Philippa Kaur

1.
Vandermeer, J. & Perfecto, I. Tropical conservation and grassroots social movements: ecological theory and social justice. Bull. Ecol. Soc. Am. 88, 171–175 (2007).
Google Scholar 
2.
Singer, B. How useful is the landscape approach? In Proceedings of the 2nd world heritage forests meeting (9–11 March 2005) (2007).

3.
Wiens, J. A. (2002). Central concepts and issues of landscape ecology. In Gutzwiller, K. J. (Eds.), Applying landscape ecology in biological conservation (pp. 3–21). Springer.

4.
Schonewald-Cox, C. M. & Bayless, J. W. The boundary model: a geographical analysis of design and conservation of nature reserves. Biol. Conserv. 38, 305–322 (1986).
Google Scholar 

5.
Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).
PubMed  Google Scholar 

6.
Driscoll, D. A., Banks, S. C., Barton, P. S., Lindenmayer, D. B. & Smith, A. L. Conceptual domain of the matrix in fragmented landscapes. Trends Ecol. Evol. 28, 605–613 (2013).
PubMed  Google Scholar 

7.
Prevedello, J. A. & Vieira, M. V. Does the type of matrix matter? A quantitative review of the evidence. Biodivers. Conserv. 19, 1205–1223 (2010).
Google Scholar 

8.
Campbell, R. E., Harding, J. S., Ewers, R. M., Thorpe, S. & Didham, R. K. Production land use alters edge response functions in remnant forest invertebrate communities. Ecol. Appl. 21, 3147–3161 (2011).
Google Scholar 

9.
Tscharntke, T., Rand, T. A. & Bianchi, F. J. J. A. The landscape context of trophic interactions: insect spillover across the crop-noncrop interface. Ann. Zool. Fennici 42, 421–432 (2005).
Google Scholar 

10.
Ng, K., Barton, P. S., Macfadyen, S., Lindenmayer, D. B. & Driscoll, D. A. Beetle’s responses to edges in fragmented landscapes are driven by adjacent farmland use, season and cross-habitat movement. Landsc. Ecol. 33, 109–125 (2018).
Google Scholar 

11.
Ruffell, J. & Didham, R. K. Towards a better mechanistic understanding of edge effects. Landsc. Ecol. 31, 2205–2213 (2016).
Google Scholar 

12.
Murcia, C. Edge effects in fragmented forests: implications for conservation. Trends Ecol. Evol. 10, 58–62 (1995).
CAS  PubMed  Google Scholar 

13.
Ruffel, J. et al. Discriminating the drivers of edge effects on nest predation: forest edges reduce capture rates of ship rats (Rattus rattus), a globally invasive nest predator, by altering vegetation structure. PLoS ONE 9, e113098 (2014).
ADS  Google Scholar 

14.
Mairota, P. et al. Very high resolution earth observation features for testing the direct and indirect effects of landscape structure on local habitat quality. Int. J. Appl. Earth Obs. Geoinf. 34, 96–102 (2015).
ADS  Google Scholar 

15.
Laurance, W. F., Didham, R. K. & Power, M. E. Ecological boundaries: a search for synthesis. Trends Ecol. Evol. 16, 70–71 (2001).
Google Scholar 

16.
Perfecto, I. & Vandermeer, J. Quality of agroecological matrix in a tropical montane landscape: ants in coffee plantations in southern mexico. Conserv. Biol. 16, 174–182 (2002).
Google Scholar 

17.
Kupfer, J. A., Malanson, G. P. & Franklin, S. B. Not seeing the ocean for the islands: the mediating influence of matrix-based processes on forest fragmentation effects. Glob. Ecol. Biogeogr. 15, 8–20 (2006).
Google Scholar 

18.
Leibold, M. A. et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).
Google Scholar 

19.
Ries, L. & Debinski, D. M. Butterfly responses to habitat edges in the highly fragmented prairies of Central Iowa. J. Anim. Ecol. 70, 840–852 (2001).
Google Scholar 

20.
de Lange, H. J., Lahr, J., Brouwer, J. H. D. & Faber, J. H. Review of available evidence regarding the vulnerability of off-crop non-target arthropod communities in comparison to in-crop non-target arthropod communities. Support. Publ. EN-348 (2012).

21.
Ppr, E. F. S. A. Scientific opinion addressing the rate of the science on risk assessment of plant protection products for non-target arthropods. EFSA J. 13, 3996 (2015).
Google Scholar 

22.
Ries, L. & Sisk, T. D. Butterfly edge effects are predicted by a simple model in a complex landscape. Oecologia 156, 75–86 (2008).
ADS  PubMed  Google Scholar 

23.
Ries, L., Murphy, S. M., Wimp, G. M. & Fletcher, R. J. Closing persistent gaps in knowledge about edge ecology. Curr. Landsc. Ecol. Rep. 2, 30–41 (2017).
Google Scholar 

24.
Wilson, D. S. Complex interactions in metacommunities, with implications for biodiversity and higher levels of selection. Ecology 73, 1984–2000 (1992).
Google Scholar 

25.
Ries, L., Fletcher, R. J. J., Battin, J. & Sisk, T. D. Ecological responses to habitat edges: mechanisms, models, and variability explained. Annu. Rev. Ecol. Evol. Syst. 35, 491–522 (2004).
Google Scholar 

26.
Ries, L. & Sisk, T. D. What is an edge species? The implications of sensitivity to habitat edges. Oikos 119, 1636–1642 (2010).
Google Scholar 

27.
Pandit, S. N., Kolasa, J., Cottenie, K., Andit, S. H. N. P. & Olasa, J. U. K. Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. Ecology 90, 2253–2262 (2009).
PubMed  Google Scholar 

28.
van Schalkwyk, J., Pryke, J. S. & Samways, M. J. Contribution of common vs. rare species to species diversity patterns in conservation corridors. Ecol. Indic. 104, 279–288 (2019).
Google Scholar 

29.
Kotze, D. J. & Samways, M. J. No general edge effects for invertebrates at Afromontane forest/grassland ecotones. Biodivers. Conserv. 10, 443–466 (2001).
Google Scholar 

30.
Rand, T. A., Tylianakis, J. M. & Tscharntke, T. Spillover edge effects: the dispersal of agriculturally subsidized insect natural enemies into adjacent natural habitats. Ecol. Lett. 9, 603–614 (2006).
PubMed  Google Scholar 

31.
Winegardner, A. K., Jones, B. K., Ng, I. S. Y., Siqueira, T. & Cottenie, K. The terminology of metacommunity ecology. Trends Ecol. Evol. 27, 253–254 (2012).
PubMed  Google Scholar 

32.
Lanta, V., Nordahl, K., Gilbert, S., Söderman, G. & Rinne, V. Biotic filtering and mass effects in small shrub patches: is arthropod community structure predictable based on the quality of the vegetation?. Ecol. Entomol. 43, 234–244 (2018).
Google Scholar 

33.
Duelli, P. & Obrist, M. K. Regional biodiversity in an agricultural landscape: the contribution of seminatural habitat islands. Basic Appl. Ecol. 4, 129–138 (2003).
Google Scholar 

34.
Katayama, N., Bouam, I., Koshida, C. & Baba, Y. G. Biodiversity and yield under different land-use types in orchard/vineyard landscapes: a meta-analysis. Biol. Conserv. 229, 125–133 (2019).
Google Scholar 

35.
Lucey, J. M. et al. Tropical forest fragments contribute to species richness in adjacent oil palm plantations. Biol. Conserv. 169, 268–276 (2014).
Google Scholar 

36.
Vink, N. & Tregurtha, N. Agriculture and mariculture first paper: structure, performance and future prospects—an overview (Department of Agriculture, Forestry, and Fisheries, Cape Town, 2007).
Google Scholar 

37.
Thorpe, P. T., Pryke, J. S. & Samways, M. J. Review of ecological and conservation perspectives on future options for arthropod management in Cape Floristic Region pome fruit orchards. Afr. Entomol. 24, 279–306 (2016).
Google Scholar 

38.
van Schalkwyk, J., Pryke, J. S., Samways, M. J. & Gaigher, R. Complementary and protection value of a Biosphere Reserve buffer zone for increasing local representativeness of ground-living arthropods. Biol. Conserv. 239, 108292 (2019).
Google Scholar 

39.
Chao, A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 43, 783 (1987).
MathSciNet  CAS  PubMed  MATH  Google Scholar 

40.
Oksanen, J. et al. vegan: community ecology package (2019).

41.
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2019).

42.
De Cáceres, M. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90, 3566–3574 (2009).
Google Scholar 

43.
Tichý, L. & Chytrý, M. Statistical determination of diagnostic species for site groups of unequal sample size. J. Veg. Sci. 17, 809–818 (2006).
Google Scholar 

44.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 

45.
Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).
ADS  PubMed  Google Scholar 

46.
Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006).
Google Scholar 

47.
Blanchet, G., Legendre, P. & Borcard, D. Forward selection of spatial explanatory variables. Ecology 89, 2623–2632 (2008).
PubMed  Google Scholar 

48.
Bauman, D., Drouet, T., Fortin, M.-J. & Dray, S. Optimizing the choice of a spatial weighting matrix in eigenvector-based methods. Ecology 99, 2159–2166 (2018).
PubMed  Google Scholar 

49.
Wagner, H. H. Direct multi-scale ordination with canonical correspondence analysis. Ecology 85, 342–351 (2004).
Google Scholar 

50.
Dray, S. et al. adespatial: multivariate multiscale spatial analysis (2019).

51.
UNESCO. Biosphere reserves—learning sites for sustainable development (2017). https://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/biosphere-reserves/. Accessed 2 March 2020.

52.
Kammerer, M. A., Biddinger, D. J., Rajotte, E. G. & Mortensen, D. A. Local plant diversity across multiple habitats supports a diverse wild bee community in pennsylvania apple orchards. Environ. Entomol. 45, 32–38 (2016).
PubMed  Google Scholar 

53.
Witt, A. B. R. & Samways, M. J. Influence of agricultural land transformation and pest management practices on the arthropod diversity of a biodiversity hotspot, the Cape Floristic Region, South Africa. Afr. Entomol. 12, 89–95 (2004).
Google Scholar 

54.
Adu-Acheampong, S., Bazelet, C. S. & Samways, M. J. Extent to which an agricultural mosaic supports endemic species-rich grasshopper assemblages in the Cape Floristic Region biodiversity hotspot. Agric. Ecosyst. Environ. 227, 52–60 (2016).
Google Scholar 

55.
Magura, T. Carabids and forest edge: spatial pattern and edge effect. For. Ecol. Manag. 157, 23–37 (2002).
Google Scholar 

56.
Kautz, M., Schopf, R. & Ohser, J. The ‘sun-effect’: microclimatic alterations predispose forest edges to bark beetle infestations. Eur. J. For. Res. 132, 453–465 (2013).
Google Scholar 

57.
Greenslade, P. Pitfall trapping as a method for studying populations of Carabidae (Coleoptera). J. Anim. Ecol. 33, 301–310 (1964).
Google Scholar 

58.
Gascon, C. et al. Matrix habitat and species richness in tropical forest remnants. Biol. Conserv. 91, 223–229 (1999).
Google Scholar 

59.
Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2017).
Google Scholar 

60.
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science (80-) 344, 296–299 (2014).
ADS  CAS  Google Scholar 

61.
Epstein, D. L., Zack, R. S., Brunner, J. F., Gut, L. & Brown, J. J. Effects of broad-spectrum insecticides on epigeal arthropod biodiversity in Pacific Northwest apple orchards. Environ. Entomol. 29, 340–348 (2000).
CAS  Google Scholar 

62.
Markó, V. & Kádár, F. Effects of different insecticide disturbance levels and weed patterns on carabid beetle assemblages. Acta Phytopathol. Entomol. Hungarica 40, 111–143 (2005).
Google Scholar 

63.
Ries, L. & Sisk, T. D. A predictive model of edge effects. Ecology 85, 2917–2926 (2004).
Google Scholar 

64.
Gerlach, J., Samways, M. & Pryke, J. Terrestrial invertebrates as bioindicators: an overview of available taxonomic groups. J. Insect Conserv. 17, 831–850 (2013).
Google Scholar 

65.
Nuyttens, D. et al. Drift from field crop sprayers using an integrated approach: results of a five-year study. Trans. ASABE 54, 403–408 (2011).
Google Scholar 

66.
Zaady, E., Katra, I., Shuker, S., Knoll, Y. & Shlomo, S. Tree belts for decreasing aeolian dust-carried pesticides from cultivated areas. Geosciences 8, 286 (2018).
ADS  Google Scholar 

67.
Blitzer, E. J. et al. Spillover of functionally important organisms between managed and natural habitats. Agric. Ecosyst. Environ. 146, 34–43 (2012).
Google Scholar 

68.
Leibold, M. A., Chase, J. M. & Ernest, S. K. M. Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes. Ecology 98, 909–919 (2017).
PubMed  Google Scholar 

69.
With, K. A. The landscape ecology of invasive spread. Conserv. Biol. 16, 1192–1203 (2002).
Google Scholar 

70.
Hickey, M. B. C. & Doran, B. A review of the efficiency of buffer strips for the maintenance and enhancement of riparian ecosystems. Water Qual. Res. J. Canada 39, 311–317 (2004).
Google Scholar 

71.
Vought, L. B. M. & Lacoursièr, J. O. Restoration of streams in the agricultural landscapes. In Restoration of Lakes, Streams, Floodplains, and Bogs in Europe Vol. 3 (ed. Eiseltová, M.) (Springer, Berlin, 2010).
Google Scholar 

72.
Samways, M. J., Osborn, R. & Carliel, F. Effect of a highway on ant (Hymenoptera: Formicidae) species composition and abundance, with a recommendation for roadside verge width. Biodivers. Conserv. 6, 903–913 (1997).
Google Scholar 

73.
Nyhus, P. J. & Adams, M. S. Biosphere Reserves of the World—Principles and Practice (University of Wisconsin, Madison, 1995).
Google Scholar 

74.
UNESCO. Management Manual for UNESCO Biosphere Reserves in Africa. (2015).

75.
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, Princeton, 1967).
Google Scholar 

76.
Mehring, M. & Stoll-Kleemann, S. How effective is the buffer zone? Linking institutional processes with satellite images from a case study in the Lore Lindu forest biosphere reserve, Indonesia. Ecol. Soc. 16, 3 (2011).
Google Scholar 

77.
Badejo, M. A. & Ola-Adams, B. A. Abundance and diversity of soil mites of fragmented habitats in a biopshere reserve in southern Nigeria. Pesqui. Agropecuária Bras. 35, 2121–2128 (2000).
Google Scholar 

78.
Dutta, P. et al. Mosquito biodiversity of Dibru-Saikhowa biosphere reserve in Assem, India. J. Environ. Biol. 31, 695–699 (2010).
CAS  PubMed  Google Scholar 

79.
González-Moreno, A., Bordera, S., Leirana-Alcocer, J., Delfín-González, H. & Ballina-Gómez, H. S. Explaining variations in the diversity of parasitoid assemblages in a biosphere reserve of Mexico: evidence from vegetation, land management and seasonality. Bull. Entomol. Res. 108, 602–615 (2018).
PubMed  Google Scholar 

80.
McIntyre, S. & Barrett, G. W. Habitat variegation, an alternative to fragmentation. Conserv. Biol. 6, 146–147 (1992).
Google Scholar 

81.
Ingham, D. S. & Samways, M. J. Application of fragmentation and variegation models to epigaeic invertebrates in South Africa. Conserv. Biol. 10, 1353–1358 (1996).
Google Scholar 

82.
Guevara, S. & Laborde, J. The landscape approach: designing new reserves for protection of biological and cultural diversity in Latin America. Environ. Ethics 30, 251–262 (2008).
Google Scholar 

83.
Brunckhorst, D. Building capital through bioregional planning and biosphere reserves. Ethics Sci. Environ. Polit. 1, 19–32 (2001).
Google Scholar  More

Field investigation
This study was performed between from May 2017 and October 2019 at Xinsheng Aquaculture Professional Cooperative (121° 0′ 56″ N, 30° 58′ 17″ E) in Qingpu District, Shanghai, Eastern China. This region has a subtropical monsoon climate with a mean monthly air temperature of 17.6 ± 2.3 °C and mean monthly precipitation of 126.9 ± 24.6 mm.
Each RC paddy (667 m2) had a rice-growing area (80% of the total area), aquaculture area (10%) and ridge area (10%; Fig. 4A). In the aquaculture area, a 1.2 m deep ditch was dug to provide a more comfortable habitat for the crayfish and eels. The ridge had a height of 40 cm, and it was covered with a high-density polyethylene film to prevent the aquatic animals from escaping. Every May, rice (Oryza sativa L., Qing-Xiang-Ruan-Geng) seedlings were transplanted from a nursery into the paddies at a planting density of 20 × 20 cm (one seedling on each hill). Moreover, the juvenile crayfish weighing 1.5 ± 0.3 g were released into the paddies according to the standard of 45,000 juveniles per hectare, and the crayfish were allowed to self-propagate inside the rice paddies. A total of nine RC paddies were divided into three groups according to the rearing density of the eels: control group (C), low-density group (LD) and high-density group (HD) with rearing densities of 0, 6000 and 12,000 ind. ha−1, respectively. The LD and HD groups were supplemented with juvenile eels at a density of 2000 and 4000 ind. ha−1 in June 2018 and 2019. The average weights of juvenile eels in 2017, 2018 and 2019 were 21.4 ± 1.8, 24.1 ± 0.9 and 26.8 ± 1.1 g, respectively. All juvenile crayfish and eels were purchased from Shanghai Xiangsheng Aquaculture Cooperative. In the aquaculture area, floating plants, such as duckweed (Lemna minor L.) and foxtail (Myriophyllum spicatum L.), covered one-third of the water surface. The soil contained 20.6–23.7 g kg−1 of organic matter, 0.7–1.2 g kg−1 of total N and 0.31–0.37 g kg−1 of total P.
Figure 4

Photographs of the paddies in the field investigation (A) and plots in the mesocosm experiment (B).

Full size image

Only basal fertilizer was used for rice cultivation, and it contained 587 kg ha−1 of urea (46.4% N), 625 kg ha−1 of superphosphate and 150 kg ha−1 of potassium chloride. Every day, 500 g of commercial fish diet (5.83% N) was applied, and no pesticides or herbicides were used in the paddies.
In late August, the mature crayfish and eels were collected using ground cages to measure the aquatic product yields. The immature crayfish and eels were returned to the paddy fields during the collection. After the rice was harvested, the rice grains were air-dried and weighed to estimate the rice yield. The N content of the rice grains and aquatic animals was determined using the semi-micro Kjeldahl method33. Before testing, rice grains, crayfish and eels were weighted, dried at 65 °C and ground. Then, all the samples were digested with concentrated sulphuric acid (H2SO4) and hydrogen peroxide.
Water samples were collected every month during the co-culture period. Three duplicate 500 mL water samples were collected from 0 to 10 cm below the surface in the aquaculture area; the three subsamples were combined to obtain one sample per paddy. In the laboratory, the total N content of the water was analysed using UV spectrophotometry after digestion by alkaline potassium persulfate oxidation.
Soil samples were collected after the rice-planting period. In each paddy, three samples were collected from a rice-planting area of 0.25 m × 0.25 m × 0.10 m. All the soil samples were air-dried, ground, passed through a 0.15 mm sieve and digested with K2SO4–CuSO4–Se solution. Then, the semi-micro Kjeldahl method was used to test the total N content of the soil.
The N2O flux rate was measured using the static chamber method34. The size of the chamber was 1.0 m × 1.0 m × 1.0 m. The N2O samples were collected every half month between 8:30 and 10:30 AM from June to October. In each paddy, four gas samples were collected using 40 mL vacuum tubes at 10 min intervals (0, 10, 20 and 30 min after chamber closure). All samples were analysed with gas chromatography (GC 2010; Shimadzu, Kyoto, Japan). The N2O flux rate was calculated using the following equation:

$$F = rho times h times left[ {{273}/left( {{273} + T} right)} right] times {text{d}}C{text{/d}}t$$
(1)

where F is the N2O flux rate (μg N m−2 h−1); ρ, density of N2O at the standard state (μg m−3); h, height of the chamber (m); T, average temperature in the chamber during gas collection and dC/dt, concentration variation rate of N2O.
The ammonia volatilization flux was measured with a continuous airflow enclosure method35. The NH3 flux was measured every half month from 09:00 to 11:00 AM during the rice-planting period. NH3 was absorbed using boric acid, and 0.01 M H2SO4 was used to titrate the solution to determine the rate of NH3 volatilization. The ammonia volatilization flux was calculated using the following equation:

$$F = 14 times V times C times A^{ – 1} times t^{ – 1}$$
(2)

where F denotes the ammonia volatilization flux (mg N m−2 h−1); V, volume of H2SO4 titrated (L); C, concentration of H2SO4 (mol L−1); A, area of the chamber base (m2) and t, continuous measurement time.
In this study, all the data were shown as mean ± standard error of the mean (SEM) values. One-way ANOVA and Tukey’s test (SPSS V.16.0) were used to compare the differences of the yields and total N content among the three groups and three investigated years.
Mesocosm experiment
Between May and October 2019, the mesocosm experiment was conducted at Shanghai Academy of Agricultural Sciences. Each mesocosm consisted of an experimental plot (1.2 m × 1.2 m × 0.6 m) covered with a high-density polyethylene film (Fig. 4B). In each experiment plot, 30 kg of soil from Xinsheng Aquaculture Professional Cooperative was used to construct a rice-planting platform and an aquaculture ditch (40 cm in depth). The platform area was about three-fourth of the cross-sectional area of the plot.
A total of six mesocosms were constructed: three experimental plots (RCE) and three control plots (RC). In each plot, the rice seedlings were planted in hills (one seedling per hill) within rows in May, with 20 cm between rows and 20 cm between hills in the same row for the experimental and control plots. The fertilizers used in each plot contained 84.5 g of urea (N content, 46.8%; 15N abundance, 10.15%), 90 g of superphosphate and 15 g of potassium chloride. The duckweed was planted in the aquaculture area, and it covered 30% of the aquaculture zone. Mudsnails (Cipangopaludina cathayensis, 500 g) were added to each plot. After a month, 12 crayfish were cultured in each simulated paddy, and two eels were reared in each experiment plot. The proportion of crayfish and eels was set according to that in the LD group of field investigation. The crayfish feed was supplied once every day, and the daily allowance was about 3% of the estimated crayfish weight in each mesocosm. The rice and aquatic products were harvested in October.
Rice, crayfish and eel samples were collected to measure the total N content and 15N abundance. The total N content of the soil and organism samples were measured using the semi-micro Kjeldahl method after digestion with concentrated H2SO4 and hydrogen peroxide. The 15N abundance was measured in all samples by using the MAT-271 isotope mass spectrometer (Finnigan MAT, California). The accumulation of N in rice, crayfish and eels from N fertilizer was calculated using the following equations:

$${text{Percentage of accumulated N from fertilizer NDFF}});( % ) = {text{A}}% ;{text{E of the organism sample/A}}% ;{text{E of the fertilizer sample}} times 100$$
(3)

$${text{Amount of accumulated N from fertilizer}} = {text{organism N accumulation amount}} times {text{NDFF}}$$
(4)

$${text{N use efficiency NUE}});(% ) = {text{amount of N accumulated by the organism accumulated from N fertilizer/total N content of the fertilizer}} times 100$$
(5)

where A% E is the difference between the 15N abundance of the samples or 15N-labelled fertilizers and natural abundance of 15N.
The independent-samples t-test was used to determine the differences in total N, N use efficiency and percentage of N derived from fertilizer between RCE and RC at 95% confidence level by using SPSS 16.0 (P value  More

1.
Marquet, P. A. Scaling and power-laws in ecological systems. J. Exp. Biol. 208, 1749–1769 (2005).
PubMed  Google Scholar 
2.
GarciaMartin, H. & Goldenfeld, N. On the origin and robustness of power-law species-area relationships in ecology. Proc. Natl. Acad. Sci. 103, 10310–10315 (2006).
ADS  Google Scholar 

3.
Taylor, L. R. Aggregation, variance and the mean. Nature 189, 732–735 (1961).
ADS  Google Scholar 

4.
Damuth, J. Population density and body size in mammals. Nature 290, 699–700 (1981).
ADS  Google Scholar 

5.
Carbone, C. & Gittleman, J. L. A common rule for the scaling of carnivore density. Science 295, 2273–2276 (2002).
ADS  CAS  PubMed  Google Scholar 

6.
White, C. R. & Seymour, R. S. Allometric scaling of mammalian metabolism. J. Exp. Biol. 208, 1611–1619 (2005).
CAS  PubMed  Google Scholar 

7.
da Silva, J. K. L., Garcia, G. J. M. & Barbosa, L. A. Allometric scaling laws of metabolism. Phys. Life Rev. 3, 229–261 (2006).
ADS  Google Scholar 

8.
Reich, P. B., Tjoelker, M. G., Machado, J.-L. & Oleksyn, J. Universal scaling of respiratory metabolism, size and nitrogen in plants. Nature 439, 457–461 (2006).
ADS  CAS  PubMed  Google Scholar 

9.
Eisler, Z., Bartos, I. & Kertész, J. Fluctuation scaling in complex systems: Taylor’s law and beyond. Adv. Phys. 57, 89–142 (2008).
ADS  CAS  Google Scholar 

10.
Reed, D. H. & Hobbs, G. R. The relationship between population size and temporal variability in population size. Anim. Conserv. 7, 1–8 (2004).
Google Scholar 

11.
Benton, T. G. & Beckerman, A. P. Population dynamics in a noisy world: lessons from a mite experimental system. in Advances in Ecological Research vol. 37, pp. 143–181 (Academic Press, Cambridge, 2005).

12.
Ramsayer, J., Fellous, S., Cohen, J. E. & Hochberg, M. E. Taylor’s law holds in experimental bacterial populations but competition does not influence the slope. Biol. Lett. 8, 316–319 (2012).
PubMed  Google Scholar 

13.
Kaltz, O., Escobar-Páramo, P., Hochberg, M. E. & Cohen, J. E. Bacterial microcosms obey Taylor’s law: effects of abiotic and biotic stress and genetics on mean and variance of population density. Ecol. Process. 1, 5 (2012).
Google Scholar 

14.
Anderson, R. M., Gordon, D. M., Crawley, M. J. & Hassell, M. P. Variability in the abundance of animal and plant species. Nature 296, 245–248 (1982).
ADS  Google Scholar 

15.
Ballantyne, F. I. The upper limit for the exponent of Taylor’s power law is a consequence of deterministic population growth. Evol. Ecol. Res. 8 (2005).

16.
Engen, S., Lande, R. & Sæther, B.-E. A general model for analyzing taylor’s spatial scaling laws. Ecology 89, 2612–2622 (2008).
PubMed  Google Scholar 

17.
Damuth, J. Interspecific allometry of population density in mammals and other animals: the independence of body mass and population energy-use. Biol. J. Linn. Soc. 31, 193–246 (1987).
Google Scholar 

18.
Blackburn, T. M. & Gaston, K. J. The relationship between animal abundance and body size: a review of the mechanisms. In Advances in Ecological Research (eds Fitter, A. H. & Raffaelli, D.) 181–210 (Academic Press, Cambridge, 1999).
Google Scholar 

19.
Jennings, S., Oliveira, J. A. A. D. & Warr, K. J. Measurement of body size and abundance in tests of macroecological and food web theory. J. Anim. Ecol. 76, 72–82 (2007).
PubMed  Google Scholar 

20.
Belgrano, A. & Reiss, J. The Role of Body Size in Multispecies Systems (Academic Press, Cambridge, 2011).
Google Scholar 

21.
Lawton, J. H. What is the relationship between population density and body size in animals?. Oikos 55, 429–434 (1989).
Google Scholar 

22.
Marquet, P. A., Navarrete, S. A. & Castilla, J. C. Scaling oopulation density to body size in rocky intertidal communities. Science 250, 1125–1127 (1990).
ADS  CAS  PubMed  Google Scholar 

23.
Silva, M. & Downing, J. A. The allometric scaling of density and body mass: a nonlinear relationship for terrestrial mammals. Am. Nat. 145(5), 704–727 (1995).
Google Scholar 

24.
Dunham, J. B. & Vinyard, G. L. Relationships between body mass, population density, and the self-thinning rule in stream-living salmonids. Can. J. Fish. Aquat. Sci. 54, 6 (1997).
Google Scholar 

25.
Enquist, B. J., Brown, J. H. & West, G. B. Allometric scaling of plant energetics and population density. Nature 395, 4 (1998).
Google Scholar 

26.
Hendriks, A. J. Allometric scaling of rate, age and density parameters in ecological models. Oikos 86, 293–310 (1999).
Google Scholar 

27.
Schmid, P. E. Relation between population density and body size in stream communities. Science 289, 1557–1560 (2000).
ADS  CAS  PubMed  Google Scholar 

28.
Morand, S. & Poulin, R. Body size–density relationships and species diversity in parasitic nematodes: patterns and likely processes. Evol. Ecol. Res. 12 (2002).

29.
Niklas, K. J., Midgley, J. J. & Enquist, B. J. A general model for mass–growth–density relations across tree-dominated communities. Evol. Ecol. Res. 5, 459–468 (2003).
Google Scholar 

30.
Makarieva, A. M., Victor, G. & Li, B.-L. Why do population density and inverse home range scale differently with body size?. Ecol. Complex. 2, 259–271 (2005).
Google Scholar 

31.
Reuman, D. C., Mulder, C., Raffaelli, D. & Cohen, J. E. Three allometric relations of population density to body mass: theoretical integration and empirical tests in 149 food webs. Ecol. Lett. 11, 1216–1228 (2008).
PubMed  Google Scholar 

32.
Reuman, D. C. et al. Allometry of body size and abundance in 166 food webs. in Advances in Ecological Research vol. 41, pp. 1–44 (Elsevier, 2009).

33.
Carbone, C., Pettorelli, N. & Stephens, P. A. The bigger they come, the harder they fall: body size and prey abundance influence predator–prey ratios. Biol. Lett. 7, 312–315 (2011).
PubMed  Google Scholar 

34.
Cohen, J. E., Xu, M. & Schuster, W. S. F. Allometric scaling of population variance with mean body size is predicted from Taylor’s law and density-mass allometry. Proc. Natl. Acad. Sci. 109, 15829–15834 (2012).
ADS  CAS  PubMed  Google Scholar 

35.
Segura, A. M. & Perera, G. The metabolic basis of fat tail distributions in populations and community fluctuations. Front. Ecol. Evol. 7, 148 (2019).
Google Scholar 

36.
Agusti, S., Duarte, C. M. & Kalff, J. Algal cell size and the maximum density and biomass of phytoplankton1. Limnol. Oceanogr. 32, 983–986 (1987).
ADS  Google Scholar 

37.
Belgrano, A., Allen, A. P., Enquist, B. J. & Gillooly, J. F. Allometric scaling of maximum population density: a common rule for marine phytoplankton and terrestrial plants. Ecol. Lett. 5, 611–613 (2002).
Google Scholar 

38.
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
Google Scholar 

39.
Barneche, D. R., Kulbicki, M., Floeter, S. R., Friedlander, A. M. & Allen, A. P. Energetic and ecological constraints on population density of reef fishes. Proc. R. Soc. B 283, 20152186 (2016).
PubMed  Google Scholar 

40.
Ghedini, G., White, C. R. & Marshall, D. J. Metabolic scaling across succession: do individual rates predict community-level energy use?. Funct. Ecol. 32, 1447–1456 (2018).
Google Scholar 

41.
Lagrue, C., Poulin, R. & Cohen, J. E. Parasitism alters three power laws of scaling in a metazoan community: Taylor’s law, density-mass allometry, and variance-mass allometry. Proc. Natl. Acad. Sci. 112, 1791–1796 (2015).
ADS  CAS  PubMed  Google Scholar 

42.
Xu, M. Ecological scaling laws link individual body size variation to population abundance fluctuation. Oikos 125, 288–299 (2016).
Google Scholar 

43.
Taylor, L. R. & Woiwod, I. P. Comparative synoptic dynamics. I. Relationships between inter- and intra-specific spatial and temporal variance/mean population parameters. J. Anim. Ecol. 51, 879 (1982).
Google Scholar 

44.
Cyr, H., Downing, J. A., Peters, R. H. & Cyr, H. Density-body size relationships in local aquatic communities. Oikos 79, 333 (1997).
Google Scholar 

45.
O’Connell, A. F., Nichols, J. D. & Karant, U. K. Camera traps in animal ecology methods and analyses (Springer, London, 2010).
Google Scholar 

46.
Anile, S. & Devillard, S. Study design and body mass influence RAIs from camera trap studies: evidence from the Felidae. Anim. Conserv. 19, 35–45 (2015).
Google Scholar 

47.
Anile, S. & Devillard, S. Camera-trapping provides insights into adult sex ratio variability in felids. Mamm. Rev. 48, 168–179 (2018).
Google Scholar 

48.
Wilson, E. E. & Wolkovich, E. M. Scavenging: how carnivores and carrion structure communities. Trends Ecol. Evol. 26, 129–135 (2011).
PubMed  Google Scholar 

49.
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).
Google Scholar 

50.
Van Valkenburgh, B., Hayward, M. W., Ripple, W. J., Meloro, C. & Roth, V. L. The impact of large terrestrial carnivores on Pleistocene ecosystems. Proc. Natl. Acad. Sci. 113, 862–867 (2016).
ADS  PubMed  Google Scholar 

51.
Albert, C., Luque, G. M. & Courchamp, F. The twenty most charismatic species. PLoS ONE 13, e0199149 (2018).
PubMed  PubMed Central  Google Scholar 

52.
Dickman, S. Felid conservation priorities. Conserv. Biol. (2015).

53.
Inskip, C. & Zimmermann, A. Human-felid conflict: a review of patterns and priorities worldwide. Oryx 43, 18 (2009).
Google Scholar 

54.
Macdonald, D. W. & Loveridge, A. J. The Biology and Conservation of Wild Felids (Oxford University Press, Oxford, 2010).
Google Scholar 

55.
Hunter, L. Wild Cats of the World (Bloomsbury Publishing, London, 2015).
Google Scholar 

56.
Rizzuto, M., Carbone, C. & Pawar, S. Foraging constraints reverse the scaling of activity time in carnivores. Nat. Ecol. Evol. 2, 247–253 (2018).
PubMed  Google Scholar 

57.
Karanth, K. U., Nichols, J. D., Samba Kumar, N., Link, W. A. & Hines, J. E. Tigers and their prey: predicting carnivore densities from prey abundance. Proc. Natl. Acad. Sci. 101, 4854–4858 (2004).
ADS  CAS  PubMed  Google Scholar 

58.
Jiang, G. et al. New hope for the survival of the Amur leopard in China. Sci. Rep. 15, 15475 (2015).
ADS  Google Scholar 

59.
Jedrzejewski, W. et al. Estimating large carnivore populations at global scale based on spatial predictions of density and distribution: application to the jaguar (Panthera onca). PLoS ONE 13, e0194719 (2018).
PubMed  PubMed Central  Google Scholar 

60.
Kitchener, A. C. et al. A revised taxonomy of the Felidae. The final report of the Cat Classification Task Force of the IUCN/SSC Cat Specialist Group. Cat News Special Issue, pp. 11–80 (2017).

61.
Sinclair, A. R. E. Mammal populations: fluctuation, regulation, life history theory and their implications for conservation. Front. Popul. Ecol. 1, 127–154 (1996).
Google Scholar 

62.
Santini, L., Isaac, N. J. B. & Ficetola, G. F. TetraDENSITY: a database of population density estimates in terrestrial vertebrates. Glob. Ecol. Biogeogr. 27, 787–791 (2018).
Google Scholar 

63.
Ceballos, G., Ehrlich, P. R. & Dirzo, R. Biological annihilation via the ongoing sixth mass extinction signaled by vertebrate population losses and declines. Proc. Natl. Acad. Sci. 114, E6089–E6096 (2017).
CAS  PubMed  Google Scholar 

64.
Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl. Acad. Sci. 114, 10678–10683 (2017).
CAS  PubMed  Google Scholar 

65.
Wikramanayake, E. et al. A landscape-based conservation strategy to double the wild tiger population: landscape-based strategy for tiger recovery. Conserv. Lett. 4, 219–227 (2011).
Google Scholar 

66.
Stearns, S. C. The Influence of size and phylogeny on patterns of covariation among life-history traits in the mammals. Oikos 41, 173 (1983).
Google Scholar 

67.
Paemelaere, E. & Dobson, F. S. Fast and slow life histories of carnivores. Can. J. Zool. 89, 692–704 (2011).
Google Scholar 

68.
Peters, R. H. & Wassenberg, K. The effect of body size on animal abundance. Oecologia 60, 89–96 (1983).
ADS  PubMed  Google Scholar 

69.
Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B. & Charnov, E. L. Effects of Body size and temperature on population growth. Am. Nat. 163, 429–441 (2004).
PubMed  Google Scholar 

70.
Gamelon, M. et al. Influence of life-history tactics on transient dynamics: a comparative analysis across mammalian populations. Am. Nat. 184, 673–683 (2014).
PubMed  PubMed Central  Google Scholar 

71.
McCallum, J. Changing use of camera traps in mammalian field research: habitats, taxa and study types: camera trap use and development in field ecology. Mamm. Rev. 43, 196–206 (2013).
Google Scholar 

72.
Johnson, P. J. et al. Rensching cats and dogs: feeding ecology and fecundity trends explain variation in the allometry of sexual size dimorphism. R. Soc. Open Sci. 4, 170453 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

73.
Efford, M. Density estimation in live-trapping studies. Oikos 106, 598–610 (2004).
Google Scholar 

74.
Luskin, M. S., Albert, W. R. & Tobler, M. W. Sumatran tiger survival threatened by deforestation despite increasing densities in parks. Nat. Commun. 8, 1783 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

75.
Royle, J. A., Chandler, R. B., Sollmann, R. & Gardner, B. Spatial Capture-Recapture (Springer, New York, 2013).
Google Scholar 

76.
Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals: ecological Archives E090–184. Ecology 90, 2648 (2009).
Google Scholar 

77.
Huaranca, J. C. et al. Density and activity patterns of Andean cat and pampas cat (Leopardus jacobita and L. colocolo) in the Bolivian Altiplano. Wildl. Res. 47(1), 68–76 (2020).
Google Scholar 

78.
Tobler, M. W. & Powell, G. V. N. Estimating jaguar densities with camera traps: problems with current designs and recommendations for future studies. Biol. Conserv. 159, 109–118 (2013).
Google Scholar  More

1.
Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: Global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).
ADS  CAS  Article  Google Scholar 
2.
Shipley, B. et al. Reinforcing loose foundation stones in trait-based plant ecology. Oecologia 180, 923–931 (2016).
ADS  Article  Google Scholar 

3.
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).
ADS  CAS  Article  Google Scholar 

4.
Onoda, Y. et al. Physiological and structural tradeoffs underlying the leaf economics spectrum. N. Phytol. 214, 1447–1463 (2017).
CAS  Article  Google Scholar 

5.
Dray, S. et al. Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation. Ecology 95, 14–21 (2014).
Article  Google Scholar 

6.
Diaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
ADS  CAS  Article  Google Scholar 

7.
Güsewell, S. Variation in nitrogen and phosphorus concentrations of wetland plants. Perspect. Plant Ecol. Evol. Syst. 5, 37–61 (2002).
Article  Google Scholar 

8.
Page, S. E. & Baird, A. J. Peatlands and global change: response and resilience. Annu. Rev. Environ. Resour. 41, 35–57 (2016).
Article  Google Scholar 

9.
Sutton-Grier, A. E. & Megonigal, J. P. Plant species traits regulate methane production in freshwater wetland soils. Soil Biol. Biochem. 43, 413–420 (2011).
CAS  Article  Google Scholar 

10.
Alldred, M. & Baines, S. B. Effects of wetland plants on denitrification rates: a meta-analysis. Ecol. Appl. 26, 676–685 (2016).
Article  Google Scholar 

11.
Moor, H. et al. Towards a trait-based ecology of wetland vegetation. J. Ecol. 105, 1623–1635 (2017).
Article  Google Scholar 

12.
Sutton-Grier, A. E., Wright, J. P. & Richardson, C. J. Different plant traits affect two pathways of riparian nitrogen removal in a restored freshwater wetland. Plant Soil 365, 41–57 (2013).
CAS  Article  Google Scholar 

13.
Pan, Y., Cieraad, E. & van Bodegom, P. M. Are ecophysiological adaptive traits decoupled from leaf economics traits in wetlands? Funct. Ecol. 33, 1202–1210 (2019).
Article  Google Scholar 

14.
Lambers, H., Chapin, F. S. & Pons, T. L. Plant Physiological Ecology. (Springer New York, 2008). https://doi.org/10.1007/978-0-387-78341-3.

15.
Colmer, T. D. & Voesenek, L. A. C. J. Flooding tolerance: suites of plant traits in variable environments. Funct. Plant Biol. 36, 665–681 (2009).
CAS  Article  Google Scholar 

16.
Douma, J. C., Bardin, V., Bartholomeus, R. P. & van Bodegom, P. M. Quantifying the functional responses of vegetation to drought and oxygen stress in temperate ecosystems. Funct. Ecol. 26, 1355–1365 (2012).
Article  Google Scholar 

17.
Ramsar Convention Secretariat. The Ramsar Convention Manual: a guide to the Convention on Wetlands (Ramsar, Iran, 1971). https://doi.org/10.1007/978-94-007-0551-7 (2013).

18.
Pan, Y. et al. Drivers of plant traits that allow survival in wetlands. Funct. Ecol. https://doi.org/10.1111/1365-2435.13541 (2020).

19.
Keddy, P. A. Wetland ecology: principles and conservation. (Cambridge University Press, UK, 2010).

20.
Ordoñez, J. C. et al. Leaf habit and woodiness regulate different leaf economy traits at a given nutrient supply. Ecology 91, 3218–3228 (2010).
Article  Google Scholar 

21.
Mommer, L., Lenssen, J. P. M., Huber, H., Visser, E. J. W. & De Kroon, H. Ecophysiological determinants of plant performance under flooding: a comparative study of seven plant families. J. Ecol. 94, 1117–1129 (2006).
Article  Google Scholar 

22.
Pierce, S., Brusa, G., Sartori, M. & Cerabolini, B. E. Combined use of leaf size and economics traits allows direct comparison of hydrophyte and terrestrial herbaceous adaptive strategies. Ann. Bot. 109, 1047–1053 (2012).
Article  Google Scholar 

23.
Reich, P. B., Ellsworth, D. S. & Walters, M. B. Leaf structure (specific leaf area) modulates photosynthesis-nitrogen relations: Evidence from within and across species and functional groups. Funct. Ecol. 12, 948–958 (1998).
Article  Google Scholar 

24.
Reich, P. B. et al. Relationships of leaf dark respiration to leaf nitrogen, specific leaf area and leaf life-span: a test across biomes and functional groups. Oecologia 114, 471–482 (1998).
ADS  Article  Google Scholar 

25.
Mommer, L., Pedersen, O. & Visser, E. J. W. Acclimation of a terrestrial plant to submergence facilitates gas exchange under water. Plant, Cell Environ. 27, 1281–1287 (2004).
Article  Google Scholar 

26.
Herzog, M. & Pedersen, O. Partial versus complete submergence: Snorkelling aids root aeration in Rumex palustris but not in R. acetosa. Plant, Cell Environ. 37, 2381–2390 (2014).
CAS  Google Scholar 

27.
Reich, P. B. The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).
Article  Google Scholar 

28.
Violle, C. et al. Plant functional traits capture species richness variations along a flooding gradient. Oikos 120, 389–398 (2011).
Article  Google Scholar 

29.
Colmer, T. D., Winkel, A. & Pedersen, O. A perspective on underwater photosynthesis in submerged terrestrial wetland plants. AoB Plants 11, 1–15 (2011).
Google Scholar 

30.
Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002).
Article  Google Scholar 

31.
Cyr, H. & Face, M. L. Magnitude and patterns of herbivory in aquatic and terrestrial ecosystems. Nature 361, 148–150 (1993).
ADS  Article  Google Scholar 

32.
Hikosaka, K. & Shigeno, A. The role of Rubisco and cell walls in the interspecific variation in photosynthetic capacity. Oecologia 160, 443–451 (2009).
ADS  Article  Google Scholar 

33.
Pedersen, O., Colmer, T. D., Borum, J., Zavala-Perez, A. & Kendrick, G. A. Heat stress of two tropical seagrass species during low tides—impact on underwater net photosynthesis, dark respiration and diel in situ internal aeration. N. Phytol. 210, 1207–1218 (2016).
CAS  Article  Google Scholar 

34.
Armstrong, W. & Beckett, P. M. Experimental and modelling data contradict the idea of respiratory down-regulation in plant tissues at an internal [O2] substantially above the critical oxygen pressure for cytochrome oxidase. N. Phytol. 190, 431–441 (2011).
CAS  Article  Google Scholar 

35.
Colmer, T. D. & Pedersen, O. Underwater photosynthesis and respiration in leaves of submerged wetland plants: gas films improve CO2 and O2 exchange. N. Phytol. 177, 918–926 (2008).
CAS  Article  Google Scholar 

36.
Jackson, M. B. & Armstrong, W. Formation of aerenchyma and the processes of plant ventilation in relation to soil flooding and submergence. Plant Biol. 1, 274–287 (1999).
CAS  Article  Google Scholar 

37.
Reich, P. B. et al. Scaling of respiration to nitrogen in leaves, stems and roots of higher land plants. Ecol. Lett. 11, 793–801 (2008).
Article  Google Scholar 

38.
Pagter, M., Bragato, C. & Brix, H. Tolerance and physiological responses of Phragmites australis to water deficit. Aquat. Bot. 81, 285–299 (2005).
Article  Google Scholar 

39.
Ellenberg, H. H. Vegetation ecology of central Europe. (Cambridge University Press, 1988).

40.
Warton, D. I., Duursma, R. A., Falster, D. S. & Taskinen, S. smatr 3- an R package for estimation and inference about allometric lines. Methods Ecol. Evol. 3, 257–259 (2012).
Article  Google Scholar 

41.
Warton, D. I., Wright, I. J., Falster, D. S. & Westoby, M. Bivariate line-fitting methods for allometry. Biol. Rev. Camb. Philos. Soc. 81, 259–291 (2006).
Article  Google Scholar 

42.
Wasserstein, R. L., Schirm, A. L. & Lazar, N. A. Moving to a World Beyond “p < 0.05”. Am. Stat. 73, 1–19 (2019). MathSciNet  Article  Google Scholar  43. Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007). Article  Google Scholar  44. R. Core Team. R: A language and environment for statistical computing. R Foundation Statistical Computing, Vienna, Austria. URL https://www.r-project.org/ (2018). More

1.
Dressler, R. L. In Phylogeny and Classification of the Orchid Family (ed. Dressler, R. L.) 7–13 (Cambridge University Press, Cambridge, 1994).
Google Scholar 
2.
Delforge, P. In Orchids of Europe, Nord Africa and the Middle East (ed. Delforge, P.) 67–68 (A & C Black Publishers, London, 2001).
Google Scholar 

3.
Barman, D. & Devadas, R. Climate change on orchid population and conservation strategies: a review. J. Crop Weed. 9(12), 1–12 (2013).
Google Scholar 

4.
Fay, M. F. Orchid conservation: how can we meet the challenges in the twenty-first century. Bot. Stud. 5, 1–6 (2018).
Google Scholar 

5.
Brovkin, V. Climate–vegetation interaction. J. Phys. IV FRANCE 12, 57–72 (2002).
Google Scholar 

6.
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148. https://doi.org/10.1038/nature02121 (2004).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

7.
Anderson, M. G. & Ferree, C. E. Conserving the stage: climate change and the geophysical underpinnings of species diversity. PLoS ONE 5(7), e11554 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

8.
Bálint, M. et al. Cryptic biodiversity loss linked to global climate change. Nat. Clim. Change. 1, 313–318. https://doi.org/10.1038/nclimate1191 (2011).
ADS  Article  Google Scholar 

9.
Kolanowska, M. Niche conservatism and the future potential range of Epipactis helleborine (Orchidaceae). PLoS ONE 8(10), e77352. https://doi.org/10.1371/journal.pone.0077352 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Kolanowska, M. The naturalization status of African Spotted Orchid (Oeceoclades maculata) in Neotropics. Plant Biosyst. 148(5), 1049–1055. https://doi.org/10.1080/11263504.2013.824042 (2014).
Article  Google Scholar 

11.
Kolanowska, M. & Konowalik, K. Niche conservatism and future changes in the potential area coverage of Arundina graminifolia, an invasive orchid species from Southeast Asia. Biotropica 46(2), 157–165. https://doi.org/10.1111/btp.12089 (2014).
Article  Google Scholar 

12.
Konowalik, K. & Kolanowska, M. Climatic niche shift and possible future spread of the invasive South African Orchid Disa bracteata in Australia and adjacent areas. PeerJ. 6, e6107. https://doi.org/10.7717/peerj.6107 (2018).
Article  PubMed  PubMed Central  Google Scholar 

13.
Kolanowska, M. et al. Global warming not so harmful for all plants – response of holomycotrophic orchid species for the future climate change. Sci. Rep. 7, 12704. https://doi.org/10.1038/s41598-017-13088-7 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Naczk, A. & Kolanowska, M. Glacial refugia and future habitat coverage of selected Dactylorhiza representatives (Orchidaceae). PLoS ONE 10(11), e0143478. https://doi.org/10.1371/journal.pone.0143478 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Kolanowska, M. & Rykaczewski, M. From the past to the future – glacial refugia, current distribution patterns and future potential range changes of Diodonopsis (Orchidaceae) representatives. Lankesteriana. 17(2), 315–327 (2017).
Google Scholar 

16.
Wang, H. H. et al. Species distribution modelling for conservation of an endangered endemic orchid. AoB Plants 7, plv039 (2015).
PubMed  PubMed Central  Google Scholar 

17.
Tsiftsis, S., Djordjević, V. & Tsiripidis, I. Neottia cordata (Orchidaceae) at its southernmost distribution border in Europe: threat status and effectiveness of Natura 2000 Network for its conservation. J. Nat. Conserv. 48, 27–35 (2019).
Google Scholar 

18.
Vollering, J., Schuiteman, A., de Vogel, E., van Vugt, R. & Raes, N. Phytogeography of New Guinean orchids: patterns of species richness and turnover. J. Biogeogr. 43(1), 204–214 (2016).
Google Scholar 

19.
Reina-Rodríguez, G. A., Rubiano Mejía, J. E., Castro Llanos, F. A. & Soriano, I. Orchid distribution and bioclimatic niches as a strategy to climate change in areas of tropical dry forest in Colombia. Lankesteriana 17(1), 17–47 (2017).
Google Scholar 

20.
Gogol-Prokurat, M. Predicting habitat suitability for rare plants at local spatial scales using a species distribution model. Ecol. Appl. 21, 33–47. https://doi.org/10.1890/09-1190.1 (2011).
Article  PubMed  Google Scholar 

21.
Dudley, T. L. & Bean, D. W. Tamarisk biocontrol, endangered species risk and resolution of conflict through riparian restoration. Biocontrol 57, 331–347. https://doi.org/10.1007/s10526-011-9436-9 (2012).
Article  Google Scholar 

22.
Antúnez, P. et al. The potential distribution of tree species in three periods of time under a climate change scenario. Forests 9(10), 628. https://doi.org/10.3390/f9100628 (2018).
Article  Google Scholar 

23.
Wilson, C. D., Roberts, D. & Reid, N. Applying species distribution modeling to identify areas of high conservation value for endangered species: a case study using Margaritifera margaritifera (L.). Biol. Cons. 144, 821–829 (2011).
Google Scholar 

24.
Koch, R., Almeida-Cortez, J. S. & Kleinschmit, B. Revealing areas of high nature conservation importance in a seasonally dry tropical forest in Brazil: combination of modelled plant diversity hot spots and threat patterns. J. Nat. Conserv. 35, 24–39 (2017).
Google Scholar 

25.
Spiers, J. A., Oatham, M. P., Rostant, L. V. & Farrell, A. D. Applying species distribution modelling to improving conservation based decisions: a gap analysis of Trinidad and Tobago’s endemic vascular plants. Biodivers. Conserv. 27(11), 2931–2949 (2018).
Google Scholar 

26.
Ramírez, S. R., Gravendeel, B., Singer, R. B., Marshall, C. R. & Pierce, N. E. Dating the origin of the Orchidaceae from a fossil orchid with its pollinator. Nature 448, 1042–1045 (2007).
ADS  PubMed  Google Scholar 

27.
Conran, J. G., Bannister, J. M. & Lee, D. E. Earliest orchid macrofossils: early Miocene Dendrobium and Earina (Orchidaceae: Epidendroideae) from New Zealand. Am. J. Bot. 96(2), 466–474 (2009).
PubMed  Google Scholar 

28.
Kenny, J. 2008. Orchids of Trinidad and Tobago (ed. Kenny, J.) 1–127 (Prospect Press, 2008).

29.
Swarts, N. D. & Dixon, K. W. Terrestrial orchid conservation in the age of extinction. Ann. Bot. 104(3), 543–556 (2009).
PubMed  PubMed Central  Google Scholar 

30.
Teketay, D. History, botany and ecological requirements of coffee. Walia 20, 28–50 (1999).
Google Scholar 

31.
Tupac, O. J., Ackerman, J. D. & Bayman, P. Diversity and host specificity of endophytic Rhizoctonia-like fungi from tropical orchids. Am. J. Bot. 89(11), 1852–1858 (2002).
Google Scholar 

32.
Pellegrino, G., Luca, A. & Bellusci, F. Relationships between orchid and fungal biodiversity: mycorrhizal preferences in Mediterranean orchids. Plant Biosyst. 150(2), 180–189 (2016).
Google Scholar 

33.
Suárez, J. P. & Kottke, I. Main fungal partners and different levels of specificity of orchid mycorrhizae in the tropical mountain forests of Ecuador. Lankesteriana 16(2), 299–305 (2016).
Google Scholar 

34.
Senthilkumar, S. Mycorrhizal fungi of endangered orchid species in Kolli, a part of eastern ghats, South India. Lankesteriana 7, 15–156 (2003).
Google Scholar 

35.
Pereira, O. L., Rollemberg, C. L., Borges, A. C., Matsuoka, K. & Kasuya, M. C. M. Epulorhiza epiphytica sp. nov. isolated from mycorrhizal roots of epiphytic orchids in Brazil. Mycoscience 44, 153–155 (2003).
Google Scholar 

36.
Tedersoo, L. Biogeography of mycorrhizal symbiosis (Springer, Cham, 2017).
Google Scholar 

37.
Waud, M., Brys, R., Van Landuyt, W., Lievens, B. & Jacquemyn, H. Mycorrhizal specificity does not limit the distribution of an endangered orchid species. Mol. Ecol. 26(6), 1687–1701 (2017).
CAS  PubMed  Google Scholar 

38.
van der Cingel, N. A. An atlas of orchid pollination: America, Africa, Asia and Australia (A.A. Balkema Publishers, Rotterdam, 2001).
Google Scholar 

39.
Pansarin, E. R. & Maria do Carmo, E. A. Biologia reprodutiva e polinização de duas espécies de Polystachya Hook. no Sudeste do Brasil: evidência de pseudocleistogamia em Polystachyeae (Orchidaceae). Rev. Bras. Bot. 29(3), 423–432 (2006).

40.
Chakraborty, D. et al. Selecting populations for non-analogous climate conditions using universal response functions: the case of Douglas-Fir in Central Europe. PLoS ONE 10(8), e0136357 (2015).
PubMed  PubMed Central  Google Scholar 

41.
Broennimann, O. & Guisan, A. Predicting current and future biological invasions: both native and invaded ranges matter. Biol. Lett. 4, 585–589 (2008).
PubMed  PubMed Central  Google Scholar 

42.
Abrams, M. D. Adaptations of forest ecosystems to air pollution and climate change. Tree Physiol. 31, 258–261 (2011).
PubMed  Google Scholar 

43.
Atwater, D. Z., Ervine, C. & Barney, J. N. Climatic niche shifts are common in introduced plants. Nat. Ecol. Evol. 2, 34–43 (2018).
PubMed  Google Scholar 

44.
Konowalik, K. & Kolanowska, M. Climatic niche shift and possible future spread of the invasive South African Orchid Disa bracteata in Australia and adjacent areas. PeerJ 6, e6107 (2018).
PubMed  PubMed Central  Google Scholar 

45.
Early, R. & Sax, D. F. Climatic niche shifts between species’ native and naturalized ranges raise concern for ecological forecasts during invasions and climate change. Global Ecol. Biogeogr. 23, 1356–1365 (2014).
Google Scholar 

46.
Baranow, P. & Mytnik-Ejsmont, J. Two new species of Polystachya Hook. (Orchidaceae) from Africa. Plant Syst Evol. 281, 11–16 (2009).
Google Scholar 

47.
Mytnik-Ejsmont, J. & Baranow, P. Taxonomic study of Polystachya Hook. (Orchidaceae) from Asia. Plant Syst. Evol. 290, 57–63 (2010).
Google Scholar 

48.
Russell, A. et al. Phylogenetics and cytology of a pantropical orchid genus Polystachya (Polystachyinae, Vandeae, Orchidaceae): Evidence from plastid DNA sequence data. Taxon 59(2), 389–404 (2010).
Google Scholar 

49.
McCartney, C. African affinities, part I: the surprising relationship of some of Florida’s wild orchids. Orchids 69(2), 130–139 (2010).
Google Scholar 

50.
Mytnik-Ejsmont, J. A monograph of the subtribe Polystachyinae Schltr. (Orchidaceae) (Wydawnictwo Uniwersytetu Gdańskiego, Gdańsk, 2011).
Google Scholar 

51.
GBIF Occurrence Download; https://doi.org/10.15468/dl.ks410t (2018).

52.
Phillips, S. J., Dudík, M. & Schapire, R. E. A maximum entropy approach to species distribution modeling. In: ICML ’04. Proceedings of the Twenty-First International Conference on Machine learning. 655–662 (ACM, New York, 2004).

53.
Phillips, S. J., Anderson, R. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 

54.
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 

55.
Barve, N. et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol. Model. 222, 1810–1819 (2011).
Google Scholar 

56.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 

57.
WorldClim (version 1.4) www.worldclim.org

58.
Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611 (2010).
Google Scholar 

59.
Chung, M. Y. et al. Comparison of genetic variation between northern and southern populations of Lilium cernuum (Liliaceae): Implications for Pleistocene refugia. PLoS ONE 13(1), e0190520. https://doi.org/10.1371/journal.pone.0190520 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

60.
Kim, S. H. et al. Phylogeography and ecological niche modeling reveal reduced genetic diversity and colonization patterns of skunk cabbage (Symplocarpus foetidus; Araceae) from Glacial Refugia in Eastern North America. Front. Plant Sci. 9, 648. https://doi.org/10.3389/fpls.2018.00648 (2018).
Article  PubMed  PubMed Central  Google Scholar 

61.
Moss, R. et al. Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies. (Intergovernmental Panel on Climate Change, 2008)

62.
Weyant, J. et al. Report of 2.6 Versus 2.9 Watts/m2 RCPP Evaluation Panel (IPCC Secretariat, 2009).

63.
Sohel, S. I., Akhter, S., Ullah, H., Haque, E. & Rana, P. Predicting impacts of climate change on forest tree species of Bangladesh: evidence from threatened Dysoxylum binectariferum (Roxb.) Hook.f. ex Bedd. (Meliaceae). Forest 10(1), 154–160 (2016).
Google Scholar 

64.
Sony, R. K., Sen, S., Kumar, S., Sen, M. & Jayahari, K. M. Niche models inform the effects of climate change on the endangered Nilgiri Tahr (Nilgiritragus hylocrius) populations in the southern Western Ghats, India. Ecol. Eng. 120, 355–363 (2018).
Google Scholar 

65.
Mason, S. J. & Graham, N. E. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves statistical significance and interpretation. Q. J. R. Meteorol. Soc. 128, 2145–2166 (2002).
ADS  Google Scholar 

66.
Evangelista, P. H. et al. Modelling invasion for a habitat generalist and a specialist plant species. Divers. Distrib. 14, 808–817 (2008).
Google Scholar 

67.
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar 

68.
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith J. Dismo: Species Distribution Modeling. R package version 1.1-4. https://cran.r-project.org/package=dismo (2017)

69.
Phillips, S. B., Aneja, V. P., Kang, D. & Arya, S. P. Modelling and analysis of the atmospheric nitrogen deposition in North Carolina. Int. J. Glob. Environ. Issues 6, 231–252 (2006).
Google Scholar 

70.
Warren, D. L., Glor, R. E. & Turelli, M. Environmental nicheequivalency versus conservatism: quantitative approaches toniche evolution. Evolution 62, 2868–2883. https://doi.org/10.1111/evo.2008.62.issue-11 (2008).
Article  PubMed  Google Scholar 

71.
Schoener, T. W. The Anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology 49, 704–726. https://doi.org/10.2307/1935534 (1968).
Article  Google Scholar 

72.
Heibl, C. & Calenge, C. Phyloclim: integrating phylogenetics and climatic Niche modeling. R package version 0.9-4 https://cran.rproject.org/web/packages/phyloclim/phyloclim.pdf (2015).

73.
Kremen, C. et al. Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science 320, 222–226 (2008).
ADS  CAS  PubMed  Google Scholar 

74.
Leps, J. & Smilauer, P. Multivariate Analysis of Ecological Data Using CANOCO (Cambridge University Press, Cambridge, 2003).
Google Scholar 

75.
Peterson, A. T. et al. Ecological Niches and Geographic Distributions (MPB-49) (Princeton University Press, Princeton, 2011).
Google Scholar  More

In-flight dosing system
The system used in this work is detailed in Fig. 1. It can be broken down into three primary modules: (1) a “coarse tracking” system that uses a pair of stereoscopic cameras to identify the three dimensional location of a target subject, which is passed on to (2) the “fine tracking” system that uses a single higher speed camera and a fast scanning mirror (FSM) to keep the target in the middle of the field of view (FOV) of the camera using a proportional-integral-derivative (PID) control loop, and (3) the laser dosing system that fires the laser pulse, which is co-aligned with the fine tracking system to ensure the laser pulse is accurately applied to the subject even while it is moving. For both the coarse and fine tracking systems, subjects are identified by the size of their silhouettes generated from near infrared LED back-illumination or reflection. As demonstrated in Fig. 1b, the subject cages were 20 cm cubes constructed from clear acrylic, but with the side facing the tracking and dosing systems made of borosilicate glass, placed two meters away from the FSM. A high-speed video camera (Vision Research Phantom) was set up to record a small subset of experiments at 2000 frames per second as well. For all experiments, each cage contained only a single subject to be illuminated, or “dosed,” with the laser, and conditions were set such that a subject could be dosed only when it was at least 2.5 to 5 cm away from any wall of the cage to ensure the subject was flying normally, rather than taking wing or alighting.
Figure 1

Schematics and images of in-flight dosing setup. (a) Schematic (not to scale) of primary dosing system components and communication lines among them. Note that the mirror control and 3D position subsystems were run on separate kernels within the same PC. (b) Image of complete system setup including the test cage location, LED backlighting, and control electronics. Cameras for coarse and fine tracking, the fast scanning mirror (FSM), and dosing laser path are contained in the white circle and better seen in (c) close-up image of core tracking and dosing system components and alignment among them. Red line represents fine tracking image path, green line the dosing laser path (laser not shown), and yellow line the combined fine tracking and dosing laser path.

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Prior to commencing the laser dosing experiments, a number of parameters were defined to analyze the results, as detailed in Table 1 and Fig. 2. The key outcome, in line with our previous study15 and with WHO guidelines on insecticide treatments17, was whether the subjects were alive or disabled (i.e. dead or moribund) 24 h after the treatment. To characterize how well the subjects were tracked during the laser pulse, the tracking error was defined as how far the insect’s centroid was from the center of the fine tracking camera’s FOV. Other parameters defined in Table 1 relate to the “occlusion factor,” or how much of the laser beam’s energy (assuming a Gaussian profile) overlapped with the target’s outline, as demonstrated in Fig. 2. From the coarse tracking system’s output of xyz position of the target, we could also define velocity, speed, and both linear and angular acceleration of the subjects before, during, and after the laser pulse.
Table 1 Definitions of all parameters tracked or calculated for each in-flight dosing event.
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Figure 2

Representative fine tracking camera images of A. stephensi silhouettes. In each frame, the approximate outline of the thorax and abdomen is drawn (thick black lines) according to a set pixel intensity threshold. The centroid of this region is then calculated (intersection of red crosshairs) and compared with the center of the camera’s field of view (green dot) to determine the current tracking error and provide input to the fine tracking PID loop controlling the direction of the scanning mirror. The green circle around the green dot represents the spot size of the laser (2.5 mm diameter for all images shown here); occlusion factor represents how much of this laser spot (assuming a Gaussian profile) overlaps with the body of the subject. The images in (a) demonstrate a typical time course for a single subject in 1 ms intervals, and those in (b) show representative depictions of tracking errors ranging from 1 to 5 pixels for various subjects.

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Initial results with 532 nm laser
Initial experiments were conducted with A. stephensi subjects and a 532 nm laser (Verdi, Coherent) using parameters for power (3 W), pulse duration (25 ms), and laser spot diameter (2.5 mm) that were defined in our previous work15 as an optimal combination of potential cost and mortality performance. As seen in Supplemental Video 1, the system could be quite effective in disabling a flying mosquito with these parameters. Of note from the video, along with flight tracking data for numerous trials (not shown), the 25 ms pulse duration appeared to be short enough that the mosquitoes did not perceptibly alter their flight pattern during the pulse to throw off the tracking algorithm. From Fig. 3a,b, though, the initial system did not have consistent enough tracking performance, such that survival of the subjects was almost entirely dictated by how well the fine tracking system kept the target near the center of its FOV. From this initial experiment on a sample of 80 subjects, we set limits for mean and maximum tracking error during the laser pulse as 2 and 3 pixels, respectively, where each pixel represents ~ 250 μm. Figure 3c,d demonstrates that the system was able to achieve and maintain this performance after modifying the PID loop parameters, and that there was no longer any association between tracking performance and mortality at these conditions. These criteria were evaluated and assured for all mortality data reported in this manuscript (i.e. a Kruskal–Wallis test indicated no significant differences in tracking errors among subjects that survived or were disabled by the laser pulses). Further, Supplemental Fig. S1 shows that flight behavior, in particular speed and linear acceleration, did correlate with tracking accuracy, but that the system was able to meet the noted tracking requirements even at the extremes of flight behavior that could be observed in this study given the restricted flight volumes (though the typical values seen here largely align with available data for other Anopheles mosquitoes18,19).
Figure 3

Influence of mean and maximum tracking errors on mortality outcomes. Initial mortality outcomes using LD90 conditions with a 532 nm laser from previous anesthetized work showed strong correlation with (a) mean and (b) maximum tracking errors over the 25 ms pulse duration. After setting and achieving new tracking error targets of less than 2 pixels mean and 3 pixels max (see Fig. 2b for depictions of overlap between the laser spot and the subject for various error magnitudes), identical experiments no longer showed any correlation with (c) mean or (d) maximum tracking errors. Moribund and dead outcomes were grouped in (c) and (d), labeled “Disabled,” and subsequent experiments since they both represent functional kills and are grouped as such in WHO guidelines for insecticide trials.

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Mortality results were analyzed as in Fig. 4. Each data point represents the mortality outcomes (i.e. percentage of targets that were dead or moribund at 24 h, assuming acceptable control mortality  2 × that of the other experiments at 445 or 532 nm. Note that this greater LD90 fluence value for the smaller spot still represented a lower pulse energy than required for the larger spots given the spot size differential.
Table 2 List of in-flight dosing experimental conditions and resulting LD90 fluence values for A. stephensi subjects.
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Figure 5

Dose–response curves for all experiments from Table 2, other than the two constant pulse energy tests. The x-axis is plotted on a log scale to allow clear visualization of the curves at both low and high fluence values. Individual data points and error bars not shown for the sake of clarity. The intersections of the dashed horizontal line at 90% mortality with the dose–response curves indicate the LD90 points, which are also presented in Table 2, along with pseudo R2 values for the logistic regression fits.

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Dosing with near infrared wavelengths
Two near infrared (NIR) laser sources were examined as well—a custom constructed 1,064 nm (1 μm) fiber laser with a maximum output of 30 W and a commercial 1,570 nm (1.5 μm) fiber laser (IPG Photonics) with a maximum deliverable output of 11 W. The right-hand side of Fig. 5 shows the dose–response curves for these laser sources in addition to those using visible wavelengths. All of the infrared experiments resulted in significantly higher LD90 fluence levels compared with the visible lasers, and given the log scale of Fig. 5, varying experimental conditions with the 1 μm source led to comparatively larger changes in LD90 compared with the visible sources. As with the blue diode, the small spot (1.5 mm) had the highest LD90 fluence, but unlike the visible lasers, the larger spot (4 mm) offered a lower LD90 fluence compared with the baseline 2.5 mm. For the 1.5 μm source, there was insufficient mortality at the maximum power available from this source with a 2.5 mm spot and 25 ms pulse duration, so Fig. 5 shows a curve using slightly longer pulse durations to make up the additional fluence required. From the available data, it appears that the LD90 fluence levels for the 1 μm and 1.5 μm sources are at least at reasonably comparable levels.
Effects of longer pulse durations with lower power
We then further explored the effects of increasing the pulse duration to determine whether this could relax the optical power required from the laser source, given that laser cost is correlated with output power. Figure 6a,b show the results for the 532 nm and 1,064 nm sources, respectively, with spot diameters of 2.5 mm and pulse duration set to 25, 50, or 100 ms. In all cases, the optical power was adjusted according to the pulse duration to supply a constant pulse energy, and therefore fluence at approximately the LD75 level determined from previous experiments, which was chosen to ensure a low likelihood of any data point being 100% mortality (i.e., a saturated signal). As evidenced from Fig. 6a and Table 3, at 532 nm there was a significant drop off in mortality at the longer pulse durations, although the effect is smaller going from 50 to 100 ms compared with the initial drop from 25 to 50 ms. Similar, but smaller magnitude effects were seen with the 1 μm source in Fig. 6b and Table 3. Supplemental Fig. S2 shows that the tracking accuracy for these experiments somewhat mirrors the mortality performance, with a notable decrement from 25 to 50 ms but no significant change from 50 to 100 ms.
Figure 6

Mortality results for experiments with constant pulse energy achieved by proportionally adjusting the power according to pulse duration (25, 50, and 100 ms). Dosing was performed with both the (a) 532 nm and (b) 1,064 nm lasers, both using a 2.5 mm spot size, and set to the approximate LD75 fluence value from the dose–response curves (solid curves with dashed line 95% confidence intervals of logistic regression fit) from the corresponding 25 ms experiments. Error bars on individual points represent 95% confidence intervals of exact binomial probabilities. Statistical differences among mortality at the various pulse durations summarized in Table 3. The slight offsets on the x axis in (a) stem from slight changes in the beam spot size as a function of power.

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Table 3 Results of χ2 tests for mortality equivalence among experiments with constant pulse energy but varied amounts of power and pulse duration (25, 50, and 100 ms).
Full size table

Comparing dosing performance across other insects
Using the same system and procedures, experiments were also run on SWD and ACP subjects as a way of cursorily exploring laser-insect interaction for species of interest besides A. stephensi. For SWD, a full dose–response curve was created for the 1 μm laser with typical exposure settings (2.5 mm spot diameter and 25 ms pulse duration). The LD90 fluence from this work was 8.6 J/cm2, compared with 12.9 J/cm2 for A. stephensi under the same conditions. Tracking performance on SWD subjects was comparable to that for A. stephensi, given that flight parameters such as speed and acceleration were comparable as well. With ACP subjects, inducing them to fly in the cages was very difficult. As such, we could not process a sufficient number of subjects to create a proper dose–response curve. Based on the limited data available, and as demonstrated in Supplemental Video 2, the system as set for the mosquito LD90 condition with the 1 μm laser, 2.5 mm spot diameter and 25 ms pulse duration was effective in tracking and disabling the ACP subjects, despite their very different flying behavior (greater speeds and accelerations, limited durations) relative to the other test species.
Longer range dosing system demonstration
As a final proof of principle test, a long-range version of the system from Fig. 1 was configured. This system worked at a distance of 30 m instead of 2 m, facilitated primarily by modifying the optics used for the coarse and fine tracking systems. Given the reduced flexibility of this system, it was only used with the 1 μm laser with a 2.5 mm spot diameter and 25 ms pulse duration. Rather than acquiring new dose–response curves, which was impractical given the setup’s location in a building ~ 30 km away from the insect-housing chamber, we verified the efficacy of the system using the LD90 conditions established by short-range dosing. Supplemental Video 3 shows this system dosing an A. stephensi subject from the same stock as used for short-range testing, and Supplemental Video 4 shows the same for a wild-sourced Culex pipiens mosquito obtained in a local pond. Note that the wild C. pipiens was substantially larger in size than the lab-reared A. stephensi. In all of the limited number of tests of this system for both species (10 A. stephensi and 5 C. pipiens), the mortality rate was 100%. More

1.
Yang, J., Kloepper, J. W. & Ryu, C. M. Rhizosphere bacteria help plants tolerate abiotic stress. Trends Plant Sci. 14, 1–4 (2009).
ADS  CAS  PubMed  Google Scholar 
2.
Kearl, J. et al. Salt-tolerant halophyte rhizosphere bacteria stimulate growth of alfalfa in salty soil. Front. Microbiol. 10, 1849. https://doi.org/10.3389/fmicb.2019.01849 (2019).
Article  PubMed  PubMed Central  Google Scholar 

3.
Khan, N. et al. Comparative physiological and metabolic analysis reveals a complex mechanism involved in drought tolerance in Chickpea (Cicer arietinum L.) induced by PGPR and PGRs. Sci. Rep. 9, 2097. https://doi.org/10.1038/s41598-019-38702-8 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

4.
Compant, S., Samad, A., Faist, H. & Sessitsch, A. A review on the plant microbiome: ecology, functions, and emerging trends in microbial application. J. Adv. Res. 19, 29–37 (2019).
CAS  PubMed  PubMed Central  Google Scholar 

5.
Bruto, M., Prigent-Combaret, C., Muller, D. & Moënne-Loccoz, Y. Analysis of genes contributing to plant-beneficial functions in plant growth-promoting rhizobacteria and related Proteobacteria. Sci. Rep. 4, 6261. https://doi.org/10.1038/srep06261 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

6.
Timmusk, S. Perspectives and challenges of microbial application for crop improvement. Front. Plant Sci. 8, 49. https://doi.org/10.3389/fpls.2017.00049 (2017).
Article  PubMed  PubMed Central  Google Scholar 

7.
Sessitsch, A., Pfaffenbichler, N. & Mitter, B. Microbiome applications from lab to field: facing complexity. Trends Plant Sci. 24, 194–198 (2019).
CAS  PubMed  Google Scholar 

8.
Olanrewaju, O. S., Glick, B. R. & Babalola, O. O. Mechanisms of action of plant growth promoting bacteria. World J. Microb. Biot 33, 197. https://doi.org/10.1007/s11274-017-2364-9 (2017).
CAS  Article  Google Scholar 

9.
Gupta, A. et al. Whole genome sequencing and analysis of plant growth promoting bacteria isolated from the rhizosphere of plantation crops coconut, cocoa and arecanut. PLoS ONE 9, e104259. https://doi.org/10.1371/journal.pone.0104259 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Pérez-Jaramillo, J. E., Carrión, V. J., de Hollander, M. & Raaijmakers, J. M. The wild side of plant microbiomes. Microbiome 6, 143. https://doi.org/10.1186/s40168-018-0519-z (2018).
Article  PubMed  PubMed Central  Google Scholar 

11.
Song, J. Y. et al. Genome sequence of the Plant Growth-Promoting Rhizobacterium Bacillus sp. strain JS. J. Bacteriol. 19, 14. https://doi.org/10.1128/JB.00676-12 (2012).
CAS  Article  Google Scholar 

12.
Duan, J., Jiang, W., Cheng, Z., Heikkila, J. J. & Glick, B. R. The complete genome sequence of the Plant Growth-Promoting bacterium Pseudomonas sp. UW4. PLoS ONE 8, e58640. https://doi.org/10.1371/journal.pone.0058640 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

13.
Li, S. et al. Complete genome sequence of Paenibacillus polymyxa SQR-21, a plant growth-promoting rhizobacterium with antifungal activity and rhizosphere colonization ability. Genome Announc. 2, e00281-e314. https://doi.org/10.1128/genomeA.00281-14 (2014).
Article  PubMed  PubMed Central  Google Scholar 

14.
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).
CAS  PubMed  Google Scholar 

15.
Penrose, D. M. & Glick, B. R. Methods for isolating and characterizing ACC deaminase-containing plant growth-promoting rhizobacteria. Physiol. Plantarum 118, 10–15 (2003).
CAS  Google Scholar 

16.
Lo, K. J., Lin, S. S., Lu, C. W., Kuo, C. H. & Liu, C. T. Whole-genome sequencing and comparative analysis of two plant-associated strains of Rhodopseudomonas palustris (PS3 and YSC3). Sci. Rep. 8, 12769. https://doi.org/10.1038/s41598-018-31128-8 (2018).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

17.
Liu, W. et al. Whole genome analysis of halotolerant and alkalotolerant plant growth-promoting rhizobacterium Klebsiella sp. D5A. Sci. Rep. 6, 20–22 (2016).
Google Scholar 

18.
Andrés-Barrao, C. et al. Complete genome sequence analysis of Enterobacter sp. SA187, a plant multi-stress tolerance promoting endophytic bacterium. Front. Microbiol. 8, 2023. https://doi.org/10.3389/fmicb.2017.02023 (2017).
Article  PubMed  PubMed Central  Google Scholar 

19.
Esmaeel, Q. et al. Draft genome sequence of plant growth-promoting Burkholderia sp. strain BE12, isolated from the rhizosphere of maize. Genome Announc. 6, e00299-18. https://doi.org/10.1128/genomeA.00299-18 (2018).
Article  PubMed  PubMed Central  Google Scholar 

20.
Wang, Z. et al. Draft genome analysis offers insights into the mechanism by which Streptomyces chartreusis WZS021 increases drought tolerance in sugarcane. Front. Microbiol. 10, 3262. https://doi.org/10.3389/fmicb.2018.03262 (2019).
Article  Google Scholar 

21.
Matteoli, F. P. et al. Genome sequencing and assessment of plant growth-promoting properties of a Serratia marcescens strain isolated from vermicompost. BMC Genomics 19, 750. https://doi.org/10.1186/s12864-018-5130-y (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

22.
Yu, Z. et al. Complete genome sequence of the nitrogen-fixing bacterium Azospirillum humicireducens type strain SgZ-5T. Stand. Genomic Sci. 13, 28. https://doi.org/10.1186/s40793-018-0322-2 (2018).
CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Westerman, R. L. Soil Testing and Plant Analysis. SSSA Book Series 3 3rd edn. (Madison, SSSA, 1990).
Google Scholar 

24.
Bharti, N., Pandey, S. S., Barnawal, D., Patel, V. K. & Kalra, A. Plant growth promoting rhizobacteria Dietzia natronolimnaea modulates the expression of stress responsive genes providing protection of wheat from salinity stress. Sci. Rep. 6, 34768. https://doi.org/10.1038/srep34768 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

25.
Sharma, S., Kulkarni, J. & Jha, B. Halotolerant rhizobacteria promote growth and enhance salinity tolerance in peanut. Front. Microbiol. 7, 1600. https://doi.org/10.3389/fmicb.2016.01600 (2016).
Article  PubMed  PubMed Central  Google Scholar 

26.
Gupta, S. & Pandey, S. ACC Deaminase producing bacteria with multifarious plant growth promoting traits alleviates salinity stress in French Bean (Phaseolus vulgaris) plants. Front. Microbiol. 10, 1506. https://doi.org/10.3389/fmicb.2019.01506 (2019).
Article  PubMed  PubMed Central  Google Scholar 

27.
Pan, J. et al. The growth promotion of two salt-tolerant plant groups with PGPR inoculation: a meta-analysis. Sustainability 11, 378. https://doi.org/10.3390/su11020378 (2019).
CAS  Article  Google Scholar 

28.
Vurukonda, S. S. K. P., Vardharajula, S., Shrivastava, M. & SkZ, A. Multifunctional Pseudomonas putida strain FBKV2 from arid rhizosphere soil and its growth promotional effects on maize under drought stress. Rhizosphere 1, 4–13 (2016).
Google Scholar 

29.
Marasco, R. et al. Salicornia strobilacea (synonym of Halocnemum strobilaceum) grown under different tidal regimes selects rhizosphere bacteria capable of promoting plant growth. Front. Microbiol. 7, 1286. https://doi.org/10.3389/fmicb.2016.01286 (2016).
Article  PubMed  PubMed Central  Google Scholar 

30.
Mukhtar, S. et al. Impact of soil salinity on the microbial structure of halophyte rhizosphere microbiome. World J. Microb. Biot. 34, 136. https://doi.org/10.1007/s11274-018-2509-5 (2018).
CAS  Article  Google Scholar 

31.
Patten, C. L. & Glick, B. R. Role of Pseudomonas putida indoleacetic acid in development of the host plant root system Appl. Environ. Microbiol. 68(3795), 3801 (2002).
Google Scholar 

32.
Etesami, H., Alikhani, H. A. & Hosseini, H. M. Indole-3-acetic acid (IAA) production trait, a useful screening to select endophytic and rhizosphere competent bacteria for rice growth promoting agents. MethodsX 2, 72–78 (2015).
PubMed  PubMed Central  Google Scholar 

33.
Abbamondi, G. R. et al. Plant growth-promoting effects of rhizospheric and endophytic bacteria associated with different tomato cultivars and new tomato hybrids. Chem. Biol. Technol. Agric. 3, 1. https://doi.org/10.1186/s40538-015-0051-3 (2016).
CAS  Article  Google Scholar 

34.
Gupta, S. & Pandey, S. Unravelling the biochemistry and genetics of ACC deaminase-An enzyme alleviating the biotic and abiotic stress in plants. Plant gene 18, 100175. https://doi.org/10.1016/j.plgene.2019.100175 (2019).
CAS  Article  Google Scholar 

35.
Khamna, S., Yokota, A., Peberdy, J. F. & Lumyong, S. Indole-3-acetic acid production by Streptomyces sp. isolated from some Thai medicinal plant rhizosphere soils. EurAsian J. BioSciences 4, 23–32 (2010).
CAS  Google Scholar 

36.
Spaepen, S. & Vanderleyden, J. Auxin and plant-microbe interactions. C. S. H. Perspect. Biol. 3, a001438. https://doi.org/10.1101/cshperspect.a001438 (2011).
CAS  Article  Google Scholar 

37.
Patten, C. L. & Glick, B. R. Bacterial biosynthesis of indole-3-acetic acid. Can. J. Microbiol. 42, 207–220 (1996).
CAS  PubMed  Google Scholar 

38.
Majeed, A., Abbasi, K. M., Hameed, S., Imran, A. & Rahim, N. Isolation and characterization of plant growth-promoting rhizobacteria from wheat rhizosphere and their effect on plant growth promotion. Front. Microbiol. 6, 198. https://doi.org/10.3389/fmicb.2015.00198 (2015).
Article  PubMed  PubMed Central  Google Scholar 

39.
Apine, O. A. & Jadhav, J. P. Optimization of medium for indole-3-acetic acid production using Pantoea agglomerans strain PVM. J. Appl. Microbiol. 110, 1235–1244 (2011).
CAS  PubMed  Google Scholar 

40.
Checcucci, A. et al. Role and regulation of ACC deaminase gene in Sinorhizobium melilotr: Is it a symbiotic, rhizospheric or endophytic gene?. Front. Genet. 8, 6. https://doi.org/10.3389/fgene.2017.00006 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

41.
Belimov, A. A. et al. Cadmium-tolerant plant growth-promoting bacteria associated with the roots of Indian mustard (Brassica juncea L. Czern.). Soil Biol. Biochem. 37, 241–250 (2005).
CAS  Google Scholar 

42.
Khan, N. A., Khan, M. I. R., Ferrante, A. & Poor, P. Editorial: ethylene: a key regulatory molecule in plants. Front. Plant Sci. 8, 1782. https://doi.org/10.3389/fpls.2017.01782 (2017).
Article  PubMed  PubMed Central  Google Scholar 

43.
Glick, B. R. Bacteria with ACC deaminase can promote plant growth and help to feed the world. Microbiol. Res. 169, 30–39 (2014).
CAS  PubMed  Google Scholar 

44.
Wang, Z. et al. Isolation and characterization of a phosphorus-solubilizing bacterium from rhizosphere soils and its colonization of Chinese Cabbage (Brassica campestris ssp. chinensis). Front. Microbiol. 8, 1270. https://doi.org/10.3389/fmicb.2017.01270 (2017).
Article  PubMed  PubMed Central  Google Scholar 

45.
Dinesh, R. et al. Isolation, characterization, and evaluation of multi-trait plant growth promoting rhizobacteria for their growth promoting and disease suppressing effects on ginger. Microbiol. Res. 173, 34–43 (2015).
PubMed  Google Scholar 

46.
Niu, X., Song, L., Xiao, Y. & Ge, W. Drought-tolerant plant growth-promoting rhizobacteria associated with foxtail millet in a semi-arid agroecosystem and their potential in alleviating drought stress. Front. Microbiol. 8, 2580. https://doi.org/10.3389/fmicb.2017.02580 (2018).
Article  PubMed  PubMed Central  Google Scholar 

47.
Singh, V. K., Singh, A. K., Singh, P. P. & Kumar, A. Interaction of plant growth promoting bacteria with tomato under abiotic stress: a review. Agricult. Ecosyst. Environ. 267, 129–140 (2018).
CAS  Google Scholar 

48.
Grover, M., Bodhankar, S., Maheswari, M. & Srinivasarao, C. Actinomycetes as mitigators of climate change and abiotic stress. In Plant Growth Promoting Actinobacteria: A New Avenue for Enhancing the Productivity and Soil Fertility of Grain Legumes (eds Subramaniam, G. et al.) 203–212 (Springer, Berlin, 2016).
Google Scholar 

49.
Sang, M. K., Jeong, J. J., Kim, J. & Kim, K. D. Growth promotion and root colonisation in pepper plants by phosphate-solubilising Chryseobacterium sp strain ISE14 that suppresses Phytophthora blight. Ann. Appl. Biol. 172, 208–223 (2018).
CAS  Google Scholar 

50.
Xiao, X., Fan, M., Wang, E., Chen, W. & Wei, G. Interactions of plant growth-promoting rhizobacteria and soil factors in two leguminous plants. Appl. Microbiol. Biot. 101, 8485–8497 (2017).
CAS  Google Scholar 

51.
Abdelkrim, S. et al. Effect of Pb-resistant plant growth-promoting rhizobacteria inoculation on growth and lead uptake by Lathyrus sativus. J. Basic Microb. 58, 579–589 (2018).
CAS  Google Scholar 

52.
Liu, Y. et al. Characterization of Lysobacter capsici strain NF87–2 and its biocontrol activities against phytopathogens. Eur. J. Plant Pathol. 155, 859–869 (2019).
CAS  Google Scholar 

53.
Edgar, R. C. Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ. https://doi.org/10.7717/peerj.4652 (2018).
Article  PubMed  PubMed Central  Google Scholar 

54.
Lee, Z. M., Bussema, C. III. & Schmidt, T. M. rrnDB: documenting the number of rRNA and tRNA genes in bacteria and archaea. Nucleic Acids Res. 37, D489–D493 (2008).
PubMed  PubMed Central  Google Scholar 

55.
Shen, X., Hu, H., Peng, H., Wang, W. & Zhang, X. Comparative genomic analysis of four representative plant growth-promoting rhizobacteria in Pseudomonas. BMC Genomics 14, 271. https://doi.org/10.1186/1471-2164-14-271 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Niazi, A. et al. Genome analysis of Bacillus amyloliquefaciens subsp. Plantarum UCMB5113: a rhizobacterium that improves plant growth and stress management. PloS ONE 9, e104651. https://doi.org/10.1371/journal.pone.0104651 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

57.
Riggs, P. J., Chelius, M. K., Leonardo, I. A., Kaeppler, S. M. & Triplett, E. W. Enhanced maize productivity by inoculation with diazotrophic bacteria. Funct. Plant Biol. 28, 829–836 (2001).
Google Scholar 

58.
Saleem, M., Arshad, M., Hussain, S. & Bhatti, A. S. Perspective of plant growth promoting rhizobacteria (PGPR) containing ACC deaminase in stress agriculture. J. Ind. Microbiol. Biot. 34, 635–648 (2007).
CAS  Google Scholar 

59.
Dorjey, S., Gupta, V. & Razdan, V. K. Evaluation of Pseudomonas fluorescens isolates for the management of Fusarium oxysporum f.sp. lycopersici and Rhizoctonia solani causing wilt complex in tomato. Indian Phytopathol. 70, 127–130 (2017).
Google Scholar 

60.
Dardanelli, M. S. et al. Changes in flavonoids secreted by Phaseolus vulgaris roots in the presence of salt and the plant growth-promoting rhizobacterium Chryseobacterium balustinum. Appl. Soil Ecol. 75, 31–38 (2012).
Google Scholar 

61.
Ogut, M., Er, F. & Brohi, A. Excessive phosphorus fertilization does not increase cadmium concentrations in soil or carrots (Daucus carota L.) grown in Konya (Turkey). Acta Agr. Scand B-S.P. 60, 420–426 (2010).
CAS  Google Scholar 

62.
Rodríguez, H., Fraga, R., Gonzalez, T. & Bashan, Y. Genetics of phosphate solubilization and its potential applications for improving plant growth-promoting bacteria. Plant Soil 287, 15–21 (2006).
Google Scholar 

63.
Kalayu, G. Phosphate solubilizing microorganisms: Promising approach as biofertilizers. Int. J. Agron. 2019, 4917256. https://doi.org/10.1155/2019/4917256 (2019).
CAS  Article  Google Scholar 

64.
Mellidou, I. et al. Silencing S-adenosyl-L-methionine decarboxylase (SAMDC) in Nicotiana tabacum points at a polyamine-dependent trade-off between growth and tolerance responses. Front. Plant Sci. 7, 379. https://doi.org/10.3389/fpls.2016.00379 (2016).
Article  PubMed  PubMed Central  Google Scholar 

65.
Michael, A. J. Biosynthesis of polyamines and polyamine-containing molecules. Biochem. J. 473, 2315–2329 (2016).
CAS  PubMed  Google Scholar 

66.
Nascimento, F. X., Hernández, A. G., Glick, B. R. & Rossi, M. J. Plant growth-promoting activities and genomic analysis of the stress-resistant Bacillus megaterium STB1, a bacterium of agricultural and biotechnological interest. Biotechnol. Rep. 25, e00406. https://doi.org/10.1016/j.btre.2019.e00406 (2020).
Article  Google Scholar 

67.
Goswami, R. et al. Optimization of growth determinants of a potent cellulolytic bacterium isolated from lignocellulosic biomass for enhancing biogas production. Clean Technol. Envir. 18, 1565–1583 (2016).
CAS  Google Scholar 

68.
Vokou, D., Giannakou, U., Kontaxi, C. & Vareltzidou, S. Axios. Aliakmon and gallikos delta complex, Νorthern Greece. In Encyclopedia of Wetlands, Vol. 4 World Wetlands (eds Finlayson, M. et al.) (Springer, Berlin, 2016).
Google Scholar 

69.
Mellidou, I., Keulemans, J., Kanellis, A. & Davey, M. W. Regulation of fruit ascorbic acid concentrations in high and low vitamin C tomato cultivars during ripening. BMC Plant Biol. 12, 239–258 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Bouyoucos, G. J. Hydrometer method improved for making particle size analysis of soils. Agron. J. 54, 464–465 (1962).
Google Scholar 

71.
Sparks, D. L. Methods of Soil Analysis – Part 3 – Chemical Methods, SSSA Book Series 5 (ASA. Madison, WI, SSSA, 1996).
Google Scholar 

72.
Karamanoli, K., Bouligaraki, P., Constantinidou, H. I. & Lindow, S. E. Polyphenolic compounds on leaves limit iron availability and affect growth of epiphytic bacteria. Ann. Appl. Biol. 159, 99–108 (2011).
CAS  Google Scholar 

73.
Eevers, N. et al. Optimization of isolation and cultivation of bacterial endophytes through addition of plant extract to nutrient media. Microb. Biotechnol. 8, 707–715 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

74.
Louden, B. C., Haarmann, D. & Lynne, A. M. Use of blue agar CAS assay for siderophore detection. J. Microbiol. Biol. Educ. 12, 51. https://doi.org/10.1128/jmbe.v12i1.249 (2011).
Article  PubMed  PubMed Central  Google Scholar 

75.
García, C. A., De Rossi, B. P., Alcaraz, E., Vay, C. & Franco, M. Siderophores of Stenotrophomonas maltophilia: detection and determination of their chemical nature. Rev. Argent. Microbiol. 44, 150–154 (2012).
PubMed  Google Scholar 

76.
Dworkin, M. & Foster, J. Experiments with some microorganisms which utilize ethane and hydrogen. J. Bacteriol. 75, 592–601 (1958).
CAS  PubMed  PubMed Central  Google Scholar 

77.
Hontzeas, N., Zoidakis, J., Glick, B. R. & Abu-Omar, M. M. Expression and characterization of 1-aminocyclopropane-1-carboxylate deaminase from the rhizobacterium Pseudomonas putida UW4: a key enzyme in bacterial plant growth promotion. Biochim. Biophys. Acta 1703, 11–19 (2004).
CAS  PubMed  Google Scholar 

78.
Bradford, M. A rapid and sensitive method for the quantitation of micro gram quantities of protein utilizing the principle of protein—dye binding. Anal. Biochem. 72, 248–258 (1976).
CAS  Google Scholar 

79.
Nautiyal, C. S. An efficient microbiological growth medium for screening phosphate solubilizing microorganisms. FEMS Microbiol. Lett. 170, 265–270 (1999).
CAS  PubMed  Google Scholar 

80.
Luziatelli, F., Ficca, A. G., Colla, G., Švecová, E. B. & Ruzzi, M. Foliar application of vegetal-derived bioactive compounds stimulates the growth of beneficial bacteria and enhances microbiome biodiversity in lettuce. Front. Plant Sci. 10, 60. https://doi.org/10.3389/fpls.2019.00060 (2019).
Article  PubMed  PubMed Central  Google Scholar 

81.
Herlemann, D. P. R. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

82.
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
CAS  PubMed  Google Scholar 

83.
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2, W537–W544 (2018).
Google Scholar 

84.
Li, D. et al. MEGAHIT v.10: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 1, 3–11 (2016).
Google Scholar 

85.
Seeman, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 15, 2068–2069 (2014).
Google Scholar 

86.
Thompson, J. D., Gibson, T. J. & Higgins, D. G. Multiple sequence alignment using ClustalW and ClustalX. Curr Protoc Bioinform. https://doi.org/10.1002/0471250953.bi0203s00 (2002).
Article  Google Scholar 

87.
Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acid S. 41, 95–98 (1999).
CAS  Google Scholar 

88.
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

89.
Felsenstein, J. Confidence limits on phylogenies: an approach using the Bootstrap. Evolution 39, 783–791 (1985).
PubMed  PubMed Central  Google Scholar 

90.
Wu, S., Zhu, Z., Fu, L., Niu, B. & Li, W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genomics 12, 444. https://doi.org/10.1186/1471-2164-12-444 (2011).
Article  PubMed  PubMed Central  Google Scholar 

91.
Kamou, N. N. et al. Isolation screening and characterisation of local beneficial rhizobacteria based upon their ability to suppress the growth of Fusarium oxysporum f. sp. radicis-lycopersici and tomato foot and root rot. Biocontrol Sci. Technol. 25(8), 928–949 (2015).
Google Scholar  More

Total P and available P
Fertilizer application significantly (P  0.05). The highest increase in available P concentration in NPK + S treatment observed in the 60–100 cm soil depth, with the increase of 111% and 115% in 60–80 cm and 80–100 cm soil depths, respectively, over CK treatment.
Figure 2

Effect of long-term application of chemical fertilizer, organic manure, and straw on total phosphorus (P) and available P concentrations. * indicates significant difference at P  3.6%) was associated with OM treatment, especially at the 0–20 and 20–40 cm soil depths (Fig. 3). The PAC values under NPK, NPK + S and OM treatments increased by 7.6%, 4.5% and 11.5% in the 0–20 cm soil depth and 4.2%, 1.3%, and 5.8% in 20–40 cm soil depth, respectively as compared to the CK treatment. However, PAC value for soil depth below 40 cm showed the trend, NPK  More