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Article

Grasses in Semi-Arid Lowlands—Community Composition and Spatial Dynamics with Special Regard to the Influence of Edaphic Factors

by
Muhammad Waheed
1,
Shiekh Marifatul Haq
2,
Fahim Arshad
1,
Rainer W. Bussmann
2,3,
Muhammad Iqbal
1,
Najat A. Bukhari
4 and
Ashraf Atef Hatamleh
4,*
1
Department of Botany, University of Okara, Okara 56300, Pakistan
2
Department of Ethnobotany, Institute of Botany, Ilia State University, 0105 Tbilisi, Georgia
3
State Museum of Natural History, Erbprinzenstrasse 13, 76133 Karlsruhe, Germany
4
Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(22), 14964; https://doi.org/10.3390/su142214964
Submission received: 21 September 2022 / Revised: 7 November 2022 / Accepted: 9 November 2022 / Published: 12 November 2022
(This article belongs to the Section Sustainability, Biodiversity and Conservation)
Browse Figures
Versions Notes

Abstract

:
Edaphic properties have been widely shown to influence community composition and distribution. However, the degree to which edaphic factors can affect grasses in semi-arid lowlands is still little researched. We assessed the significance of nine edaphic factors to explain the distributions of 65 grass species with various ecological traits (i.e., the ecological indicator values for their preferred habitat) in the semi-arid lowlands of Pakistan. To record information on species composition and related ecological conditions, we selected 10 random sampling locations between 2020 and 2021. For each species, we determined the important value index (IVI) and looked at the primary indicator species that were identified using the indicator species analysis approach. The major genera were Setaria, Brachiaria, and Cenchrus with 6.15% species in each followed by Aristida, Panicum, and Eragrostis with 4.61% wild grass species, Bothriochloa, Bromus, Phragmites, Polypogon, Saccharum, Poa, Echinochloa, and Dactyloctenium with 3.07% species, whereas other genera had a single species each. In total, 80% of the species were native, while only 20% were introduced species. Microphylls accounted for 49.23% of the leaf size spectra of the grass flora in the study area. The other frequent traits included macrophylls (21.53%), nanophylls (20%), and leptophylls (9.23%). The major life forms were therophytes (56.92%) followed by hemicryptophytes (38.46%) and geophytes (4.61%). The results of an ordination analysis indicated that the distribution of grasses was significantly (p ≤ 0.002) influenced by several edaphic parameters, with pH having the greatest impact on species distribution. The analyses of indicator species showed that pH and EC were the most powerful and important edaphic factors for determining the composition of plant communities and indicator species. The significant indicator species in various ecosystems were Cynodon dactylon (L.) Pers. [email protected] and Leptochloa chinensis (L.) Nees (agroecosystem), Brachiaria reptans (L.) C.A. Gardner and C.E. Hubb, Dichanthium annulatum (Forssk.) Stapf, and Saccharum spontaneum L. (forest ecosystem), Cenchrus biflorus Roxb., Cenchrus ciliaris L., and Desmostachya bipinnata (L.) Stapf (urban ecosystem), Arundo donax L., Echinochloa crus-galli, and Phragmites australis (wetland ecosystem), and Saccharum spontaneum and Echinochloa crus-galli (L.) P. Beauv. (riparian ecosystem). We discovered that different species groupings had different habitat preferences and that soil pH had a significant beneficial effect on plant variety. These results provide a scientific roadmap for soil and plant restoration in semi-arid lowland habitats.

1. Introduction

The composition of plant communities and the functional traits of the plants that are associated with them are influenced by the diversity of habitat that is made possible by the local environmental conditions [1]. On a range of parent materials and soil types in combination with regional diversity in environmental conditions, floristic communities with diverse functional features may grow as a result of changes in resource availability [2,3]. These filters search for particular features or feature anomalies in a methodical manner. While more fruitful and disturbed habitats favor species with an “opportunistic growth strategy”, nutrient-poor and stable conditions, for instance, tend to favor species with a “stress-tolerant development strategy” [4]. Consequently, topographic environmental gradients and disturbance regimes may alter the functional traits of the locally established plant community, such as the longevity, mean growth rate, etc., which would impact the species composition and spatial dynamics in the area [5]. In any location, environmental conditions impact the composition of the vegetation, distribution, the coexistence of species, and the adaption of species in a given area with regard to climate, edaphic, and anthropogenic effects.
Numerous studies have demonstrated that edaphic characteristics affect the location and composition of communities [6,7]. To predict how plants will react to impending environmental changes, the relationship between edaphic variation and plant diversity must be quantified [8]. The development and heterogeneity of habitats are significantly impacted by edaphic conditions, which also change the structure of communities and the dynamics of space [8,9]. In addition to encouraging functional diversity in areas with more diverse environments, such regional variance in edaphic features also selects for unique traits in local habitats within a region [10]. The effects of several connected factors can thus be separated using edaphic variation, which also provides a great environment for assessing the relative importance of the assembly mechanisms that drive diversification patterns [11,12,13].
Poaceae, the largest flowering plant family, with great environmental adaptability, dominate around 20% of all land areas in any habitat and phytogeographical zone on earth [14]. Because grasses are important for the diet of many species, including humans, Poaceae have ecological dominance in terms of nutrition. Poaceae make up about 15% of monocots [15]. Poaceae provide the bulk of human food and a variety of livestock feed and forage [16,17]. The indigenous Poaceae flora in Pakistan maintains soil integration, moisture retention, and porosity for air infiltration while also serving as a substantial source of nutrition for animals [18]. Wild Poaceae species are the main source of income, particularly for rural areas [19]. Grass plays a critical role in ecosystem formation and conservation in many types of climates, from alpine to xeric situations, by routinely enhancing primary production, meeting nutrient needs, and supplying humus to the soil [20].
Little is known about the ecological factors affecting Poaceae in Pakistan, particularly in the semi-arid lowland region of Punjab. However, the relationship between edaphic variables and plant diversity in semi-arid lowland habitats is still poorly understood [21]. Numerous quantitative factors can be used to explain the structure of vegetation. Given that it accurately analyses vegetation structure, quantitative analysis is needed for long-term ecological study planning and interpretation [22], including information on life forms and leaf size spectrum [23,24]. Few attempts have been made to study the life forms of plants in Pakistan [25,26]. The Kasur district’s distinctive geography and climate support a great variety of plant species. However, it has not received much scientific attention.
Wide-ranging categorization and ordination are useful methods for understanding the relationships and dynamics in wild grass conservation, planning, and usage [27]. Keeping these facts in mind, the current research hypothesis was designed to accomplish the following objectives: (a) to investigate the floristic diversity of the local wild grasses, and (b) to identify the edaphic factors affecting the composition and structure of wild grasses in this region. The results of this study will enable us to develop sustainable management plans and habitat restoration techniques for wild grasses, primarily in this natural environment, with ramifications for the entire planet.

2. Materials and Methods

2.1. Study Area

The Kasur district is situated 150 to 240 m above sea level (masl) and located southeast of Lahore (31°12′ N and 74°44′ E). The total area of district Kasur is 3995 km2 and it is bordered by the River Satluj in the south and the Ravi in the north. Its northern boundary is toward the Lahore District, its eastern and southern borders are toward India, its western border is toward the Okara District, and its northern border is formed by Nankana Sahib District. Topographically, the Kasur district is a semi-arid plain and basin area of the Sutlej riverine. The Kasur district is famous for Changa Manga National Forest, Balloki Headworks, and the Ganda Singh border (Figure 1). The Kasur district has a moderate climate. During summer, the climate is hot, and the temperature rises above 40 °C. The maximum temperature is recorded during the month of June and the minimum temperature in January. The highest rainfall is recorded during July with more than 120 mm. The winter season extends from December to February. The temperature fluctuates between 6 and 20 °C. The average annual rainfall is 500 mm. The climate data (precipitation, wind speed, humidity, and temperature) of the Kasur district were acquired from the Pakistan Meterological Department (PMD) (Figure 2). Floristically, it falls in the Sahara Sindian region and hosts a significant number of plant species. The vegetation includes xerophytic and thermophilic species in the open and arid area, but riverine belts host several macrophytes. The agricultural lands host weeds and ruderal species, for instance Ageratum conyzoides L., Amaranthus viridis L., Lepidium didymum L., Chenopodium album L., Convolvulus arvensis L, Cyperus rotundus L. [email protected], Cynodon dactylon (L.) Pers., Oxalis corniculata L., Rumex dentatus L., Melilotus indicus (L.) All. and Eragrostis poaeoides P. Beauv. [28].

2.2. Field Sampling

A detailed field investigation was conducted to investigate the botanical diversity in the Kasur district (Punjab) during 2020–2022. Samples were collected from the forest ecosystem (natural or planted woodland area that is suitable for the survival of both biotic and abiotic components), riparian ecosystem (floodplain areas, lands that occur along the edges of rivers, streams, lakes), agroecosystem (land managed for agricultural activities, including crop fields, farmland, and nurseries), wetland ecosystem (extensions of land that are continuously or intermittently saturated or covered with fresh, salty, brackish, or briny water), and urban ecosystem (densely settled areas including parks, roads, home gardens). On the basis of topography and vegetation richness [29], 10 sites were selected from each ecosystem. At each sampling site, 20 transects of 100 m2 in size were placed randomly at each ecosystem. On each 100 m2 transect, 20 quadrats of 5 m2 were placed systematically. From each ecosystem, 200 quadrats were sampled for exploring the ecological diversity of wild grasses. In the examined plots, the density, frequency, and cover values of each plant species were recorded, and the average values for each of the sample quadrates were computed [30,31]. Important phyto-ecological data, such as biological spectrum (life form, leaf size) and blooming seasons, were recorded for each plant. A Garmin eTrex Global Positioning System was used to determine the geographic elements of each site, such as altitude, latitude, and longitude (GPS) [2].
Grass specimens were collected during field surveys, photographed, pressed, dried, and eventually mounted as international standard-sized herbarium sheets. Using the online Flora of Pakistan (http://www.efloras.org/ accessed on 20 March 2021), all samples were identified and then cross-referenced with the floristic literature [32,33]. The taxonomy follows The Plant List ver. 1.1 (URL: http://www.theplantlist.org/ (accessed on 10 March 2021)).

2.3. Soil Sampling

The physicochemical characteristics of the soil have an impact on the dispersion of plant communities [34]. At a depth of 9 to 12 cm, soil samples were taken from each sampling site and put in a polythene bag. Rock, garbage, and gravel particles larger than 2 mm in size were removed by sifting the soil samples after the sample had been completely mixed and air-dried. The soil samples were tested for soil moisture, pH, electrical conductivity, organic carbon, and macronutrients (K, P, N, and CaCO3). A conductivity meter and a pH meter were used to determine the electrical conductivity and pH of the soil samples, respectively. The Kjeldahl method was used to get the total nitrogen (N) [35], and the Walkley–Black method was used to calculate the organic matter (OM) [36]. Calculations were made for the levels of phosphorus (P), and potassium (K), and CaCO3 was measured using the acid–base neutralization method. The moisture content (MC) of a soil sample was determined using the ScalTec moisture analyzer, which was adjusted to 110 °C [37]. The saturation percentage was calculated using the formula:
%   m o i s t u r e = W e t   s o i l − D r y   s o i l D r y   s o i l × 100

2.4. Indicator Species Analysis

To identify indicator species for each ecosystem in the semi-arid lowland region, indicator species analysis was conducted. After determining Indicator Values of each species following [38], a Monte Carlo assessment was performed to test for statistical significance. During indicator species analysis, the Relative abundance of a species in different ecosystems was calculated using the following formula:
R e l a t i v e   a b u n d a n c e   ( R A j k ) = x k j ∑ k = 1 g x k j
where RAjk means Relative abundance, xkj is the abundance of species j in group k, and g means the total number of groups.
R e l a t i v e   f r q u e n c y   ( R F k j ) = ∑ i = 1 n k b I ˙ j k n k
RFkj is the Relative frequency of plant j in group k, bijk is the presence or absence of plant j in group k sample I and I is the sample unit.
I n d i c a t o r   v a l u e   ( I V K j ) = 100 ( R A × R F )
As a cutoff value for determining indicator species, a threshold level of 25% indication and 95% significance (p ≤ 0.05) was employed. Furthermore, the indicator species with (p ≤ 0.05) value were represented graphically with the help of PAST software (version 4.10).

2.5. Data Analysis

Microsoft Excel 2016 was used to arrange and further process the gathered phytosociological data for plants and environmental factors in preparation for analysis using the CANOCO and PAST software [26]. The diversity indices for each ecosystem were calculated using PAST software (version 4.10). CANOCO software (version 4.5) was applied to perform CCA. Using the package “factoextra” in the R 4.0.0 software, Principal Component Analysis (PCA) was carried out to illustrate the associations between various ecosystems and wild grasses [39]. Pearson correlation is a linear correlation in which a positive correlation indicates that two grass species that exhibit mutualism and have comparable environmental component needs are likely to arise concurrently, whereas a negative correlation implies the exclusive presence of two grass species in the plots due to specific ecological needs, interaction, and rivalry between them.

3. Results

3.1. Diversity and Ecological Traits

Overall, 65 wild members of Poaceae family from 40 genera were documented from the semi-arid lowlands of the Kasur district. The major genera were Setaria, Brachiaria, and Cenchrus with 6.15% species each, followed by Aristida, Panicum, and Eragrostis with 4.61% species, and Dactyloctenium, Bromus, Bothriochloa, Saccharum, Phragmites, Poa, Echinochloa, and Polypogon with 3.07% species. Other genera only included single species. In total, 80% of the species were native, while only 20% were introduced species. The dominant leaf size spectrum was microphyll (49.23%) followed by nanophyll (20%), macrophyll (21.53%), and leptophyll (9.23%). The major life form was therophyte (56.92% species) followed by hemicryptophyte (38.46%), and geophyte (4.61%) (Figure 3) (Table 1). The highest species richness was recorded in the forest ecosystem with 48 species, followed by the riparian ecosystem with 28 species, the wetland ecosystem with 25 species, the agroecosystem with 22 species, and the urban ecosystem with 18 species (Figure 4). Cynodon dactylon (L.) Pers and Echinochloa colona (L.) Link occurred in all study ecosystems. The other most frequent species recorded in different ecosystems were Chrysopogon aucheri (Boiss.) Stapf, Dichanthium annulatum (Forssk.) Stapf, Echinochloa crus-galli (L.) P. Beauv. and Phragmites karka (Retz.) Trin. ex Steud.

3.2. Species Similarities in Different Ecosystem

The studied ecosystems showed a different composition of Poaceae, and only two species, Cynodon dactylon (L.) Pers and Echinochloa colona (L.) Link occurred in all ecosystems. Dichanthium annulatum (Forssk.) Stapf were recorded from four ecosystems excepting the riparian ecosystem while Echinochloa crus-galli (L.) P.Beauv and Phragmites karka (Retz.) Trin. ex Steud were absent in the urban ecosystem. The highest number of species was recorded in (FE-RE) with eight common species followed by (FE-AE), (FE-WE) and (RE-WE) with four common species in each. Fewer similar species were recorded between (AE-WE) and (RE-UE), with only a single common species in each. The forest ecosystem harbored 11 unique species not recorded from any other ecosystem followed by the wetland and riparian ecosystems with 2 unique species in each. The urban ecosystem and agroecosystem included only a single unique grass species each (Figure 5).

3.3. Principal Component Analysis

Principal component analysis was applied to elucidate the species distribution and abundance patterns of the grasses to find significant connections among the species with the sampling locations of the various ecosystems. The first principal component (PC1) of a PCA based on the grasses’ importance value index accounted for 16% of explained variations, with the riparian ecosystem and wetland ecosystem clearly differentiated from the forest ecosystem, agroecosystem, and urban ecosystem (Figure 6). The second component (PC2), which primarily influenced the forest ecosystem, urban ecosystem, and agroecosystem, accounted for 9.6% of the explained variation. The riparian ecosystem shows the highest value in PC1 while forest ecosystem shows the highest value in PC2.
The PCA biplot indicated that the grass species were considerably more prevalent in the wetland and riparian ecosystems. Polypogon monosplensis (Linn.) Desf, Panicum antidotale Retz, Sorghum halepense (L.) Pers, Brachiaria ramosa (L.) Stapf, Brachiaria deflexa (Schumach.) C.E.Hubb. ex Robyns, Aristida mutabilis Trin. and Rupr, and Arundo donax L. were most frequent species in the wetland and riparian ecosystems (Figure 5). In urban and agroecosystems, the most frequent species were Agrostis gigantean Roth, Cymbopogon jwarancusa (Jones) Schult, Echinochloa colona (L.) Link, Enneapogon persicus Boiss, Cenchrus setiger Vahl, Cenchrus ciliaris L., Dactyloctenium aegyptium (L.) Willd, and Avena fatua L. while in the forest ecosystem Aristida adscensionis L, Aristida mutabilis Trin. and Rupr, Bothriochloa bladhii (Retz.) S.T.Blake, Bromus japonicas Thunb, Desmostachya bipinnata (L.) Stapf, Dichanthium annulatum (Forssk.) Stapf, and Lolium persicum Boiss. and hohen. ex Boiss were frequent species.

3.4. Canonical Correspondence Analysis

The CCA ordination confirmed that environmental factors such as pH, moisture, organic matter, N, P, K, and CaCO3 had a significant impact on species distribution. Each triangle represents a distinct species of grass, with the distance between them showing how similar they are to one another. The species in the first quadrat were under the influence of soil moisture and CaCO3, while the species in the second quadrat were influenced by nitrogen and potassium. The distribution of the wild grasses in the third quadrat was impacted by organic soil matter and electrical conductance. The distribution of wild grasses found in the fourth quadrat of the CCA diagram was controlled by the pH and phosphorus concentration in the soil (Figure 7). The first axis identified 4.7 variations, the second, 7.8, and the third and fourth, 10.1–11.7 of the total variation, indicating that organic soil matter, electrical conductance, pH, and phosphorus have a strong relationship with the third and fourth axes and have a significant impact on the species richness patterns of grasses (Table 2).

3.5. Indicator Species Analysis

The results of the indicator species analysis showed a clear distinction of key species in different ecosystems. In the agroecosystem, Cynodon dactylon (L.) Pers and Leptochloa chinensis (L.) Nees had significant indicator value (p ≤ 0.05), while in the forest ecosystem Brachiaria reptans (L.) C.A.Gardner and C.E.Hubb, Dichanthium annulatum (Forssk.) Stapf, and Saccharum spontaneum L. had significant p-values. Cenchrus biflorus Roxb, Cenchrus ciliaris L., and Desmostachya bipinnata (L.) Stapf were indicator species of urban ecosystems. In wetland ecosystems, indicator species were Arundo donax L., Echinochloa crus-galli (L.) P.Beauv, and Phragmites australis (Cav.) Trin. ex Steud while in riparian ecosystems Saccharum spontaneum L and Echinochloa crus-galli (L.) P.Beauv were indicator species (Figure 8).

4. Discussion

The spatial variability of soil characteristics is especially essential for ecosystem function in semi-arid habitats, where the heterogeneous distributions of nutrients boost the formation and maintenance of resources beneath the plant canopy [21]. Particularly for semi-arid grassland ecosystems, the spatial heterogeneity of soil nutrient pools at scales ranging from the sizes of individual plants to vast fields, as well as the fact that individual plants and plant community composition influence the distribution of soil nutrients at a variety of spatial scales [40]. We identified 65 different species of grasses in the semi-arid ecosystems of the Kasur district of the Pakistan Himalayas. The floral composition was consistent with Shaheen et al. [41], and Majeed et al. [42], who reported 61 and 52 Poaceae from Pakistan. The most important genera were Cenchrus, Brachiaria, Setaria, Aristida, Panicum, and Eragrostis. Similar findings were previously reported by Roy et al. [43] for tropical Indian range grasses. These genera, i.e., Cenchrus, Brachiaria, Setaria, exhibit endurance to biotic and abiotic stimuli while being more resilient to climate change under unfavorable climatic conditions [44]. The maintenance of grasslands, prevention of erosion, and, most importantly, uses by people and livestock (e.g., fodder and forage) are all aspects of the relevance of the Poaceae species in semi-arid ecosystems.
Ecological traits are viewed as a potential predictor of current environmental circumstances. The study region had a higher percentage (56.92%) of therophytes than hemicryptophytes (38.46%); this typically indicates adverse arid environmental conditions and is a sign of an anthropogenic effect [45]. Therophytes are frequently associated with low precipitation and short vegetative growth seasons, which are common on disturbed habitats and the percentage of therophytes often increases when introducing alien weedy forbs such as Bromus pectinatus Thunb, Dichanthim fovelatum (Del.) Roberty, Imperata cylindrica L.) Raeusch, and Polypogon monspeliensis (Linn.) Desf. Aliens made up about 20% of the total species recorded, and thrived especially in anthropogenic settings rather than slightly fragmented habitats [2]. Regardless of their life cycle approach, alien plant species are usually more fit than native plants in many ways and have more phenotypic plasticity. As a result, they more successfully colonize disturbed areas than native species [46]. The main leaf-size types of grasses in the studied area were microphyll species, followed by nanophylls and macrophylls. The abundance of microphylls is a sign of the moderate environment. Microphyll and nanophyll species from other areas of Pakistan were reported by Khan et al. [24]. Microphyll was also described as having important leaf size in the Indian Himalaya by [47]. Small leaves have been associated with cold or hot desert settings, as an adaptation mechanism for conserving soil moisture. The results of this study are comparable to those of Majeed et al. [42], Zeb et al. [48], and Ali et al. [49], who indicated that similar leaf size spectra predominated in their study locations. The root system can be impacted by temperature changes, which leads to less effective water and nutrient uptake from the soil, favoring the growth of simple leaves. Therefore, maintaining moisture is essential and has an impact on how quickly leaves grow. In order to categorize plants into different associations and to comprehend the physiological processes of both individual plants and plant communities, it is crucial to consider the habit and persistence of a plant’s leaves [50,51].
In various semi-arid ecological conditions, the variety and composition of grasses are known to be influenced by edaphic variables and biological interactions between species [email protected]. While a number of edaphic factors (soil moisture contents, K, P, N, pH, CaCO3, and organic matter) were most strongly connected with the distributions of specific species, pH and electrical conductivity seemed to be the main determinants of the overall composition of plants and plot-level richness [52,53,54,55]. Similar results are reported by Ahmad et al. [56] from the temperate grass land of the Himalayas. The pH was the dominant environmental driver for species we tested, including Cynodon dactylon (L.) Pers, Setaria viridis (L.) P.Beauv, Aristida adscensionis L., Bothriochloa bladhii (Retz.) S.T.Blake, Leptochloa chinensis (L.) Nees. Filibeck et al. reported similar results [57]. Different species react differently to pH, and in grassland areas, moisture content and precipitation are two factors that are thought to influence the distribution of plants. The protoplasm of the root cells is extended by the pH of the soil, which also directly affects the nutrient availability, toxicity, and microbial activity [58].
The relative significance of electrical conductivity influences the distribution of species such as Phragmites karka (Retz.) Trin. ex Steud., Dichanthium annulatum (Forssk.) Stapf, Brachiaria reptans (L.) C.A. Gardner and C.E. Hubb, and Aristida abnormis Chiov. Similar relationships between edaphic characteristics and grass species composition have been proposed by other studies. These relationships can be explained by the fact that local edaphic characteristics influence the availability of water and nutrients in various soil types, selecting plant communities with a variety of ecological functions [56,59,60].
Our results further showed that grass species composition and distribution responded to the soil parameters in the particular habitats. This was further supported by multivariate analyses (PCA, indicator species analysis) that showed the strong influences of soil factors on the assemblage of grass species. Similar multivariate analyses were also carried out by previous researchers such as Majeed et al. [42]; Gupta et al. [61]; Wei et al. [62]; and Soulodre et al. [63]. The PCA and indicator species analysis diagram showed that variety, distribution, and communities of grass species reflected the differences in the edaphic factors. Grasses’ community composition in semi-arid lowlands was more frequently influenced by ecosystem type. Our research shows that the forest ecosystem and agroecosystem have different species compositions, which is supported by diagnostic species analysis. Species with specific ecological requirements are frequently restricted to a small number of habitat types and are thus more likely to be rare [64]. In our study, Acrachne racemosa (Heyne ex Roem. and Schult.) Ohwi, Agrostis gigantean Roth, Aristida adscensionis L., Brachiaria distachya (L.) Stapf, Brachiaria reptans (L.) C.A. Gardner and C.E. Hubb, Chloris barbata Sw, and Dactyloctenium scindicum Boiss were restricted only to the forest ecosystem while Lolium persicum Boiss. and Hohen. ex Bois and Brachiaria deflexa (Schumach.) C.E. Hubb. ex Robyns were found only in the riparian ecosystem. The results of the indicator species analysis of grasses highlighted key species in different ecosystems. Cynodon dactylon (L.) Pers. and Leptochloa chinensis (L.) Nees were indicator species in the agroecosystem, Brachiaria reptans (L.) C.A.Gardner and C.E. Hubb., Dichanthium annulatum (Forssk.) Stapf, and Saccharum spontaneum L. in the forest ecosystem. Similarly, Saccharum spontaneum L. and Echinochloa crus-galli (L.) P. Beauv. were indicator species in riparian ecosystems. The distribution of species within any ecosystem is determined by a variety of biotic and abiotic factors, making determining the impact of a single environmental factor difficult [65]. Grass-species richness was considerably lower in the urban ecosystem compared to the forest ecosystem, while in other ecosystems the species richness was relatively similar (Table 1). This might be because the forest habitat provides optimum temperature, moderate amounts of rainfall, and rich nutrient supplies, thus supporting the greatest diversity of species [47,66].
The indicator species analysis separated the five habitat types, which was supported by high and significant indicator values for a number of species. It is interesting to note that a Venn diagram of the habitats revealed a similar pattern, with the forest ecosystem having 11 unique species not found in other habitats. A similar indicator species analysis was carried out by Rasheed et al. [36], Hussain et al. [67], and Ahmad et al. [56]. A species will evolve to function better in one habitat than in others when conditions are favorable for a long time [47]. In conclusion, preserving and/or restoring natural habitats (forest, riverine, wetlands) would be a crucial first step in maintaining or increasing species richness.

5. Conclusions

In the current study, we investigated the wild grass composition and distribution patterns with regard to edaphic properties in various ecosystem types of semi-arid lowland of the Kasur district, Punjab, Pakistan. We collected 65 wild Poaceae members from the study region, with Cenchrus, Brachiaria, and Setaria as the dominant genera. Therophyte was the most common life form, followed by hemicryptophyte and geophyte. Through indicator species analyses, each community’s distinctive species was verified. The significant indicator species in various ecosystems were Cynodon dactylon (L.) Pers and Leptochloa chinensis (L.) Nees (AE), Brachiaria reptans (L.) C.A.Gardner and C.E.Hubb, Dichanthium annulatum (Forssk.) Stapf, and Saccharum spontaneum L. (FE), Cenchrus biflorus Roxb, Cenchrus ciliaris L., and Desmostachya bipinnata (L.) Stapf (UE), Arundo donax L., Echinochloa crus-galli (L.) P.Beauv, and Phragmites australis (Cav.) Trin. ex Steud (WE), and Saccharum spontaneum L., and Echinochloa crus-galli (L.) P.Beauv (RE). The analyses of indicator species showed that pH and EC were the most powerful and important edaphic factors for determining the composition of plant communities and indicator species. The identified indicator species may be an adequate choice for the restoration of degraded ecosystems in this area, as well as generating scientifically informed management solutions.

Author Contributions

Conceptualization, M.W. and S.M.H.; methodology, M.W. and F.A.; software, M.W., S.M.H. and F.A.; validation, R.W.B. and F.A.; formal analysis, M.W., S.M.H. and F.A.; investigation, M.W., S.M.H. and F.A.; resources, N.A.B. and A.A.H. ; data curation, M.W., F.A., M.I. and R.W.B. writing—original draft preparation, M.W.,S.M.H., F.A. and R.W.B.; writing—review and editing, R.W.B., N.A.B., A.A.H. and F.A. visualization, M.W., S.M.H., supervision, F.A. and N.A.B.; project administration, A.A.H. All authors have read and agreed to the published version of the manuscript.

Funding

The authors extend their appreciation to the Researchers supporting project number (RSP-2021/229) King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Species occurrence data are available on request to first author.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. Study area with elevations and sampling locations in the various ecosystems (riparian ecosystem, wetland ecosystem, urban ecosystem, forest ecosystem, and agroecosystem).
Figure 1. Study area with elevations and sampling locations in the various ecosystems (riparian ecosystem, wetland ecosystem, urban ecosystem, forest ecosystem, and agroecosystem).
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Figure 2. Climograph showing average temperature, rainfall, humidity, and wind speed in the Kasur district.
Figure 2. Climograph showing average temperature, rainfall, humidity, and wind speed in the Kasur district.
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Figure 3. Chord diagram showing the different ecological traits of wild grasses in semi-arid lowland of Punjab Pakistan.
Figure 3. Chord diagram showing the different ecological traits of wild grasses in semi-arid lowland of Punjab Pakistan.
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Figure 4. Chord diagram representing the distribution of grasses in different ecosystems, i.e., RE (riverine ecosystem), WE (wetland ecosystem), FE (forest ecosystem), UE (urban ecosystem), and AE (agroecosystem) of semi-arid lowland of Punjab, Pakistan.
Figure 4. Chord diagram representing the distribution of grasses in different ecosystems, i.e., RE (riverine ecosystem), WE (wetland ecosystem), FE (forest ecosystem), UE (urban ecosystem), and AE (agroecosystem) of semi-arid lowland of Punjab, Pakistan.
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Figure 5. Venn diagram depicting the similarities of grass species in different ecosystems of semi-arid lowland of Punjab, Pakistan.
Figure 5. Venn diagram depicting the similarities of grass species in different ecosystems of semi-arid lowland of Punjab, Pakistan.
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Figure 6. Principal component analysis based on importance value index of sampling sites in different ecosystems. The wetland ecosystem (WE) and riparian ecosystem (RE) are similar in species composition while the urban ecosystem (UE) and agroecosystem (AE) are more similar to the forest ecosystem (FE).
Figure 6. Principal component analysis based on importance value index of sampling sites in different ecosystems. The wetland ecosystem (WE) and riparian ecosystem (RE) are similar in species composition while the urban ecosystem (UE) and agroecosystem (AE) are more similar to the forest ecosystem (FE).
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Figure 7. CCA analysis of wild grasses in semi-arid region of Punjab, Pakistan.
Figure 7. CCA analysis of wild grasses in semi-arid region of Punjab, Pakistan.
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Figure 8. Indicator species analysis of wild grasses in various ecosystems of semi-arid lowland region of Punjab. The species with significant (p ≤ 0.05) are highlighted in box in each ecosystem type.
Figure 8. Indicator species analysis of wild grasses in various ecosystems of semi-arid lowland region of Punjab. The species with significant (p ≤ 0.05) are highlighted in box in each ecosystem type.
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Table
Table 1. List of grasses with ecological traits and p-value of indicator species analysis in different ecosystems of semi-arid lowland of Punjab, Pakistan. Legend: Nap, Nanophyll; Mip, Microphyll; Map, Macrophyll; Lep, Leptophyll; Th, Therophyte; Hc, Hemicryptophyte; Ge, Geophyte; AE, Agroecosystem; FE, Forest ecosystem; UE, Urban ecosystem; WE, Wetland ecosystem; RE, Riparian ecosystem.
Table 1. List of grasses with ecological traits and p-value of indicator species analysis in different ecosystems of semi-arid lowland of Punjab, Pakistan. Legend: Nap, Nanophyll; Mip, Microphyll; Map, Macrophyll; Lep, Leptophyll; Th, Therophyte; Hc, Hemicryptophyte; Ge, Geophyte; AE, Agroecosystem; FE, Forest ecosystem; UE, Urban ecosystem; WE, Wetland ecosystem; RE, Riparian ecosystem.
SpeciesNativityLeaf SpectraLife FormIndicator Species Analysis with p-Value
AEFEUEWERE
Acrachne racemosa (Heyne ex Roem. and Schult.) OhwiNativeNapTh10.6475111
Agrostis gigantea RothNativeMipHc10.4726111
Apluda mutica L.NativeNapHc10.397710.18430.2014
Aristida abnormis Chiov.NativeMipTh10.355510.10961
Aristida adscensionis L.NativeMipTh10.3565111
Aristida mutabilis Trin. and Rupr.NativeMipTh10.1417110.062
Arundo donax L.NativeMapHc10.705110.03150.1613
Avena fatua L.NativeNapTh0.19530.6724111
Bothriochloa bladhii (Retz.) S.T. BlakeNativeMipTh10.4167110.277
Bothriochloa ischaemum (L.) KengNativeMipTh1110.38760.2147
Brachiaria deflexa (Schumach.) C.E. Hubb. ex RobynsNativeMipTh11110.3774
Brachiaria distachya (L.) StapfNativeMipTh10.306111
Brachiaria ramosa (L.) StapfNativeMipTh1110.16480.1857
Brachiaria reptans (L.) C.A. Gardner and C.E. Hubb.NativeMipTh10.0182111
Bromus japonicus Thunb.NativeMipHc0.2381110.20920.2913
Bromus pectinatus Thunb.IntroducedMipHc0.329810.222811
Cenchrus biflorus Roxb.NativeNapHc0.14060.54350.01611
Cenchrus ciliaris L.NativeNapTh0.22960.23330.029911
Cenchrus pennisetiformis Steud.NativeNapHc1110.12460.2561
Cenchrus setiger Vahl.NativeNapHc10.1372111
Chloris barbata Sw.IntroducedNapTh10.0927111
Chrysopogon aucheri (Boiss.) StapfNativeNapTh10.36670.18130.29150.2289
Cymbopogon jwarancusa (Jones) Schult.NativeNapHc10.434910.15450.3372
Cynodon dactylon (L.) Pers.NativeLepTh0.0160.30720.09420.36850.2554
Dactyloctenium aegyptium (L.) Willd.NativeNapTh0.09280.348810.09631
Dactyloctenium scindicum BoissNativeNapTh10.5851111
Desmostachya bipinnata (L.) StapfNativeMapHc0.216710.046610.1582
Dichanthim fovelatum (Del.) Roberty IntroducedLepTh0.18230.03110.26140.28051
Dichanthium annulatum (Forssk.) StapfNativeLepTh10.7005110.431
Digitaria sanguinalis (L.) Scop.NativeMipTh0.28370.5638111
Echinochloa colona (L.) LinkNativeMipHc0.0650.277210.04660.0175
Echinochloa crus-galli (L.) P.Beauv.NativeMipHc0.11790.26640.10870.33910.1112
Eleusine indica (L.) Gaertn.NativeMipTh0.2688110.3021
Enneapogon persicus Boiss. NativeMipTh110.242610.359
Eragrostis cilianensis (All.) Janch.NativeMipTh10.52260.1450.15811
Eragrostis ciliaris (L.) R. Br.NativeMipTh10.65540.140711
Eragrostis papposa (Roem. and Schult.) Stued.NativeMipTh10.74540.223311
Heteropogon contortus L.) P. Beauv.ex Roem. and Schult.IntroducedMipTh0.3130.3917111
Imperata cylindrica (L.) Raeusch.IntroducedMipTh10.1666110.0858
Leptochloa chinensis (L.) NeesNativeMapHc0.032210.06711
Lolium persicum Boiss. and Hohen. ex BoissIntroducedMipHc11110.2988
Ottochloa compressa (Forssk.) HiluNativeMapHc1110.33350.4395
Panicum antidotale Retz.NativeMapHc10.45530.199311
Panicum maximum Jacq.NativeMapHc0.07730.1397110.154
Panicum turgidum Forssk.IntroducedMipHc0.320810.285311
Paspalidium flavidum (Retz.) A. CamusIntroducedMipHc1110.20211
Paspalum distichum L.IntroducedMipGe1110.22681
Pennisetum divisum (Forssk. ex J.F. Gmel.) HenrardNativeMipTh10.564410.35521
Phalaris minor Retz.NativeMapTh0.06570.53280.184211
Phragmites australis (Cav.) Trin. ex SteudIntroducedMapHc10.741710.01450.09
Phragmites karka (Retz.) Trin. ex Steud.IntroducedMapGe0.1570.251810.09770.0641
Poa annua L.NativeMipTh0.26440.1765111
Poa pratensis L.NativeMipTh10.2196111
Polypogon fugax Nees ex Steud.NativeMapTh0.18531111
Polypogon monspeliensis (L.) Desf.IntroducedMapTh10.060210.05780.4273
Saccharum bengalense Retz.NativeMapHc110.076411
Saccharum spontaneum L.NativeMapHc10.0472110.0294
Setaria intermedia Roem. and Schult.NativeMipTh10.4935111
Setaria italica (L.) P. BeauvNativeMipTh10.6285110.3222
Setaria verticillata (L.) P. Beauv.NativeMipTh10.6305110.3675
Setaria viridis (L.) P. Beauv.NativeMipTh10.422810.25721
Sorghum halepense (L.) Pers.NativeMipGe0.10630.19950.115711
Sporobolus ioclados (Trin.) Nees NativeMipHc10.6308110.1196
Stipagrostis plumosa Munro ex T. AndersonNativeNapHc10.078510.26341
Tetrapogon bidentatus Pilg.IntroducedMapHc10.2076111
Table
Table 2. Summary of CCA analysis of wild grasses in semi-arid lowland of Punjab, Pakistan.
Table 2. Summary of CCA analysis of wild grasses in semi-arid lowland of Punjab, Pakistan.
Axes1234
Eigen values0.3130.2060.1550.103
Species-environment associations0.6910.680.6880.569
Accumulative percentage variance of wild grass species data4.77.810.111.7
Accumulative percentage variance of species–environment relation30.650.765.976
Total inertia6.638
Sum of all eigen values6.638
Sum of all canonical eigenvalues1.021
Monte Carlo Test
Test of significance of first canonical axis: eigenvalue0.586
F-ratio2.034
p-value0.0210
Test of significance of total canonical axes; Trace1.021
F-ratio1.323
p-value0.0350
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Waheed, M.; Haq, S.M.; Arshad, F.; Bussmann, R.W.; Iqbal, M.; Bukhari, N.A.; Hatamleh, A.A. Grasses in Semi-Arid Lowlands—Community Composition and Spatial Dynamics with Special Regard to the Influence of Edaphic Factors. Sustainability 2022, 14, 14964. https://doi.org/10.3390/su142214964

AMA Style

Waheed M, Haq SM, Arshad F, Bussmann RW, Iqbal M, Bukhari NA, Hatamleh AA. Grasses in Semi-Arid Lowlands—Community Composition and Spatial Dynamics with Special Regard to the Influence of Edaphic Factors. Sustainability. 2022; 14(22):14964. https://doi.org/10.3390/su142214964

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Waheed, Muhammad, Shiekh Marifatul Haq, Fahim Arshad, Rainer W. Bussmann, Muhammad Iqbal, Najat A. Bukhari, and Ashraf Atef Hatamleh. 2022. "Grasses in Semi-Arid Lowlands—Community Composition and Spatial Dynamics with Special Regard to the Influence of Edaphic Factors" Sustainability 14, no. 22: 14964. https://doi.org/10.3390/su142214964

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