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Article

Elevation and Soil Properties Determine Community Composition, but Not Vascular Plant Richness in Tropical Andean Roadside

by
Patricio Guzmán
1,2,*,
Ángel Benítez
1,3,
Vinicio Carrión-Paladines
3,
Paul Salinas
1 and
Nixon Cumbicus
3
1
Maestría en Biología de la Conservación y Ecología Tropical, Universidad Técnica Particular de Loja, San Cayetano s/n, Loja 1101608, Ecuador
2
Bosques y Vida Silvestre, Ministerio del Ambiente, Agua y Transición Ecológica, Zamora 190101, Ecuador
3
Biodiversidad de Ecosistemas Tropicales-BIETROP, Herbario HUTPL, Departamento de Ciencias Biológicas y Agropecuarias, Universidad Técnica Particular de Loja, San Cayetano s/n, Loja 1101608, Ecuador
*
Author to whom correspondence should be addressed.
Forests 2022, 13(5), 685; https://doi.org/10.3390/f13050685
Submission received: 14 February 2022 / Revised: 30 March 2022 / Accepted: 18 April 2022 / Published: 28 April 2022
(This article belongs to the Section Forest Biodiversity)
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Versions Notes

Abstract

:
Roadsides are common ecosystems worldwide, with specific environmental characteristics and multiple effects on plant diversity. As such, they represent examples of highly dynamic anthropogenic ecosystems. Our objective was to assess patterns of vascular species diversity in response to elevation and soil characteristics on a roadside in the Andean mountains of Ecuador. The study area was located in the southern Ecuadorian Andes, at five elevations ± 400 m a.s.l. (2600, 2200, 1800, 1400 and 1000 m a.s.l.), where we recorded species richness and abundance in transects perpendicular to the road. The effects of elevation and soil characteristics on species abundance and richness were analyzed using generalized linear mixed models (GLMMs), while species composition was assessed with a non-metric multidimensional scaling analysis (NMDS) and its relationship to environmental variables. We used indicator species analyses (ISA) to identify which species significantly characterized specific elevation and soil factors from primary succession for restoration processes at the roadside. Although elevation and soil characteristics do not condition vascular species richness, the composition is more similar at elevations E1 and E2 (2600 m and 2200 m a.s.l.), differing from low elevations E4 and E5 (1400 m and 1000 m a.s.l.), which in turn are more similar to each other, while intermediate elevation E3 is similar to the highest and lowest elevations. Soil variables that limited plant communities were pH, bulk density (gr/cm3), silt (%), and sand (%) contents. The indicator species showed a preference for specific environmental and soil condition requirements associated with the different microhabitats and, thus, can be suggested for potential use in roadside revegetation processes in tropical areas. These results can help decision-makers in the implementation of biodiversity conservation and roadside environmental restoration projects in areas of Andean mountain ecosystems which have been affected by the construction of road infrastructure.

1. Introduction

In recent decades, ecologists and environmental restoration practitioners have agreed that successful restoration requires a solid scientific foundation grounded in an understanding of ecological principles [1,2,3]. Therefore, understanding how species assemble, function, and interact with environmental and soil factors is necessary for the development of ecosystem restoration projects [4,5]. In addition, areas degraded by road infrastructure have become a matter of scientific concern [6,7], due to the advancement of road networks and the negative ecological impacts caused to the landscape, such as habitat fragmentation, soil erosion, edge effects, and alien species invasion [7,8,9,10,11,12]. Consequently, roadsides represent an ideal scenario for studying the influence of environmental filters on the composition of plant communities [13], and, at the same time, they represent a great challenge for ecological restoration and selection of the most appropriate species in road revegetation processes [14].
Studies on roadside colonization reveal that plant establishment in the early stages follows a process of primary succession, whereby the floristic composition is mainly shaped by the arrival of diaspores from nearby communities [15,16]. Once diaspores have reached exposed slopes, abiotic conditions represent the first limiting filter for plant recruitment [17,18]. Furthermore, elevational gradients in mountainous areas are often composed of a series of complex biotic and abiotic changes that affect species distribution [19], such as temperature decrease, which favors plant growth, and elevation, which acts as a filter for plant species richness and abundance [2,20,21,22,23]. At a general level, investigations of roadside plant richness have suggested various patterns in response to biotic and abiotic changes associated with altitude [2,24]. For example, previous research generally reports that richness decreases with elevation [1,25,26]. However, other studies indicate that species richness has a hump-shaped response to elevation, and that patterns are different for native and non-native species [3,27].
On the other hand, on a small scale, other species distribution patterns are associated with physical and chemical soil conditions, such as a lack of nutrients in coarse-textured soils, high stoniness [28], a lack of organic matter [29], and highly consolidated bedrock [30], all of which will influence a suitable substrate for the establishment of plant diversity [29]. Thus, it has been reported that soil moisture, organic matter content, bulk density, and pH, change significantly along roadsides, which affects the composition of the plant community [31,32,33,34]. However, the ecological functioning of roadside plant communities has been little studied [6,18]. Most of the investigations that describe the altitudinal distribution of roadside species have been carried out in temperate ecosystems [3,22,24,35,36], including limited studies in the Neotropical region [1,23,24,25].
Currently, only one study evaluated changes in plant diversity on roadside in northern Ecuador related to elevation; however, soil physicochemical properties were not included in this study to understand plant diversity and responses along an elevation gradient. Therefore, new studies that provide more data are required, since roadsides are anthropized ecosystems that contain unique environmental, topographical, and edaphic characteristics [27]. In this context, to obtain information on the effect of elevation and soil edaphic factors on the occurrence and distribution of vascular species along an altitude gradient that goes from 2600 m to 1000 m a.s.l. at roadside located in Andean Mountain biomes, our research determined the following: (1) the diversity of roadside vascular species; and (2) the effect of elevation and edaphic factors on the distribution of species at the roadside. The data from this study can be used to generate an ecological base that serves for the selection of species in different microsites generated by the altitudinal gradient and by the characteristics of the soil. Knowledge of these potentially beneficial processes can be used for road revegetation in Andean biomes.

2. Materials and Methods

2.1. Study Area

The study was carried out between November 2019 and June 2020 in the Andes of southern Ecuador, in the province of Zamora Chinchipe, on the Zamora-Loja road. The altitude gradient goes from 2600 m to 1000 m a.s.l. (Figure 1). The experimental area is characterized by similar slopes between concave and convex topography, which defines its floristic composition and forest structure [37]. Bioclimatic data for annual temperature (°C), and total annual precipitation (mm), were extracted from Worldclim ([38], Table 1).
According to the Classification System of Terrestrial Ecosystems for Continental Ecuador [39], the vegetation is characterized ecologically as containing an evergreen montane forest at the Southeastern Cordillera of the Andes (BSMN02), and an evergreen low montane forest at the Southeastern Cordillera of the Andes (BSBN02). Selected roadsides are characterized by moderately steep slopes (20%–30%), that were measured with a clinometer (PM-5/360 PC Clinometer, Suunto, Finland).

2.2. Design and Data Collection

Along the studied path, five zones with different elevations were considered, which were separated ± 400 m.a.s.l. from one zone to another (2600 m.a.s.l., 2200 m.a.s.l., 1800 m.a.s.l., 1400 m.a.s.l., 1000 m.a.s.l.). In each area, two replicas were considered, which in turn were located at ± 100 m.a.s.l. from each other (Figure 1). For each elevation (E1–E5), two 10 m2 (2 m × 5 m) transects were established perpendicular to the road [40]. The length was measured from five linear meters from the edge of the road. Each transect was subdivided into 10 nested plots of one square meter, of which five nested plots were considered for sampling (2, 4, 6, 8 and 10). A total of 10 transects were installed, and 50 1 m2 nested plots were sampled. The frequency and abundance of the species were quantified in each nested plot, using a 1 × 1 m2 frequency grid (made of wood), divided into 0.10 × 0.10 cm2 cells [41]. The native and non-native species were taxonomically identified in situ, and those that could not be identified were collected for comparison with the collections in the Herbarium of the Universidad Técnica Particular de Loja (HUTPL).

2.3. Soil Sampling and Laboratory Analytical Methods

To determine soil quality, samples were taken from each transect and the odd nested plots (1, 3, 5, 7 and 9) to avoid mechanical damage to the plants sampled in the even-numbered nested plots. Sampling for bulk density (BD) consisted of taking a sample from each odd plot at a depth of 0–5 cm by using standardized metallic cores (5.5 cm in diameter, 4 cm in height, 95 cm3 in volume). Therefore, 10 individual samples were obtained for BD analysis at each elevation gradient, giving a total of 50 samples. Sampling for texture, pH, and macronutrient content consisted of using standardized metal cylinders (6 cm diameter, 10 cm height, 283 cm3 volume) at a depth of 0–10 cm [42], obtaining five subsamples for each sampling site. The subsamples were mixed to obtain a composite sample, obtaining 2 samples for each altitudinal gradient and 10 composite samples in total.
In the laboratory, BD was first determined using the cylinder method, for which individual BD samples were oven-dried for 48 h at 105 °C [43]. Samples for the determination of texture, pH, and macronutrient contents were dried at room temperature for 72 h. Subsequently, all visible roots were removed, and the samples were sieved through a 2 mm mesh. Soil texture was determined using the Bouyoucos-hydrometer method [44], while the soil pH was measured with a pH meter using the standard method [44]. In addition, soil organic carbon (SOC) and soil organic matter (SOM) were determined using the Walkley and Black method [45], for which the sample was placed in an oven at 125 °C for 45 min, after oxidation in a solution of K2Cr2O7/H2SO4. Total nitrogen (TN) was determined by the Kjeldahl method, phosphorus content (mg/kg) by the modified Olsen method [46], and potassium content (cmol/kg) by atomic absorption spectrophotometry [47].

2.4. Data Analysis

Species richness was defined as specific richness at plot level. A rarefaction curve based on samples and a Chao 2 non-parametric richness estimator was used to determine the sampling effort at each elevation. To visualize changes in species richness, a box plot was performed.
The effects of altitude and soil variables on richness and abundance were analyzed separately using generalized mixed linear models (GLMMs) at plot level. In these models, altitude and soil physicochemical properties (PCA1 and PCA2) were used as predictors (fixed factors), whereas locality were included as random sources of variation. We assumed Poisson errors for the response variables with the log link function. Effects of random factors were tested using the Wald Z-statistic test, and GLMMs were fit using package “lme4” with the function “glmer” [48]. Following Bolker et al. [49], we used the Laplace approximation for likelihood estimates. For GLMMs, the minimal adequate model was selected based on Akaike’s information criterion (AIC). Before GLMMs, a principal component analysis (PCA) was performed for the soil variables to avoid linearity effects. The residuals of the models were checked to verify that the assumptions of normality and homoscedasticity were met. Differences in plant community composition were observed by non-metric multidimensional scaling analysis (NMDS), using the Bray-Curtis distance and 999 Monte Carlo permutations. To analyze the effect of environmental variables (elevation, slope, and soil variables), a correlation between the two fitted axes and the environmental variables was performed with the “envfit” function. Finally, an indicator species analysis (ISA) [50] was carried out to determine the indicator species of primary succession in the restoration processes in the roadside. All statistical analyses were carried out using R software and functions in the package “vegan” [51].

3. Results

We recorded a total of 22 families represented by 44 genera and 54 native species, of which two were endemic (Chusquea loxensis and Neurolepis laegaardii) along the altitudinal gradient ranging from 2600 to 1000 m.a.s.l. The maximum species richness occurs in gradients E1 and E4 with 24 species each, followed by E2 and E5 with 21 and 20 species, respectively, and finally E3 with 18 species (Appendix A). Of the 22 families, the most diverse were as follows: Poaceae (15%) Orchidaceae (12%), Asteraceae (10%), Melastamotaceae (8%), Lycopodiaceae (7%), and Ericaceae (7%). In contrast, the least diverse were as follows: Pteridaceae, Polygonaceae, and Polipodiaceae, with a representativeness of (2%). With regards to genera, 38 are represented by a single species, while the remaining 14% are represented by two species, namely, Baccharis, Blechnum, Elleanthus, Gaultheria, Licopodium, Tibouchina, and Weimania. The most abundant species throughout the altitudinal gradient of the study area were Baccharis latifolia (2%).
The species rarefaction curve and the non-parametric richness estimator indicated the highest estimated species richness occurring in gradients E4, with 36 species, followed by E2, with 32 species, E1, with 28 species, and E5 and E3, with 22 and 20 estimated species, respectively (Figure 2).

3.1. Patterns of Richness and Abundance in Relation to Elevation and Edaphic Factors

Although richness did not show a distinct pattern with elevation, abundance showed a hump-shaped relationship with elevation (Figure 3). This hump shape may be due to edaphic factors, such as bulk density (E1: 1.43 g cm−3 and E2: 1.18 g cm−3, respectively), sandy loam texture, and nitrogen content (E1: 0.7%), which were higher at higher elevations. On the other hand, sandy loam and clay loam textures (with higher sand content), and pH (E4: 5.3 and E5: 5.4, respectively), were higher at lower elevations (Figure 3, Appendix A). However, E3 is where bulk density is better (lower compaction; 0.9 g cm−3) than at the other elevations.
This relationship was confirmed by GLMMs, where abundance had a maximum peak at middle elevations (E3 and E4), while for richness no significant effects of elevation and soil factors were detected (Figure 3, Appendix B and Appendix C).

3.2. Beta Diversity in Relation to Elevation and Edaphic Factors

The vascular plant composition of the higher elevations E1 and E2 (2600 and 2200 m a.s.l., respectively), clearly differed from the lower elevations E4 and E5 (1400 and 1000 m a.s.l., respectively), while the intermediate elevation E3 shares a similarity with the higher and lower elevations (Figure 4).
A total of 49% of the variability in community composition is conditioned by elevation and soil variables, such as higher silt content, pH, and sand with 47%, 46%, and 37% respectively (Table 2). At higher elevations (E1 and E2), the higher silt content (sandy loam textures), bulk density, and slope are conditioning the composition of the communities, compared to the higher pH and sand content (sandy loam and sandy clay loam textures), which are determinant for the composition at lower elevations (E4 and E5, Table 2).

3.3. Indicator Species

The indicator species analysis (ISA) determined a total of 20 species that are significantly more abundant and frequent in their respective microhabitats than the other plants, where species with an indicator value of >0.25% are considered as the best indicators (Table 3). At elevation E3, five indicator species were recorded, representing the highest number of indicator species among the five elevations, while for elevation E2, three indicator species were recorded, being the elevation with the fewest indicator species. At elevations, E1, E4, and E5, four species were recorded for each elevation (Table 3). However, these findings reflects the fact that two of these species, Elleanthus aurantiacus and Baccharis genistelloides, were found, at least occasionally, in all five microhabitats. Eight species were found in a single microhabitat. The other 10 were occasionally found in 2, 3, and up to 4 of the study elevations (Appendix D).

4. Discussion

4.1. Patterns of Richness and Abundance in Relation to Elevation and Edaphic Factors

Our results indicated that species richness was not conditioned by elevation, although community composition was limited by elevation and edaphic factors at the roadside (e.g., pH, different texture types, silt, sand, and Bd content). Haider et al. [24] indicated that species richness presents a hump shape regarding elevation. However, in some areas there is no relationship between richness and elevation, as evidenced in our study. Disturbance and the presence of non-native species condition species richness related with elevation, as shown by Sandoya et al. [27] on roadsides in northern Ecuador.
The abundance of species pointed to a hump shape as a function of elevation, as has been documented for species richness in temperate and boreal zones (3, 24) as well as for tropical regions [27]. Thus, our results suggest that roadside in Andean Mountain biomes showed higher abundance at mid-elevation (e.g., 1800 m), highly correlated with abiotic changes (precipitation, temperature, soil, and topography). For example, Baccharis genistelloides, Carex lehmanniana, Sticherus bifidus, and Calamagrostis intermedia are more abundant in E3, where the bulk density is lower (0.9 g cm−3) and, therefore, the soil is looser (less compacted), facilitating root growth. In this context, McGrath and Henry [52] and Jim [53] showed that the more compacted the soil is, the more moisture retention conditions are affected, limiting root growth [54] which shows that E3 has the best soil conditions for plant growth. Therefore, the pattern found in our study for abundance is consistent with other studies related with species richness that is higher at mid-elevations and decreases at higher elevations, where climate harshness increases and acts as an important filter [24,27,55].

4.2. Beta Diversity in Relation to Elevation and Edaphic Factors

Plant community composition was influenced by elevation and edaphic factors, as in the findings of several previous studies that have documented that elevation [25,26,56,57] and soil characteristics [16,55,58,59] are driving factors in the composition of roadside plant communities. In this context, our results are consistent with those reported by Karin and Mallik [60] and Arenas et al. [55] who point out that roadside soil characteristics influence different plant species communities. Furthermore, Solivers and Garcia [61] showed that at roadside, soil-plant interactions are more important than plant-plant interactions, so physical and chemical parameters are key in these anthropized ecosystems.
High gradient communities (e.g., Gaultheria vaccinioides, Eriosorus aureonitens, Maxillaria sp., and Elleanthus aurantiacus) were adapted to soil conditions with higher bulk density (higher compaction; 1.43 and 1.18 gr cm−3, respectively, Appendix B), and in addition, as they grew in sandy loam textures (higher silt concentration, but with less sand concentration than the other contrasted gradients), erosive processes were greater, which explains why in E1 and E2 there is greater soil compaction. On the contrary, low gradient communities (e.g., Lycopodiella andicola, Lycopodium clavatum L., Axinaea sp., Neurolepis laegaardii, Dennstaedtia mathewsii, and Melpomene moniliformis) have adapted to sandy loam and sandy clay loam soil texture conditions (higher sand concentrations), which could lead to a relatively muted erosive process as demonstrated in other studies [56,62]. However, pH is a determining factor in the composition of roadside plants, as previous studies have shown [55,58,60]. Although the soil pH values found in our study are suboptimal (Appendix B), considering that values between 6.5 and 8.0 are required for adequate assimilation of macronutrients by plants [63], the pH values were higher in the lower gradients and, thus, the less compacted soil could facilitate the absorption of these nutrients, as reported by Schoonover [64]. In addition, the higher concentration of sand and a more favorable texture (sandy-loam and sandy-clay-loam textures in E4 and E5 respectively), allow for a higher concentration of K, which can determine different communities better than high gradients, where plant communities have adapted to more acidic pH conditions (low pH).
The indicator species (ISA) in our study were abundant and frequent in their respective microhabitats, e.g., for E1 and E2 species, such as Gaultheria vaccinioides, Elleanthus aurantiacus, Bejaria resinosa, and Digitaria ciliaris, were the better indicators, while for E4 and E5, Lycopodium clavatum, Neurolepis laegaardii, Dennstaedtia mathewsii, Eleusine indica, and Melpomene moniliformis have the highest indication values. Supporting our results, Karim and Mallik [60] note that species indicate their preference for particular sets of environmental and edaphic conditions associated with different roadside microhabitats. These results suggest that elevation and roadside soil properties in different microhabitats play an important role in determining plant community composition [34,55,58]. Although the functional traits of these species were not assessed in our study, they can be categorized as a potential for roadside revegetation processes in tropical areas.
Although there are commonalities with widely studied temperate regions, there are significant differences in how vascular species are structured as a function of environmental and edaphic variables in the highlands of the southern Andes of Ecuador. In this context, other factors not measured in this study, such as disturbance, adjacent vegetation patches, timing of intervention, roadside effect, and the presence of non-native species conditioning plant diversity should also be considered [24,27]. Further comprehensive studies prioritizing these approaches will make an important contribution to the development of guidelines for roadside revegetation.

5. Conclusions

Species composition on the roadside in mountainous areas of the southern Andes of Ecuador was limited by elevation and soil characteristics. Therefore, we found that community composition is a more sensitive indicator than species richness in the area of roadside studied. The indicator species with specific needs of altitude, bulk density, pH, amount of sand, and silt identified in our study (Elleanthus aurantiacus, Digitaria ciliaris, Baccharis genistelloides, Lycopodiella andicola, and Dennstaedtia mathewsii) can potentially be used in some roadside restoration programs implemented in tropical areas. On the other hand, it is essential to understand the interactions between elevation, physicochemical soil composition, and plant composition, as these factors are very useful in shaping the distribution of species. In this context, these results can help those responsible for generating policies and technical proposals for the design of biodiversity conservation projects and environmental restoration of roadside in areas of Andean Mountain ecosystems, which are highly intervened because of road infrastructure construction.

Author Contributions

Conceptualization, P.G., Á.B., V.C.-P., P.S. and N.C.; methodology, P.G., Á.B., V.C.-P., P.S. and N.C.; formal analysis, P.G., Á.B., V.C.-P. and N.C.; investigation, P.G., Á.B., V.C.-P., P.S. and N.C.; writing—original draft preparation, P.G., Á.B., V.C.-P., P.S. and N.C.; writing—review and editing, P.G., Á.B., V.C.-P., P.S. and N.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Universidad Técnica Particular de Loja (I-CON-TFT).

Data Availability Statement

Data is contained within the article.

Acknowledgments

This research was funded by Private Technical University of Loja (UTPL-PROY_INV_CCBIO_2020_2773). We thank the Ministerio del Ambiente, Agua y Transición Ecológica del Ecuador for providing access to the study areas. Special thanks to Gregory Gedeon for english text revision.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Description of soil variables in each altitudinal gradient (E1–E5). Mean values and standard deviation are given. SOM = soil organic matter; SOC = soil organic carbon; Bd = bulk density.
Table A1. Description of soil variables in each altitudinal gradient (E1–E5). Mean values and standard deviation are given. SOM = soil organic matter; SOC = soil organic carbon; Bd = bulk density.
E1E2E3E4E5
pH4.82 ± 0.014.89 ± 0.1 4.8 ± 0.02 5.3 ± 0.01 5.4 ± 0.5
SOM (%) 1.3 ± 2.9 0.6 ± 0.7 1.2 ± 1.5 0.7 ± 0.7 1.4 ± 0.8
N (%)0.7 ± 0.01 0.04 ± 0.040.06 ± 0.07 0.04 ± 0.04 0.07 ± 0.04
P (mg/kg)3.5 ± 0.00 3.7 ± 0.28 3.5 ± 0.00 3.9 ± 0.56 3.5 ± 0.00
K (cmol/kg)0.01 ± 0.00 0.01 ± 0.00 0.06 ± 0.00 0.02 ± 0.010.17 ± 0.00
SOC (%)0.73 ± 0.04 0.38 ± 0.41 0.69 ± 0.88 0.42 ± 0.46 0.81 ± 0.49
C/N ratio11.3 ± 0.49 9.4 ± 2.61 9.5 ± 3.53 11.5 ± 1.55 11.6 ± 0.00
Bd (gr cm3)1.43 ± 0.29 1.18 ± 0.35 0.90 ± 0.24 1.15 ± 0.30 1.17 ± 0.23
Sand (%)60.6 ± 19.8 58.9 ± 8.0 62.6 ± 8.5 77.6 ± 1.4 65.6 ± 9.9
Silt (%)22.0 ± 14.1 21.6 ± 2.3 20.0 ± 5.7 7.0 ± 4.2 10.0 ± 11.3
Clay (%)17.4 ± 5.7 19.4 ± 5.7 17.4 ± 2.8 15.4 ± 2.8 24.4 ± 1.4
Textural classSandy loamSandy loamSandy loamSandy loamSandy clay loam

Appendix B

Table
Table A2. Contribution of each soil variable to each axis of the principal component analysis. SOM = soil organic matter; Bd = bulk density.
Table A2. Contribution of each soil variable to each axis of the principal component analysis. SOM = soil organic matter; Bd = bulk density.
PC1PC2
pH0.05210.3578
SOM−0.4725−0.0172
N−0.4725−0.0487
P0.32450.1257
K−0.20170.2309
SOC−0.4725−0.0172
C/N−0.37040.2317
Bd−0.0737−0.0503
Sand0.04240.5875
Silt0.0398−0.6117
Clay−0.1885−0.1561

Appendix C

Table
Table A3. GLMMs data with the relationship between altitudes (E1–E5), slope, and soil characteristics (represented as the two components of the PCA analyses PC1 and PC2, which bring together all soil characteristics) on roadside vascular species richness along an altitudinal gradient in mountainous Andean ecosystems.
Table A3. GLMMs data with the relationship between altitudes (E1–E5), slope, and soil characteristics (represented as the two components of the PCA analyses PC1 and PC2, which bring together all soil characteristics) on roadside vascular species richness along an altitudinal gradient in mountainous Andean ecosystems.
Richness EstimateStd ErrorZ Valuep Value
AltitudeE11.7680.15311.5710.467
AltitudeE20.1310.1850.7110.477
AltitudeE3−0.1720.189−0.9070.364
AltitudeE40.2810.2830.9930.321
AltitudeE50.0210.2410.0860.931
PC1-soil−0.0300.034−0.8930.372
PC2-soil−0.0590.067−0.8790.380
Abundance
AltitudeE17.3160.12061.1420.437
AltitudeE20.0410.1540.2670.789
AltitudeE31.0380.1477.052<0.0001
AltitudeE40.8420.2243.7620.0001
AltitudeE50.3310.1901.7440.081
PC1-soil0.0680.0272.5600.060
PC2-soil−0.0610.055−1.1230.261

Appendix D

Table
Table A4. Families and number of species per elevation recorded.
Table A4. Families and number of species per elevation recorded.
Elevations
Family/SpeciesE1E2E3E4E5
Asteraceae
Baccharis genistelloides (Lam.) Pers.16121272827
Baccharis latifolia (Ruiz & Pav.) Pers. 31
Liabum sp. 1 5
Sonchus oleraceus (L.) L. 5
Tessaria sp. 1
Triplaris sp. 1
Blechnaceae
Blechnum binervatum (Poir.) C.V. Morton & Lellinger. 11
Blechnum stipitatum A. Rojas.25
Bromeliaceae
Guzmania sp.414
Pitcairnia cf pungens.63
Clethraceae
Clethra finbriata Kunth8
Clethra revoluta (Ruiz & Pav.) Spreng. 2
Cunoniaceae
Weinmannia fagaroides Kunth 2
Weinmannia glabra Lam. 2
Cyperaceae
Carex lehmanniana Boott ex Walp. 93
Rhynchospora vulcani Boeckeler.1121
Dennstaedtiaceae
Dennstaedtia mathewsii (Hook.) C. Chr. 17
Pteridium sp. Gled. ex Scop.2858289
Dryopteridaceae
Elaphoglossum sp. Schott ex J. Sm.1
Ericaceae
Bejaria resinosa Mutis ex L. f.4 3364
Disterigma acuminatum (Kunth) Nied.2
Gaultheria erecta Vent.324718811
Gaultheria vaccinioides Wedd.401611 5
Fabaceae
Desmodium sp. 11
Medicago sp. 7
Gleicheniaceae
Sticherus bifidus (Willd.) Ching 2351523
Gunneraceae
Gunnera sp. 36
Lycopodiaceae
Lycopodiella andicola B. Øllg. 382
Lycopodium clavatum L. 13091
Lycopodium clavatun subsp. Contiguum 75
Lycopodium jussiaei Desv. ex Poir. 6
Melastomataceae
Axinaea sp. Ruiz & Pav. 16
Clidemia sp. 28
Miconia sp. 6
Tibouchina laxa (Desr.) Cogn. 58
Tibouchina lepidota (Bonpl.) Baill. 67 2118
Orchidaceae
Elleanthus aurantiacus Rchb.f14812194
Elleanthus sp. 823 5
Maxillaria sp. 73
Sobralia candida (Poepp. & Endl.) Rchb. f. 5117
Phyllanthaceae
Hyeronima sp. 5
Piperaceae
Piper sp. 52
Poaceae
Axonopus scoparius H 69
Calamagrostis intermedia (J. Presl) Steud.1 168
Chusquea loxensis L.G. Clacrk18
Cortaderia jubata (Lemoine) Stapf1767 155
Digitaria ciliaris (Kents.) Koeler146
Eleusine indica (L.) Gaertn. 3382
Festuca subulifolia Benth.12
Neurolepis laegaardii L.G. Clark1 386051
Setaria parviflora (Poir.) Kerguélen 41
Polygonaceae
Muehlenbeckia tamnifolia (Kunth) Meisn. 20
Polypodiaceae
Melpomene moniliformis (Lag. ex Sw.) A.R. Sm. & R.C. Moran146 174
Pteridaceae
Eriosorus aureonitens (Hook.) Copel.12

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Forests 13 00685 g001 550
Figure 1. Study area in the southern Ecuadorian Andes, throughout the altitudinal gradient 2600 m to 1000 m a.s.l. on the Zamora-Loja road, covering the five elevations (E1–E5), with the location of two transects per elevation.
Figure 1. Study area in the southern Ecuadorian Andes, throughout the altitudinal gradient 2600 m to 1000 m a.s.l. on the Zamora-Loja road, covering the five elevations (E1–E5), with the location of two transects per elevation.
Forests 13 00685 g001
Forests 13 00685 g002 550
Figure 2. Rarefaction curves with 95% confidence intervals and a Chao 2 estimator (points on the right of the figure) for the five elevations.
Figure 2. Rarefaction curves with 95% confidence intervals and a Chao 2 estimator (points on the right of the figure) for the five elevations.
Forests 13 00685 g002
Forests 13 00685 g003 550
Figure 3. Box-plot of species richness at the five elevations throughout the altitudinal gradient of the study. Data is depicted through quartiles and different lowercase letters above the whiskers indicate significant differences altitudinal gradient (GLMMs p < 0.05).
Figure 3. Box-plot of species richness at the five elevations throughout the altitudinal gradient of the study. Data is depicted through quartiles and different lowercase letters above the whiskers indicate significant differences altitudinal gradient (GLMMs p < 0.05).
Forests 13 00685 g003
Forests 13 00685 g004 550
Figure 4. NMDS analysis of the influence of environmental variables on vascular plant composition at the five elevations of the study’s altitudinal gradient (E1–E5).
Figure 4. NMDS analysis of the influence of environmental variables on vascular plant composition at the five elevations of the study’s altitudinal gradient (E1–E5).
Forests 13 00685 g004
Table
Table 1. Mean values of environmental variables for different elevation levels (E1–E5) and the type of ecosystems present in the study area.
Table 1. Mean values of environmental variables for different elevation levels (E1–E5) and the type of ecosystems present in the study area.
Variable/ElevationE1E2E3E4E5
Altitude (asl)2.6002.2001.8001.4001.000
Temperature (°C)14.2 ± 0.4514.9 ± 0.0017.3 ± 0.1320.5 ± 0.0023.0 ± 0.22
Precipitation (mm)1 148.5 ± 19.501 101 ± 3.00802 ± 0.001 027 ± 0.001 581 ± 65.00
Ecosystem BSMN02BSMN02BSMN02BSBN02BSBN02
Table
Table 2. NMDS ordination data by environmental factors and roadside vascular composition of the Loja-Zamora Road. The squared correlation coefficients (R2) for axes 1 and 2, the p value and the contribution of each variable to the variability on each axis are shown.
Table 2. NMDS ordination data by environmental factors and roadside vascular composition of the Loja-Zamora Road. The squared correlation coefficients (R2) for axes 1 and 2, the p value and the contribution of each variable to the variability on each axis are shown.
NMDS1NMDS2R2p Value
Slope−0.48131−0.876550.13090.035964
pH−0.411880.911240.46810.000999
MOS−0.03460.99940.02390.529471
N−0.116750.993160.01410.675325
P−0.477550.87860.13140.031968
K0.235840.971790.23220.003996
C−0.03460.99940.02390.529471
CN−0.621290.783580.19070.007992
Bd−0.89457−0.446930.37120.000999
Sand0.843950.536420.33820.000999
Silt−0.71846−0.695570.47960.000999
Clay−0.486010.873950.08380.12987
Altitude 0.49190.000999
AltitudeG1−0.4402−0.4933
AltitudeG20.0644−0.4341
AltitudeG30.3862−0.0282
AltitudeG40.0120.5335
AltitudeG5−0.02240.4222
Table
Table 3. Selected roadside vascular species as indicators across an altitudinal gradient in mountainous Andean ecosystems.
Table 3. Selected roadside vascular species as indicators across an altitudinal gradient in mountainous Andean ecosystems.
ZoneSpeciesIndValp-Value
Elevation 1 Gaultheria vaccinioides Wedd.0.3890.01
Eriosorus aureonitens (Hook.) Copel.0.30.032
Maxillaria sp.0.40.007
Elleanthus aurantiacus Rchb.f0.4030.009
Elevation 2 Bejaria resinosa Mutis ex L. f.0.4070.025
Cortaderia jubata (Lemoine) Stapf0.4410.005
Digitaria ciliaris (Kents.) Koeler0.5950.001
Elevation 3 Baccharis genistelloides (Lam.) Pers.0.590.015
Carex lehmanniana Boott ex Walp.0.30.025
Sticherus bifidus (Willd.) Ching0.4190.006
Lycopodium clavatun subsp. Contiguum0.30.04
Calamagrostis intermedia (J. Presl) Steud.0.4980.001
Elevation 4 Lycopodiella andicola B. Øllg.0.50.002
Lycopodium clavatum L.0.2980.029
Axinaea sp.0.40.005
Neurolepis laegaardii L.G. Clark.0.3820.009
Elevation 5Dennstaedtia mathewsii (Hook.) C. Chr.0.3560.01
Clidemia sp.0.40.004
Eleusine indica (L.) Gaertn.0.270.038
Melpomene moniliformis (Lag. ex Sw.)0.310.028
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Guzmán, P.; Benítez, Á.; Carrión-Paladines, V.; Salinas, P.; Cumbicus, N. Elevation and Soil Properties Determine Community Composition, but Not Vascular Plant Richness in Tropical Andean Roadside. Forests 2022, 13, 685. https://doi.org/10.3390/f13050685

AMA Style

Guzmán P, Benítez Á, Carrión-Paladines V, Salinas P, Cumbicus N. Elevation and Soil Properties Determine Community Composition, but Not Vascular Plant Richness in Tropical Andean Roadside. Forests. 2022; 13(5):685. https://doi.org/10.3390/f13050685

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Guzmán, Patricio, Ángel Benítez, Vinicio Carrión-Paladines, Paul Salinas, and Nixon Cumbicus. 2022. "Elevation and Soil Properties Determine Community Composition, but Not Vascular Plant Richness in Tropical Andean Roadside" Forests 13, no. 5: 685. https://doi.org/10.3390/f13050685

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