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Study wetlands and datasets
Four salt marshes located in Waquoit Bay and adjacent estuaries at Cape Cod, MA, USA were used as the case study sites: (1) Sage Lot Pond (SL), (2) Eel Pond (EP), (3) Great Pond (GP), and (4) Hamblin Pond (HP) (Fig. 1). The marshes represent a moderate gradient in nitrogen loading and a wide range of human population density31, 32. On the basis of nitrogen loading influx, SL is in relatively pristine condition (~ 5 kg/ha/year), whereas HP (~ 29 kg/ha/year), EP (~ 63 kg/ha/year), and GP (~ 126 kg/ha/year) represent a medium to high nitrogen loading31, 33. The vegetation community of the marshes is mostly dominated by Spartina alterniflora (a C4 plant) in the low marsh zone.
Figure 1

Locations of the case study salt marshes along the southern shore of Cape Cod in the Waquoit Bay and adjacent estuaries, MA. Nitrogen loading rates of the Sage Lot Pond, Hamblin Pond, Eel Pond, and Great Pond were 5, 29, 63, and 126 kg/ha/year, respectively.

Full size image

A comprehensive detail on the collections and processing of gas fluxes and environmental variables for the four salt marshes were presented in Abdul-Aziz et al.7. Closed chamber-based measurements of the net ecosystem exchange (NEE) of CO2 were made using a cavity ring-down spectrometer (CRDS) gas-analyzer (Model G2301, Picarro, Inc., Santa Clara, CA; frequency: 1 Hz; precision: 0.4 ppm) for different days during the extended growing season (May to October) in 2013 at the low marsh zones of the four salt marshes. The spectrometer analyzer was connected to the transparent, closed acrylic chamber (60 cm × 60 cm × 60 cm) through tubes. We calculated the molar concentrations of CO2 in the chamber using the ideal gas law. The instantaneous molar concentrations of CO2 were then linearly regressed with time (s). The regression slopes (i.e., rates of changes in CO2 concentrations) were normalized by the chamber area (60 cm × 60 cm = 3,600 cm2 = 0.36 m2) to compute the corresponding fluxes of CO2 (i.e., changes in CO2 concentrations per unit area and per unit time in μmol/m2/s) between the wetland soil and the atmosphere inside the chamber for each sampling period (typically ~ 5 min)6, 7. To avoid impacts of any experimental error, a coefficient of determination (R2) of 0.90 was set as the minimum threshold for the regression to qualify the computed CO2 fluxes as accurate and acceptable for analyses6, 7.
The employed enclosed chamber-based technique of measuring CO2 fluxes is a widely-used method in the carbon research domain34,35,36,37. The technique provides an effective way to measure surface-atmospheric gas fluxes. As demonstrated above, the method first involves the calculation of the gradient of molar concentrations of CO2 in time, which is then divided by the chamber area to compute the vertical CO2 fluxes. Since the measurement chamber is small (e.g., 60 cm × 60 cm × 60 cm for our equipment) and enclosed, the vertical fluxes of CO2 between soil and atmosphere (and not the divergence of CO2 fluxes) drives the changes in CO2 concentrations with time inside the chamber. Therefore, the chamber area-normalized rates of changes in CO2 molar concentrations represent the vertical CO2 gas fluxes between the soil and atmosphere inside the chamber.
The associated instantaneous environmental variables such as the photosynthetically active radiation (PAR), air temperature (AT), soil temperature (ST), and porewater salinity (SS) were concurrently measured7. The corresponding observations of atmospheric pressure (Pa) were collected from the nearby NOAA National Estuarine Research Reserve System (NOAA-NERRS) monitoring station located at Carriage House, MA38. The filtered daytime net uptake fluxes of CO2 (NEECO2,uptake) represented the measurements made between 8 a.m. and 4.30 p.m. (Eastern Standard Time, EST), with the corresponding PAR higher than 1.5 µmole/m2/s. AT was used to calculate the fluxes of NEECO2,uptake using the ideal gas law7; AT was, therefore, excluded as an environmental driver from further analyses. Instead, soil temperature (ST) was considered to represent the impact of temperature on NEECO2,uptake. The dataset included 137 observational panels from the four study wetlands for 25 sampling days (Table S1, Figure S1 and S2 in Supplemental notes).
Theoretical formulation of dimensionless numbers through parametric reductions
Dimensional analysis using Buckingham pi ((Pi)) theorem were applied to formulate wetland ecological similitudes and derive dimensionless functional groups or (Pi) numbers18, 20. According to the pi theorem, a combination of ({text{n}}) dimensional variables would lead to (({text{n}} – {text{r}})) dimensionless Π numbers (({text{r}} =) number of relevant fundamental dimensions). NEECO2,uptake, PAR, ST, SS, Pa, and the time-scale of measurement or estimation (t) were used for the dimensional analysis. PAR, ST, SS were the most dominant drivers of NEECO2,uptake, as identified in the study of Abdul-Aziz et al.7. Furthermore, Pa negatively correlates with net photosynthesis as stomatal conductance increases with decreasing pressure39. The selected variables for the dimensional analysis included four fundamental dimensions (mass: M; length: L; temperature: K; time: T) (Table 1). Since the variables were in different unitary systems, they were converted to the SI units by using appropriate conversion factors (Table 1). As the temperature dimension (K) was only represented by ST, specific heat of wet soil (cp = 1.48 kJ/kg/K) was further incorporated in the dimensional analysis to normalize ST. Following the pi theorem, a functional relationship ((f)) among the response (NEECO2,uptake) and the potential predictors was expressed as follows:

$$fleft( {NEE_{CO2,uptake} , PAR, ST, SS,{ }P_{a} ,{ }c_{p} , t} right) = 0$$
(1)

where the total number of variables, (n = 7); number of fundamental dimensions, (r = 4). Therefore, the total number of possible ({Pi }) numbers (= {text{n}} – {text{r}} = 3). The functional relation of Eq. (1) was then represented with (Phi) in terms of dimensionless numbers as follows:

$$Phi left( {Pi _{1} ,Pi _{2} , Pi _{3} } right) = 0$$
(2)

Table 1 List of variables, units and dimensions used for the dimensional analysis.
Full size table

Based on the pi theorem, four variables ((r = 4)) could be considered as “repeating variables” in each iteration to formulate a dimensionless number by involving any of the remaining variables. Although the repeating variables should include all relevant fundamental dimensions (M, L, K, and T in this study), they should not form a dimensionless number among themselves. For example, considering PAR, ST, SS, and t as the “repeating variables”, the first pi number (left( {Pi _{1} } right)) was expressed as follows:

$$Pi _{1} = PAR^{a} cdot ST^{b} cdot SS^{c} cdot t^{d} cdot NEE_{CO2,uptake}$$
(3)

where (a), (b), (c), and (d) were exponents. For (Pi _{1 }) to be dimensionless, the following equation was obtained using the principle of dimensional homogeneity (i.e., equal dimensions on both sides):

$$M^{0} cdot L^{0} cdot T^{0} cdot K^{0} = left( {frac{M}{{L^{2} T}}} right)^{{text{a}}} cdot left( K right)^{b} cdot left( {frac{M}{{L^{3} }}} right)^{c} cdot left( T right)^{d} cdot frac{M}{{L^{2} T}}$$
(4)

Therefore,

$$M^{0} cdot L^{0} cdot T^{0} cdot K^{0} = M^{{{text{a}} + {text{c}} + 1}} cdot L^{{ – 2{text{a}} – 3c – 2}} cdot T^{ – a + d – 1} cdot K^{b}$$
(5)

Equating the exponents of M, L, K, and T on both sides, we obtained the following matrix–vector form:

$$left[ {begin{array}{*{20}c} 1 & 0 & 1 & 0 \ { – 2} & 0 & { – 3} & 0 \ { – 1} & 0 & 0 & 1 \ 0 & 1 & 0 & 0 \ end{array} } right] left[ {begin{array}{*{20}c} a \ b \ c \ d \ end{array} } right] = left[ {begin{array}{*{20}c} { – 1} \ 2 \ 1 \ 0 \ end{array} } right]$$
(6)

The system of linear equations was algebraically solved to compute the exponents as: (a = – 1), (b = 0), (c = 0), and (d = 0) (see Text S1 in Supplemental notes for detailed algebraic equations and solutions). Therefore, from Eq. (3), we obtained the first pi number as

$$Pi _{1} = frac{{NEE_{CO2,uptake} }}{PAR}$$
(7)

Similarly, the other two ({Pi }) numbers were formulated as (see Text S1 in Supplemental notes)

$$Pi_{2} = frac{{SS cdot P_{a} }}{{PAR^{2} }}$$
(8)

$$Pi_{3} = frac{{ST cdot c_{p} cdot SS^{2} }}{{PAR^{2} }}$$
(9)

The pi theorem also allowed the derivation of new (Pi) numbers by combining any two (or more) original (Pi) numbers through multiplication or division as follows:

$$Pi _{4} =Pi _{2} timesPi _{3} = frac{{ST cdot c_{p } cdot SS^{3} cdot P_{a} }}{{PAR^{4} }}$$
(10)

$$Pi _{5} = frac{{Pi _{3} }}{{Pi _{2} }} = frac{{ST cdot c_{p} cdot SS}}{{P_{a} }}$$
(11)

Thus, the functional relationship of Eq. (2) could be represented in any of the following forms:

$$Phi left( {Pi _{1} ,Pi _{4} } right) = 0$$
(12)

$$Phi left( {Pi _{1} ,Pi _{5} } right) = 0$$
(13)

Therefore, dimensional analysis reduced the 7 original variables to 2–3 dimensionless numbers. Recalling the definition of similitude from the physical domain18, 20, such parametric reductions for the daytime net uptake fluxes of CO2 and the associated environmental drivers were termed as “wetland ecological similitudes” in this research. As apparent, (Pi _{1}) represented the dimensionless CO2 flux number (i.e., response), whereas (Pi _{2}) to (Pi _{5}) represented the environmental driver numbers (i.e., predictors).
Various sets of dimensionless numbers were obtained by iteratively changing the “repeating variables” (Table S2; see Text S1 in Supplemental notes for full derivations). However, only the unique ({Pi }) numbers were considered for further analysis with empirical data. For example, (frac{{ SS^{2} cdot ST cdot c_{p} }}{{PAR^{2} }}) (iteration-1 or 4 in Table S2) and (frac{{SS cdot sqrt {ST cdot c_{p} } }}{PAR}) (iteration-3) were considered non-unique ({Pi }) numbers, because the latter could be obtained as a square root function of the former. Similarly, (frac{{P_{a} }}{{PAR cdot sqrt {ST cdot c_{p} } }}) (iteration-3) could be obtained from a square root and inversion of (frac{{PAR^{2} cdot ST cdot c_{p} }}{{P_{a}^{2} }}) (iteration-2 or 5), and were considered the same number. Based on the pi theorem, the response ({Pi }) number (i.e., dimensionless CO2 flux number) were expressed as a general function ((psi)) of all unique dimensionless environmental numbers as follows:

$$frac{{NEE_{CO2,uptake} }}{PAR} = psi left[ {left( {frac{{SS cdot P_{a} }}{{PAR^{2} }}} right),left( {frac{{ST cdot c_{p} cdot SS^{2} }}{{PAR^{2} }}} right),left( {frac{{ST cdot c_{p } cdot SS^{3} cdot P_{a} }}{{PAR^{4} }}} right),left( {frac{{ST cdot c_{p} cdot SS}}{{P_{a} }}} right),left( {frac{{PAR^{2} cdot ST cdot c_{p} }}{{P_{a}^{2} }}} right),left( {frac{{SS cdot P_{a}^{3} }}{{PAR^{4} cdot ST cdot c_{p} }}} right)} right]$$
(14)

Empirical analysis to determine the linkages among the derived numbers
The multivariate method of principal component analysis (PCA) was applied to the observational dataset from the salt marshes of Waquoit Bay to identify the important environmental driver number(s) that had dominant linkage(s) with the response pi number40. PCA can resolve multicollinearity (mutual correlations) among the environmental driver numbers in a multivariate space, identifying the relatively unbiased information on their individual linkages with the response40, 41. To incorporate any non-linearity in the data matrix, observed (i.e., calculated) values of all pi numbers were log10-transformed, which were further standardized (centralized and scaled) as follows: (Z = left( {X – overline{X}} right)/s_{X}); (X) = log10-transformed pi number, (overline{X}) = mean of (X), and (s_{X}) = standard deviation of (X). More

NEWS
25 September 2020

Infernos in South America’s Pantanal region have burnt twice the area of California’s fires this year. Researchers fear the rare ecosystem will never recover.

Emiliano Rodríguez Mega

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Firefighters and volunteers in the Pantanal, Brazil, have been scrambling to rescue jaguars from extreme fires.Credit: Andre Penner/AP/Shutterstock

When Luciana Leite arrived in the Pantanal on 2 September, she thought she would be celebrating her wedding anniversary. Instead, the biologist and her husband spent their eight-day planned holiday aiding volunteers and firefighters struggling to extinguish the burning landscape.
A common destination for ecotourists, the Pantanal is the world’s largest tropical wetland, home to Indigenous peoples and a high concentration of rare or endangered species, such as jaguars and giant armadillos. Small fires occur every year in the region, which sprawls over parts of western Brazil and extends into Bolivia and Paraguay.

But 2020’s fires have been unprecedented in extent and duration, researchers say. So far, 22% of the vast floodplain — around 3.2 million hectares (see ‘Biodiversity Hotspot Under Threat’) — has succumbed to the flames, according to Renata Libonati, a remote-sensing specialist at the Federal University of Rio de Janeiro, Brazil, whose data are being used by firefighters to plan containment. That’s more than twice the area that has burnt in the record-breaking fires in California this year.
Scientists worry that the extreme blazes will profoundly alter the already fragile ecosystem of the Pantanal, and that research programmes investigating the region’s ecology and biodiversity will never recover.
“It’s apocalyptic,” says Leite, who studies humanity’s relationship with nature at the Federal University of Bahia in Salvador, Brazil. “It is a tragedy of colossal proportions.”
Scorched earth
Unlike in the nearby Amazon Rainforest, vegetation in the Pantanal has evolved to coexist with fire — many plant species there require the heat from fires to germinate. Often caused by lightning strikes, those natural fires tend to spring up at the end of the dry season, in September. They run out of fuel quickly, and the surrounding floodplains prevent them from spreading.
What’s different this season is that the region is facing its worst drought in 47 years, says Luisa Diele-Vegas, a Brazilian ecologist at the University of Maryland in College Park. And 2019’s fires were also intense, contributing even further to the unusually dry conditions and exacerbating the fire risk this year.

Source: Laboratory for Environmental Satellite Applications, Federal University of Rio de Janeiro

The desiccated vegetation was perfect tinder for fires intentionally set by ranchers clearing land for their cattle. But some of those fires got out of control, adding to the wildfire damage, says Diele-Vegas.
In July, Brazilian President Jair Bolsonaro announced a 120-day moratorium on setting fires in the Amazon and the Pantanal. However, those regulations were not strictly enforced, says José Marengo, a climatologist at the National Center for Monitoring and Early Warning of Natural Disasters in São Paulo. According to news reports, the Bolsonaro government, which has a reputation for being unfriendly towards environmental regulations, reduced the number of environmental inspectors and blocked funding for fire prevention this year.

What worries scientists further is that this year’s fire season might not be an isolated incident. Climate modelling suggests that the Pantanal could become hotter and drier, with a rise in temperature of up to 7 ºC by the end of the century1. Unpublished data from Diele-Vegas project an even grimmer outlook: by 2050, if climate-change trends continue, annual mean temperatures in the Pantanal could increase by 10.5%, and the annual volume of rain could decrease by 3%.
According to Marengo, these changes could lead to a collapse of the Pantanal’s current vegetation, making it even more susceptible to fires, and could push the region to transform into a different type of ecosystem.
A race against the flames
One of the biggest losses in this year’s fires is the region’s wildlife, says Douglas Morton, a remote-sensing specialist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, who has studied fires and deforestation across Brazil for two decades. Many creatures thrive in the mosaic landscape of the Pantanal, which includes flooded areas, grasslands, lakes and forests. Scientists have so far documented more than 580 species of bird, 271 of fish, 174 mammals, 131 reptiles and 57 amphibians in the region2. “My lasting memory from being in the Pantanal is the cacophony of life,” Morton says. “To me, that’s what’s so heart wrenching about seeing the extent of fires.”

Luciana Leite surveys the burnt landscape of the Pantanal during her trip on 2 September.Credit: Ben Phalan

The flames have also breached five territories in the Pantanal where Indigenous communities live. More than 80% of the land in each of the three most affected — Baía dos Guató, Perigara and Tereza Cristina — has been consumed by fire.
A number of locals have jumped in to rescue as many animals as possible from the flames and smoke. Eduarda Fernandes Amaral, who works as a guide in the Encontro das Águas State Park, is among them. As of 20 September, more than 83% of the park, which is home to a large number of jaguars, capybaras and alligators, had been destroyed.
In the past month, a team including Fernandes Amaral has rescued more than 20 animals, although some had to be euthanized. To deal with the situation, Fernandes Amaral and her colleagues have adopted a mantra. “When we see an animal dying, we have to look at it, be sad for two minutes and understand that there is another in need of help,” she says.

As the blazes advance, animal research in the Pantanal might also suffer. Two years ago, Diele-Vegas started a project to study the distribution of frogs, tree frogs and toads across the Pantanal, and how it might shift owing to land-use change and climate variations. But she doesn’t know whether the amphibian populations she’s monitoring will even survive the blazes.
“We are seeing our fauna and flora burning. And there’s a lot of this fauna and flora that we haven’t had time to study yet,” she says. “We are trying to race against time, but the fire is coming and taking everything down.”
After her initial trip to the Pantanal, Leite couldn’t leave it behind. She returned a few days ago to keep helping the locals. What she’s seen has convinced her that the wetlands will be forever changed.
“If climate trends, land-management trends and the current anti-environment politics persist,” says Leite, “the Pantanal as we know it will cease to exist.”

doi: 10.1038/d41586-020-02716-4

References

1.
Marengo, J. A., Oliveira, G. S. & Alves, L. M. in Dynamics of the Pantanal Wetland in South America (eds Bergier, I. & Assine, M) https://doi.org/10.1007/698_2015_357 (Springer, 2015).

2.
Tomas, W. M. et al. Trop. Conserv. Sci. https://doi.org/10.1177/1940082919872634 (2019).

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Volatile contents of S. caucasica shoots during the stages of oviposition by G. pecorum
Overall, 60 volatile compounds were identified in S. caucasica shoots during the preoviposition (I), oviposition (II), and postoviposition (III) stages of G. pecorum. These comprised 16 aldehydes, 14 ketones, 12 esters, 9 alcohols, 3 alkanes, 3 aromatic hydrocarbons, 1 acid, 1 ether, and 1 other. Among them, 35 volatiles were identified in I-L, 36 in II-L, and 37 in III-L. In addition, 18 volatiles were common to I-L, II-L, and III-L; 5 to I-L and II-L; 5 to II-L and III-L; and 2 to I-L and III-L. Ten volatiles were unique to I-L, 8 to II-L, and 12 to III-L (Table 1). The main chemical classes of I-L, II-L, and III-L were alcohols, esters, and others; alcohols and others; and alcohols and esters, respectively (Fig. 1).
Table 1 Volatiles detected from shoots of Stipa caucasica during preoviposition, oviposition, and postoviposition of Gasterophilus pecorum.
Full size table

Figure 1

Volatiles classes from shoots of Stipa caucasica during preoviposition, oviposition, and postoviposition of Gasterophilus pecorum. I-L, II-L, and III-L represent Stipa caucasica shoots during the preoviposition, oviposition, and postoviposition stages of Gasterophilus pecorum. (A) alcohols, (B) esters, (C) aldehydes, (D) ketones, (E) others, (F) acids, (G) alkanes, (H) aromatic hydrocarbons, and (I) ethers. Data are mean (n = 3) ± SE. Different letters indicate significant differences at p  0.05) (Fig. 1A). Of the alcohols, 3-hexen-1-ol,(Z)- had the highest relative contents, 25.68%, 55.65%, and 32.35% in I-L, II-L, and III-L, respectively, with no significant differences among these three (P  > 0.05). The relative content of 1-hexanol was higher in II-L (1.52%) than in III-L (1.01%) (P = 0.002) or I-L (0.89%) (P = 0.001), whereas III-L and I-L showed no significant difference (P  > 0.05). The relative contents of the other volatile alcohols were less than 0.8% (Table 1).
Twelve esters were identified from the three stages of S. caucasica. Among them, three, i.e., acetic acid hexyl ester, ethyl acetate, and acetic acid phenylmethyl ester, were common to all three stages; and four, i.e., 3-cyclohexen-1-ol,acetate, 2(3H)-furanone,5-ethyldihydro-, 3-hexen-1-ol,formate,(Z)-, and acetic acid pentyl ester, were common to two of the three stages. The relative contents of esters were lower in II-L (3.16%) than in III-L (40.61%) or I-L (27.81%) (P = 0.000; P = 0.000), whereas there was no significant difference between III-L and I-L (P  > 0.05) (Fig. 1B). The relative contents of acetic acid hexyl ester in II-L (1.47%) and III-L (1.14%) were not significantly different (P  > 0.05), but were higher in both than in I-L (0.52%) (P = 0.001 and 0.005, respectively). The relative contents of 3-hexen-1-ol,acetate,(Z)- (24.8%), a specific volatile of I-L, and 3-hexen-1-ol,acetate(E)- (38.7%), which was specific to III-L, were highest in esters in stages specifically containing them. The relative content of propanoic acid,2-methyl-,3-hydroxy-2,4,4-trimethylpentyl ester, which was detected only in I-L, was 1.12%, whereas those of the other volatiles in esters were lower than 0.8% (Table 1).
Sixteen aldehydes were identified from the three stages of S. caucasica. Among them, seven, i.e., hexanal, nonanal, decanal, heptanal, undecanal, 2-octenal, (E)-, and 2-heptenal,(Z)-, were common to all three stages; and two, i.e., 3-hexenal and 2,4-hexadienal, (E,E)-, were common to two of the three stages. The relative contents of aldehydes in I-L, II-L, and III-L were 10.83%, 6.84%, and 9.9%, and those of hexanal were 0.62%, 2.38%, and 1.16%, respectively; none of these differences was significant (P  > 0.05) (Fig. 1C). The relative contents of nonanal in I-L (1.45%) and II-L (1.9%) did not differ significantly (P  > 0.05), and both were higher than that in III-L (0.96%) (P  > 0.05 and P = 0.018, respectively). The relative content of decanal was higher in II-L (1.20%) than in I-L (0.78%) (P = 0.043) or III-L (0.65%) (P = 0.016), but those in I-L and III-L did not differ significantly (P  > 0.05). The following two volatiles were present in two of the three stages: 3-hexenal, with higher content in I-L (7.10%) than in III-L (5.03%) (P  > 0.05); and 2,4-hexadienal,(E,E)-, with content higher in II-L (0.3%) than in III-L (0.22%) (P = 0.00). Benzaldehyde was specific to III-L (0.99%), with the relative contents of other volatile aldehydes  0.05), with no significant difference between II-L and III-L (P  > 0.05) (Fig. 1D). The content of 2(5H)-Furanone,5-ethyl- was specific to II-L (2.38%), and the relative contents of the other ketones were  0.05), and both were higher than those for III-L (12.9%) (P = 0.017 and P  > 0.05, respectively) (Fig. 1E). The relative content of acetic acid, the only volatile in the class of acids, was lower in III-L (0.61%) than in II-L (3.36%) or I-L (2.14%) (P = 0.022 and P  > 0.05, respectively); there was no significant difference between the latter two (P  > 0.05). The relative contents of alkanes, aromatic hydrocarbons, and ethers were less than 0.22% (Fig. 1G–I). These included three alkanes, one in I-L and two each in II-L and III-L; three aromatic hydrocarbons, one of them specific to each stage; and one ether, which was not found in III-L (Table 1).
The five volatile compounds with the highest relative contents, in order, during the three stages of S. caucasica were as follows: I-L, caprolactam (30.66%)  > 3-hexen-1-ol,(Z)- (25.68%)  > 3-hexen-1-ol,acetate,(Z)- (24.8%)  > 3-hexenal (7.1%)  > acetic acid (2.14%); II-L, 3-hexen-1-ol,(Z)- (55.65%)  > caprolactam (22.68%)  > acetic acid (3.36%)  > hexanal (2.38%) = 2(5H)-furanone,5-ethyl- (2.38%); III-L, 3-hexen-1-ol,acetate,(E)- (38.7%)  > 3-hexen-1-ol,(Z)- (32.35%)  > caprolactam (12.9%)  > 3-hexenal (5.03%)  > hexanal (1.16%) (Table 1). A total of eight volatiles were detected: two (i.e., 3-hexen-1-ol,(Z)- and caprolactam) were common to the three stages, and three (i.e., acetic acid, 3-hexenal, and hexanal) to two of the three stages. Finally, 2(5H)-furanone,5-ethyl- was in the top 5 volatile compounds of only II-L.
Relative contents of volatiles in three plant species during the oviposition stage of G. pecorum
During the oviposition stage of G. pecorum, a total of 60 volatiles were identified in S. orientalis (II-D), S. caucasica (II-L), and C. latens (II-T). These comprised 18 esters, 13 aldehydes, 11 alcohols, 10 ketones, 2 alkanes, 2 aromatic hydrocarbons, 1 acid, 1 alkene, 1 ether, and 1 other. Of these, 35 were identified in II-D, 36 in II-L, and 27 in II-T. In addition, 11 were common to II-D, II-L, and II-T, 14 to II-D and II-L, and 2 to II-L and II-T; 10 were unique to II-D, 9 to II-L, and 14 to II-T (Table 2). The main chemical classes of II-D and II-L were alcohols and others, and those of II-T were alcohols, esters, and others (Fig. 2).
Table 2 Volatiles detected from shoots of three plant species during oviposition of Gasterophilus pecorum.
Full size table

Figure 2

Volatiles classes from shoots of three plant species during oviposition of Gasterophilus pecorum. II-D, II-L, and II-T represent shoots of Stipa orientalis, Stipa caucasica, and Ceratoides latens during the oviposition stage of Gasterophilus pecorum. (A) alcohols, (B) esters, (C) aldehydes, (D) ketones, (E) others, (F) acids, (G) alkanes, (H) aromatic hydrocarbons, (I) ethers, and (J) alkenes. Data are mean (n = 3) ± SE. Different letters indicate significant differences at p  0.05) (Fig. 2A). The relative content of 3-hexen-1-ol,(Z)- was lower in II-T (14.28%) than in II-L (55.65%) or II-D (44.64%) (P = 0.002 and 0.008), but there was not significant difference between II-L and II-D (P  > 0.05). The relative contents of 1-hexanol and 2-hexen-1-ol,(E)- in II-D, II-L, and II-T were 1.67%, 1.52%, 2.79%, and 0.72%, and 0.59% and 2.66%, respectively; these differences were not significant (P  > 0.05). Finally, 3-hexen-1-ol was specific to II-D (1.57%), and the relative contents of other alcohols were  0.05) (Fig. 2B). The relative content of acetic acid hexyl ester in II-D, II-L, and II-T was 0.4%, 1.47%, and 4.25%, respectively; these differences were not significant (P  > 0.05). The relative content of 2(3H)-furanone, 5-ethyldihydro- was higher in II-T (0.71%) than in II-D (0.27%) or II-L (0.26%) (P = 0.000; P = 0.000), but II-D and II-L were not significantly different (P  > 0.05). Both 3-hexen-1-ol,acetate,(Z)- (13.13%) and propanoic acid,2-methyl-,3-hydroxy-2,4,4-trimethylpentyl ester (1.07%) were unique to II-D, and benzoic acid methyl ester (1.88%), methyl salicylate (2.52%), and cis-3-hexenyl isovalerate (8.45%) were all unique to II-T. The relative contents of other esters were  0.05) (Fig. 2C). The relative contents of hexanal, nonanal, decanal, and heptanal were 0.25–2.38% and were higher in II-L than in II-D or II-T, although the differences were not significant (P  > 0.05). Finally, 3-hexenal (6.57%) was unique to II-D, and benzaldehyde (0.94%) to II-T. The relative contents of other aldehydes were  0.05) (Fig. 2D). Five ketones, i.e., 5-hepten-2-one,6-methyl-, 2(3H)-furanone,dihydro-5-methyl-, 2-hexanone,4-methyl-, 2-undecanone,6,10-dimethyl-, and acetophenone, were common to II-D and II-L, and 2(5H)-furanone,5-ethyl- (2.38%) was unique to II-L. The relative contents of other ketones were  0.05) (Fig. 2E). Acetic acid was the only substance in the class ‘acids,’ and its relative content was lower in II-D (1.44%) than in II-T (3.62%) (P = 0.046) or II-L (3.36%) (P  > 0.05); contents in II-T and II-L did not differ significantly (P  > 0.05). The only alkene, 1,3,6-Octatriene,3,7-dimethyl-, was unique to II-T (12.67%). The relative contents of other alkanes and ethers were  caprolactam (21.76%)  > 3-hexen-1-ol,acetate,(Z)- (13.13%)  > 3-hexenal (6.57%)  > 1-hexanol (1.67%); II-L, 3-hexen-1-ol,(Z)- (55.65%)  > caprolactam (22.68%)  > acetic acid (3.36%)  > hexanal (2.38%) = 2(5H)-furanone,5-ethyl- (2.38%); II-T, caprolactam (34.2%)  > 3-hexen-1-ol,(Z)- (14.28%)  > 1,3,6-octatriene,3,7-dimethyl- (12.67%)  > cis-3-hexenyl isovalerate (8.45%)  > acetic acid hexyl ester (4.25%) (Table 2). Eleven volatiles were included: two (3-hexen-1-ol,(Z)- and caprolactam) were common to all three plant species; the other nine were in the top five of only one species.
Relative contents of volatiles from S. caucasica in different growth periods
From S. caucasica at the early, flourishing, and late growth periods (GP1, GP2, and GP3, respectively), a total of 69 volatile compounds were identified. These comprised 17 ketones, 13 aldehydes, 11 esters, 10 alcohols, 4 alkanes, 4 aromatic hydrocarbons, 2 acids, 2 alkenes, 1 ether, and 5 others. Of these, 35 were found in GP1, 36 in GP2, and 40 in GP3. In addition, 11 were common to all three stages, 10 to both GP2 and GP3, 6 to both GP1 and GP2, and 4 to both GP1 and GP3; 14 were unique to GP1, 9 to GP2, and 15 to GP3 (Table 3). The main chemical classes of GP1 and GP2 were alcohols and others, and those of GP3 were esters and others (Fig. 3).
Table 3 Volatiles detected from shoots of Stipa caucasica during its different growth periods.
Full size table

Figure 3

Volatiles classes from shoots of Stipa caucasica during its different growth periods. GP1, GP2, and GP3 represent Stipa caucasica shoots during the early, flourishing, and late growth periods, respectively. Note that GP2 was actually the same sample as II-L in Figs. 1 and 2. Thus, the three groups had a total of seven rather than nine samples. (A) alcohols, (B) esters, (C) aldehydes, (D) ketones, (E) others, (F) acids, (G) alkanes, (H) aromatic hydrocarbons, (I) ethers, and (J) alkenes. Data are mean (n = 3) ± SE. Different letters indicate significant differences at p  0.05). The 3-hexen-1-ol,(Z)- content, which was the highest among all alcohols, was lower in GP3 (15.42%) than in GP1 (49.5%) or GP2 (55.65%) (P = 0.005 and 0.002, respectively); the latter two did not differ significantly (P  > 0.05). The relative content of 1-hexanol was lower in GP3 (0.59%) than in GP1 (1.98%) or GP2 (1.52%) (P = 0.001 and 0.007, respectively); the latter two were not significantly different (P  > 0.05). The relative contents of other alcohols were  0.05) (Fig. 3B). The relative content of acetic acid hexyl ester was higher in GP2 (1.47%) than in GP1 (0.52%) (P = 0.022) or GP3 (0.98%) (P  > 0.05), with no significant difference between GP1 and GP3 (P  > 0.05). Propanoic acid,2-methyl-,3-hydroxy-2,4,4-trimethylpentyl ester (1.48%) was unique to GP1, and 3-hexen-1-ol,acetate,(Z)- (28.42%) to GP3. The relative contents of other esters were  0.05) (Fig. 3E). The relative contents of the remaining four ‘others’ were  0.05) (Fig. 3C). The relative contents of hexanal and decanal decreased with growth period from 4.77% and 1.38% to 1.51% and 1.06%, respectively; but there were no significant differences between periods (P  > 0.05). Six volatiles were common to two of the three periods. There were no significant differences between the relative contents of 3-hexenal in GP3 (6.13%) and in GP1 (5.50%) (P  > 0.05) or between those of nonanal in GP2 (1.90%) and GP3 (1.43%) (P  > 0.05). Finally, 2-hexenal (4.51%) was unique to GP3, and the relative contents of other aldehydes were  0.05) (Fig. 3D). The relative content of 5-hepten-2-one, 6-methyl- was higher in GP1 (0.7%) than in GP3 (0.33%) (P = 0.020), with no significant difference between that in GP2 (0.45%) and that in GP1 or GP3 (both P  > 0.05). The relative content of 2-undecanone,6,10-dimethyl- was higher in GP1 (3.12%) than in GP2 (0.14%) (P = 0.05). Finally, 2(5H)-furanone,5-ethyl- (2.38%) was specific to GP2, and the relative contents of other ketones were  0.05) (Fig. 3F). The relative content of acetic acid, which was common to all three periods, was higher in GP2 (3.36%) than in GP3 (0.97%) (P = 0.035), but there was no significant difference between GP1 (1.87%) and GP2 or GP3 (both P  > 0.05). The other acid, propanoic acid,2-methyl-,2,2-dimethyl-1- (1%), was specific to GP3 (Table 3).
Four alkanes were identified, and the relative contents of individual alkanes ranged from 0.06% to 0.89%. The relative contents of all alkanes were higher in GP1 (1.56%) than in GP3 (0.15%) (P = 0.022), with no significant difference between GP2 (0.22%) and GP1 or GP3 (both P  > 0.05) (Fig. 3G). Two alkenes were found only in GP3; they had a total relative content of 4.76% (Fig. 3J); one, 1,3,6-octatrine,3,7-dimethyl-, accounted for 4.70% of this total. The relative aromatic hydrocarbon and ether contents were  caprolactam (19.78%)  > 3-hexenal (5.5%)  > hexanal (4.77%)  > 2-undecanone,6,10-dimethyl- (3.12%); GP2, 3-hexen-1-ol,(Z)- (55.65%)  > caprolactam (22.68%)  > acetic acid (3.36%)  > hexanal (2.38%) = 2(5H)-furanone,5-ethyl-(2.38%); GP3, caprolactam (28.8%)  > 3-hexen-1-ol,acetate,(Z)- (28.42%)  > 3-hexen-1-ol,(Z)- (15.42%)  > 3-hexenal (6.13%)  > 1,3,6-octatriene,3,7-dimethyl- (4.70%) (Table 3). Overall, nine volatiles were detected: two (3-hexen-1-ol,(Z)- and caprolactam) were in the top five in all three growth periods, two (3-hexenal and hexanal) in two growth periods, and the other five were in the top five of in only one of the three growth periods. More

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The GM maize material used was the GM insect-resistant maize variety (line) GIF, and the maize was a yellow grain strain provided by the Lai Jinsheng Teacher Laboratory of China Agricultural University. The conventional maize variety Meiyu 11 with white kernels was selected as the pollen receptor of GM maize. The inheritance of the seed (kernel) color can be considered to be a single gene, with one pair of alleles (yellow vs. white). The yellow allele is dominant, and the white allele is recessive. The experimental site was sown at the base of the agricultural GM environmental safety assessment of the Institute of Tropical Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Wujitangxia Village, Maihao Town, Wenchang City, Hainan Province (110° 45′ 44″ E, 19° 32′ 14″ N). Transgenic insect-resistant maize was sown three times, once every other week, so that the pollination period of GM maize overlapped with the silking period of the non-GM maize. Artificial on-demand sowing with three seeds per hole and a 4–5 cm sowing depth was adopted.
Field experiments were carried out during two seasons in 2016–2017 and 2017–2018. In the first planting season of 2016–2017, the farthest investigated distance of flow frequency was 60 m (Fig. 1A, Table 1). According to the results from the first investigation, the frequency of gene flow in the eight directions beyond 30 m was very low, almost zero (Table 1). Thus, in 2017–2018, the farthest investigated distance of flow frequency was adjusted to 30 m. In the second planting season, the total area was approximately 14,000 m2 (Fig. 1B, Table 2). As in Hainan off-season reproduction regions the work of breeding research institutes is particularly intensive, it is generally difficult to meet conventional isolation conditions. At the same time, this area also provided a reference for regions around the world that need close isolation. Therefore, we added bagging measures in the treatment areas during the maize tassel pollination period in the second planting season in order to further reduce the flow frequency.
Figure 1

Design of the experimental area. (A) In the period of 2016–2017, the design of the experimental area included one control area (A) and one isolation area (B). The dimensions of control area A and isolation area B in the figure are the same. (B) In the period from 2017 to 2018, the design of the experimental area included one control area (D) and three isolation areas (A, B and C). The solid line represents the isolation area, and the dashed line represents the control area without isolation devices. A1–A8 and B1–B8 in (A) and A1–A8, B1–B8, C1–C8 and D1–D8 in (B) represent eight directions of NE, N, NW, W, SW, S, SE and E, respectively. The dimensions of control area D and isolation areas A, B and C in the figure are the same. The blue numbers represent the size of the experimental areas. The green arrows represent the main wind direction during flowering.

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In the first year of the experiment, control and treatment areas were set up. The area of the control region was 10,000 m2 (100 m × 100 m). A 100 m2 (10 m × 10 m) plot was designated in the center for GM insect-resistant maize, and non-GM maize was planted around this central area. The treatment area with isolation measures was 10,000 m2 (100 m × 100 m). A 100 m2 (10 m × 10 m) plot was designated in the center for GM insect-resistant maize, and non-GM maize was planted around this area. Colored steel plates were used as an isolation measure. The isolation height was 4 m.
A colored steel plate was the isolation material used in these experiments (Fig. 2). Colored steel plates and steel plates are two different materials. At present, there are many colors of colored steel plates. As for which color was used in our isolation experiments, there was no strict requirement, only a desire to match with the surrounding environment. Colored steel plates have the advantages of having both an organic polymer and a steel plate, and many organic polymers have good colorability, formability, corrosion resistance, decoration and high-strength. This combines with the workability of a steel plate, which can be easily finished by stamping, cutting, bending, deep drawing, and other processing to form virtually any shape. This makes the products made of colored steel plates have excellent practicability, decoration, processing and durability.
Figure 2

Isolation device for natural ecological risk control of GM maize. (A) Schematic of the isolation device; (B) partial diagram of the isolation device; (C) sectional view of figure (B); (D) structural detail diagram of the square card; 1: rectangular steel frame, 1.1: steel frame wall, 1.1a: horizontal steel rod, 1.1b: vertical steel rod, 2: inclined support rod, 3: colored steel plate, 4: door for entry and exit, 5: hot-dip galvanized steel frame. 6: structure of the square card, 6.1: screw.

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When maize was harvested after ripening, the investigated directions of control plots were NE, N, NW, W, SW, S, SE and E, labeled with A1–A8, respectively, and those of the isolation plots were labeled with B1–B8, respectively. The location of GM insect-resistant maize from 1 m, 3 m, 5 m, 10 m, 15 m, 20 m, 30 m, 40 m, 50 m and 60 m was investigated along these eight directions. The farthest investigation distances for NE, NW, SW and SE were 60 m, and other directions were 40 m. Ten maize plants were harvested randomly at each point (the first ear). Plants were marked in the order of P1, P2, P3, … P10, dried and stored for further testing. The total number of kernels harvested per corn ear was recorded.
In the second year of the experiment, one control and three treatments were set up. The control plot and the three treatment areas with isolation measures covered an area of 3500 m2 (50 m × 70 m). A 100 m2 (10 m × 10 m) plot was designated in the center of the plot to plant GM maize, and non-GM maize was planted around this central area. Colored steel plates were used as an isolation measure. Bagging of tassels of transgenic maize plants was performed during the pollination period. No bagging was conducted in the control area.
When the maize was harvested after ripening, the investigated directions of control plots were NE, N, NW, W, SW, S, SE and E, labeled D1, D2, D3, D4, D5, D6, D7 and D8, respectively. Isolation area A was marked A1, A2, A3, A4, A5, A6, A7 and A8 along the same eight directions. Isolation areas B and C were marked with B1, B2, B3, B4, B5, B6, B7 and B8, and C1, C2, C3, C4, C5, C6, C7 and C8, respectively. The location of GM insect-resistant maize from 1 m, 3 m, 5 m, 10 m, 15 m, 20 m and 30 m was investigated along these eight directions. The farthest investigation distances for NE, NW, SW and SE were 30 m, and the farthest investigation distances for N, W, S and E were 20 m. Ten maize plants were harvested randomly at each point (the first ear). Plants were marked in the order of P1, P2, P3, … P10, dried and stored for further testing. The total number of kernels harvested per corn ear was recorded.
The endosperm was identified by dominant and recessive traits. According to the number of endosperm traits of GM insect-resistant maize harvested at different directions and distances from GM insect-resistant maize, the pollen transmission distance and outcrossing rate of GM insect-resistant maize were then determined. This method can only be applied to dominant endosperm traits such as yellow or non-waxy grains.
The outcrossing rate was calculated according to formula (1):

$$ P = frac{N}{T} times 100, $$
(1)

where P is the outcrossing rate percentage (%), N is the number of corn kernels containing exogenous genes (the number of the yellow seeds) per ear of corn in units of granules, and T is the total grains (the number of the yellow seeds and white seeds) per ear in units of granules. The outcrossing rates of exogenous genes in different directions and distances were determined, and then the pollen flow distance was determined.
As descriptive statistics, the arithmetic mean as well the standard deviation of outcrossing rates were calculated. The outcrossing rate at each point (1 m, 3 m, 5 m, … 60 m) in the experiment was the mean of the outcrossing rate (P1, P2, P3, … P10) of 10 corn plants at that point.
Details of the isolation device for gene flow risk control of GM maize
The isolation device for gene flow risk control of GM maize, as shown in Fig. 2, comprises a rectangular steel frame (1). The rectangular steel frame 1 was composed of four steel frame walls (1.1), each of which was composed of multiple horizontal steel poles (1.1a) and vertical steel poles (1.1b). Each vertical steel pole was fixed 20–30 cm deep in the soil, and the angle between the inclined support pole (2) and the vertical steel pole was 30°–45°. The vertical steel pole of the four steel frame walls intersected the horizontal steel pole of the top. There were eight inclined supporting poles at the intersection of the vertical steel pole at the four corners of the rectangular steel frame and the horizontal steel pole at the top of the rectangular steel frame, and one inclined supporting pole was fixed through the square card structure (6). The four-sided steel frame wall of the rectangular steel frame was equipped with a colored steel plate (3), and one side of the isolation device was provided with an entry and exit (4). Horizontal steel bars at the top of the rectangular steel frame were provided with a hot-dip galvanized steel frame (5). The hot-dip supporting steel frame was a quadrilateral, and the four corners of the hot-dip supporting steel frame were fixed in the middle of the horizontal steel pole through the hoop. The clamp structure (6) included a side opening and a hollow rectangular frame. The top of the inclined support rod was obliquely inserted into the square clamp structure and fixed on the vertical steel rod through a screw (6.1). The dimensions of the steel rod and the inclined supporting rod were 6000 mm in length, 40 mm in diameter and 2 mm in thickness, and the colored steel plate was 0.425 mm in thickness. The size of the device and the number of inclined supporting rods were determined according to the actual situation in the field. More