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Article

Fire-Induced Vegetation Dynamics: An In-Depth Discourse on Revealing Ecological Transformations of the Mahaban and Surrounding Forests

by
Azra Israr
1,
Shujaul Mulk Khan
1,2,*,
Abdullah Abdullah
1,
Ujala Ejaz
1,
Sadia Jehangir
1,
Zeeshan Ahmad
3,
Abeer Hashem
4,
Graciela Dolores Avila-Quezada
5 and
Elsayed Fathi Abd_Allah
6
1
Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
2
Pakistan Academy of Sciences, Islamabad 44010, Pakistan
3
CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
4
Botany and Microbiology Department, College of Science, King Saud University, P.O. Box. 2460, Riyadh 11451, Saudi Arabia
5
Facultad de Ciencias Agrotechnológicas, Universidad Autónoma de Chihuahua, Chihuahua 31350, Mexico
6
Plant Production Department, College of Food and Agricultural Sciences, King Saud University, P.O. Box. 2460, Riyadh 11451, Saudi Arabia
*
Author to whom correspondence should be addressed.
Fire 2024, 7(1), 27; https://doi.org/10.3390/fire7010027
Submission received: 7 November 2023 / Revised: 28 December 2023 / Accepted: 4 January 2024 / Published: 15 January 2024
(This article belongs to the Section Fire Science Models, Remote Sensing, and Data)
Browse Figures
Review Reports Versions Notes

Abstract

:
Since the Palaeozoic era, fire as a potent driver of environmental changes, has dramatically shaped the terrestrial ecosystems. Fire affects soil structure and composition, which in turn affects the floral diversity of an area. This research work aims to examine the impact of fire on vegetation and the physicochemical nature of the soil in fire-affected and fire-free sites across the Mahaban and the surrounding forests, Swabi District, Khyber Pakhtunkhwa, Pakistan. Quadrat quantitative ecological techniques were used for vegetation sampling in fire-free and fire-affected sites. In total, 219 plant species belonging to 173 genera and 70 families were recorded. Among the 219 plant species, 173 species were recorded from fire-free sites and the remaining 122 species were from fire-affected sites. The incidence of fire results in elevated organic matter, nitrogen, phosphorus, and lower calcium carbonate concentrations in the soil. The greatest species richness and evenness were observed across the fire-free sites. Our study concludes that the influence of edaphic and topographic factors on species richness varies between fire-affected and fire-free sites. Fire has significantly altered the nutrient availability in the studied region, and this is confirmed by soil analysis and vegetation research. It is suggested that further research in the field of fire ecology can produce valuable insights.

1. Introduction

Fire has been a significant component of the Earth’s system for the past 350 million years [1], influencing the evolution of plants and animals [2]. The natural growth of coniferous species, particularly since the Miocene epoch, has increased the occurrence of fires, making them dominant in all biomes, except deserts and scarcely vegetated areas [3]. Approximately half of global ecosystems, spanning from tropical savannas to boreal forests, are considered ‘fire-prone’ [4]. In recent decades, the heightened frequency of forest fire incidence has been connected to late 20th-century trends favoring warm and early springs [5].
This trend is attributed to various factors, such as the continuous increase of the human population and extreme climatic conditions, that contribute to forest fire incidence at a global scale [6]. Predictions based on a scenario of climatic changes show a significant temperature rise, leading to frequent fire incidences in the 21st century [7]. Frequent forest fires cause threats to the forest’s ecosystem services, with about 15 million hectares of forests lost annually [8]. Vegetation is a prominent manifestation of an ecosystem and shows indispensable bonds between biotic and abiotic components controlled by edaphic, topographic, and climatic factors [9]. Moreover, vegetation is pivotal for projecting future changes in plant distribution, linking them to anthropogenic impacts on the ecosystem, and addressing conservation challenges [10,11]. In addition, environmental factors, including soil, topography, and various other non-climatic factors, play key roles in controlling vegetation structure and regeneration [12,13,14,15]. Altitude, aspect, slope, and slope location are topographic elements that may have a direct or indirect impact on soil and microclimatic conditions as well as on seedling establishment and growth [16,17]. Moreover, ground fires not only affect vegetation cover but also severely alter soil characteristics [18]. Sometimes, the soil surface is affected by fire leading to significant variations in soil nutrients [19] and soil textures, which tend to become rougher and harder [20], contributing to stable aggregates from clay and silt fractions influenced by tremendous heat [21]. Fire-induced changes in humus composition result in a complex interplay of physical, chemical, and biological features within soils. Different nutrients in organic matter (OM) are gradually released during decomposition and contribute to maintaining minimal loss during the leaching process to establish a reliable source of nutrients [22]. The impact of soil burning extends to the nutrient cycling process [23]. Burning OM makes certain nutrients available while causing the volatilization of others, such as phosphorus (P) nitrogen (N) and sulfur (S) [24,25,26]. The rise in accessible phosphate and nitrate following a fire is probably due to elevated microbial activity. Following a fire, ions may be moved as particulate matter through wind, surface runoff, or groundwater, even though the top layers of soil readily absorb cations [27]. Since the combustibility thresholds of woody fuels are higher than those of sulfur, phosphorus, nitrogen, and potassium, these nutrients are easily emitted from organic matter during a fire [22]. Several studies have been conducted to assess the impact of forest fires on vegetation and soil health across different regions of the globe (e.g., [26,28,29,30,31,32]). Concerning the simultaneous effects of forest fire on soil characteristics and plants, there is a substantial research vacuum. Little information is available on how soil characteristics, nitrogen cycling, and vegetation are affected by fire disturbances [25,33]. Like other areas of the world, the forests of Mahaban and the surrounding region in the Swabi District of Khyber Pakhtunkhwa, Pakistan, are facing a high incidence of fire, but no research has been carried out do date to incorporate the impact of forest fires on soil features and vegetation [34]. This study was designed to assess the impact of fire regimes in the forests of Mahaban and the surrounding region. Fires are initiated accidentally by lightning or intentionally by the indigenous people of this region. Ecologically, the impact of fire extends beyond biodiversity to overall ecosystem growth and nutrient availability. Ground fires affect vegetation cover and the soil’s physical and chemical features.
This study aims to bridge an important research gap by studying the simultaneous impact of fire regimes on both soil and plants. We hypothesized that fire regimes in these regions cause drastic changes to soil fertility and subsequently affect species diversity and richness. By comparing fire-affected regions with fire-free ones, the study tries to analyze changes in the soil’s physical and chemical features in relation to vegetation, contributing to a more detailed understanding of fire regimes in the forests of Mahaban and the surrounding region.

2. Materials and Methods

2.1. Study Area

The study area is located between latitudes 34–07 and 34–20 in the north and 72–39 and 72–46 in the east of Swabi District, Khyber Pakhtunkhwa, Pakistan. The average elevation ranges from 400 m to 2250 m. A major part of the hilly region belongs to Gadoon Hills in the northeast of the district; these hills are the continuation of the Mahaban Hills (on the gorges of the river Indus). The name “Mahaban” means dense forests, which is fitting given that the entire region is encircled by enormous forests [35]. At lower altitudes, the mountains are covered by forests of Pinus roxburghii whereas, at higher altitudes, dense forests of Quercus leucotrichophora dominate. In the lower levels, the climate is subtropical and semi-arid, transitioning to a temperate environment in the upper regions. Mahaban has a moderate summer and a harsh winter climate. The average summer temperature is around 10–15 °C for roughly seven months of the year, while the average winter temperature is considerably below zero [35]. The hottest months are June and July, with the highest average temperatures of 40 to 45 °C in the lower piedmont. Winters are chilly, with average monthly temperatures ranging from 4 to 10 °C. High-elevation areas experience snowfall during the coldest months of winter [10]. The annual precipitation ranges from 60 to 145 mm and increases as one travels further north [10].
These forests are highly fire-prone with frequently reported natural and anthropogenic instances of forest fires. This area was selected for this study because it has completely fire-free areas as well as forests that regularly experience forest fires (Figure 1 and Figure 2).

2.2. Data Collection

The whole study area was divided into two categories, i.e., fire-free and fire-affected areas. These areas were selected based on fire history and further confirmed based on their environmental conditions. The study area was split up into 12 different sites. Out of these 12 sites, six sites were in fire-free areas, having no recent incidents of fire, and six sites were in fire-affected areas. Data collection was carried out at the end of March 2022 to record spring data and a second field survey was carried out at the end of August 2022, following the monsoon season, to record summer plant species from the same quadrat across the fire-affected and fire-free sites of the studied region.

2.3. Vegetation Sampling

Vegetation sampling was carried out at each of these 12 sites along the altitudinal gradient to cover the whole forest. Sampling was carried along the entire stretch of the mountain from the bottom up to the highest peak. The quadrat method was utilized for data collection. Quadrats with sizes of 10  ×  10 m square, 5  ×  5 m square, and 1  ×  1 m square were employed for trees, shrubs, and herb species, respectively. Two neighboring sample sites were separated by more than 100 m. A total of 315 quadrats were recorded for each season. Soil samples were collected and stored in zipper bags labeled with the quadrat particulars. Additionally, herb spread characteristics were documented [36,37]. DBH (diameter at breast height) measurements were taken for all the trees in a quadrat and calculated.

2.4. Environmental Gradients

The coordinates of each sampling site, including latitude, longitude, and altitude, were also documented by utilizing GPS (Global Positioning System, Garmin Corp., Olathe, Kansas, 2000). A compass was used to determine the mountain’s aspect, and a clinometer was employed to determine the slope angle.

2.5. Soil Analysis

The collected soil samples were shifted to the drying trays for air drying. After two days, when the soil samples were well dried, the samples were ground using an electric grinder and then sieved using a No. 30 laboratory test sieve. These samples were then ready for analysis. Each sample of 10 g was placed into a clean beaker (100 mL) and the final volume was diluted up to 500 mL with distilled water. This was and allowed to rest for 30 min. The pH of each soil sample was noted using a pH meter [38]. For electrical conductivity (EC), a suspension of 1:5 (soil: water) was prepared in the same way as for pH determination, mixed, and allowed to stand for 30 min. The EC of each soil sample was then recorded using an EC meter. Organic matter was quantified through extraction with orthophosphoric acid [39]. Nitrogen and phosphorus were quantified through extraction with sulfuric acid. Calcium carbonate was extracted using hydrochloric acid [40].

2.6. Data Analysis

For analysis, MS Excel was used to sort the datasets of all plants and their parameters, and then their phytosociological attributes, including density, frequency, relative cover density, relative frequency, relative cover, importance value index (IVI), and species richness were calculated. Simpson’s dominance index [41] (D = plant dominance) was calculated as,
D = 1 − ∑ n i ( n − 1 ) N ( N − 1 )
Species richness was computed by the formula,
d = s / √ N
where s = the total number of species in a community; N = the total number of individuals of all species [42].
The Shannon–Wiener diversity indices were calculated using the formula,
− Σ = ∑ i s = 1 p i l n p i
where pi is the proportion of individuals of a species i [43]. The Shannon index, abbreviated as H’, gauges the degree of order present in a community. A higher H index represents more order in a community. A low rating would signify a lack of order in the neighborhood. Species evenness was calculated using Shannon’s evenness index,
E = H′/ln S
where S is the species number and H′ is the Shannon–Wiener diversity index [44].
R programming was used for regression analysis, as was structure equation modeling (SEM). The regression analysis was conducted to find the influence of environmental factors (both topographic and edaphic) on species richness in both fire-free and fire-affected areas. The environmental components were soil pH, EC, phosphorus, nitrogen, and organic matter, whereas the topographic variables were aspect, slope angle, and altitude. The regression line provides the strength of the influence. The blue color indicates data from fire-free areas whereas the red color represents fire-affected areas.
Structural equation modeling is a multivariate statistical analysis technique employed to examine structural relationships. Using structural equation modeling the impact of edaphic and topographic variables on species richness for the fire-free and fire-affected areas was analyzed.
As a function of the sampling effort, species richness can be quantified using rarefaction curves [45]. Species rarefaction curves for the fire-free and fire-affected areas were analyzed using an online version of the iNEXT package at a confidence limit of 95% [46]. Species richness was placed on the y-axis and the number of sampling units (quadrats) was placed on the x-axis.

3. Results

3.1. Floristic Composition

A total of 219 plant species were reported during this study. In the first field, 114 plant species were recorded and, during the second field that was carried out after the monsoon season, an additional 104 new plant species were recorded. These plant species belong to 173 genera and 70 families. Among these, 14 (7%) were trees, 30 (14%) were shrubs and 175 (79%) were herbs. The habit-wise distribution of plant species from fire-free areas, fire-affected areas and the total study area is displayed in Figure 3. On the basis of species richness and the dominance of the top thirty plant species mentioned in (Figure 3), herbs were the most dominant species. In the fire-free areas, out of a total of 30 species, 12 were herbs, 11 shrubs, and 7 trees; however, in the fire-affected areas, a total of 17 species were herbs, whereas 9 were shrubs and only 4 were trees. The top dominant species of both the sampled areas are tabulated in Appendix A (Table A1).

3.2. Species Diversity Indices

The Shannon diversity indices revealed that, out of a total of 219 species, 173 species were from fire-free areas and the remaining 122 species belonged to fire-affected areas. Only 97 species were exclusive to fire-free areas while 46 species were limited to fire-affected areas. The remaining 76 species were found in both the burned and unburned areas. The species rarefaction curves demonstrate that, with the same sampling efforts, the species richness of vegetation in the fire-free areas was visibly higher than that of the in the fire-affected areas (at p < 0.01), (Figure 4). The highest and lowest values for species richness were recorded in the fire-affected areas but the average species richness and Shannon diversity index were higher for the fire-free areas with a mean or average value of 16.83 and an average Shannon diversity index value of 2.45. The fire-affected areas had a lower average species richness of 13.83 and an average Shannon diversity index of 2.21. Average species evenness was higher in the fire-free areas, having a value of 18.74 whereas species evenness was negative in the fire-affected areas, with a value of −47.08 (Table 1). Regression analysis also supported these results, the majority of the variables had a negative correlation with species richness. Species richness and the species evenness of individual quadrats are tabulated in Appendix B Table A2).

3.3. Impact of Edaphic Factors on Species Richness

The influence of edaphic variables on species richness was demonstrated by regression analysis. The results indicated that organic matter in both the fire-affected and fire-free regions displayed a strongly positive correlation with species richness (with R = 0.52, p = 0.00007 and R = 0.36, p = 0.0084, respectively) (Figure 5a). Similarly, nitrogen also showcased a positive correlation in the fire-free areas (R = 0.32, p = 0.019) and fire-affected areas (R = 0.05, p = 0.00003) (Figure 5b). Phosphorus concentration in the fire-affected regions showed a strongly positive correlation (R = 0.36, p = 0.09) whereas, in the fire-free regions, it showed a non-significant slightly positive correlation with species richness (at R = 0.21, p = 0.12) (Figure 5c). This means that a rise in phosphorus leads to a rise in species richness as well. pH and EC showed negative correlations with species richness in both the fire-free and affected areas. The pH of the fire-free areas displayed a relatively stronger correlation with species richness (R = −0.47, p = 0.0003) than was the case for the fire-free areas (R = −0.07, p = 0.63), (Figure 5d). The EC of both the fire-free and fire-affected areas showed no significant correlation with species richness. (Figure 5e). Calcium carbonate did not display a significant correlation with species richness in the fire-free areas (R = 0.04, p = 0.76), whereas, in the fire-affected areas, it displayed a strongly negative correlation with species richness (R = −0.38, p = 00053). So, a higher level of calcium carbonate in the fire-affected soils resulted in a lower species richness (Figure 5f).

3.4. Impact of Environmental Gradients on Species Richness

The species richness of both the fire-free and fire-affected areas showed a negative correlation from north to south, and this was more visible in the fire-affected areas (R = −0.03, p = 0.83) (Figure 6a). Slope angle and species richness did not appear to be correlated in any visible way, with no significant R and p-values (Figure 6b). A visibly negative correlation between altitude and species richness was observed in the fire-free areas (R = −0.47, p = 0.0004) and a slightly negative correlation was evident in the fire-affected areas (R = −0.16, p = 0.24) indicating that species richness decreases with increasing elevation (Figure 6c). The species richness and complete environmental data of all quadrats are tabulated in Appendix B (Table A3).

3.5. Structure Equation Modeling

The structure equation model was used to analyze the effect of environmental variables on the species richness of fire-affected and fire-free areas separately. The SEM of the fire-free region showed that the organic matter of the soil had a positive and significant effect on nitrogen concentration and species richness with a p-value of 0.000 and β-value of 0.64. Organic matter and altitude have a negative and non-significant relation, having a p-value of 0.1948 and a β-value of −0.2345. Altitude has a positive and significant relationship with pH at a p-value of 0.0107 and a β-value of 0.3408. On the other hand, altitude has a negative and significant relationship with the species richness of fire-free areas with a p-value of 0.0124 and a β-value of 0.3470. pH has a negative and highly significant relationship with species richness, indicating that an increase in pH results in a decline in the species richness of fire-free areas with a p-value of 0.0046 and β-value of −0.3864 (Figure 7a), (Table 2).
Our results show that, in the fire-affected areas, organic matter has a significantly positive impact on the phosphorus content with a p-value of 0.0449 and a β-value of 0.1860. Additionally, organic matter has a highly significant and positive impact on the nitrogen content of the soil with a p-value of 0.0002 and a β-value of 0.3530. In other words, with the addition of organic matter to the soil, phosphorus and nitrogen content also increases. In contrast, organic matter has a significantly negative effect on the pH of the soil with a p-value of 0.0130 and a β-value of −0.2651, indicating that, at a high pH, organic matter concentrations in the soil decrease. Phosphorus also had a positive and slightly significant impact on species richness with a p-value of 0.0112 and a β-value of 0.2093. Organic matter also has a significant and positive impact on species richness with a p-value of 0.0015 and a β-value of 0.2962. Nitrogen also displayed a highly significant and positive impact on species richness with a p-value of 0.0002 and a β-value of 0.3256. It turned out that the higher the nitrogen, phosphorus, and organic matter concentrations are in the soil, the higher the species richness in that area would be (Figure 7b).

4. Discussion

Pakistan is a country with various sorts of climatic zones, ranging from tropical and desert ecosystems to the alpine and fridged zones of perpetual glaciers. In the subtropical, moist, and dry temperate regions of the country, coniferous forests are common. Sometimes these forests are highly affected by fire incidents. In this article, we tried to disentangle the impact of forest regimes on the vegetation of the study area. Our findings revealed that herbs were the dominant life form in the Mahaban forest, representing 79% (175 Species) of the total (219) plant species. Instances of fire at regular intervals are one of the reasons for the visible dominance of herbal life forms. Regular fires harm the major woody species, but appear to be advantageous for perennial and annual herbs [47]. Disturbances that leave gaps in the perennial grass-dominated landscape tend to favor annuals or short-lived perennials [48].
It is a general perception that changes in species diversity result from disturbance [49]. Diversity indices have been employed in numerous ecological studies to evaluate the effects of environmental disturbance [49]. According to our results, although average species richness, Shannon diversity index, and species evenness were high in the fire-free areas, the highest and lowest values of species richness were observed in the fire-affected areas, while the fire-free areas had a relatively narrow range of species richness. Repeated forest fires at regular intervals, which make the environmental conditions unsuitable for most species but favorable for some other species, could be a possible reason for the comparatively wider range of species richness values in the fire-affected areas. Significant changes in species diversity and species richness have also been documented by Biswa and Malik, [50] at various levels of disturbance. The intermediate disturbance hypothesis (IDH), which argues that species diversity would be highest at a moderate amount of disturbance, is further supported by our findings [49]. Similarly, Li et al. also reported that disturbance brought about due to grazing increased species diversity in northeastern China [51]. The reason for the difference in our results could be because grazing reduces competition, but fire is more severe than grazing, altering the physical and chemical characteristics of the soil and making the environment less favorable for certain species.
We observed that fire causes a slight decrease in the concentrations of organic matter and nitrogen in the soil, thereby causing an increase in species richness (Figure 8). Moreover, Ullah et al. reported that higher organic matter concentrations lead to higher species richness [52]. In our studied regions, we observed that those areas in which fire incidents happened are easily occupied by plant species. The fire leads to a significant amount of ash which is considered an important source of Phosphorus and a suitable substrate for various species. The ash of grass species is used by farmers in rural areas as a fertilizer for different crops. In indigenous societies, people believe that ash is crucial for root elongation due to its smooth texture. Therefore, we are of the opinion that the ash is the main driver of species richness. Phosphorus concentrations showed a noticeable effect on species richness in the fire-free areas but in the fire-affected areas, the higher percentage of phosphorus in the soil resulted in a steady rise in species richness. The phosphorus in organic matter is converted by forest fires into the soluble orthophosphate form which is easily taken up by plants [53]. We also observed that fire has a crucial impact on soil parameters (Figure 8).
According to the research, nutrients in the soil drop after a forest fire, but their availability to plants rises [54]. High-intensity wildfires deplete nitrogen in the soil [55,56]; however, low-intensity wildfires result in higher nitrogen [57]. Kutiel and Naveh noted that different areas of the same forest that are dominated by distinct species show a noticeable variation in soil nutrient levels assessed after a wildfire. Most of these nutrients were most abundant in the pine forest-dominated soil, moderate in the oak soil, and least abundant in the open grass soil [58]. In comparison to the available nitrogen and phosphorus in the burned soil, the available nitrogen and phosphorus in the burned soil below pine trees was higher [59]. Verma and Jayakumar reported that fire decreases the amount of total nitrogen while increasing the readily available ammonium form. Volatilization is the cause of this nitrogen decline [60,61]. Research suggests that fire causes an increase in the water-soluble forms of nitrogen and phosphorus and higher species richness [62,63,64]. It has been reported that pH and nitrogen have a positive impact on the vegetation of an area [65].
In our study, the pH of the fire-affected areas remained relatively constant but in the fire-free areas increases in pH led to lower species richness. The soil in the Pinus-dominated forests has a relatively high pH since the humus produced by Pinus leaves increases the pH of the soil. This attracts some species, such as Morchella fungus, which is an indicator of humus produced from Pinus leaves. The average pH recorded in our study of the fire-free areas was 7.36. According to Pausas and Austin, most species occur when the pH is between 6.1 and 6.5. Species diversity decreased in both the acidic and alkaline soils [66]. Previous research has also shown that, at high pH levels, phosphorus and iron become scarcely soluble, which has an impact on the species variety of a region [67,68]. A rise in pH following a fire has been observed by several researchers [69,70,71,72], and others have argued that pH remains unchanged after a fire [73,74,75]. Environmental conditions after a fire could be considered a key factor [76].
According to our results, higher calcium carbonate in the fire-affected areas leads to lower species richness. An increase in the temperature decreases the solubility of CaCO3 and accelerates the formation of calcium carbonate crystals [77], which cannot be absorbed by plants. In Pyrenean woods, Pausas also discovered a humped relationship between species richness and calcium concentration [66]. CaCO3 had an inverse relationship with abundance [65]. Conversely, Ullah et al. have reported that in an environment free from fire disturbance, higher CaCO3 concentrations in soil cause an increase in species richness [67].
The terrain of mountainous regions provides a wide range of varied ecosystems as well as a climate change buffer because temperature varies significantly over different elevation ranges [78,79]. In our study, environmental gradients, particularly topography, elevation, aspect, and slope angle values were recorded for each quadrat and analyzed. Our results indicated that slope angle had no visible effect on the species richness. The mountains of Mahaban are part of the outer Himalayan mountains with steep slopes; moreover, almost all our data had a very narrow range of slope angle data (i.e., 70–90 degrees). Steeper slopes are more resilient to changes in the composition of the vegetation [80]. Our study revealed that the north-facing aspect had higher species diversity as compared to the south-facing aspect. Temperatures are believed to be lower and soil and air moisture levels are believed to be higher on the northern slopes because of reduced solar radiation [81,82,83]. With increases in altitude species richness decreased due to the less favorable environment.
The vegetation study from Shangla District by Ullah et al. [52] also reported similar results [67]. The same correlation of species abundance and richness with altitude was reported by Kharkwal et al., from Kumaon, India [84]. Our findings concur with those of Bano et al., who also reported that the number and variety of species dropped as altitude rose [7]. One of the most significant factors that directly affected the distribution of species in the Durand Line district of Kurram was altitude [85]. In the Bhabha Valley of the western Himalayas in India, the greater elevation ranges reduced species variety [86]. Topographic factors (aspect and elevation), had a substantial impact on the abundance of Pinus roxburghii trees [65,87,88,89].
There is an urgent need for research on the impact of fire on soil health and vegetation on a broad scale in the Himalayan coniferous and other surrounding forest ecosystems. A considerable number of species are affected by fires incidents that happened intentionally or unintentionally in the forest ecosystems. To advance this research area, we need to have an accurate parametrization of how people cause incidences of fire, and what solutions can save our forests and natural biodiversity.

5. Conclusions

This study attempted to compare the species richness of fire-free and fire-affected areas. In total, 219 plant species were identified, of which 14 were trees, 30 were shrubs, and 175 were herbs. Diversity indices revealed that species richness and species evenness were higher in the unburned areas. The absence or presence of fire had visible effects on different environmental variables. The pH and calcium carbonate levels showed a highly negative correlation with species richness in the fire-free areas, whereas phosphorus and organic matter showed a highly positive correlation with species richness in the fire-affected areas. Among the environmental gradients studied, slope angle and aspect had no significant impact on species richness: however, an increase in altitude resulted in lower species richness.
It is concluded that fire incidence significantly affected the environmental variables of the study area, which in turn affected the species composition and diversity of the study area. The investigation of the burned and unburned areas revealed that the forest has significant potential for regeneration due to high nutrient levels in the soil and the availability of water; however, the frequent occurrence of forest fires in the research area has adversely affected this regeneration capacity.

6. Recommendations

Our study provides a direct and relatively simple approach to gaining an understanding of complex relationships in fire ecology. Based on the current study, it is recommended fire ecology be further explored and investigated to bolster our understanding of its crucial role in shaping ecosystems. By delving deeper into this field of research, we can gain valuable insights into community ecology, enabling us to devise more effective conservation strategies. To advance this research area, we need to have an accurate parametrization of how people cause fires, and how fire affects soil factors like soil moisture, soil texture, and soil porosity, as well as what solutions will save our forests and our natural biodiversity. To monitor the fluctuations of nutrients in the soil, we advise different modeling approaches using advanced techniques.

Author Contributions

Conceptualization, A.I. and S.M.K.; methodology A.A.; lab work, plant identification, data assessment and manuscript editing, A.I.; investigation, A.I.; data analysis, A.I., Z.A. and S.J.; writing the original draft, A.I.; supervision, S.M.K.; review and editing, S.M.K., U.E., S.J., G.D.A.-Q., A.H. and E.F.A.; funding acquisition A.H. and E.F.A. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to extend their sincere appreciation to the Researchers Supporting Project Number (RSP2024R356), King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors would like to extend their sincere appreciation to the Researchers Supporting Project Number (RSP2024R356), King Saud University, Riyadh, Saudi Arabia. The authors would like to thank the Soil and Water Testing Laboratory, Agricultural Research Station Swabi, Pakistan. Lastly, the authors would like to extend their gratitude to Muhammad Sheraz and Israr Ali Shah for their enormous support during the fieldwork.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Plant species of the fire-free and fire-affected areas on the basis of Simpson’s index of dominance.
Table A1. Plant species of the fire-free and fire-affected areas on the basis of Simpson’s index of dominance.
Fire-Free AreasFire-Affected Areas
Sr.No.Plant NameHabitDPlant NameHabitD
1Pinus roxburghii Sarg.Tree0.23777Pinus roxburghii Sarg.Tree0.308946
2Berberis lycium RoyleShrub0.074977Cynodon dactylon (L.) Pers.Herb0.073945
3Cynodon dactylon (L.) Pers.Herb0.070309Zanthoxylum armatum DC.Shrub 0.072871
4Zanthoxylum armatum DC.Shrub 0.048806Carissa spinarum L.Shrub0.048141
5Quercus leucotrichophora A.CamusTree0.037879Rubus ellipticus Sm.Shrub0.042647
6Dodonaea viscosa (L.) Jacq.Shrub 0.034698Berberis lycium RoyleShrub0.04145
7Mallotus philippensis (Lam.) Müll.Arg.Shrub 0.029749Agrostis stolonifera L.Herb0.024808
8Debregeasia saeneb (Forssk.) Hepper and J.R.I.WoodShrub 0.027356Dodonaea viscosa (L.) Jacq.Shrub 0.022899
9Digitaria sanguinalis (L.) Scop.Herb0.022099Digitaria sanguinalis (L.) Scop.Herb0.020579
10Clinopodium serpyllifolium subsp. fruticosum (L.) BräuchlerShrub0.016923Desmostachya bipinnata (L.) StapfHerb0.015824
11Plantago lanceolata L.Herb0.015587Salvia moorcroftiana Wall. ex Benth.Herb0.013698
12Oplismenus compositus P.Beauv.Herb0.015148Oxalis corniculata L.Herb0.013586
13Desmostachya bipinnata (L.) StapfHerb0.01429Calendula arvensis M.Bieb.Herb0.013026
14Rubus ellipticus Sm.Shrub0.014026Geranium mascatense Boiss.Herb0.012859
15Mimosa rubicaulis subsp. himalayana (Gamble) H.OhashiShrub0.01381Mimosa rubicaulis subsp. himalayana (Gamble) H.OhashiShrub0.011801
16Juncus inflexus L.Herb0.012916Gymnosporia royleana M.A.LawsonShrub0.010727
17Adiantum capillus-veneris L.Herb0.012766Rumex hastatus D. DonShrub0.010598
18Rumex hastatus D. DonShrub0.012337Colebrookea oppositifolia Sm.Shrub0.01038
19Launaea secunda Hook.f.Herb0.010385Adiantum pedatum L.Herb0.010187
20Carissa spinarum L.Shrub0.009804Achyranthes aspera L.Herb0.010099
21Oxalis corniculata L.Herb0.009693Grewia optiva J.R.Drumm. ex BurretTree0.009993
22Gymnosporia royleana M.A.LawsonShrub0.009395Erigeron floribundus (Kunth) Sch.BipHerb0.00843
23Ficus carica L.Tree0.008816Cyperus alopecuroides Rottb.Herb0.008414
24Melia azedarach L.Tree0.007613Avena barbata Pott ex LinkHerb0.008146
25Olea ferruginea Wall. ex Aitch. Tree0.007564Brachiaria ramosa StapfHerb0.00736
26Mentha longifolia (L.) L.Herb0.00739Eragrostis minor HostHerb0.007223
27Agrostis stolonifera L.Herb0.007087Bombax ceiba L.Tree0.005659
28Rhododendron arboreum Sm.Tree0.006693Heteropogon contortus Beauv. ex Roem. and Schult.Herb0.004973
29Grewia optiva J.R.Drumm. ex BurretTree0.006517Solanum nigrum L.Herb0.004874
30Rumex dentatus L.Herb0.006235Melia azedarach L.Tree0.004398
Species richness and Shannon diversity of all the quadrats.

Appendix B

Table A2. Species richness and Shannon diversity of all the quadrats.
Table A2. Species richness and Shannon diversity of all the quadrats.
Fire-Free AreasFire Affected Areas
QuadratsSpecies RichnessShannon Diversity QuadratsSpecies RichnessShannon Diversity
S1Q1182.487659FS1Q1222.814788
S1Q2152.396967FS1Q2212.655287
S1Q3162.324348FS1Q3142.316964
S2Q1162.381747FS1Q4132.252507
S2Q2172.346416FS1Q5142.245333
S2Q3162.347237FS1Q6142.255042
S2Q4142.260971FS2Q1172.580328
S2Q5242.741961FS2Q2142.268368
S2Q6162.420426FS2Q3152.369721
S2Q7172.260509FS2Q4122.258033
S2Q8172.431093FS2Q5142.018089
S2Q9162.44746FS2Q6122.062945
S2Q10162.513053FS2Q7132.198842
S3Q1202.620321FS2Q8142.334764
S3Q2192.597952FS2Q9172.434357
S3Q3162.548764FS2Q10122.185835
S3Q4222.794055FS2Q11172.618541
S3Q5142.335251FS3Q1122.079266
S4Q1182.668152FS3Q2112.135406
S4Q2172.488219FS3Q3111.98121
S4Q3222.837677FS3Q4102.058286
S4Q4172.520994FS3Q581.77076
S4Q5222.720286FS3Q671.595613
S4Q6222.783348FS3Q771.62007
S4Q7172.54875FS3Q840.97633
S4Q8202.709978FS3Q940.941318
S4Q9152.383271FS3Q1040.998696
S4Q10162.442801FS4Q1162.435287
S4Q11222.864515FS4Q2182.422332
S4Q12182.565379FS4Q3182.639734
S4Q13212.665254FS4Q4162.459811
S5Q1152.417465FS4Q5182.451564
S5Q2162.466072FS4Q6132.186223
S5Q3152.294344FS4Q7122.115488
S5Q4172.48977FS4Q8122.080375
S5Q5142.272177FS4Q9132.297515
S5Q6162.504384FS4Q10122.198479
S5Q7182.619202FS5Q1162.378599
S5Q8132.154757FS5Q2152.36489
S5Q9172.589061FS5Q3132.091665
S5Q10152.538078FS5Q4162.251122
S5Q11152.272501FS5Q5152.395034
S5Q12132.053219FS5Q6132.048712
S5Q13172.332756FS5Q7142.32654
S6Q1172.328227FS5Q8132.271589
S6Q2142.239102FS5Q9132.21683
S6Q3121.89413FS5Q10111.979097
S6Q4162.517507FS6Q1222.936839
S6Q5122.202981FS6Q2212.685807
S6Q6122.031984FS6Q3192.57531
S6Q7162.507694FS6Q4222.950197
S6Q8182.576293FS6Q5282.39228
S6Q9182.338526
Table A3. Species richness and environmental data of all quadrats.
Table A3. Species richness and environmental data of all quadrats.
Sr.NoStationsSpecies RichnessCaCO3OMNitrogenPhosphoruspHECAltitudeSlope Angle AspectRegion
1S1Q1177.30.870.0435.77.450.121227.134882.5FF
2S1Q21411.120.940.0427.77.590.21285.061792.5FF
3S1Q3158.340.850.042197.020.011317.988872.5FF
4S2Q11510.620.620.027107.280.051282.622862FF
5S2Q2167.660.490.0247.996.910.151318.902732.25FF
6S2Q3154.750.840.022106.960.071340.244763FF
7S2Q4139.870.810.0413.27.080.131364.634732.25FF
8S2Q5238.870.840.042107.30.121390.549713FF
9S2Q6158.120.780.03115.48.080.191401.524801.25FF
10S2Q7165.620.870.043167.340.081380.793711.25FF
11S2Q81611.50.940.03897.160.011359.756741.25FF
12S2Q9157.370.320.015127.810.321337.5871.25FF
13S2Q10157.870.510.0210.717.110.311319.512742.75FF
14S3Q11911.51.010.0586.560.11066.463892FF
15S3Q218100.520.05114.97.350.011072.866872FF
16S3Q3159.210.680.034107.310.111095.427691.5FF
17S3Q42111.621.040.057137.270.121117.988752FF
18S3Q5138.060.890.024227.080.111138.415762FF
19S4Q11711.450.730.02227.380.321053.659881.5FF
20S4Q2163.530.990.01487.330.021087.5842.25FF
21S4Q3218.621.110.0518.447.150.041104.878892FF
22S4Q41611.750.850.02420.017.150.251130.793821FF
23S4Q5219.50.920.046226.720.151155.183851.5FF
24S4Q6215.250.820.041156.50.011187.805841.5FF
25S4Q71612.430.510.02127.450.011206.402841.5FF
26S4Q8198.870.850.04211.327.420.021239.939861.5FF
27S4Q9146.620.840.042107.750.021255.793841.5FF
28S4Q101511.750.470.023117.450.021279.573761.5FF
29S4Q112111.250.890.04417.17.430.011300841.5FF
30S4Q12177.80.850.042147.80.011315.549882FF
31S4Q13207.750.690.01811.17.450.131331.402882FF
32S5Q11410.50.840.042127.620.171457.012881.25FF
33S5Q2154.51.020.058.66.820.21484.451791.25FF
34S5Q3148.750.670.03310.017.810.141510.366841FF
35S5Q4169.620.910.025117.240.121530.488861FF
36S5Q5134.250.940.04711.17.550.391558.841841FF
37S5Q6158.250.510.03107.130.041586.28891FF
38S5Q717100.70.03597.120.121617.988872FF
39S5Q81211.750.490.04247.380.081649.085842FF
40S5Q91612.430.490.04514.217.190.061675882FF
41S5Q10149.370.680.0347.77.580.041685.976872.75FF
42S5Q11149.770.950.0478.87.350.151729.573882.5FF
43S5Q12129.120.590.02613.18.10.031728.049871FF
44S5Q13167.121.040.035167.550.031740.244791.25FF
45S6Q1162.370.920.046137.010.121840.244841FF
46S6Q2137.750.840.04211.127.510.111878.354861FF
47S6Q3119.370.370.018108.30.021937.5842.75FF
48S6Q41590.770.038147.820.211971.037871.25FF
49S6Q5116.810.720.03677.770.22004.573843FF
50S6Q61110.470.580.026107.860.112029.268832.75FF
51S6Q7159.50.720.036127.180.022068.293873FF
52S6Q8174.870.610.03157.030.352120.732842.5FF
53S6Q9179.130.730.035107.620.012088.72862.25FF
54S1Q1177.30.870.0435.77.450.121227.134882.5FF
55FS1Q1218.10.980.0549.27.320.121004.268872FA
56FS1Q2219.011.320.051187.80.051021.341712FA
57FS1Q3148.80.310.06597.90.071039.634512FA
58FS1Q4129.250.720.06187.810.351070.427892FA
59FS1Q51410.21.40.01897.20.181098.171571.5FA
60FS1Q61411.120.820.022127.280.211110.061592.5FA
61FS2Q1167.250.910.06212.157.120.151148.78832.5FA
62FS2Q21311.750.740.02787.660.051157.317772FA
63FS2Q3144.091.520.036117.820.041179.268842.75FA
64FS2Q4123.120.820.041188.180.121205.183862.75FA
65FS2Q5147.51.540.031117.930.11241.463892.75FA
66FS2Q61111.250.770.03812.18.210.081218.902791.25FA
67FS2Q7132.750.940.0238.97.290.031168.293892.5FA
68FS2Q8144.550.510.02517.197.650.061156.402771.25FA
69FS2Q9165.620.740.037157.150.111101.22892.75FA
70FS2Q10126.250.680.0347.87.350.141070.427832.75FA
71FS2Q11165.50.980.04912.146.460.11044.207832.75FA
72FS3Q112100.810.048.137.180.31115.549882FA
73FS3Q210100.810.048.137.180.31088.11772FA
74FS3Q310110.490.02487.430.031067.683881.5FA
75FS3Q4910.751.020.02613.26.780.211021.646842.25FA
76FS3Q5710.620.460.035117.370.161117.988881.5FA
77FS3Q6610.150.480.0227.997.920.291148.476862FA
78FS3Q79110.70.0319.77.890.131167.073821.5FA
79FS3Q839.750.320.035.678.090.061195.122881FA
80FS3Q9310.50.40.03787.460.111217.073833FA
81FS3Q10310.220.450.01610.376.880.121245.732801FA
82FS4Q11510.750.90.049.17.390.1111227.134871.5FA
83FS4Q21710.870.940.053107.690.21261.89881FA
84FS4Q31711.620.850.025187.910.041293.293881.25FA
85FS4Q41511.370.940.033147.080.051313.72781FA
86FS4Q51710.370.630.0779.97.560.031349.695871FA
87FS4Q612100.540.02710.27.150.251372.561861.25FA
88FS4Q7116.50.770.038147.550.311406.098761FA
89FS4Q8810.220.780.02210.376.880.121443.598791.25FA
90FS4Q91210.750.850.042117.440.081478.354862.25FA
91FS4Q101110.250.740.021136.770.181460.061882.25FA
92FS5Q11510.870.820.041157.210.021522.561861.5FA
93FS5Q21410.250.670.03297.520.031546.951861.5FA
94FS5Q31210.110.70.05387.320.061574.39871.5FA
95FS5Q41512.251.040.02488.240.141599.39861FA
96FS5Q51450.730.0227.57.070.031643.598871FA
97FS5Q61211.370.940.042106.880.071672.256851.5FA
98FS5Q7136.750.960.063107.020.121677.744741.5FA
99FS5Q8129.370.540.027127.80.171707.012641FA
100FS5Q91210.750.70.0378.47.680.051744.512821.5FA
101FS5Q101010.750.410.0396.87.980.091750.305892FA
102FS6Q12151.060.0612.127.090.211090.549751.25FA
103FS6Q2204.590.850.042187.360.081098.171871.25FA
104FS6Q3194.370.710.035147.190.051121.951871.25FA
105FS6Q42211.620.540.07612.317.380.041139.634831FA
106FS6Q5282.371.370.0688.67.380.151175.61861FA
FF indicates Fire-Free and FA indicates Fire Affected.

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Figure 1. Map of the study area along with the geographical locations of the sampling points expressed as green dots for fire-free areas and red dots for fire-affected areas.
Figure 1. Map of the study area along with the geographical locations of the sampling points expressed as green dots for fire-free areas and red dots for fire-affected areas.
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Figure 2. Images of the study area (a,c) fire-free areas, (b,d) fire-affected areas.
Figure 2. Images of the study area (a,c) fire-free areas, (b,d) fire-affected areas.
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Figure 3. Habit-wise distribution of the recorded plant species from fire-free, fire-affected and the total study area.
Figure 3. Habit-wise distribution of the recorded plant species from fire-free, fire-affected and the total study area.
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Figure 4. Species rarefaction curves for the Fire-free (FF) and Fire-affected (FA) areas.
Figure 4. Species rarefaction curves for the Fire-free (FF) and Fire-affected (FA) areas.
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Figure 5. Regression analysis of plant species richness (y-axis) with soil factors (x-axis). Impact of soil (a) organic matter, (b) Nitrogen, (c) Phosphorus, (d) pH, (e) EC, and (f) Calcium carbonate on species richness.
Figure 5. Regression analysis of plant species richness (y-axis) with soil factors (x-axis). Impact of soil (a) organic matter, (b) Nitrogen, (c) Phosphorus, (d) pH, (e) EC, and (f) Calcium carbonate on species richness.
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Figure 6. Regression analysis of plant species richness (y-axis) with topographic factors (x-axis). Impact of (a) aspect (b) slope angle, and (c) altitude on species richness.
Figure 6. Regression analysis of plant species richness (y-axis) with topographic factors (x-axis). Impact of (a) aspect (b) slope angle, and (c) altitude on species richness.
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Figure 7. Structural equation model showing the impact of environmental factors on species richness of (a) the fire-free and (b) fire-affected areas. The solid lines indicate a highly significant effect among various variables, whereas the dotted lines indicate a significant effect among various variables. Red denotes a negative impact, while green depicts a positive effect. In this figure (* represents p < 0.05, ** represents p < 0.01, and *** represents p < 0.0001).
Figure 7. Structural equation model showing the impact of environmental factors on species richness of (a) the fire-free and (b) fire-affected areas. The solid lines indicate a highly significant effect among various variables, whereas the dotted lines indicate a significant effect among various variables. Red denotes a negative impact, while green depicts a positive effect. In this figure (* represents p < 0.05, ** represents p < 0.01, and *** represents p < 0.0001).
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Figure 8. Comparative analysis of soil variables across the fire-free and fire-affected sites of the study area.
Figure 8. Comparative analysis of soil variables across the fire-free and fire-affected sites of the study area.
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Table 1. Summary of Shannon diversity indices for the fire-free and fire-affected areas.
Table 1. Summary of Shannon diversity indices for the fire-free and fire-affected areas.
Summary of Shannon Diversity Indices of Fire Free Areas
Specieschaochao.sejack1jack1.sejack2bootboot.sen
173207.25513.37985217.15099.611166233.0838194.75485.55347553
Summary of Shannon Diversity Indices of fire-affected areas
Specieschaochao.sejack1jack1.sejack2bootboot.sen
122137.54317.86368148.48085.981573152.7606135.85374.16607152
Table 2. Fit measurements of the SEM of edaphic factors on the species richness of the fire-free and fire-affected areas.
Table 2. Fit measurements of the SEM of edaphic factors on the species richness of the fire-free and fire-affected areas.
Fit Measurements of SEM of Edaphic Factors on Species Richness of Fire-Free Areas
ChisqPvalueNFICFIRMSEAGFIAGFISRMRRMRAIC
4.1650.3840.9310.9970.0280.9590.9460.0630.062408.022
Fit measurements of SEM of edaphic factors on species richness of fire-affected areas.
ChisqPvalueNFICFIRMSEAGFIAGFISRMRRMRAIC
1.640.650.9780.9760.0010.9730.9630.0320.0311135.254
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MDPI and ACS Style

Israr, A.; Khan, S.M.; Abdullah, A.; Ejaz, U.; Jehangir, S.; Ahmad, Z.; Hashem, A.; Avila-Quezada, G.D.; Abd_Allah, E.F. Fire-Induced Vegetation Dynamics: An In-Depth Discourse on Revealing Ecological Transformations of the Mahaban and Surrounding Forests. Fire 2024, 7, 27. https://doi.org/10.3390/fire7010027

AMA Style

Israr A, Khan SM, Abdullah A, Ejaz U, Jehangir S, Ahmad Z, Hashem A, Avila-Quezada GD, Abd_Allah EF. Fire-Induced Vegetation Dynamics: An In-Depth Discourse on Revealing Ecological Transformations of the Mahaban and Surrounding Forests. Fire. 2024; 7(1):27. https://doi.org/10.3390/fire7010027

Chicago/Turabian Style

Israr, Azra, Shujaul Mulk Khan, Abdullah Abdullah, Ujala Ejaz, Sadia Jehangir, Zeeshan Ahmad, Abeer Hashem, Graciela Dolores Avila-Quezada, and Elsayed Fathi Abd_Allah. 2024. "Fire-Induced Vegetation Dynamics: An In-Depth Discourse on Revealing Ecological Transformations of the Mahaban and Surrounding Forests" Fire 7, no. 1: 27. https://doi.org/10.3390/fire7010027

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