Management Implications
To protect biodiversity in strictly protected areas, land managers must prevent the establishment and invasion of non-native species. Identifying pathways of introduction is essential to achieve this goal. Our findings suggest two main management implications for monitoring and early detection of potentially invasive species. First, targeted efforts should be implemented to reduce anthropogenic disturbances along roads and trails within the protected area. This may involve developing and implementing strategies to minimize human-induced disturbances, such as controlled access, designated trails, and educational campaigns. Second, monitoring and early detection programs should focus on areas with high public use intensity, particularly where recreational activities are concentrated. These areas appear to be hotspots for non-native species richness. Rapid response and management actions can be initiated in these hotspots to prevent the further establishment and spread of invasive plant species.
Introduction
The spread of invasive non-native species is increasing worldwide and is recognized as a major threat to biodiversity conservation in protected areas (IPBES 2023; Padmanaba et al. Reference Padmanaba, Tomlinson, Hughes and Corlett2017). Anthropogenic activities such as human population growth, tourism expansion, and climate change facilitate the establishment and spread of non-native species in these areas, potentially causing drastic changes in the native communities and ecosystems meant to be protected (Dar et al. Reference Dar, Reshi and Shah2018; Duque et al. Reference Duque, Stevenson and Feeley2015; Gottfried et al. Reference Gottfried, Pauli, Futschik, Akhalkatsi, Barančok, Benito Alonso, Coldea, Dick, Erschbamer, Fernández Calzado and Kazakis2012; Kueffer et al. Reference Kueffer, McDougall, Alexander, Daehler, Edwards, Haider, Milbau, Parks, Pauchard, Reshi, Rew, Schroder and Rew2013). This issue is particularly significant in mountain protected areas, which have seen a rapid increase in non-native species worldwide, with roads identified as one of the main pathways for their spread (Kueffer Reference Kueffer2017; Paiaro et al. Reference Paiaro, Cabido and Pucheta2011; Pauchard et al. Reference Pauchard, Milbau, Albihn, Alexander, Burgess, Daehler, Englund, Essl, Evengård, Greenwood and Haider2016). Understanding the patterns and drivers of non-native plant invasions in mountain protected areas is essential for management actions aimed at reducing their impact on native communities.
Roads, tracks, and trails are primary pathways for the spread of non-native species in both mountain ecosystems and protected areas (Foxcroft et al. Reference Foxcroft, Spear, van Wilgen and McGeoch2019; Pauchard and Alaback Reference Pauchard and Alaback2004; Pickering and Mount Reference Pickering and Mount2010). Human-modified habitats provide large-scale (e.g., roads and facilities) and small-scale (e.g., small gaps and informal trails) disturbances that promote non-native species establishment (Lembrechts et al. Reference Lembrechts, Pauchard, Lenoir, Nuñez, Geron, Ven, Bravo-Monasterio, Teneb, Nijs and Milbau2016). The relationship between non-native species establishment and public use is attributed to anthropogenic disturbances such as facilities, trails, and roadsides that enhance water runoff, change soil moisture, composition, and chemistry, and increase light availability and propagule delivery, thus favoring non-native plant occurrences (Daniels et al. Reference Daniels, Iacona, Armsworth and Larson2019; Karr et al. Reference Karr, Crisafulli and Gerwing2018). Consequently, the diversity and number of non-native plant species in mountains are generally higher along roads than in adjacent habitats (Pauchard et al. Reference Pauchard, Kueffer, Dietz, Daehler, Alexander, Edwards, Arévalo, Cavieres, Guisan, Haider and Jakobs2009). Additionally, human activities are important determinants of non-native species richness, increasing the chances of successful establishment and invasions by boosting propagule pressure to suitable sites (Marini et al. Reference Marini, Bertolli, Bona, Federici, Martini, Prosser and Bommarco2013; Seipel et al. Reference Seipel, Kueffer, Rew, Daehler, Pauchard, Naylor, Alexander, Edwards, Parks, Arevalo and Cavieres2012). More disturbed areas with higher tourist use intensity are thus expected to harbor more non-native species.
The variations in elevation inherent to mountain ecosystems produce significant environmental changes that greatly affect biodiversity patterns. One of the most noteworthy patterns is species richness along elevational gradients, which can exhibit three main patterns: linear decreases with increasing altitude, hump-shaped patterns with a maximum at mid-elevations, and constant richness from low to mid-elevations followed by declines farther up (Rahbek Reference Rahbek1995). However, richness patterns of native and non-native species in mountains may differ due to different ecological processes (Averett et al. Reference Averett, McCune, Parks, Naylor, DelCurto and Mata-González2016). Native species patterns result from long periods of coevolution with the environment, involving various biotic and abiotic interactions and evolutionary changes, leading to distinct native species pools in each ecosystem (Alexander et al. Reference Alexander, Kueffer, Daehler, Edwards, Pauchard, Seipel, Miren Consortium, Cavieres, Dietz and Jakobs2011, Reference Alexander, Lembrechts, Cavieres, Daehler, Haider, Kueffer, Liu, McDougall, Milbau, Pauchard and Rew2016; Otto et al. Reference Otto, Arteaga, Delgado, Arévalo, Blandino and Fernández-Palacios2014). In contrast, non-native species composition in an area is determined by the accumulation of species transported through human agency (Alexander et al. Reference Alexander, Kueffer, Daehler, Edwards, Pauchard, Seipel, Miren Consortium, Cavieres, Dietz and Jakobs2011, Reference Alexander, Lembrechts, Cavieres, Daehler, Haider, Kueffer, Liu, McDougall, Milbau, Pauchard and Rew2016; Ricciardi 2007). Two main patterns of non-native species richness are expected along environmental gradients: (1) a decline in species richness with increasing elevation, and (2) non-native species at higher altitudes being generalists that also occur at lower altitudes (Alexander et al. Reference Alexander, Kueffer, Daehler, Edwards, Pauchard, Seipel, Miren Consortium, Cavieres, Dietz and Jakobs2011; Marini et al. Reference Marini, Bertolli, Bona, Federici, Martini, Prosser and Bommarco2013).
To understand the disturbances driving plant species richness patterns along roads and trails in a tropical mountain forest, we examined the distribution of plant species in relation to elevational gradients, public use, and disturbance by roads and trails, and contrasted the results for native and non-native species. We aimed to contribute knowledge on species richness patterns along elevational gradients and the role of anthropogenic disturbances in non-native species invasions. Using field surveys of plant data, we sought to answer the following questions: (1) How are native and non-native species richness affected by altitudinal gradient, public use, and disturbance by roads and trails? (2) Do native and non-native species richness respond similarly to the altitudinal gradient? (3) Are the non-native species communities at higher altitudes a subsample of the lower-altitude non-native species pool?
Materials and Methods
Study Site
The Itatiaia National Park (hereafter Itatiaia Park) is a protected area conserving a remnant of the Atlantic Forest biodiversity hotspot (Myers et al. Reference Myers, Mittemeier, Mittemeier, da Fonseca and Kent2000). The park spans 28,084 ha in southeastern Brazil (22.37°S, 44.63°W). According to the Köppen climate classification, Itatiaia Park experiences a subtropical highland climate (Cwb) at higher elevations (1,600 m above sea level [m asl]) and a warm summer continental climate (Cfb) at lower elevations (ICMBio 2014). Data from the Alto Itatiaia weather station, located at 2,199 m asl, indicate an annual average temperature of 11.5 C, with negative temperatures and frequent hail during the winter months (FBDS 2000; ICMBio 2014). The mean annual precipitation in the municipalities within Itatiaia Park ranges from 1,250 to 2,500 mm, with the driest months from June to August and the wettest months during the Southern Hemisphere summer (ICMBio 2014). The park’s phytophysiognomy consists of 62% Montane and Upper Montane Ombrophilous Forests and 17% high-altitude grasslands, with the remaining 21% composed of agricultural areas, urban areas, and rocky outcrops (ICMBio 2014). Itatiaia Park received around 100,000 visitors per year between 2005 and 2017, with annual visitor numbers ranging from 72,703 to 139,616 (ICMBio 2020). The most frequently used mode of transport to visit the park was by car, accounting for 90.5% of all transportation modes (ICMBio 2014).
Fieldwork
Fieldwork was conducted during the rainy season from October 2018 to February 2019 to ensure the collection of fertile individuals for accurate identification. Specimens were identified using vouchers deposited at the ESAL Herbarium at the Federal University of Lavras and the Herbarium of the Rio de Janeiro Botanical Garden. The species’ status (native or non-native) to the Atlantic Forest was determined using the Brazilian Flora 2020 (http://floradobrasil.jbrj.gov.br) and the Royal Botanic Gardens Kew website Plants of the World Online (http://www.plantsoftheworldonline.org). Following the criteria of Richardson et al. (Reference Richardson, Pysek, Rejmánek, Barbour, Panetta and West2000) and Blackburn et al. (Reference Blackburn, Pyšek, Bacher, Carlton, Duncan, Jarošík, Wilson and Richardson2011), non-native species were defined as those introduced, intentionally or accidentally, to areas beyond their native ranges.
To address our research questions, we selected two roads and two trails representative of all areas of the park for plant surveying. The chosen roads provide the main access to the upper and lower parts of the park, while the trails cross the northern area, meeting at the Serra Negra mountain ridge. The northeast trail is called Santa Clara, and the northwest trail is called Serra Negra (Figure 1A). The study area’s elevation ranged from 501 to 2,448 m asl.
Figure 1. Itatiaia National Park location in Brazil, the distribution of the sampling points in each area of the park (A) and the plot design (B). The sampling points are separated by 500 m in a straight line (A). At each sampling point, three plots of 1 × 10 m were set up, they were parallel to the road or trail and to each other (B).
Sampling points were placed at 500-m intervals along each road and trail. The distances were measured using Google Earth Pro 7.3.2 (free version). We established 24 points in the upper part, 16 in the lower part, 14 in Serra Negra, and 13 in Santa Clara (Figure 1A). At each sampling point, three plots were set up: the first plot at the edge of the road or trail (plot 0 m away), the second plot 5 m away, and the third plot 10 m away. Each plot measured 1 by 10 m and was parallel to the road or trail (Figure 1B). The positions of the plots along the road or trail (left or right) were determined randomly. These plots were established at varying distances to assess the influence of disturbances from roads and trails on native and non-native plant species richness. The 0-m plots experienced the highest levels of anthropogenic disturbance due to road and trail maintenance, traffic, and tourism. The 5-m plots had intermediate disturbance levels, while the 10-m plots had the lowest disturbance levels, representing more natural ecological conditions (Figure 1B). We recorded herbaceous plants, herbs, and grasses in each plot. Altitude was measured at each sampling point using a Garmin 62s GPS.
To measure public use intensity, we calculated the distance of each sampling point from tourist facilities and attractions. For each transect, we assigned a value of 0 to the farthest sampling point from any tourist facilities or attractions. We then measured the distance of consecutive sampling points along the road or trail using the Ruler Tool in Google Earth Pro 7.3.2 (free version). To avoid disproportionate influence from absolute distances, we standardized each sampling point’s value in proportion to the total distance of the corresponding transect, resulting in values ranging from 0 to 1. Sampling points closer to tourist facilities or attractions had higher proportional values (closer to 1), indicating higher public use intensity.
Data Analyses
To account for spatial autocorrelation between plots at each sampling site, we used principal coordinates of neighbor matrices (PCNMs) as spatial explanatory variables (Borcard et al. Reference Borcard, Gillet and Legendre2011; Dray et al. Reference Dray, Legendre and Peres-Neto2006). The PCNM eigenvectors are orthogonal spatial variables representing the spatial structure of the data at different spatial scales, with the first axes representing broad scales that become progressively finer as the axes increase (Borcard et al. Reference Borcard, Gillet and Legendre2011).
Next, we created three generalized linear models containing explanatory variables related to space against the absolute native and non-native species richness and relative non-native species richness. We applied backward selection using the drop1 function to progressively remove nonsignificant variables with smaller F-values (R package stats; R Core Team 2022). The residuals of the three final models related to each response variable were used as measures of richness without the influence of spatial distances.
Following this, we used a generalized mixed model to test whether altitudinal gradient, public use intensity, disturbance level by roads and trails, and type of access (i.e., roads or trails) affected absolute native and non-native species richness and relative non-native richness. The residuals from the previously mentioned models were used as response variables. We created the linear mixed-effects models using lmer from the package lme4 (Bates et al. Reference Bates, Mächler, Bolker and Walker2015) and included area as a random effect in the models to remove bias caused by the nested sampling. Considering the potential unimodal patterns of species richness along the elevational gradient, we created a model with predictor variables including a second-order polynomial of elevation using the poly function in R (R Core Team 2022), which uses QR factorization to generate monic orthogonal polynomials.
To compare the models, we ranked them according to the corrected Akaike’s information criterion (AICc) using the model.sel function in the MuMIn package (Barton Reference Barton2019). The model with the lowest ΔAICc was considered the best-fit model (Akaike Reference Akaike1974; Sugiura Reference Sugiura1978). Models were considered significantly better when they presented 2 ΔAIC units lower than another. The coefficient of determination was obtained for the models with the r2 function from the sjstats package (Lüdecke Reference Lüdecke2022).
We checked residual distributions, impacts of outliers on results, and dispersion using diagnostic tests implemented in the DHARMa package v. 0.4.6 (Hartig Reference Hartig2022). We tested for underdispersion in the residuals of the models using the testDispersion function, which tests the quantiles of scaled simulated residuals against a uniform distribution (Hartig Reference Hartig2022). The fit of the models was validated using the simulateResiduals function (Hartig Reference Hartig2022; Supplementary Figures S1–S4).
Finally, to determine whether the non-native species community composition of the sampling points at higher altitudes was nested within sampling points from lower altitudes, we used the functions nestednodf and oecosimu from the vegan package v. 2.6-4 (Almeida-Neto et al. Reference Almeida-Neto, Guimarães, Guimarães, Loyola and Ulrich2008; Almeida-Neto and Ulrich Reference Almeida-Neto and Ulrich2011; Oksanen et al., Reference Oksanen, Simpson, Blanchet, Kindt, Legendre, Minchin, O’Hara, Solymos, Stevens, Szoecs, Wagner, Barbour, Bedward, Bolker and Borcard2022). We used the nestedness metric based on overlap and decreasing fill (NODF), because this measure is less dependent on the size and shape of the interaction matrix than other measures of nestedness, providing an unbiased estimate of the degree of nestedness (Almeida-Neto et al. Reference Almeida-Neto, Guimarães, Guimarães, Loyola and Ulrich2008). The nestednodf calculated nestedness was compared with a null model using oecosimu with default settings, except for the Methods parameter, where we used “r0” to maintain the site (row) frequencies and fill presences anywhere on the row with no respect to species (column) frequencies. The nestednodf output gives a statistic for nestedness of rows (sites), where 0 indicates no nesting and 100 indicates perfect nesting. This analysis requires two basic properties for a matrix to have the maximum degree of nestedness: (1) complete overlap of 1s from right to left columns and from down to up rows and (2) decreasing marginal totals between all pairs of columns and all pairs of rows (Almeida-Neto et al. Reference Almeida-Neto, Guimarães, Guimarães, Loyola and Ulrich2008). The nestedness statistic is evaluated separately for columns (N columns) and for rows (N rows) and combined for the whole matrix (NODF). We set order = FALSE so the statistic evaluates with the current matrix ordering, in our case, the altitude values. The weighted argument, when TRUE, finds the weighted version of the index, but instead, we used weighted = FALSE, considering our matrix as presence/absence data (Almeida-Neto et al. Reference Almeida-Neto, Guimarães, Guimarães, Loyola and Ulrich2008; Almeida-Neto and Ulrich Reference Almeida-Neto and Ulrich2011).
All statistical analyses were performed using the software RStudio v. 4.3.1 (R Core Team 2022).
Results and Discussion
We identified a total of 112 species encompassing 34 botanical families. Among these, 82 species (73%) belonging to 24 families were native, and 30 species (27%) belonging to 16 families were non-native (Supplementary Table S1). Most native species belonged to the families Poaceae and Asteraceae, while most non-native species were in the families Poaceae, Asteraceae, Zingiberaceae, and Fabaceae. The number of native species was higher than the number of non-native species in all areas of Itatiaia Park. Despite this, Itatiaia Park is listed among the protected areas with the highest occurrence of non-native invasive species in Brazil (Sampaio and Schmidt Reference Sampaio and Schmidt2013). Non-native species can promote biotic homogenization (e.g., Winter et al. Reference Winter, Schweiger, Klotz, Nentwig, Andriopoulos, Arianoutsou, Basnou, Delipetrou, Didžiulis, Hejda, Hulme, Lambdon, Pergl, Pyšek, David, Roy and Kühn2009) and impact native species (e.g., Heringer et al. Reference Heringer, Thiele, Meira-Neto and Neri2019), potentially becoming invasive and spreading from points of introduction to other areas of the park.
The quadratic models presented the smallest ΔAIC in the three sets of models for non-native, native, and proportion of non-native species (Table 1). Non-native species richness was affected by the disturbance level caused by roads and trails, with richness decreasing progressively with distance from these features (Table 1). The quadratic model’s total explanatory power was conditional R2 = 0.34, and the part related to the fixed effects alone was marginal R² = 0.30. This was close to the linear models, which had a conditional R² = 0.32 and marginal R² = 0.30. The best model representing relative non-native species richness showed a similar result, with disturbance caused by roads and trails negatively affecting the proportion of non-native species (Table 1). However, the explanatory power for the quadratic model (conditional R² = 0.19 and marginal R² = 0.15) and the linear model (conditional R² = 0.16 and marginal R² = 0.15) was smaller.
Table 1. Results from the analysis for comparisons between models: model parameter results of linear and quadratic models for absolute and relative native and non-native species richness.
a AIC, Akaike’s information criterion.
* Statistically significant values at alpha < 0.05.
We did not find that non-native plant species were concentrated at lower altitudes in montane areas, although a decreasing richness pattern for non-native species has been often observed in previous studies (Chytrý et al. Reference Chytrý, Pyšek, Tichý, Knollová and Danihelka2005, Reference Chytrý, Pyšek, Wild, Pino, Maskell and Vilà2009; Dar et al. Reference Dar, Reshi and Shah2018; McDougall et al. Reference McDougall, Morgan, Walsh and Williams2005; Pauchard and Alaback Reference Pauchard and Alaback2004; Pauchard et al. Reference Pauchard, Kueffer, Dietz, Daehler, Alexander, Edwards, Arévalo, Cavieres, Guisan, Haider and Jakobs2009; Zhang et al. Reference Zhang, Yin, Huang, Du, Liu, Guo and Wang2015). In our study, the positive effect of roads and trails on the spread of non-native species likely favors their distribution regardless of the altitudinal gradient and public use intensity. Thus, non-native species may exhibit an even distribution across the altitudinal public use intensity gradients if they are closer to roads and trails.
The high proportion of non-native species close to roads and trails supports the idea that disturbances caused by these networks play a key role in the introduction of species to mountain systems, being a main driver of non-native species introduction and establishment (Alexander et al. Reference Alexander, Lembrechts, Cavieres, Daehler, Haider, Kueffer, Liu, McDougall, Milbau, Pauchard and Rew2016; Padilha et al. Reference Padilha, Loregian and Budke2015). The decline in non-native species could be due to decreased human disturbance intensity, lower propagule pressure, or greater resistance to invasion of more natural communities away from frequently disturbed sites (Seipel et al. Reference Seipel, Kueffer, Rew, Daehler, Pauchard, Naylor, Alexander, Edwards, Parks, Arevalo and Cavieres2012). Proximity to roads and trails may represent a more favorable, high-light environment for non-native species occurrence (Dar et al. Reference Dar, Reshi and Shah2018; Dawson et al. Reference Dawson, Burslem and Hulme2015). Seeds from various plants can be unintentionally dispersed to protected areas on clothing, potentially traveling many kilometers (Ansong and Pickering Reference Ansong and Pickering2014). The high number of visitors could mean higher propagule pressure in these areas, facilitating the introduction and establishment of non-native species, mainly in roadside and trailside plots, similar to previous records from other mountain ecosystems (Khuroo et al. Reference Khuroo, Weber, Malik, Reshi and Dar2011; Lembrechts et al. Reference Lembrechts, Alexander, Cavieres, Haider, Lenoir, Kueffer, McDougall, Naylor, Nuñez, Pauchard and Rew2017; Liedtke et al. Reference Liedtke, Barros, Essl, Lembrechts, Wedegärtner, Pauchard and Dullinger2020; Pauchard et al. Reference Pauchard, Kueffer, Dietz, Daehler, Alexander, Edwards, Arévalo, Cavieres, Guisan, Haider and Jakobs2009). Although it might be expected that public use would affect the richness of non-native species, we did not find any such effect.
For native species, the quadratic model best represented the richness pattern. We observed a unimodal pattern of altitude with a negative effect of second-degree altitude (Table 1; Figure 2). In other words, native species richness reaches a maximum at an intermediate altitude around 1,750 m. Disturbances caused by roads and trails also affected native species richness, with middle and interior plots having progressively fewer species than plots closer to roads. The quadratic model’s total explanatory power was substantial, with conditional R² = 0.50 and the part related to the fixed effects alone being marginal R² = 0.42, very close to the linear model’s (conditional R² = 0.49 and marginal R² = 0.40).
Figure 2. The hump-shaped pattern of native species (red circles) in Itatiaia National Park, with species peaking around 1,750 m asl, and the non-native species (blue triangles). Only the curve for the significant relationship was plotted.
Our findings on native plant species richness along an altitudinal gradient align with those of other studies on plant species richness in temperate forests in Mexico (Sánchez-González and López-Mata Reference Sánchez-González and López-Mata2005) and Korea (Lee and Chun Reference Lee and Chun2016), bryophyte species (Ah-Peng et al. Reference Ah-Peng, Wilding, Kluge, Descamps-Julien, Bardat, Chuah-Petiot, Strasberg and Hedderson2012; Marline et al. Reference Marline, Ah-Peng and Hedderson2020), and global mountain plant richness (Haider et al. Reference Haider, Kueffer, Bruelheide, Seipel, Alexander, Rew, Arévalo, Cavieres, McDougall, Milbau and Naylor2018). Species richness may be highest in middle elevations due to intermediate temperature and rainfall, which provide optimal conditions and higher species richness, while lower humidity at lower altitudes and lower temperature at higher elevations constrain species diversity (Kluge et al. Reference Kluge, Kessler and Dunn2006). Additionally, middle-elevation regions often see overlapping plant communities, making these regions highly diverse (Haider et al. Reference Haider, Kueffer, Bruelheide, Seipel, Alexander, Rew, Arévalo, Cavieres, McDougall, Milbau and Naylor2018). In Itatiaia Park, middle elevations may be considered a transitional region where montane forest habitat (500 to 2,000 m asl) meets high-altitude grassland (above 2,000 m asl), forming an ecotone (Aximoff et al. Reference Aximoff, Alves and Rodrigues2014; Safford Reference Safford1999). These phytophysiognomic encounters likely explain the unimodal native species richness observed in our study area.
Regarding anthropogenic disturbances, we found a clear effect of roads and trails on the richness of both non-native and native species. The similar response of native and non-native species richness we observed, complementary to the widely accepted notion that frequently disturbed communities are more vulnerable to invasions (Pauchard et al. Reference Pauchard, Kueffer, Dietz, Daehler, Alexander, Edwards, Arévalo, Cavieres, Guisan, Haider and Jakobs2009; Sandoya et al. Reference Sandoya, Pauchard and Cavieres2017), suggests that native species are also favored and may be expanding their local ranges (Lembrechts et al. Reference Lembrechts, Alexander, Cavieres, Haider, Lenoir, Kueffer, McDougall, Naylor, Nuñez, Pauchard and Rew2017). It is frequently observed that road and trail edge vegetation harbors a higher number of both native and non-native species compared with interior areas (Ullmann et al. Reference Ullmann, Bannister and Wilson1995). Possibly, ruderal native species are favored by the anthropogenic environmental conditions near roads and trails, but this question requires further investigation.
Nestedness analysis, evaluating whether the species community composition of sampling points at higher altitudes was nested within those from lower altitudes, indicated that non-native and native species communities were not nested within the lower-altitude species pool (NODF = 3.34, matrix fill = 0.079, turnover = 0.9705, nestedness = 0.0119; and NODF = 8.29, matrix fill = 0.071, turnover = 0.9752, nestedness = 0.009, respectively) (Figure 3). Thus, both non-native and native species compositions at high altitudes were composed of different assemblages. This result may be explained by the evolutionary development of native plant species in relation to edaphoclimatic variances to altitude. Differences in community composition along the altitudinal gradient highlight that distinct environmental gradients affect species composition, likely due to local and fine-scale factors (Gastauer et al. Reference Gastauer, Thiele, Porembski and Neri2020; Pinto-Junior et al. Reference Pinto-Junior, Villa, de Menezes and Pereira2020).
Figure 3. The nestednodf analyses plot of non-native plant species ordered in relation to altitude. The species names are abbreviated with the three first letters of its genus and the specific epithet (see Supplementary Table S1). The analyses showed that there is no non-native species nesting from lower to higher altitudes. The non-native species communities of higher altitudes are not formed by the non-native species pool from lower sites.
We suggest that each area of Itatiaia Park favors a particular assemblage of non-native species along trailsides and roadsides due to different climates. Previous studies have shown that non-native species communities in high-elevation areas are linked to the alien species pool from the surrounding lowlands, where only broadly climate-tolerant species and species pre-adapted to harsher climatic conditions can spread and sustain populations at higher elevations (Fernández-Palacios and de Nicolás Reference Fernández-Palacios and de Nicolás1995; Kueffer Reference Kueffer2017; Pauchard et al. Reference Pauchard, Milbau, Albihn, Alexander, Burgess, Daehler, Englund, Essl, Evengård, Greenwood and Haider2016). In our study, however, there was no nesting from lower to higher altitudes. Although environmental filters in high-altitude environments constrain the establishment of non-native species not adapted to extreme conditions, our results suggest that other non-native species adapted to harsher climates reach higher-altitude environments via roads and trails.
Our findings provide evidence that anthropogenic disturbances associated with roads and trails (i.e., gap openings, propagule pressure) increase non-native plant species introduction, facilitating their arrival and establishment. In Itatiaia Park, this impact occurs evenly along roads and trails, as tourists must traverse these paths to access park attractions. Practical actions to reduce the spread of non-native species include raising awareness among visitors about the risks of introducing species into protected areas, implementing prevention measures at park entrances (e.g., cleaning vehicles), and initiating early detection and rapid response close to frequently used roads and trails.
In conclusion, our study contributes new insights into the distributional patterns of non-native species richness along altitudinal gradients, highlighting the significant role of anthropogenic disturbances. Anthropogenic disturbances associated with roads and trails influence the richness of non-native species along the altitudinal gradient. In contrast, for native species, environmental and edaphoclimatic factors also play crucial roles in driving richness patterns. Moreover, our findings indicate that the non-native species community at higher altitudes does not merely represent a subset of the community found at lower altitudes, contrary to previous observations. This diverse community of non-native species at higher elevations suggests their association with public visitation and the park environment. Therefore, the increasing tourist activities in conservation areas may heighten the threats posed by non-native species to native and endemic species, given the concurrent rise in disturbances and propagule pressure. Managers of protected areas such as Itatiaia National Park should thus prioritize efforts to prevent the introduction of new non-native species into high-altitude environments due to the ecological significance and vulnerability of these areas.
Supplementary material
To view supplementary material for this article, please visit https://doi.org/10.1017/inp.2024.29
Data availability statement
Non-native species occurrence data are available at http://sigeei.ufla.br.
Acknowledgments
We thank the staff of the Itatiaia National Park and the Altomontana da Serra Fina Institute for the logistical and administrative support during the work.
Funding statement
Fieldwork for this project was financed by ICMBio/CNPq (grant no. 421254/2017-3). RDZ is grateful for support from CNPq (grant no. 304701/2019-0). HHMdR and RADS received scholarships from Coordenação Nacional de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. GH’s postdoctoral research is supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (Capes)—Finance Code 001 and Alexander von Humboldt Foundation.
Competing interests
The authors have no conflict of interest to declare.
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