Effects of Fire on Pyrodiversity of Terricolous Non-Tracheophytes Photoautotrophs in a P á ramo of Southern Ecuador

: The p á ramos have a great diversity of ﬂora, including terricolous non-tracheophyte pho-toautotrophs (bryophytes and lichens). Bryophytes and lichens are very sensitive to environmental changes related to anthropogenic ﬁres, livestock, and agricultural activities. We determined for the ﬁrst time in Ecuador the effects of prescribed ﬁres on the pyrodiversity of terricolous non-tracheophyte photoautotroph in a p á ramo of South Ecuador. Three permanent sampling plots (T1, T2, and control) were established, each with a dimension of 4 m × 20 m and separated by 3 m (T1: one with ﬁre-induced uphill and T2: one with ﬁre-induced downhill and one control). They were installed in three different blocks, obtaining a total of nine plots. Three samplings (2, 6, and 12 months) were carried out in each plot, where the cover and richness of terricolous bryophytes and lichens were estimated in 216 quadrats of 20 × 30 cm. A total of 27 species (11 lichens and 16 bryophytes) were studied, where the lichen families, that is, Cladoniaceae and Baeomycetaceae, as well as the bryophytes families, namely, Dicranaceae, Jungermanniaceae, Bartramiaceae, Rhacocarpaceae, and Pallaviciniaceae, have been recorded as pioneers in areas under ﬁre effects. Richness and diversity (calculated using the Shannon–Weaver and Simpson indexes) were affected by ﬁre treatments; on the other hand, monitoring time (M3) positively affected species diversity. The composition of terrestrial non-tracheophyte photoautotroph communities showed slight changes between the control and T1 and T2, but the changes were more marked with time after the burns (M1 vs. M3), related to ﬁre severity. Therefore, terricolous lichen and bryophyte communities (richness and diversity) can be used as model organisms for the assessment of the effects of prescribed ﬁres on tropical p á ramos for subsequent management and conservation.


Introduction
Páramos have a high diversity of endemic plants dominated by grasses, rosettes, and small shrubs, including non-tracheophyte photoautotrophs [1][2][3].They play an important role in the regulation of regional hydrology; in addition, they are the source of drinking water for most of the population in the Andean areas.Therefore, they are of great ecological and socioeconomic importance [3][4][5][6].Despite this, páramos are one of the most threatened ecosystems by anthropogenic activities related to burning for agricultural and livestock activities [3,[7][8][9], thus fires are one of the main factors affecting the diversity and functioning of these ecosystems, such as nutrient cycling [10,11], accelerating the decomposition process, and generating drier soils [12].Fire regimes are an important force of natural selection in biodiversity [13][14][15].In this context, a new concept called pyrodiversity has been denoted based on the spatial or temporal variability of fire effects in a landscape, where several investigations have been conducted on terrestrial mammals, bats, birds, reptiles, invertebrates, plant-pollinator interactions, and plant diversity [16].
Within the wide variety of species in the páramos, we can identify non-tracheophyte photoautotrophs (NVA), such as bryophytes, lichens, terrestrial algae, and cyanobacteria, that play key roles in the diversity and functioning of ecosystems, which is why deforestation and climate change (e.g., fire) cause negative effects [17].Lichens and bryophytes are important components of non-tracheophyte photoautotrophs (NVA) due to their ability to retain water and improve infiltration in soils, which reduces erosion and provides organic matter to the soil [18][19][20].These organisms have a high capacity to adapt to changes in environmental conditions.For this reason, they are known as soil formers [21,22], have good water retention capacity [4], and are considered pioneer species in revegetation of degraded soil [23], increasing soil stability and water input rates that provide greater success in seedling establishment [24].Therefore, previous studies in the páramos point out that the variables limiting lichen and bryophyte diversity are elevation, topography, microclimate, and edaphic properties [2, 20,25], as well as some studies pointing out the effects of fire on diversity [26,27].
In this context, the application of prescribed fire, consisting of the planned use of lowintensity fire, is used to achieve very different objectives given certain meteorological, fuel, and topographic conditions [28] and has thus been implemented in several regions [29].This approach has been studied in tracheophytes [30][31][32][33][34], along with factors related to edaphic properties [29,35,36].Despite this, studies in the páramos of Ecuador have focused on understanding the relationship of tracheophyte diversity with fire [37][38][39][40][41].
Along the same lines, studies applying the prescribed fire method to the diversity of bryophytes and lichens have been limited in temperate zones [26,42], and only in the Brazilian páramos Wienskoski and Santos [27] have shown the effects of natural fire on the richness and composition of bryophytes, but in South America, prescribed fire has not been applied.Therefore, the present research determined for the first time in Ecuador the effect of prescribed fire on the diversity of bryophyte and lichen communities in an herbaceous páramo in the southern region of Ecuador.Fire severity may imply changes in the richness and composition of bryophytes and lichens because previous studies indicate a reduction in the cover and diversity of cryptogams in soils degraded by fire, but the effect is temporary.Therefore, it is possible that a certain part of their structure is maintained, allowing their populations to recover [43,44].

Study Area
The study was carried out in the herbaceous páramo located in the parish of San Lucas, canton Loja (latitude of 3 • 43 38.20 S and longitude of 79 • 12 30.25 W) (Figure 1).This ecosystem is located above 3057 m asl.The herbaceous páramo presents a regular topography as it is a high plateau like other South American páramos.

Experimental Burns and Determination of Fire Severity
In an area of 15% slope, 3 blocks were installed (Block 1, Block 2, and Block 3), each of which included 3 permanent sampling plots (PSP), obtaining 9 plots in total (Figure 2).

Experimental Burns and Determination of Fire Severity
In an area of 15% slope, 3 blocks were installed (Block 1, Block 2, and Block 3), each of which included 3 permanent sampling plots (PSP), obtaining 9 plots in total (Figure 2).

Experimental Burns and Determination of Fire Severity
In an area of 15% slope, 3 blocks were installed (Block 1, Block 2, and Block 3), each of which included 3 permanent sampling plots (PSP), obtaining 9 plots in total (Figure 2).The blocks were separated 8 m apart to avoid the spread of fire to surrounding areas.The permanent plots per block (T1, T2, and control), each with a dimension of 4 m × 20 m, were separated by 3 m, following the protocol performed by Hueso-González et al. [45].To protect these plots from possible disturbances, we surrounded them with wooden posts and barbed wire, thus protecting them from possible interference from cattle (Bos taurus) that occasionally roamed the area, as well as from interactions with native species such as the spectacled bear (Tremarctos ornatus) (Figure 1c).
To prepare for the experimental burn, we conducted fuel load estimation four days prior to the event.To do this, we collected random samples from the páramo area near the experimental site but outside the designated plots.We used ten wood squares, each of 1 square meter, randomly distributed throughout the area.Herbaceous vegetation within these squares was cut evenly at ground level with the use of a sickle.Subsequently, the collected samples were placed in properly labeled paper bags and transported to the UTPL laboratory.In this laboratory, we subjected the samples to an oven-drying process for 48 h at a temperature of 60 • C.After completing the drying process, we proceeded to weigh the samples using a Rice Lake TC balance.
The fuel load per square meter was expressed in grams of dry matter.To calculate the percentage of fuel moisture, we considered the difference between the wet and dry weights [46].With this analysis, we determined that the fuel moisture content of the vegetation was relatively high, reaching 23.6%.This translated into a wet biomass of 2213.7 g per square meter (equivalent to 22.1 tons per hectare) and a dry biomass of 1790.25 g per square meter (equivalent to 17.9 tons per hectare).It is relevant to mention that soils in the páramo zone are usually saturated, resulting in a fuel moisture content classification considered moderate for the herbaceous páramo ecosystem.
Once the fuel load was calculated, the experimental burn was executed as follows: The burns were conducted in October 2021 during the Veranillo del Niño (VdN) phenomenon, characterized by a period of dry and sunny weather in the southern equatorial Andes.Typically, the VdN phenomenon occurs for about 15 days between October and November [11].The burns were conducted between 12:00 and 14:00 h, as per the time recommended by Geron and Hays [47].This period coincides with the highest recorded values of solar radiation (W/m 2 ) and temperature ( • C), together with the lowest relative humidity (%), thus offering favorable conditions for controlled burning.In addition, the Fine Dead Fuel Moisture (FDFM) was calculated following the procedures detailed in the widely recognized Interagency Fire Use Module Field Guide [48].This calculation considered percent relative humidity (RH%), wind speed (m/s) and direction ( • ), percent cloud cover, and temperature ( • C).These data were closely monitored using a Davis Instruments USA (San Lucas_UTPL) Vantage Pro Plus automatic weather station located near the experimental site.Solar radiation (W/m 2 ) was also measured using the same equipment.
Following the hazard index stipulated in the Interagency Fire Use Module Field Guide [48], the atmospheric conditions at the time of the experimental burn were classified as alert.This was based on a 40% probability of ignition, a relatively high wind speed of 8.9 m per second, an average relative humidity of 72.6%, an average air temperature of 15.6 • C, and a maximum solar radiation of 1135.1 (W/m 2 ).The absence of precipitation at that time provided optimal conditions to carry out the burning of the permanent sampling plots (PSP) with herbaceous páramo vegetation (VdN phenomenon).It is critical to note that the reference guide was originally developed for ecosystems in the United States and may not be directly applicable to the unique conditions of the páramo ecosystem in the high tropical Andes.Therefore, it is necessary to incorporate páramo-specific climate data to adapt the Interagency Fire Use Module Field Guide [48] to the particularities of high Andean ecosystems.
The burns were conducted following two ignition patterns.T1 was sloping uphill (up slope), T2 was sloping downhill, and a control was maintained without burning.The ignition technique consisted of a strip burn starting 20 cm from the beginning of each burn pattern (Figure 2).Finally, to assess the severity of the experimental burn, after the flames had been naturally extinguished, we collected three random ash samples within each Permanent Sampling Plot (PSP) (27 samples in total).These samples were carefully handled and placed in properly labeled plastic Petri dishes sealed with Parafilm sheets.The ash samples were transported to the UTPL soil laboratory for analysis.The severity analysis was based on the color method and Munsell code, as proposed by recent research [49,50].
Furthermore, an assessment of the fire severity was carried out using imagery captured by the Sentinel 2B satellite for the year 2021, employing its multispectral MSI sensor.Dates with less than 20% cloud cover during the last months of the year were selected, as this period experiences a higher incidence of forest fires in the southern region of Ecuador, as previously documented [51].In this analysis, the normalized burn ratio (NBR) was used, which was designed to highlight fire-affected areas due to their characteristic spectral signature [52].The formula used in this analysis corresponds to the one described by Parker et al. [53].
Furthermore, the dNBR index was calculated, representing the difference between pre-fire and post-fire conditions (NBR pre-fire-NBR post-fire), to estimate severity using the respective formula [54].

Sampling of Terricolous Non-tracheophyte Photoautotrophs
Three monitorings were performed: the first one two months (M1) after the burns, the second one after 6 months (M2), and the third one after 12 months (M3).For sampling bryophyte and lichen, block 1 was used with the three plots (T1, T2, and control).In each plot, 6 subplots of 1 m × 1 m were established, and within each subplot, 4 quadrats of 25 cm × 25 cm [25] were placed in each of the corners, where the coverage and richness of terricolous non-tracheophyte photoautotrophs (bryophytes and lichens) were estimated, with a total of 216 sampling quadrats.

Statistical Analysis
To determine the sampling effort in the three treatments and monitoring, the Chao 2 and Jack1 non-parametric richness estimators were used.Species richness and diversity (Shannon-Weaver and Simpson indices) were determined.Box plots were used to visualize changes in richness, abundance, and diversity in the three treatments (T1, T2, and control) and monitoring (M1, M2, and M3).To determine changes in the richness, abundance, and diversity of terricolous bryophytes and lichens in relation to treatment and monitoring, we used Kruskal-Wallis's non-parametric analysis, followed by Dunn's pairwise comparison.Analyses were done in R 3.2.2 using the "dunn.test"package [55].Shapiro-Wilk confirmed that the model met a non-normal distribution (p-value < 0.05).
A non-metric multidimensional scaling analysis (NMDS) was performed to detect changes in species composition in relation to treatment and monitoring.NMDS was run using the Bray-Curtis distance and 999 Monte Carlo permutations.To find treatment and monitoring effects on terrestrial cryptogam composition, a permutational multivariate analysis of variance (PERMANOVA) was performed.All analyses were performed with the statistical software R version 3.6.3and the statistical package "vegan" [56].

Fire Severity of Experimental Burns
It is evident that all ash samples collected from the burned plots exhibit an extremely dark brown color.This observation is of great importance, as it allows discerning between different degrees of fire severity, from a lighter shade to a noticeably dark one.According to the color assessment method and the Munsell coding system, a very dark brown color indicates low fire severity (Figure 3).pairwise comparison.Analyses were done in R 3.2.2 using the ''dunn.test''package [55].Shapiro-Wilk confirmed that the model met a non-normal distribution (p-value < 0.05).
A non-metric multidimensional scaling analysis (NMDS) was performed to detect changes in species composition in relation to treatment and monitoring.NMDS was run using the Bray-Curtis distance and 999 Monte Carlo permutations.To find treatment and monitoring effects on terrestrial cryptogam composition, a permutational multivariate analysis of variance (PERMANOVA) was performed.All analyses were performed with the statistical software R version 3.6.3and the statistical package "vegan" [56].

Fire Severity of Experimental Burns
It is evident that all ash samples collected from the burned plots exhibit an extremely dark brown color.This observation is of great importance, as it allows discerning between different degrees of fire severity, from a lighter shade to a noticeably dark one.According to the color assessment method and the Munsell coding system, a very dark brown color indicates low fire severity (Figure 3).The remote sensing has revealed that during the year in which the experimental burns were conducted, both in the páramo ecosystem and in the other ecosystems of the parish of San Lucas (see Figure 1), low-severity fires were recorded (Figure 4).This supports and corroborates the results obtained through the color assessment method and the Munsell code system applied to the analyzed ash samples.In the specific context of the páramo ecosystem (Figure 4b), it is observed that 12% of the fires that occurred in that year were characterized by their low severity, and there is no evidence of medium-tohigh-severity fires.The remote sensing has revealed that during the year in which the experimental burns were conducted, both in the páramo ecosystem and in the other ecosystems of the parish of San Lucas (see Figure 1), low-severity fires were recorded (Figure 4).This supports and corroborates the results obtained through the color assessment method and the Munsell code system applied to the analyzed ash samples.In the specific context of the páramo ecosystem (Figure 4b), it is observed that 12% of the fires that occurred in that year were characterized by their low severity, and there is no evidence of medium-to-highseverity fires.

Effect of Fire on the Diversity of Bryophytes and Lichens
A total of 27 species were found, of which 11 were lichens and 16 were bryophytes (Table 1), with 23 species for T2 and 21 species for T1 and the control (Table 1).We

Effect of Fire on the Diversity of Bryophytes and Lichens
A total of 27 species were found, of which 11 were lichens and 16 were bryophytes (Table 1), with 23 species for T2 and 21 species for T1 and the control (Table 1).We registered the most common lichen families, that is, Cladoniaceae and Baeomycetaceae, as well as the bryophytes families, namely, Dicranaceae, Jungermanniaceae, Bartramiaceae, Rhacocarpaceae, and Pallaviciniaceae.The Chao 1 and Jack1 estimators showed similar richness values in the control compared with T1 and T2.Following this pattern, in the monitoring, these estimators showed similar values in M1 with 24, in M2 with 22, and in M3 with 21 species, respectively (Table 2).Box plots showed higher values of richness and diversity indices (Shannon and Simpson) in the control compared to the two treatments; however, in abundance, there is a similar trend for the treatments (T1 and T2) compared to the control (Figure 5).Box plots showed higher values of richness and diversity indices (Shannon and Simpson) in the control compared to the two treatments; however, in abundance, there is a similar trend for the treatments (T1 and T2) compared to the control (Figure 5).

Box plots indicated higher values of richness and diversity indices (Shannon and
Simpson) in M3 compared to M1 and M2; however, in abundance, there is a higher richness in M2 compared to M1 (Figure 6).

M1
M2 M3 M1 0.4935 <0.0001 M2 0.4935 <0.0001 M3 <0.0001 <0.0001 NMDS analysis indicated a slight clustering of lichen and bryophyte compositions related to fire treatment (Figure 7A).However, the composition as a function of monitoring indicated a greater clustering between M1 and M2 when compared to M3 (Figure 7B).The PERMANOVA indicated that treatment significantly explains 6% (p = 0.001) of the variability in lichen and bryophyte community composition (Table 4).On the other hand, monitoring significantly explains 15% (p = 0.001) of the variability in lichen and bryophyte community composition.

Discussion
The results obtained from the analysis of fire severity in the burned plots, based on ash color assessment and corroborated by remote sensing methods, consistently indicate low-severity fires [49][50][51].This low severity aligns with the findings of a study conducted by Díaz et al. [11], which documented low-severity fires over four years of monitoring in the studied páramo.Low-severity burns are typically considered ecologically beneficial as they contribute to the reduction of dead vegetative material and promote vegetation regeneration while preserving habitat structure [57,58].For instance, recent research in ecosystems near the study area in southern Ecuador, such as wet high-altitude shrublands, has demonstrated that approximately a 2-year recovery period is necessary to restore the physicochemical properties of the soil after low-severity fires [51,59].Consequently, it is reasonable to assume that in our ecosystem, soil properties will recover over a similar timeframe following low-severity burns.This suggests that, after such burns, soil conditions should be conducive to vegetation growth, potentially benefiting ecosystem recovery and overall health in the future.
On the other hand, our results have shown for the first time the effects of fire on the richness and composition of terricolous non-tracheophyte photoautotrophs (bryophytes and lichens) in a páramo of southern Ecuador during one year of monitoring.In addition, the diversity of bryophytes and lichens was higher during the monitoring year compared to the first months of the experimental burns (M1 and M2).This is possibly related to the recovery time for low-severity burns.Supporting our results, previous studies have indicated the effects of fire on the richness, diversity, and composition of bryophytes and lichens in temperate and boreal zones [26,[60][61][62][63][64][65] The richness of lichen and bryophyte species is higher in the control because species belonging to bryophyte group (e.g., Breutelia, Rhacocarpus, and Syzygiella) were exclusive in the control plots.Supporting our results, previous studies have documented that the richness of cryptogams decreases in soils degraded by fire [43,44,66].Likewise, Johansson and Reich [60] and Calabria et al. [26] showed in their studies that the diversity and cover of cryptogams are lower in burned areas when compared to unburned areas.These changes in diversity could be limited due to some soil factors (e.g., carbon availability) because fire modifies the physical and chemical characteristics of the soil [67][68][69].For example, several studies have documented that fire alters nutrient availability (e.g., increase in K, Ca, Mg, and P levels and decrease in Zn, Cu, Fe, and Mn levels), and this implies changes in the diversity of terricolous bryophytes and lichens [63,70].
In the same line, the richness and diversity were higher at one year of monitoring (M3) compared to the monitoring of 2 months (M1) and 6 months (M2), respectively.In this context, Greene et al. [43] and Zabala et al. [66] showed that the effect of fire on the diversity of lichens and bryophytes can be temporary; therefore, their diversity can recover with the passage of time monitoring [43,44,66].Similarly, Orumaa et al. [63] and Pharo et al. [71] showed that the post-fire effect has positive consequences on the diversity and richness of terricolous cryptogams due to the increase in nutritional contents such as N, K, Ca, Mg, Fe, and Mn.Finally, Wienskoski and Santos [27] point out that the richness and diversity of terricolous cryptogams (e.g., bryophytes) are limited by the time after the fire, that is, the diversity is greater when the time after the fire increases.
The composition of terricolous cryptogam communities showed slight changes between the control and T1 and T2 related to fire severity.In agreement with our results, Calabria et al. [26] pointed out that the composition of terricolous cryptogam communities is influenced by fire.Similarly, Wienskoski and Santos [27] showed variations in species composition among treatments of areas that had suffered more recent fires.Following the same pattern, Pharo et al. [71] mentioned that the species composition of terricolous cryptogams was strongly related to the severity of the fire, which was probably of low severity, as reported by Díaz et al. [11], who used remote sensing methods in their study area.Generally, low-severity burns increase organic matter contents and thus the contents of macro-and microelements essential for vegetation development [72,73], and therefore, bryophytes and lichens have sufficient nutritional supply for their growth.
Community composition in terms of time since the burns shows the most notable changes between 2 months of monitoring and 1 year of monitoring.Supporting our results, previous studies have documented that there are changes in terricolous cryptogam species composition with time since wildfire [61].Similarly, Hylander et al. [62] showed that variation in bryophyte species composition was strongly related to time since fire.Likewise, Orumaa et al. [63] mentioned that the time since the last forest fire affected the composition patterns of vascular plants and terricolous cryptogams.In addition, the most representative lichen families reported in this study were Cladoniaceae and Baeomycetaceae, and as for bryophytes, Dicranaceae, Jungermanniaceae, Bartramiaceae, Rhacocarpaceae, and Pallaviciniaceae.These families have been previously reported in several studies as characteristic elements of the páramos of Ecuador [2,20,74] and other areas of tropical regions [75].Therefore, the families noted above have been recorded as pioneers in areas under fire effects [27,62,65,71].Although our results indicate the effect of fire on terricolous non-tracheophyte photoautotrophs (bryophytes and lichens), other variables such as páramo type, elevation, topography, and microclimatic factors, such as humidity and temperature, are limiting the diversity of these organisms [74]; therefore, these factors should be considered in future research.

Conclusions
The richness and diversity (calculated using the Shannon-Weaver and Simpson indexes) of terricolous non-tracheophyte photoautotrophs (bryophytes and lichens) were negatively affected by the fire treatments in a páramo in southern Ecuador, as well as during the first months after the burns were applied (M1 and M2).However, with the passage of time after the experimental burning method was applied, the diversity and abundance of lichens and bryophytes were high (M3).The composition of the bryophyte and lichen communities showed slight changes between the control and the two treatments (T1 and T2) due to the low severity of the fire, which probably did not alter the edaphic properties.However, the changes were more marked with time (one year after the burns), related to the natural succession of the species.The lichen families, namely Cladoniaceae and Baeomycetaceae, as well as the bryophytes families, namely Dicranacea, Jungermanniaceae, Bartramiaceae, Rhacocarpaceae, and Pallaviciniaceae, have been recorded as pioneers in areas under fire effects.Therefore, terricolous bryophyte and lichen communities can be used as model organisms for the evaluation of the effects of the prescribed fire on tropical páramos for subsequent management and conservation.

Figure 1 .
Figure 1.Design of the experimental burn in the study area: (a) Location map of the páramo in San Lucas parish, southern Ecuador.(b) Satellite image (Google Earth, 2023) showing the study plots.(c) Photo of the study area during the experimental burn.

Figure 2 .
Figure 2. Diagram of the design for the burning experiment.

Figure 1 .
Figure 1.Design of the experimental burn in the study area: (a) Location map of the páramo in San Lucas parish, southern Ecuador.(b) Satellite image (Google Earth, 2023) showing the study plots.(c) Photo of the study area during the experimental burn.

Figure 1 .
Figure 1.Design of the experimental burn in the study area: (a) Location map of the páramo in San Lucas parish, southern Ecuador.(b) Satellite image (Google Earth, 2023) showing the study plots.(c) Photo of the study area during the experimental burn.

Figure 2 .
Figure 2. Diagram of the design for the burning experiment.Figure 2. Diagram of the design for the burning experiment.

Figure 2 .
Figure 2. Diagram of the design for the burning experiment.Figure 2. Diagram of the design for the burning experiment.

Figure 3 .
Figure 3. Diagram of fire severity assessment in the experiment burn using the color method and the Munsell code: (a) corresponds to block 1, (b) corresponds to block 2, and (c) corresponds to block 3. The circles represent the photographs taken when the color of the ashes was compared with the color values of the Munsell table (5YR 2.5/2).The value of 5YR 2.5/2 indicates the presence of ashes with a very dark brown tone.

Figure 3 .
Figure 3. Diagram of fire severity assessment in the experiment burn using the color method and the Munsell code: (a) corresponds to block 1, (b) corresponds to block 2, and (c) corresponds to block 3.The circles represent the photographs taken when the color of the ashes was compared with the color values of the Munsell table (5YR 2.5/2).The value of 5YR 2.5/2 indicates the presence of ashes with a very dark brown tone.

Figure 4 .
Figure 4. Severity of wildfire in the study area calculated with the dNBR index representing the difference between pre-fire and post-fire conditions (pre-fire NBR-post-fire NBR): (a) fire severity at the San Lucas parish level and (b) fire severity in the studied páramo.The diamond with the red line indicates the experimental burn area.

Figure 4 .
Figure 4. Severity of wildfire in the study area calculated with the dNBR index representing the difference between pre-fire and post-fire conditions (pre-fire NBR-post-fire NBR): (a) fire severity at the San Lucas parish level and (b) fire severity in the studied páramo.The diamond with the red line indicates the experimental burn area.

Figure 5 .
Figure 5. Box plots of richness, abundance, and diversity indices (Shannon and Simpson) for the two different types of fire treatment and control (without burning).T1 = in favor of the slope (uphill), and T2 = against the slope (downhill).

Figure 5 .
Figure 5. Box plots of richness, abundance, and diversity indices (Shannon and Simpson) for the two different types of fire treatment and control (without burning).T1 = in favor of the slope (uphill), and T2 = against the slope (downhill).

Figure 7 .
Figure 7. Non-parametric multidimensional scaling analysis (NMDS) of species composition in: (A). the two different types of fire treatment and control (without burning).T1 is in favor of the slope

Table 1 .
Species of bryophytes and lichens occurring in the two different types of fire treatment and control (without burning).T1 is in favor of the slope (uphill), and T2 is against the slope (downhill).

Table 2 .
The Chao 2 and Jack1 richness estimators for the two different types of fire treatment and control (without burning).T1 is in favor of the slope (uphill), and T2 is against the slope (downhill).

Table 3 .
Results of the post hoc Dunn's multiple comparisons test, showing the significance of differences in two different types of fire treatment and control (without burning).T1 is in favor of the slope (uphill), and T2 is against the slope (downhill).The different monitoring times (M1, M2, and M3).M1 = 2 months, M2 = 6 months, and M3 = 12 months.

Table 4 .
PERMANOVA results of species composition at treatment and monitoring levels.Df = degrees of freedom; SS = sum of squares; R2 = coefficient of variation; F = F-statistics.