Different species of Tillandsia can be biomonitors of carbon and nitrogen emissions: the case of a tropical metropolitan area in Mexico

Different species of Tillandsia can be biomonitors of carbon and nitrogen emissions: the case of a tropical metropolitan area in Mexico | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Different species of Tillandsia can be biomonitors of carbon and nitrogen emissions: the case of a tropical metropolitan area in Mexico Paula Zamora Tirado, Yareni Perroni, Edison Armando Diaz Álvarez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4378000/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Jan, 2025 Read the published version in Acta Physiologiae Plantarum → Version 1 posted 4 You are reading this latest preprint version Abstract Air pollution affects human health and ecosystems all over the world. However, little attention is paid to its monitoring in tropics, mainly due to the high costs of automated monitoring systems. Biomonitoring may be an alternative, particularly for species of Tillandsia genus, although most species are not yet calibrated for this purpose. Therefore, 1) to determine the biomonitoring potential of T. juncea and T. schiedeana and, 2) to compare the sources and magnitudes of atmospheric pollutants at five urban parks and one rural site in a tropical metropolitan area in Mexico, we measured the elemental and isotopic composition of carbon (C) and nitrogen (N) of four Tillandsia species. The C content averaged 44.6 ± 0.5% (dry weight; p > 0.05). The N content ranged from 0.6 ± 0.1% for the rural site and 2.0 ± 0.1% for an urban site (p < 0.001). The lowest value of δ 13 C was − 15.9 ± 0.1‰ for T. usneoides for all urban parks, and the highest was − 14.3 ± 0.2‰ for T. juncea in the rural area (p < 0.001). The lowest δ 15 N of − 12.1 ± 0.2‰ was recorded for T. usneoides in the rural area, and the highest of − 0.5 ± 0.5‰ were recorded for T. schiedeana in one of the urban sites. The four species can be used as biomonitors of C and N emissions, since their specific variations reflect the source and concentration of these atmospheric pollutants. Furthermore, the tillandsias showed that pollution in the metropolitan area is different depending on the activity at each site. air quality environmental pollution global change plant atmosphere interactions stables isotopes in ecology urban ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Transportation and the global production of goods and services generate millions of tons of pollutants to be emitted into the atmosphere each year (McDuffie et al, 2020 ). In particular, carbon and nitrogen emissions contribute to poorer air quality in cities and rural areas (Galloway et al. 2004; Salameh, 2014). For example, in urban areas these air pollutants come mainly from industrial, domestic, and transportation sources. In rural areas the emission sources are mainly from agriculture and livestock origin (Phoenix et al. 2006; Galloway et al. 2008 ). Carbon emissions, such as CO 2 , contribute to climate change, and their concentration is expected to double by 2050 compared to pre-industrial levels (IPCC 2014 ; Goudie 2019). On the other hand, anthropic activities break down atmospheric N 2 to produce reactive nitrogen (Nr), which combines with various compounds in the atmosphere to form NOx and NHy. In turn, these compounds are precursors of other air pollutants, such as tropospheric ozone, the main component of photochemical smog, and which in turn are precursors of particulate matter (PM 2.5 , PM 10 ). Particulate matter causes serious health problems worldwide (Jimenez et al. 2009 ; Miri et al. 2017). Exposure to air pollution is responsible for cardiovascular and respiratory diseases that already cause 7 million premature deaths worldwide each year (Landrigan et al. 2018 ; WHO, 2022). Nevertheless, air pollution will continue to be an important health determinant, with lung cancer cases expected to double by 2050, reaching 2.5 million in 2022 (IARC, 2024 ). For these reasons, the WHO recommends limits for some air pollutant concentrations that are not being followed in many cities around the world (WHO, 2023). Such is the case in Mexico, where, despite strict government regulations (NOM-156-SEMARNAT-2012), many cities do not have air quality monitoring systems due to budgetary constraints of local governments (Díaz-Álvarez et al. 2019 ). For example, in the Metropolitan Area of Xalapa (ZMX acronym in Spanish), there is only one monitoring station for a population of more than half a million; this lack of a more robust monitoring system can be a risk factor for residents, who have little information available to take protective measures (González-Rocha et al. 2016; INECC 2024; INEGI, 2020). To supplement air quality information, there is a more affordable alternative, the use of biomonitors. In Mexico and elsewhere in the Americas, two epiphytic bromeliads of the genus Tillandsia (tillandsias from now on), T. recurvata and T. usneoides , have been broadly used as biomonitors (Figuereido et al. 2007; Zambrano et al. 2009; Felix et al. 2016 ; Díaz-Álvarez et al. 2018 ; Díaz-Álvarez and de la Barrera 2018 , 2020 ). In addition to these two species widely distributed in the Americas, their genus includes another 790 species. Most of them are characterized by obtaining water and nutrients mostly from the atmosphere, and for this reason they are called atmospheric bromeliads (Reyes-García and Griffiths 2009 ; Gouda et al. 2024 ). This allows their tissues to reflect the sources and concentrations of atmospheric pollution in the places where they grow (White et al. 2012 , Pellegrini et al. 2014 ; Díaz-Álvarez and de la Barrera, 2018 ). For example, the δ 13 C tend to be more negative in cities than in rural areas (Díaz-Álvarez and de la Barrera, 2018 ). On the other hand, they increase their N content in urban environments where this element is more common (Hietz and Popp, 1999; Díaz-Álvarez and de la Barrera, 2018 ). In addition, the δ 15 N also change according to the source, being more positive in cities and more negative in the rural areas (Wania et al. 2002 ; Díaz-Álvarez and de la Barrera, 2018 ). The accurate and measurable responses of these tillandsias to atmospheric pollution suggests that other atmospheric bromeliads of this group could be biomonitors, this is important because T. recurvata and T. usneoides , despite their wide distribution, are not always established in all regions of interest. Hence, characterizing the responses of other atmospheric species of this genus can help supplement air quality information in regions and locations where monitoring networks are lacking or unavailable. In addition, where T. recurvata and T. usneoides do not grow some species such as T. juncea and T. schiedeana can thrive. Therefore, 1) to determine the biomonitoring potential of T. juncea and T. schiedeana and, 2) to compare the sources and magnitudes of atmospheric pollutants at six sites (five urban parks and one rural site) in a tropical metropolitan area in Mexico, we measured the elemental and isotopic composition of carbon and nitrogen of four Tillandsia species. Materials and Methods Study area The metropolitan area of Xalapa has a humid temperate climate and is located in the mountainous region on the slope the Gulf of Mexico in the central part of Veracruz (INEGI, 2000). The ZMX is composed of 10 municipalities that together cover an area of 1187 km 2 . Its elevation ranges from 700 to 1850 m.a.s.l. and its population reaches up to 800,000 (CONAPO 2020). The most important city is Xalapa, which in turn is the capital city of Veracruz state. Xalapa is characterized by its important vehicular movement, and its air quality goes from moderate to bad quality, where the PM 10 and PM 2.5 surpassed the WHO guidelines and Mexican standards. Besides, NO 2 exceeds the WHO guidelines, and O 3 and SO 2 show from good to moderate quality (González-Rocha et al. 2016). The second and the third most important municipalities of the ZMX are Coatepec and Xico, whose most important activities are agriculture, particularly coffee production, other activities include tourism and high road transportation (Rivera et al. 2022 ). We selected five urban public parks from the three main municipalities of the ZMX, Xalapa, Coatepec and Xico (Fig. 1 ; Table 1 ). Three parks were located in the urban area of Xalapa, Parque Juárez (PJ), Parque Los Berros (PB), and Parque Revolución (PR). These parks have an urban environment characterized by important traffic, and commerce in its surroundings. The Miguel Hidalgo Central Park (PMH) is in Coatepec. This park is characterized by important traffic from tourism and commerce, but in smaller quantities than for Xalapa (Table 1 ). In the same municipality, we selected a rural area (RA) where the population is much smaller than for the other sites, there is no commerce, and traffic is almost nonexistent (Table 1 ). Xico Park (PX) is in downtown Xico, this park is characterized by less traffic than for parks in Xalapa and Coatepec but high road transportation (Table 1 ). Table 1 Environmental and urban characteristics of the study sites at the Metroplitan Area of Xalapa. Study site Altitude m s.n.m. Mean annual temperature (°C) Mean annual precipitation (mm) Municipality and/or city Number of residents Number of vehicles (municipality) Rural area (AR) 1486 18 1849 Coatepec < 50 < 50 Parque Juárez (PJ) 1387 18 1757 Xalapa 443,063 215,807 Parque Revolución (PR) 1361 18 751 Xalapa 443,063 215,807 Parque Los Berros (PB) 1355 18 1740 Xalapa 443,063 215,807 Parque Xico (PJ) 1337 19 1879 Xico 21,585 5,770 Parque Miguel Hidalgo (PMH) 1197 19 1720 Coatepec 55,720 27,271 Plant material We selected four epiphytic bromeliads T. juncea, T. recurvata, T. schiedeana and T. usneoides because of their atmospheric habit (Reyes-García et al. 2022b ), abundance and wide distribution in the ZMX. These bromeliads have a CAM metabolism (Craig and William 1986; Cecchi et al. 1996; Hietz and Wanek 2003 , Winkler et al. 2006). T. juncea has a rosette shape and is found between 150 and 1900 m.a.s.l. Its range extends from Mexico to Bolivia (Espejo-Serna et al. 2004). T. recurvata is spherical in shape and established between 200 and 1430 m.a.s.l. from the southern United States to Argentina (Espejo-Serna et al. 2004). T. schiedeana is tufted and is distributed from Mexico to Venezuela from sea level to 1800 m (Espejo-Serna et al. 2004). T. usneoides is a hanging bromeliad, established from sea level to 2400 m a from the southern United States to Argentina (Espejo-Serna et al. 2004). In September 2021, we collected five healthy and fully developed leaves of each of the four bromeliad species available at each site, the leaves were collected from the lower canopy (2 to 4 meters; n = 5 for each species). In total, N = 90 plant tissue samples were collected. Elemental and isotopic analysis To determine the C, N content and the δ 13 C and δ 15 N isotopic composition, we dried the samples at 60°C for 72 hours in a gravity convection oven (Díaz-Álvarez & de la Barrera, 2019). We ground these samples to a fine powder in a ball mill (MiniBead-Beater-16, Biospec Products, Inc, Bartlesville, USA). We wrapped this material in 2.5 mm tin capsules (Costech Analytical, Inc. Valencia, California, USA) and weighed it on a semi-micro analytical balance (0.01 mg, ADAM EQUIPMENT, Maidstone Road, UK) to obtain from 2 to 2.5 mg. For each sample, the C and N contents, as well as the isotopic ratios of these elements were determined at the University of Wyoming Stable Isotope Facility (Laramie, Wyoming, USA). A Carlo Erba EA 1110 elemental analyzer (Costech Analytical Inc., Valencia, CA, USA) connected to a mass spectrometer (Finnigan Delta Plus XP, Termo Electron Corp, Waltham, MA) was used to obtain the values. Isotopic ratios of C and N are expressed in parts per thousand (‰) and were calculated relative to Vienna-Pee Dee Belemnite (V − PDB) standard for C, and atmospheric nitrogen for N. The natural abundances of 13 C and 15 N were calculated as: δ 13 (‰ versus V − PDB) = (R sample / R standard − 1) × 1000 δ 15 N (‰ versus at−air ) = (R sample / R standard − 1) × 1000 where, R is the ratio of 13 C/ 12 C for C and 15 N/ 14 N for N isotope abundance for a given sample (Ehleringer & Osmond 1989; Evans et al. 1996). Statistical analyses To determine the differences in C and N content, C:N ratio, as well as the isotopic composition of the four tillandsias at different sites, we used two-way ANOVA models. However, because not all species were established at all sites, we fitted four models in this research with the species that were established at the same sites to maintain the analyses orthogonality. For model 1, the main factors were species, with three levels ( T. juncea, T. schiedeana and T. usneoides ), and sites with three levels AR, PR, PB. For model 2, the main factors were species, with three levels T. juncea, T. schiedeana and T. recurvata and sites with two levels (PB and PJ). For model 3, the main factors were species with three levels ( T. schiedeana, T. recurvata and T. usneoides ) and sites with two levels (PB, PMH). For model 4, the main factors were species with two levels ( T. schiedeana and T. usneoides ) and sites with four levels (AR, PB, PMH, PR and PX). In all cases, when the variables evaluated did not satisfy the normality premise and variance homoscedasticity, they were transformed into hierarchical ranks for analysis (Conover and Iman 1981 ). When the models were significant, we performed post hoc multiple comparisons of means by LSD (least significant differences; Steel et al. 1997). We conducted the analyzes with R (R Core Team, R Foundation for Statistical Computing, Vienna, Austria, version 4.0.2, 2020). In addition, the agricolae package in R was used for post hoc tests analyses. In all cases, the alpha value was 0.05. Results Carbon and nitrogen content and C:N ratio The mean C content for all species was 44.6 ± 0.5%. The lowest value was 41.7 ± 2.1% for T. schiedeana in the PR. The highest value was 46.9 ± 1.1 for T. schiedeana in the PJ. However, no differences were found between species (p > 0.05; Table 2 ), nor for sites (p > 0.05; Table 2 ), nor for the species × site interaction (p > 0.05; Table 2 ). Table 2 Table 2 . Two-way ANOVA for carbon and nitrogen content, C:N ratio, δ 13 C and δ 15 N that satisfy orthogonality. Model 1 ( T. juncea, T. schiedeana, T. usneoides in the AR, PB, PR); model 2 ( T. juncea, T. recurvata, T. schiedeana in PJ, PB); model 3 ( T. recurvata, T. schiedeana, T. usneoides in PB, PMH); model 4 ( T. schiedeana and T. usneoides in AR, PB, PMH, PR, PX). Model Carbon content Nitrogen content C:N ratio δ 13 C δ 15 N df F p df F p df F p df F p df F P 1 Species 2 0.6837 0.5112 2 16.439 < 0.0001 2 24.0182 < 0.0001 2 5.0071 < 0.0001 2 11.673 < 0.0001 Sites 2 0.9902 0.3814 2 24.234 < 0.0001 2 33.6474 < 0.0001 2 6.4318 < 0.0001 2 118.907 < 0.0001 Species × site 4 1.6569 0.1814 4 1.3985 0.2541 4 3.9151 < 0.0001 4 0.5823 0.6774 4 11.14 < 0.0001 2 Species 2 1.1018 0.3485 2 2.2455 0.1277 2 1.7314 0.1984 2 0.8089 0.4571 2 6.9635 < 0.0001 Sites 1 0.3336 0.5689 1 0.0829 0.7758 1 0.0497 0.8254 1 0.2497 0.6219 1 12.3657 < 0.0001 Species × site 2 1.028 0.373 2 2.0361 0.1525 2 2.9354 0.0724 2 0.5325 0.5939 2 3.3207 < 0.0001 3 Species 2 0.9757 0.3914 2 0.6807 0.5158 2 0.7117 0.5009 2 1.7539 0.1946 2 0.2859 0.7539 Sites 1 1.3589 0.2552 1 16.8456 < 0.0001 1 31.8317 < 0.0001 1 7.6494 < 0.0001 1 20.9518 < 0.0001 Species × site 2 1.4897 0.2456 2 23.372 < 0.0001 2 34.1669 < 0.0001 2 0.035 0.9657 2 11.7797 < 0.0001 4 Species 1 3.1166 0.0851 1 0.0054 0.9418 1 0.0309 0.8614 1 13.5142 < 0.0001 1 4.3206 < 0.0001 Sites 4 0.0449 0.996 4 12.6327 < 0.0001 4 10.0779 < 0.0001 4 4.5947 < 0.0001 4 36.5544 < 0.0001 Species × site 4 1.2886 0.2907 4 5.5109 < 0.0001 4 4.0693 < 0.0001 4 0.9352 0.4534 4 7.493 < 0.0001 The mean N content for the species was 1.2 ± 0.1%. The lowest N content was 0.6 ± 0.1% in the AR for T. juncea , and the highest content was 2.0 ± 0.1% for T. recurvata in the PMH. Both the species (p < 0.001; Tables 2 , 3 ), and sites (p < 0.001; Tables 2 , 3 ) in model 1 showed differences. The species × site interaction (p 0.05; Table 2 ). Table 3 Nitrogen content (dry weight) and δ13C isotopic composition for the tillandsias and for the sites of each model that showed differences. Model 1 ( T. juncea, T. schiedeana, T. usneoides in the AR, PB, PR); model 2 ( T. juncea, T. recurvata, T. schiedeana in PJ, PB); model 3 (T. recurvata, T. schiedeana, T. usneoides in PB, PMH); model 4 ( T. schiedeana and T. usneoides in AR, PB, PMH, PR, PX). Model Factor Nitrogen (%) δ 13 C (‰) 1 Species T. juncea 0.9 ± 0.1b -14.9 ± 0.2a T. schiedeana 1.1 ± 0.1a -15.0 ± 0.2a T. usneoides 1.3 ± 0.1a -15.6 ± 0.2b Sites Parque Berros 1.3 ± 0.1a -15.5 ± 0.4b Parque Revolución 1.1 ± 0.1a -14.7 ± 0.2ab Área rural 0.8 ± 0.1b -15.3 ± 0.1a 3 Sites Parque Berros -15.5 ± 0.4b Parque Miguel Hidalgo -14.7 ± 0.2a 4 Species T. schiedeana -14.9 ± 0.2a T. usneoides -15.4 ± 0.2b Sites Parque Berros -15.5 ± 0.4b Parque Miguel Hidalgo -14.8 ± 0.2a Parque Revolución -15.4 ± 0.1ab Parque Xico -15.3 ± 0.1ab Área rural -14.9 ± 0.2ab Data are shown as mean ± SE. For each element, different letters indicate statistical differences (P < 0.05). Empty cells showed no difference. The C:N ratio of the species was on average 41.0 ± 2.8. The lowest value was 23.0 ± 1.4, for T. recurvata in the PMH, and the highest value was 86.9 ± 10.5, for T. juncea in the AR. For models 1, 2 and 3, the species × site interaction was significant (p 0.05; Table 3 ). Carbon and nitrogen isotopic composition The average δ 13 C values reached − 15.2 ± 0.2‰. The lowest value was − 15.9 ± 0.1‰ for T. usneoides in the PR, and the highest value was − 14.3 ± 0.2‰ for T. juncea in the AR. There were differences in species (p < 0.001; Tables 2 , 3 ), and sites (p < 0.001; Tables 2 , 3 ) for models 1 and 4. Model 3 showed differences only for species (p 0.05; Table 2 ). The average δ 15 N value for the species studied was − 4.3 ± 0.7‰. The lowest value was − 12.1 ± 0.2‰ for T. usneoides in the AR. The highest value was 0.5 ± 0.5‰ for T. schiedeana in the PMH. All models showed differences for the species × site interaction (p < 0.001; Fig. 4 ; Table 2 ). Discussion Environmental factors regulate C content in plants, as environment shapes their metabolism and functioning (Reich and Oleksyn, 2004; Reich, 2005; Zhang et al. 2012). Thus, the carbon content in epiphytic bromeliads is an indicator of environmental change, because, environmental factors such as water availability, solar radiation, temperature, or CO 2 concentration change, CO 2 assimilation also changes (Díaz-Álvarez et., 2015; Ma et al. 2018 ). For example, in CAM epiphytes, low water availability and increased temperature can cause a reduction in CO 2 assimilation due to reduced stomatal conductance (Lambers et al. 1998; Stancato et al. 2001 ). Epiphytic bromeliads, which are exposed to environments with nutrient and humidity limitations, are very efficient at integrating the environmental changes where they are established, either in rural or urban sites (Ruzana and Ainuddin, 2011 ). For example, the epiphytic bromeliad T. recurvata has a C content of 41.6% dry weight in both in urban and rural areas of central Mexico (Díaz-Álvarez and de la Barrera, 2020 ). In our study, the average C content for the four species studied at six sites had similar values. These suggest that environmental conditions did not alter the C content in these species. All this points out that the four studied species were in optimal conditions for their growth, in consequence in optimal condition for biomonitor use. The nitrogen content in plants is affected by several factors, including the species, growth type, as well as the environment in which they grow (Díaz-Álvarez et al. 2015; Martínez et al. 2021 ). Thus, when Nr increases in the environment, the N content in plant tissues also increases (Martínez et al. 2021 ). For three of the four species studied, T. recurvata, T. schiedeana and T. usneoides , no differences in N content were observed among them. This suggests that they have similar responses to environmental Nr at each site. However, T. juncea exhibited lower N content, possibly due to its morphology or leaf structure, that impedes further nitrogen take up. However, our data do not allow us to make this claim directly, so future studies should understand its nitrogen uptake mechanism in anthropic environments and how this may affect its potential as biomonitors. Although its response was not the same as that of the other three species, T. juncea responded effectively to nitrogen pollution at each site, therefore, it can be used as a biomonitor of N emissions. Anthropogenic activities can determine the foliar nitrogen content in atmospheric epiphytic bromeliads (Díaz-Álvarez and de la Barrera, 2018 ). For example, in rural areas where the NOx concentration is less than 5 ppb, the N content in atmospheric bromeliads can reach 0.7%. But in urban areas such as the megalopolis of Mexico City where the NOx reaches 57.4 ppb, their N content can reach 3.6% (Hietz and Popp, 1999; Díaz-Álvarez and de la Barrera, 2018 ). Our species followed this trend, as we observed differences between the rural area and the urban parks. However, our highest N content was not as high as that of Mexico City, this suggests that the NOx concentrations for our study sites were not as high as in Mexico City, this is an expected result when comparing the population of the two cities. This is a good example of how the number of residents and vehicles can determine the N content in plants. However, two sites did not follow this trend, the PX and PMH, as discussed below. Both PX and PMH had the highest N content, but the population there is smaller than in Xalapa, as is the number of vehicles. This result could be explained by a couple of factors. First, the constant use of fireworks for many religious and cultural celebrations round year close to the collecting points (Madrazo and Urdapilleta, 2008 ). Gunpowder utilized for fireworks can contain up to 75% potassium nitrate (KNO 3 ), which can release NH 4 and HCO 3 during combustion (Russell, 2009 ). These nitrogen compounds, like others released into the atmosphere, are potentially assimilated by vegetation, including epiphytic bromeliads (Díaz-Álvarez and de la Barrera 2018 ). Second, at both sites, street food preparation using propane gas and charcoal as fuel is very common, and particularly at PMH there are several coffee roasters in the vicinity, which are potential sources of N for the tillandsias. Another explanation is the greater abundance of road transport compared to the sites in Xalapa, given that both parks (PX and PMH) are close to the only local market. This pattern of higher N emissions near sites with large markets can also be observed for the Mercado de la Merced sites in Mexico City (González-Rocha et al. 2016). The responses of atmospheric tillandsias have to environmental factors can be understood through the foliar C:N ratio, which indicates the existing ratio between these two essential elements for their development (Mardegan et al. 2011). In our study, the C:N ratio followed the trend of N content, since it was higher in natural areas, indicating low N uptake, and lower in urban areas, indicating high N uptake. This has also been observed in other tillandsias in urban and natural environments such as in the Valley of Mexico (Díaz-Álvarez and de la Barrera, 2018 ). In epiphytic bromeliads, the isotopic enrichment or depletion of 13 C is affected by three main environmental factors, first temperature and water availability, second, their photosynthetic metabolism, and third, CO 2 source and concentration (Craig, 1961 , Ruzana and Ainuddin, 2011 ). For our study, all species responded in a similar way to the environmental conditions in which they grew. But temperature and water availability are similar for the study sites, which indicates that these are not significant factors that affected our results. This contrasts with findings for other tillandsias along a precipitation gradient and with other ecosystems where water and temperature are determining factors (Hietz et al. 1999 ; Cach, 2013). On the other hand, the four species had δ 13 C that correspond with CAM plants, that is, between − 10 and − 22‰ (Ehleringer and Squeo, 2004 ). This is important because under ideal conditions i.e., similar C sources, no differences in δ 13 C between species are expected. However, the different anthropic activities in our study area were evident by changes in the δ 13 C as discussed below. Another important factor that affects the plant δ 13 C is the C source (Díaz-Álvarez and de la Barrera, 2020 ). This is due to the fact that atmospheric CO 2 has specific isotopic compositions depending on its source. For instance, the δ 13 C of air in natural environments is -8‰ (Pichlmayer et al. 1998 ; Widory and Javoy, 2003 ). However, δ 13 C are more negative where transportation, industrial, or domestic activities are common. For example, δ 13 C from the combustion of coal, gasoline, diesel, and natural gas ranges from − 25 to -42‰ (Pataki, et al. 2003 ; Widory and Javoy, 2003 ; Semmens et al. 2014 ; Naus et al. 2018 ). These activities leave a particular isotopic signature on the nearby vegetation, which is why there are different isotopic values between plants in the countryside and in the city. For example, CAM bromeliads growing in natural environments generally have δ 13 C close to -14‰. Meanwhile, bromeliads from anthropized environments, such as cities, have values that can reach − 19‰ (Díaz-Álvarez and de la Barrera, 2020 ). We observed a similar pattern in most of our sites, but one of them was an exception, the PMH, which we discuss below. The PMH is in Coatepec, a small town with high vehicle traffic due to tourism every weekend, in addition to other activities, as we already mentioned for N content (Campion, 2023 ; Rivera et al. 2022 ). Therefore, we expected that bromeliads from this site showed δ 13 C typical of cities, but this was not the case. On the contrary, they were similar to those from the rural site. There are several possible explanations for this finding, but the most likely one is that the tillandsias at this site were exposed to more solar radiation, resulting in higher air temperatures and changes in CO 2 fixation (Farquhar and O'Leary, 1982; Díaz-Álvarez eta., 2017). However, this should also have been observed for N content, since in urban sites there is a close relationship between N content and δ 13 C. In particular, as N content increases, δ 13 C decreases (Díaz-Álvarez and de la Barrera 2018 , 2020 ). However, at this site it was not the case, instead the N content and the δ 13 C were high, which was similar to that of the rural area. At this point, we do not have a satisfactory explanation for these results. Other bromeliads that are important to mention are those from Xico since they had δ 13 C similar to the Xalapa species. However, Xico is a smaller town than Xalapa. Its number of vehicles is lower, so we expected the 13 C there would be more enriched than for Xalapa, but this was not the case. This can be explained because of the use of fireworks, as discussed above for the N content. Gunpowder in fireworks, like other human activities, has a very negative δ 13 C of up to -27.6‰ (Mizota and Yamanaka, 2014 ). Fireworks use is an important factor that could explain the very negative δ 13 C at this site, as they are a source of C compounds potentially assimilable by the surrounding vegetation (Díaz-Álvarez and de la Barrera; 2018 ). The four species studied responded effectively to the environmental context in which they developed, especially for δ 13 C, which directly indicates the intensity of nearby human activities. Therefore, they can be used for biomonitoring studies of atmospheric carbon pollution in urban and rural environments. Sources of atmospheric reactive nitrogen directly affect the δ 15 N of atmospheric tillandsias (Díaz-Álvarez and de la Barrera, 2018 ). For example, plants growing in urban areas tend to have positive δ 15 N when the pollutants emitted are mostly nitrogen oxides from industry and vehicles (Díaz-Álvarez et al. 2018 ; Díaz-Álvarez and de la Barrera, 2018 ). However, when the dominant emission source is NHy such as fertilizers from agricultural areas, the δ 15 N tend to be very negative (Stewart et al. 2002 ; Heaton et al. 2004 ; Díaz-Álvarez et al. 2018 ; Díaz-Álvarez and de la Barrera, 2021). On the other hand, atmospheric tillandsias growing in rural and natural areas with low human activity have negative δ 15 N, but close to zero (Wania et al. 2002 ). In our study, we recorded the most negative δ 15 N in the AR, which are similar to those already reported for atmospheric tillandsias in this region and other rural sites whose δ 15 N reach − 10.9 and − 11.2‰ (Hietz et al. 1999 ; Felix et al. 2016 ). Thus, the δ 15 N of the AR suggests that little anthropogenic activity is taking place there, which is also confirmed by the low N content recorded at this site. In the megalopolis of Mexico City (CDMX), massive industrial and vehicular activities are reflected in the δ 15 N of T. recurvata , since it reaches values of 5.1‰ (Zambrano et al. 2009; Díaz-Álvarez and de la Barrera, 2018 ). Some of our sites had positive δ 15 N, but the majority were negative close to zero, with a clear difference with the rural area, that is, they followed the trend of isotopic values that reflect the volume of human activity in the study area. However, our tillandsias were not as positive as those in the CDMX due to a couple of likely factors. First, the population density and the volume of industrial and vehicular activity are much lower in the ZMX than in CDMX, and therefore there are fewer emissions and less pollution (INEGI, 2020). Second, the topography CDMX and ZMX is different since Mexico City is located in a valley surrounded by volcanoes, which does not allow the free flow of pollutants, whereas, the ZMX is located at the slope of a mountainous region, which allows the free flow of air through the landscape (Nowak, 2020). Third, the buildings structure and configuration in the two metropolitan areas may also be a factor for the dissipation of atmospheric pollutants (Nowak, 2018). In general, all four species showed differences between sites, which is a clear response to human activities at each site. In particular, Tillandsia juncea can be utilized as biomonitor for C and N emissions, but not definitely, considering that its response is not as clear as that observed for the other three bromeliads studied. On the other hand, T. recurvata, T. usneoides and T. schiedeana can be utilized interchangeably for monitoring studies in the urban and rural landscape. T. schiedeana is particularly interesting because it grows in sites where T. recurvata and T. usneoides do not, for example, at different areas of the ZMX. In this urban landscape atmospheric pollution can exceed the regulated limits and has a lack of an efficient monitoring network. Therefore, it is an obvious laboratory for biomonitoring with T. schiedeana. Finally, future studies could utilize this assembly of species for monitoring atmospheric pollution in vast land extensions, to understand the risk that residents are facing due to air pollution. Declarations Competing Interests: The authors declare no competing financial interests. Author Contributions Statement P.E.Z.T. Conceived and designed the analysis, collected the data, performed the analysis, and wrote the paper; Y.P. Contributed analysis tools, collected the data, and wrote the paper. E.A.D.A. Procured funding, conceived, and designed the analysis, collected the data, performed the analysis, and wrote the paper. Acknowledgements This study was developed under the project “Biogeochemistry of nutrients in natural and anthropized environments of eastern Mexico”, registered at the Universidad Veracruzana in SIREI with No. 354822022107. We also thank fundings by SIREI project No. 51396202294. We thank Lenin Rios Figeroa for his aid in mapping. Authors are grateful to staff of the Instituto de Biotecnología y Ecología Aplicada (INBIOTECA) of Universidad Veracruzana for their logistic and administrative support in conducting this research. PEZT was supported by the National Council on Humanities, Science and Technology (CONAHCYT, grant 226643). References Cach-Pérez MJ (2013) Bromeliáceas epífitas de la Península de Yucatán como indicadoras de los posibles efectos del cambio climático regional. 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Acta Oecol 36:659–665. https://doi.org/10.1016/j.actao.2010.10.003 Cite Share Download PDF Status: Published Journal Publication published 23 Jan, 2025 Read the published version in Acta Physiologiae Plantarum → Version 1 posted Reviewers agreed at journal 26 May, 2024 Reviewers invited by journal 21 May, 2024 Editor assigned by journal 08 May, 2024 First submitted to journal 06 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4378000","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305163186,"identity":"3021a39c-01b8-4718-a659-e053bd80014a","order_by":0,"name":"Paula Zamora Tirado","email":"","orcid":"","institution":"Universidad Veracruzana","correspondingAuthor":false,"prefix":"","firstName":"Paula","middleName":"Zamora","lastName":"Tirado","suffix":""},{"id":305163187,"identity":"d5c447e8-a0a0-4aa5-833c-4f0c448aa923","order_by":1,"name":"Yareni Perroni","email":"","orcid":"","institution":"Universidad Veracruzana","correspondingAuthor":false,"prefix":"","firstName":"Yareni","middleName":"","lastName":"Perroni","suffix":""},{"id":305163188,"identity":"2f69730d-baff-4bde-bf74-9179a3d1ab97","order_by":2,"name":"Edison Armando Diaz Álvarez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYFCCxAYgwcwDxAdI1sKWQKwWsEJmIOYxIE4DP3ty84ePO6xl+PvPfJPmYdgGthQvkOx52CY580w6j8SBs9uAWm4bE7TF4EZiGzNv22EehoO92yRnMNyWI6jF/kZi82eQFvnDPM9AWngI2yKR2CAN0mJwjIdN4gMxtkicAfmlLZ3H8AybscUHAyL8wt+e/vjDxzZre7nzhx/eSKi4TTjE0N1JovpRMApGwSgYBdgBAFlDOi34NOI3AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5372-0250","institution":"Universidad Veracruzana","correspondingAuthor":true,"prefix":"","firstName":"Edison","middleName":"Armando Diaz","lastName":"Álvarez","suffix":""}],"badges":[],"createdAt":"2024-05-06 15:45:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4378000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4378000/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11738-024-03762-5","type":"published","date":"2025-01-23T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57873680,"identity":"7f7b78cb-8eb4-4b95-88a0-7154e47879b4","added_by":"auto","created_at":"2024-06-06 18:41:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5759831,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the collecting sites in the Metropolitan Area of Xalapa\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4378000/v1/e3ba26ffb8a985bf76853abc.jpg"},{"id":57873678,"identity":"bf86b6a7-08cb-4d1b-8dcd-9c62f30302c4","added_by":"auto","created_at":"2024-06-06 18:41:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":334582,"visible":true,"origin":"","legend":"\u003cp\u003eMean values of nitrogen content for the species × site interaction, from the two-way ANOVA models that were significant. Different letters represent differences in the values of model 3 (a, b, c), model 4 (A, B, C). Data are shown as mean ± SE (n = 5 individuals per species)\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4378000/v1/e697fbe024017df178c9ca35.png"},{"id":57873679,"identity":"e0b93e4c-d620-4f12-bf48-3546736a196d","added_by":"auto","created_at":"2024-06-06 18:41:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":326993,"visible":true,"origin":"","legend":"\u003cp\u003eMean values of the C:N ratio, for the species × site interaction of the two-way ANOVA models that were significant. Different letters represent significant differences in values. Model 1: w, x, y, z. Model 3: a, b, c. Model 4: A, B, C. Data are shown as mean ± SE (n = 5 individuals per species)\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4378000/v1/ffb9637e48ef8e6835fca2fe.png"},{"id":57873677,"identity":"29d3aafa-8f40-4c68-b6b4-ea5ad2241756","added_by":"auto","created_at":"2024-06-06 18:41:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":362908,"visible":true,"origin":"","legend":"\u003cp\u003eMean values of the δ\u003csup\u003e15\u003c/sup\u003eN isotopic composition, for the species × site interaction from the two-way ANOVA models that were significant. Different letters represent significant differences in values. Model 1 letters: u, v, w, x, y, z. Model 3 letters: a, b, c. Model 4 letters: A, B, C, D, E, F. Data are shown as mean ± SE (n = 5 individuals per species)\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4378000/v1/1d36562ec09670a7912367a4.png"},{"id":74858367,"identity":"3f2fdeec-7bb7-4386-b97e-58e4b3496f78","added_by":"auto","created_at":"2025-01-27 16:08:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7954194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4378000/v1/0ecf3738-4994-4a7f-bbc5-f237685c48e4.pdf"}],"financialInterests":"","formattedTitle":"Different species of Tillandsia can be biomonitors of carbon and nitrogen emissions: the case of a tropical metropolitan area in Mexico","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTransportation and the global production of goods and services generate millions of tons of pollutants to be emitted into the atmosphere each year (McDuffie et al, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In particular, carbon and nitrogen emissions contribute to poorer air quality in cities and rural areas (Galloway et al. 2004; Salameh, 2014). For example, in urban areas these air pollutants come mainly from industrial, domestic, and transportation sources. In rural areas the emission sources are mainly from agriculture and livestock origin (Phoenix et al. 2006; Galloway et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Carbon emissions, such as CO\u003csub\u003e2\u003c/sub\u003e, contribute to climate change, and their concentration is expected to double by 2050 compared to pre-industrial levels (IPCC \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Goudie 2019). On the other hand, anthropic activities break down atmospheric N\u003csub\u003e2\u003c/sub\u003e to produce reactive nitrogen (Nr), which combines with various compounds in the atmosphere to form NOx and NHy. In turn, these compounds are precursors of other air pollutants, such as tropospheric ozone, the main component of photochemical smog, and which in turn are precursors of particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e). Particulate matter causes serious health problems worldwide (Jimenez et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Miri et al. 2017).\u003c/p\u003e \u003cp\u003eExposure to air pollution is responsible for cardiovascular and respiratory diseases that already cause 7\u0026nbsp;million premature deaths worldwide each year (Landrigan et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; WHO, 2022). Nevertheless, air pollution will continue to be an important health determinant, with lung cancer cases expected to double by 2050, reaching 2.5\u0026nbsp;million in 2022 (IARC, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For these reasons, the WHO recommends limits for some air pollutant concentrations that are not being followed in many cities around the world (WHO, 2023). Such is the case in Mexico, where, despite strict government regulations (NOM-156-SEMARNAT-2012), many cities do not have air quality monitoring systems due to budgetary constraints of local governments (D\u0026iacute;az-\u0026Aacute;lvarez et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, in the Metropolitan Area of Xalapa (ZMX acronym in Spanish), there is only one monitoring station for a population of more than half a million; this lack of a more robust monitoring system can be a risk factor for residents, who have little information available to take protective measures (Gonz\u0026aacute;lez-Rocha et al. 2016; INECC 2024; INEGI, 2020).\u003c/p\u003e \u003cp\u003eTo supplement air quality information, there is a more affordable alternative, the use of biomonitors. In Mexico and elsewhere in the Americas, two epiphytic bromeliads of the genus \u003cem\u003eTillandsia\u003c/em\u003e (tillandsias from now on), \u003cem\u003eT. recurvata\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e, have been broadly used as biomonitors (Figuereido et al. 2007; Zambrano et al. 2009; Felix et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; D\u0026iacute;az-\u0026Aacute;lvarez et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition to these two species widely distributed in the Americas, their genus includes another 790 species. Most of them are characterized by obtaining water and nutrients mostly from the atmosphere, and for this reason they are called atmospheric bromeliads (Reyes-Garc\u0026iacute;a and Griffiths \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gouda et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This allows their tissues to reflect the sources and concentrations of atmospheric pollution in the places where they grow (White et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Pellegrini et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, the δ\u003csup\u003e13\u003c/sup\u003eC tend to be more negative in cities than in rural areas (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). On the other hand, they increase their N content in urban environments where this element is more common (Hietz and Popp, 1999; D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, the δ\u003csup\u003e15\u003c/sup\u003eN also change according to the source, being more positive in cities and more negative in the rural areas (Wania et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe accurate and measurable responses of these tillandsias to atmospheric pollution suggests that other atmospheric bromeliads of this group could be biomonitors, this is important because \u003cem\u003eT. recurvata\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e, despite their wide distribution, are not always established in all regions of interest. Hence, characterizing the responses of other atmospheric species of this genus can help supplement air quality information in regions and locations where monitoring networks are lacking or unavailable. In addition, where \u003cem\u003eT. recurvata\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e do not grow some species such as \u003cem\u003eT. juncea\u003c/em\u003e and \u003cem\u003eT. schiedeana\u003c/em\u003e can thrive. Therefore, 1) to determine the biomonitoring potential of \u003cem\u003eT. juncea\u003c/em\u003e and \u003cem\u003eT. schiedeana\u003c/em\u003e and, 2) to compare the sources and magnitudes of atmospheric pollutants at six sites (five urban parks and one rural site) in a tropical metropolitan area in Mexico, we measured the elemental and isotopic composition of carbon and nitrogen of four \u003cem\u003eTillandsia\u003c/em\u003e species.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe metropolitan area of Xalapa has a humid temperate climate and is located in the mountainous region on the slope the Gulf of Mexico in the central part of Veracruz (INEGI, 2000). The ZMX is composed of 10 municipalities that together cover an area of 1187 km\u003csup\u003e2\u003c/sup\u003e. Its elevation ranges from 700 to 1850 m.a.s.l. and its population reaches up to 800,000 (CONAPO 2020). The most important city is Xalapa, which in turn is the capital city of Veracruz state. Xalapa is characterized by its important vehicular movement, and its air quality goes from moderate to bad quality, where the PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e surpassed the WHO guidelines and Mexican standards. Besides, NO\u003csub\u003e2\u003c/sub\u003e exceeds the WHO guidelines, and O\u003csub\u003e3\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e show from good to moderate quality (Gonz\u0026aacute;lez-Rocha et al. 2016). The second and the third most important municipalities of the ZMX are Coatepec and Xico, whose most important activities are agriculture, particularly coffee production, other activities include tourism and high road transportation (Rivera et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe selected five urban public parks from the three main municipalities of the ZMX, Xalapa, Coatepec and Xico (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Three parks were located in the urban area of Xalapa, Parque Ju\u0026aacute;rez (PJ), Parque Los Berros (PB), and Parque Revoluci\u0026oacute;n (PR). These parks have an urban environment characterized by important traffic, and commerce in its surroundings. The Miguel Hidalgo Central Park (PMH) is in Coatepec. This park is characterized by important traffic from tourism and commerce, but in smaller quantities than for Xalapa (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the same municipality, we selected a rural area (RA) where the population is much smaller than for the other sites, there is no commerce, and traffic is almost nonexistent (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Xico Park (PX) is in downtown Xico, this park is characterized by less traffic than for parks in Xalapa and Coatepec but high road transportation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental and urban characteristics of the study sites at the Metroplitan Area of Xalapa.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003cp\u003em s.n.m.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean annual temperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean annual precipitation (mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMunicipality and/or city\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of residents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of vehicles (municipality)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural area (AR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoatepec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParque Ju\u0026aacute;rez (PJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXalapa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e443,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e215,807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParque Revoluci\u0026oacute;n (PR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXalapa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e443,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e215,807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParque Los Berros (PB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXalapa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e443,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e215,807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParque Xico (PJ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParque Miguel Hidalgo (PMH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoatepec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55,720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27,271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePlant material\u003c/h2\u003e \u003cp\u003eWe selected four epiphytic bromeliads \u003cem\u003eT. juncea, T. recurvata, T. schiedeana\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e because of their atmospheric habit (Reyes-Garc\u0026iacute;a et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), abundance and wide distribution in the ZMX. These bromeliads have a CAM metabolism (Craig and William 1986; Cecchi et al. 1996; Hietz and Wanek \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Winkler et al. 2006). \u003cem\u003eT. juncea\u003c/em\u003e has a rosette shape and is found between 150 and 1900 m.a.s.l. Its range extends from Mexico to Bolivia (Espejo-Serna et al. 2004). \u003cem\u003eT. recurvata\u003c/em\u003e is spherical in shape and established between 200 and 1430 m.a.s.l. from the southern United States to Argentina (Espejo-Serna et al. 2004). \u003cem\u003eT. schiedeana\u003c/em\u003e is tufted and is distributed from Mexico to Venezuela from sea level to 1800 m (Espejo-Serna et al. 2004). \u003cem\u003eT. usneoides\u003c/em\u003e is a hanging bromeliad, established from sea level to 2400 m a from the southern United States to Argentina (Espejo-Serna et al. 2004).\u003c/p\u003e \u003cp\u003eIn September 2021, we collected five healthy and fully developed leaves of each of the four bromeliad species available at each site, the leaves were collected from the lower canopy (2 to 4 meters; n\u0026thinsp;=\u0026thinsp;5 for each species). In total, N\u0026thinsp;=\u0026thinsp;90 plant tissue samples were collected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eElemental and isotopic analysis\u003c/h2\u003e \u003cp\u003eTo determine the C, N content and the δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN isotopic composition, we dried the samples at 60\u0026deg;C for 72 hours in a gravity convection oven (D\u0026iacute;az-\u0026Aacute;lvarez \u0026amp; de la Barrera, 2019). We ground these samples to a fine powder in a ball mill (MiniBead-Beater-16, Biospec Products, Inc, Bartlesville, USA). We wrapped this material in 2.5 mm tin capsules (Costech Analytical, Inc. Valencia, California, USA) and weighed it on a semi-micro analytical balance (0.01 mg, ADAM EQUIPMENT, Maidstone Road, UK) to obtain from 2 to 2.5 mg.\u003c/p\u003e \u003cp\u003eFor each sample, the C and N contents, as well as the isotopic ratios of these elements were determined at the University of Wyoming Stable Isotope Facility (Laramie, Wyoming, USA). A Carlo Erba EA 1110 elemental analyzer (Costech Analytical Inc., Valencia, CA, USA) connected to a mass spectrometer (Finnigan Delta Plus XP, Termo Electron Corp, Waltham, MA) was used to obtain the values. Isotopic ratios of C and N are expressed in parts per thousand (\u0026permil;) and were calculated relative to Vienna-Pee Dee Belemnite (V\u0026thinsp;\u0026minus;\u0026thinsp;PDB) standard for C, and atmospheric nitrogen for N. The natural abundances of \u003csup\u003e13\u003c/sup\u003eC and \u003csup\u003e15\u003c/sup\u003eN were calculated as:\u003c/p\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003e (\u0026permil;\u003csub\u003eversus\u003c/sub\u003e V\u0026thinsp;\u0026minus;\u0026thinsp;PDB) = (R\u003csub\u003esample\u003c/sub\u003e / R\u003csub\u003estandard\u003c/sub\u003e \u0026minus; 1) \u0026times; 1000\u003c/p\u003e \u003cp\u003eδ\u003csup\u003e15\u003c/sup\u003eN (\u0026permil;\u003csub\u003eversus at\u0026minus;air\u003c/sub\u003e) = (R\u003csub\u003esample\u003c/sub\u003e / R\u003csub\u003estandard\u003c/sub\u003e \u0026minus; 1) \u0026times; 1000\u003c/p\u003e \u003cp\u003ewhere, R is the ratio of \u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC for C and \u003csup\u003e15\u003c/sup\u003eN/\u003csup\u003e14\u003c/sup\u003eN for N isotope abundance for a given sample (Ehleringer \u0026amp; Osmond 1989; Evans et al. 1996).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eTo determine the differences in C and N content, C:N ratio, as well as the isotopic composition of the four tillandsias at different sites, we used two-way ANOVA models. However, because not all species were established at all sites, we fitted four models in this research with the species that were established at the same sites to maintain the analyses orthogonality. For model 1, the main factors were species, with three levels (\u003cem\u003eT. juncea, T. schiedeana\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e), and sites with three levels AR, PR, PB. For model 2, the main factors were species, with three levels \u003cem\u003eT. juncea, T. schiedeana\u003c/em\u003e and \u003cem\u003eT. recurvata\u003c/em\u003e and sites with two levels (PB and PJ). For model 3, the main factors were species with three levels (\u003cem\u003eT. schiedeana, T. recurvata\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e) and sites with two levels (PB, PMH). For model 4, the main factors were species with two levels (\u003cem\u003eT. schiedeana\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e) and sites with four levels (AR, PB, PMH, PR and PX). In all cases, when the variables evaluated did not satisfy the normality premise and variance homoscedasticity, they were transformed into hierarchical ranks for analysis (Conover and Iman \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). When the models were significant, we performed post hoc multiple comparisons of means by LSD (least significant differences; Steel et al. 1997). We conducted the analyzes with R (R Core Team, R Foundation for Statistical Computing, Vienna, Austria, version 4.0.2, 2020). In addition, the agricolae package in R was used for post hoc tests analyses. In all cases, the alpha value was 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCarbon and nitrogen content and C:N ratio\u003c/h2\u003e \u003cp\u003eThe mean C content for all species was 44.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5%. The lowest value was 41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1% for \u003cem\u003eT. schiedeana\u003c/em\u003e in the PR. The highest value was 46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 for \u003cem\u003eT. schiedeana\u003c/em\u003e in the PJ. However, no differences were found between species (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), nor for sites (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), nor for the species \u0026times; site interaction (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Two-way ANOVA for carbon and nitrogen content, C:N ratio, δ\u003csup\u003e13\u003c/sup\u003eC and δ\u003csup\u003e15\u003c/sup\u003eN that satisfy orthogonality. Model 1 (\u003cem\u003eT. juncea, T. schiedeana, T. usneoides\u003c/em\u003e in the AR, PB, PR); model 2 (\u003cem\u003eT. juncea, T. recurvata, T. schiedeana\u003c/em\u003e in PJ, PB); model 3 (\u003cem\u003eT. recurvata, T. schiedeana, T. usneoides\u003c/em\u003e in PB, PMH); model 4 (\u003cem\u003eT. schiedeana\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e in AR, PB, PMH, PR, PX).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"22\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eCarbon content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eNitrogen content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eC:N ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c18\" namest=\"c16\"\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c22\" namest=\"c20\"\u003e \u003cp\u003eδ\u003csup\u003e15\u003c/sup\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c21\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c22\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e24.0182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e5.0071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e11.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e33.6474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6.4318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e118.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies \u0026times; site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.6569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.3985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.2541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e3.9151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.5823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.6774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.1018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.2455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.1277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.7314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.1984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.8089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.4571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e6.9635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.7758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.0497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.8254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.2497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.6219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e12.3657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies \u0026times; site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.0361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.1525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.9354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.0724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.5325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.5939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e3.3207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.5158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.7117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.5009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.7539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.1946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e0.2859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e0.7539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.3589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16.8456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e31.8317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e7.6494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e20.9518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies \u0026times; site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e34.1669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.9657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e11.7797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.1166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.9418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.0309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.8614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e13.5142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e4.3206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12.6327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e10.0779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e4.5947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e36.5544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies \u0026times; site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.5109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e4.0693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.9352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.4534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c20\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c21\"\u003e \u003cp\u003e7.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c22\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean N content for the species was 1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1%. The lowest N content was 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% in the AR for \u003cem\u003eT. juncea\u003c/em\u003e, and the highest content was 2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% for \u003cem\u003eT. recurvata\u003c/em\u003e in the PMH. Both the species (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and sites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) in model 1 showed differences. The species \u0026times; site interaction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) for models 3 and 4. However, model 2 did not show differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNitrogen content (dry weight) and δ13C isotopic composition for the tillandsias and for the sites of each model that showed differences. Model 1 (\u003cem\u003eT. juncea, T. schiedeana, T. usneoides\u003c/em\u003e in the AR, PB, PR); model 2 (\u003cem\u003eT. juncea, T. recurvata, T. schiedeana\u003c/em\u003e in PJ, PB); model 3 (T. recurvata, T. schiedeana, T. usneoides in PB, PMH); model 4 (\u003cem\u003eT. schiedeana and T. usneoides\u003c/em\u003e in AR, PB, PMH, PR, PX).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNitrogen (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC (\u0026permil;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT. juncea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.9\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT. schiedeana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.0\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT. usneoides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.6\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParque Berros\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.5\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.4b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParque Revoluci\u0026oacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.7\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026Aacute;rea rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.3\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParque Berros\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.5\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.4b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParque Miguel Hidalgo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.7\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT. schiedeana\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.9\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT. usneoides\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.4\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParque Berros\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.5\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.4b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParque Miguel Hidalgo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.8\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParque Revoluci\u0026oacute;n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.4\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParque Xico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.3\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.1ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026Aacute;rea rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.9\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.2ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE. For each element, different letters indicate statistical differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Empty cells showed no difference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe C:N ratio of the species was on average 41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8. The lowest value was 23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4, for \u003cem\u003eT. recurvata\u003c/em\u003e in the PMH, and the highest value was 86.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5, for \u003cem\u003eT. juncea\u003c/em\u003e in the AR. For models 1, 2 and 3, the species \u0026times; site interaction was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, model 2 did not show differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCarbon and nitrogen isotopic composition\u003c/h2\u003e \u003cp\u003eThe average δ\u003csup\u003e13\u003c/sup\u003eC values reached \u0026minus;\u0026thinsp;15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026permil;. The lowest value was \u0026minus;\u0026thinsp;15.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u0026permil; for \u003cem\u003eT. usneoides\u003c/em\u003e in the PR, and the highest value was \u0026minus;\u0026thinsp;14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026permil; for \u003cem\u003eT. juncea\u003c/em\u003e in the AR. There were differences in species (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and sites (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) for models 1 and 4. Model 3 showed differences only for species (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While model 2 did not show differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe average δ\u003csup\u003e15\u003c/sup\u003eN value for the species studied was \u0026minus;\u0026thinsp;4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u0026permil;. The lowest value was \u0026minus;\u0026thinsp;12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026permil; for \u003cem\u003eT. usneoides\u003c/em\u003e in the AR. The highest value was 0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026permil; for \u003cem\u003eT. schiedeana\u003c/em\u003e in the PMH. All models showed differences for the species \u0026times; site interaction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eEnvironmental factors regulate C content in plants, as environment shapes their metabolism and functioning (Reich and Oleksyn, 2004; Reich, 2005; Zhang et al. 2012). Thus, the carbon content in epiphytic bromeliads is an indicator of environmental change, because, environmental factors such as water availability, solar radiation, temperature, or CO\u003csub\u003e2\u003c/sub\u003e concentration change, CO\u003csub\u003e2\u003c/sub\u003e assimilation also changes (D\u0026iacute;az-\u0026Aacute;lvarez et., 2015; Ma et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, in CAM epiphytes, low water availability and increased temperature can cause a reduction in CO\u003csub\u003e2\u003c/sub\u003e assimilation due to reduced stomatal conductance (Lambers et al. 1998; Stancato et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Epiphytic bromeliads, which are exposed to environments with nutrient and humidity limitations, are very efficient at integrating the environmental changes where they are established, either in rural or urban sites (Ruzana and Ainuddin, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For example, the epiphytic bromeliad \u003cem\u003eT. recurvata\u003c/em\u003e has a C content of 41.6% dry weight in both in urban and rural areas of central Mexico (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In our study, the average C content for the four species studied at six sites had similar values. These suggest that environmental conditions did not alter the C content in these species. All this points out that the four studied species were in optimal conditions for their growth, in consequence in optimal condition for biomonitor use.\u003c/p\u003e \u003cp\u003eThe nitrogen content in plants is affected by several factors, including the species, growth type, as well as the environment in which they grow (D\u0026iacute;az-\u0026Aacute;lvarez et al. 2015; Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, when Nr increases in the environment, the N content in plant tissues also increases (Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For three of the four species studied, \u003cem\u003eT. recurvata, T. schiedeana\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e, no differences in N content were observed among them. This suggests that they have similar responses to environmental Nr at each site. However, \u003cem\u003eT. juncea\u003c/em\u003e exhibited lower N content, possibly due to its morphology or leaf structure, that impedes further nitrogen take up. However, our data do not allow us to make this claim directly, so future studies should understand its nitrogen uptake mechanism in anthropic environments and how this may affect its potential as biomonitors. Although its response was not the same as that of the other three species, \u003cem\u003eT. juncea\u003c/em\u003e responded effectively to nitrogen pollution at each site, therefore, it can be used as a biomonitor of N emissions.\u003c/p\u003e \u003cp\u003eAnthropogenic activities can determine the foliar nitrogen content in atmospheric epiphytic bromeliads (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, in rural areas where the NOx concentration is less than 5 ppb, the N content in atmospheric bromeliads can reach 0.7%. But in urban areas such as the megalopolis of Mexico City where the NOx reaches 57.4 ppb, their N content can reach 3.6% (Hietz and Popp, 1999; D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our species followed this trend, as we observed differences between the rural area and the urban parks. However, our highest N content was not as high as that of Mexico City, this suggests that the NOx concentrations for our study sites were not as high as in Mexico City, this is an expected result when comparing the population of the two cities. This is a good example of how the number of residents and vehicles can determine the N content in plants. However, two sites did not follow this trend, the PX and PMH, as discussed below.\u003c/p\u003e \u003cp\u003eBoth PX and PMH had the highest N content, but the population there is smaller than in Xalapa, as is the number of vehicles. This result could be explained by a couple of factors. First, the constant use of fireworks for many religious and cultural celebrations round year close to the collecting points (Madrazo and Urdapilleta, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Gunpowder utilized for fireworks can contain up to 75% potassium nitrate (KNO\u003csub\u003e3\u003c/sub\u003e), which can release NH\u003csub\u003e4\u003c/sub\u003e and HCO\u003csub\u003e3\u003c/sub\u003e during combustion (Russell, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These nitrogen compounds, like others released into the atmosphere, are potentially assimilated by vegetation, including epiphytic bromeliads (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Second, at both sites, street food preparation using propane gas and charcoal as fuel is very common, and particularly at PMH there are several coffee roasters in the vicinity, which are potential sources of N for the tillandsias. Another explanation is the greater abundance of road transport compared to the sites in Xalapa, given that both parks (PX and PMH) are close to the only local market. This pattern of higher N emissions near sites with large markets can also be observed for the Mercado de la Merced sites in Mexico City (Gonz\u0026aacute;lez-Rocha et al. 2016).\u003c/p\u003e \u003cp\u003eThe responses of atmospheric tillandsias have to environmental factors can be understood through the foliar C:N ratio, which indicates the existing ratio between these two essential elements for their development (Mardegan et al. 2011). In our study, the C:N ratio followed the trend of N content, since it was higher in natural areas, indicating low N uptake, and lower in urban areas, indicating high N uptake. This has also been observed in other tillandsias in urban and natural environments such as in the Valley of Mexico (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn epiphytic bromeliads, the isotopic enrichment or depletion of \u003csup\u003e13\u003c/sup\u003eC is affected by three main environmental factors, first temperature and water availability, second, their photosynthetic metabolism, and third, CO\u003csub\u003e2\u003c/sub\u003e source and concentration (Craig, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1961\u003c/span\u003e, Ruzana and Ainuddin, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For our study, all species responded in a similar way to the environmental conditions in which they grew. But temperature and water availability are similar for the study sites, which indicates that these are not significant factors that affected our results. This contrasts with findings for other tillandsias along a precipitation gradient and with other ecosystems where water and temperature are determining factors (Hietz et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Cach, 2013). On the other hand, the four species had δ\u003csup\u003e13\u003c/sup\u003eC that correspond with CAM plants, that is, between \u0026minus;\u0026thinsp;10 and \u0026minus;\u0026thinsp;22\u0026permil; (Ehleringer and Squeo, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This is important because under ideal conditions i.e., similar C sources, no differences in δ\u003csup\u003e13\u003c/sup\u003eC between species are expected. However, the different anthropic activities in our study area were evident by changes in the δ\u003csup\u003e13\u003c/sup\u003eC as discussed below.\u003c/p\u003e \u003cp\u003eAnother important factor that affects the plant δ\u003csup\u003e13\u003c/sup\u003eC is the C source (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is due to the fact that atmospheric CO\u003csub\u003e2\u003c/sub\u003e has specific isotopic compositions depending on its source. For instance, the δ\u003csup\u003e13\u003c/sup\u003eC of air in natural environments is -8\u0026permil; (Pichlmayer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Widory and Javoy, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, δ\u003csup\u003e13\u003c/sup\u003eC are more negative where transportation, industrial, or domestic activities are common. For example, δ\u003csup\u003e13\u003c/sup\u003eC from the combustion of coal, gasoline, diesel, and natural gas ranges from \u0026minus;\u0026thinsp;25 to -42\u0026permil; (Pataki, et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Widory and Javoy, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Semmens et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Naus et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These activities leave a particular isotopic signature on the nearby vegetation, which is why there are different isotopic values between plants in the countryside and in the city. For example, CAM bromeliads growing in natural environments generally have δ\u003csup\u003e13\u003c/sup\u003eC close to -14\u0026permil;. Meanwhile, bromeliads from anthropized environments, such as cities, have values that can reach \u0026minus;\u0026thinsp;19\u0026permil; (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We observed a similar pattern in most of our sites, but one of them was an exception, the PMH, which we discuss below.\u003c/p\u003e \u003cp\u003eThe PMH is in Coatepec, a small town with high vehicle traffic due to tourism every weekend, in addition to other activities, as we already mentioned for N content (Campion, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rivera et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, we expected that bromeliads from this site showed δ\u003csup\u003e13\u003c/sup\u003eC typical of cities, but this was not the case. On the contrary, they were similar to those from the rural site. There are several possible explanations for this finding, but the most likely one is that the tillandsias at this site were exposed to more solar radiation, resulting in higher air temperatures and changes in CO\u003csub\u003e2\u003c/sub\u003e fixation (Farquhar and O'Leary, 1982; D\u0026iacute;az-\u0026Aacute;lvarez eta., 2017). However, this should also have been observed for N content, since in urban sites there is a close relationship between N content and δ\u003csup\u003e13\u003c/sup\u003eC. In particular, as N content increases, δ\u003csup\u003e13\u003c/sup\u003eC decreases (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, at this site it was not the case, instead the N content and the δ\u003csup\u003e13\u003c/sup\u003eC were high, which was similar to that of the rural area. At this point, we do not have a satisfactory explanation for these results.\u003c/p\u003e \u003cp\u003eOther bromeliads that are important to mention are those from Xico since they had δ\u003csup\u003e13\u003c/sup\u003eC similar to the Xalapa species. However, Xico is a smaller town than Xalapa. Its number of vehicles is lower, so we expected the \u003csup\u003e13\u003c/sup\u003eC there would be more enriched than for Xalapa, but this was not the case. This can be explained because of the use of fireworks, as discussed above for the N content. Gunpowder in fireworks, like other human activities, has a very negative δ\u003csup\u003e13\u003c/sup\u003eC of up to -27.6\u0026permil; (Mizota and Yamanaka, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Fireworks use is an important factor that could explain the very negative δ\u003csup\u003e13\u003c/sup\u003eC at this site, as they are a source of C compounds potentially assimilable by the surrounding vegetation (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera; \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The four species studied responded effectively to the environmental context in which they developed, especially for δ\u003csup\u003e13\u003c/sup\u003eC, which directly indicates the intensity of nearby human activities. Therefore, they can be used for biomonitoring studies of atmospheric carbon pollution in urban and rural environments.\u003c/p\u003e \u003cp\u003eSources of atmospheric reactive nitrogen directly affect the δ\u003csup\u003e15\u003c/sup\u003eN of atmospheric tillandsias (D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, plants growing in urban areas tend to have positive δ\u003csup\u003e15\u003c/sup\u003eN when the pollutants emitted are mostly nitrogen oxides from industry and vehicles (D\u0026iacute;az-\u0026Aacute;lvarez et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e ). However, when the dominant emission source is NHy such as fertilizers from agricultural areas, the δ\u003csup\u003e15\u003c/sup\u003eN tend to be very negative (Stewart et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Heaton et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; D\u0026iacute;az-\u0026Aacute;lvarez et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, 2021). On the other hand, atmospheric tillandsias growing in rural and natural areas with low human activity have negative δ\u003csup\u003e15\u003c/sup\u003eN, but close to zero (Wania et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In our study, we recorded the most negative δ\u003csup\u003e15\u003c/sup\u003eN in the AR, which are similar to those already reported for atmospheric tillandsias in this region and other rural sites whose δ\u003csup\u003e15\u003c/sup\u003eN reach \u0026minus;\u0026thinsp;10.9 and \u0026minus;\u0026thinsp;11.2\u0026permil; (Hietz et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Felix et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Thus, the δ\u003csup\u003e15\u003c/sup\u003eN of the AR suggests that little anthropogenic activity is taking place there, which is also confirmed by the low N content recorded at this site.\u003c/p\u003e \u003cp\u003eIn the megalopolis of Mexico City (CDMX), massive industrial and vehicular activities are reflected in the δ\u003csup\u003e15\u003c/sup\u003eN of \u003cem\u003eT. recurvata\u003c/em\u003e, since it reaches values of 5.1\u0026permil; (Zambrano et al. 2009; D\u0026iacute;az-\u0026Aacute;lvarez and de la Barrera, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some of our sites had positive δ\u003csup\u003e15\u003c/sup\u003eN, but the majority were negative close to zero, with a clear difference with the rural area, that is, they followed the trend of isotopic values that reflect the volume of human activity in the study area. However, our tillandsias were not as positive as those in the CDMX due to a couple of likely factors. First, the population density and the volume of industrial and vehicular activity are much lower in the ZMX than in CDMX, and therefore there are fewer emissions and less pollution (INEGI, 2020). Second, the topography CDMX and ZMX is different since Mexico City is located in a valley surrounded by volcanoes, which does not allow the free flow of pollutants, whereas, the ZMX is located at the slope of a mountainous region, which allows the free flow of air through the landscape (Nowak, 2020). Third, the buildings structure and configuration in the two metropolitan areas may also be a factor for the dissipation of atmospheric pollutants (Nowak, 2018).\u003c/p\u003e \u003cp\u003eIn general, all four species showed differences between sites, which is a clear response to human activities at each site. In particular, \u003cem\u003eTillandsia juncea\u003c/em\u003e can be utilized as biomonitor for C and N emissions, but not definitely, considering that its response is not as clear as that observed for the other three bromeliads studied. On the other hand, \u003cem\u003eT. recurvata, T. usneoides\u003c/em\u003e and \u003cem\u003eT. schiedeana\u003c/em\u003e can be utilized interchangeably for monitoring studies in the urban and rural landscape. \u003cem\u003eT. schiedeana\u003c/em\u003e is particularly interesting because it grows in sites where \u003cem\u003eT. recurvata\u003c/em\u003e and \u003cem\u003eT. usneoides\u003c/em\u003e do not, for example, at different areas of the ZMX. In this urban landscape atmospheric pollution can exceed the regulated limits and has a lack of an efficient monitoring network. Therefore, it is an obvious laboratory for biomonitoring with \u003cem\u003eT. schiedeana.\u003c/em\u003e Finally, future studies could utilize this assembly of species for monitoring atmospheric pollution in vast land extensions, to understand the risk that residents are facing due to air pollution.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eCompeting Interests:\u003c/strong\u003e \u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e \u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e \u003cp\u003eP.E.Z.T. Conceived and designed the analysis, collected the data, performed the analysis, and wrote the paper; Y.P. Contributed analysis tools, collected the data, and wrote the paper. E.A.D.A. Procured funding, conceived, and designed the analysis, collected the data, performed the analysis, and wrote the paper.\u003c/p\u003e \u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study was developed under the project \u0026ldquo;Biogeochemistry of nutrients in natural and anthropized environments of eastern Mexico\u0026rdquo;, registered at the Universidad Veracruzana in SIREI with No. 354822022107. We also thank fundings by SIREI project No. 51396202294. We thank Lenin Rios Figeroa for his aid in mapping. Authors are grateful to staff of the Instituto de Biotecnolog\u0026iacute;a y Ecolog\u0026iacute;a Aplicada (INBIOTECA) of Universidad Veracruzana for their logistic and administrative support in conducting this research. PEZT was supported by the National Council on Humanities, Science and Technology (CONAHCYT, grant 226643).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCach-P\u0026eacute;rez MJ (2013) Bromeli\u0026aacute;ceas ep\u0026iacute;fitas de la Pen\u0026iacute;nsula de Yucat\u0026aacute;n como indicadoras de los posibles efectos del cambio clim\u0026aacute;tico regional. PhD Thesis, Centro de Investigaci\u0026oacute;n Cient\u0026iacute;fica de Yucat\u0026aacute;n. M\u0026eacute;xico\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCach-P\u0026eacute;rez MJ, Andrade JL, Reyes-Garc\u0026iacute;a C (2014) La susceptibilidad de las bromeli\u0026aacute;ceas ep\u0026iacute;fitas al cambio clim\u0026aacute;tico. Bot Sci 92:157\u0026ndash;168\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCameron KC, Di HJ, Moir JL (2013) Nitrogen losses from the soil/plant system: a review. 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Acta Oecol 36:659\u0026ndash;665. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actao.2010.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.actao.2010.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-physiologiae-plantarum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acpp","sideBox":"Learn more about [Acta Physiologiae Plantarum](http://link.springer.com/journal/11738)","snPcode":"11738","submissionUrl":"https://www.editorialmanager.com/acpp/default2.aspx","title":"Acta Physiologiae Plantarum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"air quality, environmental pollution, global change, plant atmosphere interactions, stables isotopes in ecology, urban ecology","lastPublishedDoi":"10.21203/rs.3.rs-4378000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4378000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAir pollution affects human health and ecosystems all over the world. However, little attention is paid to its monitoring in tropics, mainly due to the high costs of automated monitoring systems. Biomonitoring may be an alternative, particularly for species of \u003cem\u003eTillandsia\u003c/em\u003e genus, although most species are not yet calibrated for this purpose. Therefore, 1) to determine the biomonitoring potential of \u003cem\u003eT. juncea\u003c/em\u003e and \u003cem\u003eT. schiedeana\u003c/em\u003e and, 2) to compare the sources and magnitudes of atmospheric pollutants at five urban parks and one rural site in a tropical metropolitan area in Mexico, we measured the elemental and isotopic composition of carbon (C) and nitrogen (N) of four \u003cem\u003eTillandsia\u003c/em\u003e species. The C content averaged 44.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5% (dry weight; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The N content ranged from 0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% for the rural site and 2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% for an urban site (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The lowest value of δ\u003csup\u003e13\u003c/sup\u003eC was \u0026minus;\u0026thinsp;15.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u0026permil; for \u003cem\u003eT. usneoides\u003c/em\u003e for all urban parks, and the highest was \u0026minus;\u0026thinsp;14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026permil; for \u003cem\u003eT. juncea\u003c/em\u003e in the rural area (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The lowest δ\u003csup\u003e15\u003c/sup\u003eN of \u0026minus;\u0026thinsp;12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026permil; was recorded for \u003cem\u003eT. usneoides\u003c/em\u003e in the rural area, and the highest of \u0026minus;\u0026thinsp;0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026permil; were recorded for \u003cem\u003eT. schiedeana\u003c/em\u003e in one of the urban sites. The four species can be used as biomonitors of C and N emissions, since their specific variations reflect the source and concentration of these atmospheric pollutants. Furthermore, the tillandsias showed that pollution in the metropolitan area is different depending on the activity at each site.\u003c/p\u003e","manuscriptTitle":"Different species of Tillandsia can be biomonitors of carbon and nitrogen emissions: the case of a tropical metropolitan area in Mexico","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 18:41:06","doi":"10.21203/rs.3.rs-4378000/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-26T16:23:16+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-21T18:24:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-08T09:03:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Physiologiae Plantarum","date":"2024-05-06T11:45:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-physiologiae-plantarum","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"acpp","sideBox":"Learn more about [Acta Physiologiae Plantarum](http://link.springer.com/journal/11738)","snPcode":"11738","submissionUrl":"https://www.editorialmanager.com/acpp/default2.aspx","title":"Acta Physiologiae Plantarum","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bad5b723-1f0c-4819-aa85-9373614549fe","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T16:00:58+00:00","versionOfRecord":{"articleIdentity":"rs-4378000","link":"https://doi.org/10.1007/s11738-024-03762-5","journal":{"identity":"acta-physiologiae-plantarum","isVorOnly":false,"title":"Acta Physiologiae Plantarum"},"publishedOn":"2025-01-23 15:57:24","publishedOnDateReadable":"January 23rd, 2025"},"versionCreatedAt":"2024-06-06 18:41:06","video":"","vorDoi":"10.1007/s11738-024-03762-5","vorDoiUrl":"https://doi.org/10.1007/s11738-024-03762-5","workflowStages":[]},"version":"v1","identity":"rs-4378000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4378000","identity":"rs-4378000","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}