Vegetation Composition and Environmental Relationships of Two Amaranthus Species Communities in Variant Agroecosystems at Fayoum Depression, Egypt

Abstract

Amaranthus is appointed as a common weed associated with crops. The research was designed to survey the Amaranth existence pattern throughout the Fayoum Depression, Egypt, accompanied with a community vegetation analysis. The study was extended to collect and analyze associated soil samples. The obtained results figured out the prevalence of dicot families, herb growth forms, therophyte followed by phanerophyte life forms, the Pantropical monoregional chorotype, and the Mediterranean and Sudano-Zambezian followed by the Irano-Turanian pluri-regional chorotype. Multilevel pattern analysis stated that Gossypium barbadense, Corchorus olitorius, Sorghum bicolor, Sesamum indicum, and Zea mays are indicator species most related to Amaranth occurrence and prediction. NMDS analysis denoting that the Ibshaway, Youssef Al Seddik, Itsa, and Fayoum districts are the most representative districts for Amaranth existence on the basis of edaphic resources. Itsa and Youssef Al Seddik, in addition to Itsa and Fayoum, resemble each other in species composition. High pH and CaCO₃ percentages were discriminatory in Ibshaway, Itsa, and Youssef Al Seddik. Ni was the cornerstone for districts partitioning in pruned trees. Finally, Amaranth was flourishing in both comfortable and harsh habitats with cultivated crops and orchards, as well as on the outskirts. The findings are considered to be valorized by decision makers in arable land management.

1. Introduction

Weeds are undesirable plants coexisting and interfering with cultivated ones [1,2]. Like coins, weeds have two different faces that act as an angel as well as a ghost. They are holistically hindering crops in available spatial and water resources. However, on the bright side, weeds are considered as a source of food, fodder, and medication. Also, weeds offer a rich pool of genetic resources for biodiversity [3,4].

The agroecosystem is a landscape representing balanced interactions between soils, climates, plants, animals, other organisms from one side, and humans from the other side. Weeds are not a negligible part of the yield of a cropping system [1]. In agroecosystems, we should move from weed management to weed investment. In other words, how to benefit from weeds instead of resisting them to conserve biological diversity for sustainable agriculture [5,6]. In a sustainable approach to agriculture, while facing environmental change and habitat degradation, non-native weeds are expected to have greater impacts on crop yield than native weeds. Invasive plant species may be more successful than native species because of their environmental resilience. They have the ability to evolve more rapidly and have the potential to perform better during global environmental change [7].

Ammranthus, or Amaranth (Pigweed), belongs to the family Amaranthaceae and comprises about 65 species; some of them are cultivated as vegetables or ornamentals while the others are listed as weeds. North and Central America is the native country of Amaranth, but nowadays, pigweed plants have become widely distributed in Europe, Asia, Africa, and Australia as a result of agricultural practice [8]. As an annual summer herb, Amaranthus has a wide ecological amplitude and amazing phenotypic plasticity; also, it can thrive in arid climates and poor sandy soil [9].

As explained in [10], A. viridis and A. hybridus are highly recorded species of Amaranthus at the Fayoum Depression (FD), Egypt. According to [11], the Fayoum region is one of the oldest agricultural cities in the world. Healthy land, salt affected soil, wasteland, orchards, and outskirts are different habitats where A. viridis is spreading at the FD. On the other hand, A. hybridus was found in healthy land, reclaimed land, wasteland, and orchards [10]. The proliferation traits of the two Amaranthus species in agroecosystems usually coincide with soil fertility. The propagation capacity is also extended to extremely harsh conditions [12,13]. Additionally, the chemical composition of the Amaranthus viridis aerial parts was influenced by soil variables [14].

The floristic composition of weed communities has usually clashed with anthropogenic pressure and land abuse [15]. Owing to the emergence of Amaranthus species, novel aspects may appear in the soil and in the plant community as well. The integrated study of weed physiognomy, chorology, and soil characteristics at suitable intervals of time may contribute to an agroecosystem’s sustainability. The main objective of this paper is to throw a light spot on two Amaranthus species autecology. The study is extended to describe life span, life form, floristic composition, and phytogeographic distribution of the weed community associated with the studied species in their natural habitats. Furthermore, the goal of this work has broadened to assess the impact of soil and location on the distribution of the candidate weed communities throughout the Fayoum Depression.

2. Materials and Methods

2.1. Study Area

The Fayoum circular depression is a part of Egypt and is situated about 95 km southwest of Cairo around longitudes of 30°23′ and 31°5′ E and latitudes of 29°5′ and 29°35′ N [16]. The Yousof Al-seddik, Ibsheway, Senouris, Tameyah, Fayoum, and Itsa districts constitute an assemblage of desert, coastal, and agricultural ecosystems of the depression [16]. Various habitats across all six districts were surveyed during the growth period of the two Amaranthus species (Figure 1) in July and August for two successive years. Out of 100 sites, Amaranth only occurred in 59 sites. The selected sites depicting the spreading paradigm of the targeted two Amaranthus species were plotted in Figure 2. Latitudes and longitudes were listed for each site, respectively, using GPS (Table S1).

2.2. Field Measurements

2.2.1. Vegetation Analysis

Owing to the standards of weed communities, a 1 m² (1 m × 1 m) wooden quadrat was designed. Quadrats were outlined and laid out in such a way to represent the variations in the plant community at each site. Dominance and overall influence of plant species in a community is expressed as the importance value (IV) [17]. IV = RD + RF + RC where

RD: Relative density           RF: Relative frequency
RC: Relative cover                                                 

where absolute density is the number of species within the unit area of the quadrat, frequency is the proportion of quadrats in which a species occurs, and dominance is the estimated area of a quadrate that is covered by a plant’s canopy.

$$\mathrm{Absolute} \mathrm{Density} (\mathrm{A}.\mathrm{D}.)= {\displaystyle \frac{\mathrm{N}\mathrm{o}.\mathrm{o}\mathrm{f} \mathrm{p}\mathrm{l}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{s} \mathrm{i}\mathrm{n}\mathrm{d}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{d}\mathrm{u}\mathrm{a}\mathrm{l}\mathrm{s}}{\mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a} \mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}\mathrm{d}}}$$

$$\mathrm{Relative} \mathrm{Density} (\mathrm{R}.\mathrm{D}.)= {\displaystyle \frac{\mathrm{D}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{d}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s}}}\times 100$$

$$\mathrm{Absolute} \mathrm{Frequency} (\mathrm{A}.\mathrm{F}.)= {\displaystyle \frac{\mathrm{N}\mathrm{o}.\mathrm{o}\mathrm{f} \mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}\mathrm{s} \mathrm{a}\mathrm{t} \mathrm{w}\mathrm{h}\mathrm{i}\mathrm{c}\mathrm{h} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s} \mathrm{o}\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{o}.\mathrm{o}\mathrm{f} \mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}\mathrm{s} \mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}\mathrm{d}}}$$

$$\mathrm{Relative} \mathrm{Frequency} (\mathrm{R}.\mathrm{F}.)={\displaystyle \frac{\mathrm{F}\mathrm{r}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{f}\mathrm{r}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s} \mathrm{v}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{e}\mathrm{s} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s}}}\times 100$$

$$\mathrm{Absolute} \mathrm{Dominance} (\mathrm{A}.\mathrm{D}.)={\displaystyle \frac{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{o}\mathrm{f} \mathrm{b}\mathrm{a}\mathrm{s}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a} \mathrm{o}\mathrm{r} \mathrm{c}\mathrm{o}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}}{\mathrm{A}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}\mathrm{d}}}$$

$$\mathrm{Relative} \mathrm{Dominance} (\mathrm{R}.\mathrm{D}.)={\displaystyle \frac{\mathrm{D}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{d}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{n}\mathrm{c}\mathrm{e} \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{s}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{s}}}\times 100$$

IV = R.D. + R.F. +R.D.

The surveyed plant species were identified obeying the existing literature [18,19,20,21], and the names were updated according to POWO [22]. The life form of all recorded taxa were listed into classes according to the Raunkiaer (1934) Zohary (1972) [23,24] methods.

2.2.2. Sampling and Physiochemical Analysis of Soil

From each sampling site, soil samples were collected at 20 cm depth. Subsequently, samples were transferred to the lab in plastic bags, allowed to be air dried and standing for analysis. Soil texture was analyzed more effectively according to the modified method of Orhan et al., (2020) [25]. After gently crushing each sample using a wooden pestle and mortar, they were carefully mixed with distilled water, turning to soil paste. Electric conductivity (ECe), pH, sodium, chlorides, sulphates, carbonates, and bicarbonates were measured as per the described method in [26]. Some macroelements (Ca, Mg, and K), along with some heavy metals (Cd, Ni, and Pb), were determined using inductively coupled plasma mass spectroscopy (Agilent 7500a, Santa Clara, CA, USA). The stability of the device was evaluated every ten samples by examining the internal standard. Reagent blanks were also prepared to detect potential contamination during the digestion and analytical procedure [27]. The experimental work was extended to quantify the total organic matter (TOC) [5] and calcium carbonate (CaCO₃) percentage using a modified pressure calcimeter method [28].

2.3. Data Analysis

Multivariate analyses were applied to evaluate the Fayoum Depression’s vegetation using classification and ordination techniques. Relying on relative density, relative frequency, and cover data from 59 vegetation plots, species importance values were calculated and compiled into a matrix.

2.3.1. Community Classification and Clustering

To classify plant communities, hierarchical clustering was performed on the species importance matrix using the Bray–Curtis dissimilarity index and Ward’s linkage method. Clustering was implemented in R version 4.2.2 [29] using the vegan and cluster packages. Dendrograms were overlaid on heatmaps constructed with the gplots package (heatmap 2 function) to visualize similarities in the floristic composition among sites. Two correlation-based heatmaps—one for floristic and one for environmental variables—were produced to support pattern recognition and visualization of spatial similarity among plots.

2.3.2. Ordination and Environmental Fitting

Non-metric multidimensional scaling (NMDS) was conducted to ordinate plots based on species composition. NMDS was performed using the Bray–Curtis dissimilarity with the metaMDS function from the vegan package [30]. Dimensionality was evaluated using stress values and Shepard plots, and a 2-dimensional solution was selected due to acceptable stress (<0.2). To assess relationships between species composition and environmental variables, the envfit function (vegan package) was used to fit environmental vectors onto the NMDS ordination. This assumes monotonic species–environment relationships. The environmental data included edaphic variables recorded from the same plots (n = 59). This procedure allowed for the inference of gradients, such as soil nutrients and salinity structure species composition. The NMDS methodology is cited following Kruskal (1964) [31] and Oksanen et al. (2019) [30].

2.3.3. Indicator Species Analysis

After vegetation groups were identified, a multilevel pattern analysis was applied to identify species significantly associated with each group. We used the Indicator Value (IndVal.g) method [32,33] implemented via the indicspecies package (version 1.7.12) in R. The multipatt function was used with 999 permutations and a significance threshold of α = 0.05. This method calculates species specificity and fidelity to each group, identifying statistically meaningful associations. The analysis included 56 species and successfully identified ecological indicators that help characterize vegetation patterns in relation to environmental settings.

2.3.4. Multivariate Regression Tree (MRT)

To model the effects of environmental variables on Amaranthus species occurrence, a Multivariate Regression Tree (MRT) was constructed using the mvpart package in R. The response variables were normalized presence/absence data for the two Amaranthus species, while explanatory variables included a suite of soil and environmental predictors. A leave-one-out cross-validation approach (n = 59) was applied to ensure a robust model assessment. The complexity parameter (cp) was set at 0.05, balancing interpretability and performance, and pruning was based on minimizing the cross-validation error (xerror).

Although gradient analysis tools like CCA or NMDS + envfit were considered, MRT was selected for its ability to handle nonlinear species–environment relationships and detect hierarchical splits, which align with our study’s goals of identifying threshold effects across environmental gradients. Despite the moderate sample size, the relatively large number of environmental variables justified the use of the MRT.

3. Results

3.1. Floristic Composition of the Study Area

A total of 56 species (28 annual, 28 perennial) of vascular plants belonging to 49 genera in 28 families constituted the flora of the study area and were recorded from 59 vegetation stands (

Table 1. Life span, life form, chorotype, and habit of the recorded species in the study area of Fayoum Depression.

Family	Plant Species	Life Span	Life Form	Chorotype	Habit	Abbreviation
Aizoaceae	Trianthema portulacastrum L.	Annual	Th	PAN	Herb	Tri por
Amaranthaceae	Amaranthus hybridus L.	Annual	Th	PAN	Herb	Ama hyb
	Amaranthus viridis L.	Annual	Th	COS	Herb	Ama vir
Anacardiaceae	Mangifera indica L.	Perennial	Ph	PAN	Tree	Man ind
	Schinus terebinthifolia Raddi	Perennial	Ph	PAN	Tree	Sch ter
Asteraceae	Artemisia herba-alba Asso	Perennial	Ch	IT	Shrub	Art her
	Helianthus annus L.	Annual	Th	ME + IT	Shrub	Hel ann
	Pluchea dioscoridis (L.) DC.	Perennial	Ph	SA + SZ	Shrub	Plu dio
	Xanthium strumarium L.	Annual	Th	NEO	Herb	Xan str
Arecaceae	Phoenix dactylifera L.	Perennial	Pha	SA + IT	Tree	Pho dac
Brassicaceae	Lepidium coronopus (L.) Al-Shehbaz	Annual	Th	ME + IT + ES	Herb	Lep cor
Chenopodiaceae	Chenopodiastrum murale (L.) S.Fuentes, Uotila & Borsch	Annual	Th	COS	Herb	Che mur
Convolvulaceae	Convolvulus arvensis L.	Perennial	He	COS	Herb	Con arv
Cucurbitaceae	Citrullus lanatus (Thunb.) Matsum. & Nakai	Annual	He	PAN	Herb	Cit lan
	Cucumis melo L.	Annual	Th	IT	Herb	Cuc mel
	Cucumis sativus L.	Annual	Th	IT	Herb	Cuc sat
	Cucurbita pepo L.	Annual	Th	NEO.	Herb	Cuc pep
Cyperaceae	Cyperus rotundus L.	Perennial	Ge	COS	Herb	Cyp rot
Euphorbiaceae	Euphorbia peplus L.	Annual	Th	ES + IT + ME + SS + SZ	Herb	Eup pep
	Ricinus communis L.	Perennial	Ph	SZ	Shrub	Ric com
Fabaceae	Alhagi graecorum Boiss	Perennial	Ch	IT + ME + SA + SZ	Sub-shrub	Alh gra
Lamiaceae	Mentha piperita L.	Perennial	Ch	EU + CA	Herb	Men pip
	Origanum majorana L.	Perennial	Ch	ME	Sub-shrub	Ori maj
Malvacea	Abelmoschus esculentus (L.) Moench	Annual	Th	PAL	Herb	Abe esc
	Gossypium barbadense L.	Annual	Ch	NEO	Shrub	Gos bar
	Malva parviflora L.	Annual	Th	ME + IT	Herb	Mal par
Moraceae	Morus nigra L.	Perennial	Ph	PAN	Tree	Mor nig
Oleaceae	Olea europaea L.	Perennial	Ph	ME	Tree	Ole eur
Pedaliaceae	Sesamum indicum L.	Annual	Th	PAL	Herb	Ses ind
Poaceae	Cymbopogon citratus (DC.) Stapf	Perennial	Th	SA	Herb	Cym cit
	Cynodon dactylon (L.) Pers.	Perennial	Ge	COS	Herb	Cyn dac
	Dinebra retroflexa (Vahl) Panz	Annual	Th	PAN	Herb	Din ret
	Diplachne fusca (L.) P.Beauv. ex Roem. & Schult.	Perennial	He	PAN	Herb	Dip fus
	Echinochloa colonum (L.) Link	Annual	Th	PAL	Herb	Ech col
	Imperata cylindrica (L.) Raeusch.	Perennial	Ge	ME + IT + SA + SZ	Herb	Imp cyl
	Paspalum distichum L.	Perennial	Ge	NEO	Herb	Pas dis
	Phragmites australis (Cav.) Trin. ex Steud.	Perennial	GH	COS	Herb	Phr aus
	Polypogon monspeliensis (L.) Desf.	Annual	Th	ME + IT + SZ + SA	Herb	Pol mon
	Sorghum bicolor (L.) Moench	Annual	Th	PAL	Herb	Sor bic
	Sorghum halepense (L.) Pers.	Perennial	He	PAN	Herb	Sor hal
	Zea mays L.	Annual	Th	PAN	Herb	Zea may
Polygonaceae	Persicaria decipiens (R.Br.) K.L.Wilson	Annual	GH	IT + ME + SS+ SZ	Herb	Per dec
Portulacaceae	Portulaca oleracea L.	Annual	Th	IT + ME +SA + SZ	Herb	Por ole
Punicaceae	Punica granatun L.	Perennial	Ph	ME + IT	Tree	Pun gra
Rhamnaceae	Ziziphus spina-christi (L.) Desf.	Perennial	Ph	IT + ME + SA + SZ	Tree	Ziz spi
Rosaceae	Pyrus communis L.	Perennial	Ph	ME	Tree	Pyr com
Rutaceae	Citrus aurantiifolia (Christm.) Swingle	Perennial	Ph	PAL	Tree	Cit aur 1
	Citrus aurantium L.	Perennial	Ph	PAL	Tree	Cit aur 2
Solanaceae	Capsicum annum L.	Annual	Th	NEO	Herb	Cap ann
	Capsicum frutescens L.	Perennial	Th	PAN	Herb	Cap fru
	Solanum lycopersicum L.	Annual	Th	NEO	Herb	Sol lyc
	Solanum melongena L.	Annual	Ph	GC	Herb	Sol mel
	Solanum nigrum L.	Annual	Th	COS	Herb	Sol nig
Tiliaceae	Corchorus olitorius L.	Annual	Th	PAL	Herb	Cor oli
Verbinaceae	Lantana camara L.	Perennial	Ph	NEO	Sub-shrub	Lan cam
Vitaceae	Vitis vinifera L.	Perennial	Ph	Hol	Shrub	Vit vin

Chorotypes abbreviations: Pal, Palaeotropical; ME, Mediterranean; SA, Saharo Arabian; CA, Central Asian; GC, Guineo-Congolian; Hol, Holarctic; COS, Cosmopolitan; SZ, Sudano-Zambezian; Eu, Eurasian; ES, Euro-Siberian; SS, Saharo-Sindian; Pan, Pantropical; Neo, Neotropical; IT, Irano-Turanian.; and SJ, Sino-Japonic. Life form abbreviations: Ch, Chamaephyte; Ge, Geophyte, GH, Geophyte-Helophyte; He, Hemicryptophyte; Ph, Phanerophyte; and Th, Therophyte.

). A total of 18% of the recorded species are trees, 16% are shrubs and sub-shrubs, meanwhile 66% are herbs (Figure 3). Dicots are represented by 25 families while monocots have only 3 families. Generally, all families in the study are of small size. Family Poaceae (richest) exhibited the highest number of genera, amounting to 12 taxa, followed by family Solanaceae (five genera) and then the families Cucurbitaceae and Asteraceae (four genera). Family Malvaceae is represented by three taxa, meanwhile, the rest of families show two to one taxon (monospecies).

3.2. Biological Spectrum of Species

According to Raunkiaer (1934) [23], six life forms were observed throughout the species of the study area as shown in Figure 3B. Therophyte was the most frequent form (46%), followed by phanerophytes (27%). Chamaephytes, geophytes, hemicryptophytes, and geophyte-helophyte scores were 9%, 7%, 7%, and 4%, respectively.

3.3. Phytogeographical Affinities

A phytogeographical analysis of the listed 56 species indicated that 62.5% were monoregional (Figure 3C), and the Pantropical element was the most prominent chorotype (20%), whereas both Neotropical and Paleotropical elements counted for 13% each (Figure 3D). A total of 14.29% of the identified species were pluri-regional chorotypes, where the most represented elements were Mediterranean and Sudano-Zambezian followed by Irano-Turanian. Cosmopolitan chorotypes were recorded at 12.5%, which were clear in seven species: Amaranthus viridis, Chenopodiastrum murale, Convolvulus arvensis, Cyperus rotundus, Cynodon dactylon, Polypogon monspeliensis, and Solanum nigrum. However, only 10.7% of the noticed species were of a bi-regional chorotype, with more replications of Saharo-Arabian and Irano-Turanian over others. In wrapping up, 62.5% of species are monoregional, 10.7% are bi-regional, 14.29% are pluri-regional, and 12.5% are cosmopolitan.

3.4. Indicator Species

Multilevel pattern analysis was chosen to assess the indicator species that predict the existence of Amaranthus for each district as listed in

Table 2. Multilevel pattern analysis assessing the indicator species.

District	Indicator Species	Stat. p Value	Significance
Ibshaway	Gossypium barbadense	0.99	0.001 ***
	Cucumis sativus	0.707	0.037 *
	Abelmoschus esculentus	0.679	0.024 *
Ibshaway + Itsa	Corchorus olitorius	0.865	0.003 **
Tameyah	Sorghum bicolor	0.993	0.001 ***
Fayoum	Sesamum indicum	0.99	0.001 ***
Itsa + Youssef Al Seddik	Zea mays	0.963	0.001 ***

Significance differences with p-value < 0.05; Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’.

. Although the targeted two species are weeds, all indicator species were cultivated crops. Gossypium barbadense, Cucumis sativus, and Abelmoschus esculentus were indicator species for the Ibshaway district, but G. barbadense recorded the most significant value. Corchorus olitorius was the indicator species for both Ibshaway and Itsa. The analysis gave no indicator species for the Senouris district. Sorghum bicolor and Sesamum indicum were indicator species for Tameyah and Fayoum, respectively; Zea mays was noted as an indicator species for both Itsa and Youssef Al Seddik.

3.5. Multivariate Regression Tree

The final tree model (Figure S1) revealed that Nickel (Ni) concentration is the most influential environmental variable in predicting the species richness associated with Amaranthus viridis, with other variables like calcium carbonate, sodium, electrical conductivity, and the sodium adsorption ratio (SAR) contributing to finer splits in specific tree nodes. Initially, the tree had a high relative error, which reduced after pruning, yielding a model with two main splits and a low cross-validation error indicating good performance with limited overfitting (Figure 4). Each split in the tree represents a stepwise reduction in error, with observations grouped based on environmental thresholds. The final model identified Ni as the primary variable influencing Amaranthus occurrence, with additional contributions from variables like (CaCO₃), Na, (ECe), and the sodium adsorption ratio (SAR). The pruned tree firstly split the sampling points into two sections, as Ni is higher than 26.7 ppm and Ni is lower than 26.7 ppm. Secondly, the left side split the sites again into two groups, as Ni is higher than 28.5 ppm and Ni is lower than 28.5 ppm.

Figure S2 and Figure 5 reveal the similarity and dissimilarity among plots. The overall view for both heatmaps clarifies the high similarities among plots. Based on environmental data, the side dendrogram attached to the heatmap in Figure S2 figured out a sort of close-likeness concerning Tameyah (plot 31) and Fayoum (plot 48). Based on species composition, the side dendrogram attached to the heatmap of Figure 5 made an impression of the close association concerning the Itsa and Youssef Al Seddik districts on one hand and between the Itsa and Fayoum districts on the other hand. NMDS analysis (Figure 6, Figure 7 and Figure S3) enabled the determination of the significance for abiotic variables. Nickel, lead, and calcium concentrations are the most effective variables in plot homogeneity expectations, followed by pH value, CaCO₃, and sand percentage. The studied plots are divided into six clusters with environmental variables. Clusters 2, 3, 4, and 5 comprised a large number of plots compared to Cluster 1 and 2 with a limited number of plots. All of the Ibshaway, Youssef Al Seddik, Itsa, and Fayoum districts are dominant throughout Clusters 2, 3, 4, and 5.

4. Discussion

The current study was conducted to evaluate the spreading paradigm of two Amaranthus species in relation to the environmental variables and floristic composition of associated species. The study found that the family Poaceae is the most representative one with twelve genera followed by Solanaceae and Asteraceae. As stated by [34], it is well known that the families Poaceae and Asteraceae have an extensive ecological range of tolerance, effective capacity for dispersing seeds, ecological adaptability, resilience to disturbances, successful establishment, and the capacity to alter ecosystems. Additionally, it was reported that the Poaceae, Fabaceae, and Asteraceae families were the most prevalent in North Africa, the Mediterranean, northern Zambia, and eastern Ethiopia [35].

The therophyte life form, along with herb habit, was the preponderant over other life forms and plant habits, respectively, in the study area of the Fayoum Depression. Our finding is in line with that of [36], who observed that grasses are particularly common in old, cultivated areas in the Nile Valley and can be found growing in a variety of environments in Egypt, including salt marshes and arid regions. In the intervening time, the Ibshaway, Youssef Al Seddik, Itsa, and Fayoum districts are dominant throughout Clusters 2, 3, 4, and 5 in NMDS. This high repetition of district representation may be the result of similarities among the main crops that the Amaranth weed was associated with like Zea mays, Sorghum bicolor, Sesamum indicum, and Gossypium barbadense. The similarity among Ibshaway, Youssef Al Seddik, and Itsa was previously verified by [14]. In addition, a helicopter view throughout the heatmap for species data entails a positive correlation of Amaranth to some weeds like Paspalum distichum, Echinochloa colonum, and Cyperus rotundus. Thus, weed species distribution, especially for Amaranthus, may be flourish in old, cultivated lands followed by orchards. Herein, large trees Mangifera indica in orchards provide shade, which keeps the soil moist for longer. Consequently, orchard habitats offer favorable conditions for weed species to germinate, grow, and reproduce [10,37,38]. According to [39,40], the broad distribution of certain weeds in the current study may be interpreted as species with an expansive ecological niche and phenotypic plasticity and heterogeneity. These habitats give weed species ideal conditions for germination, growth, and reproduction. In contrast to monocultures, weed abundance has increased recently due to diversified crop rotations [41]. One important feature of vegetation that indicates the health and productivity of the agroecosystem, which is influenced by a variety of environmental factors, is species diversification [42,43]. Phanerophytes were the second represented category in plant life forms; the result is conflicting with that of [10], because the present work’s goal was oriented to Amaranth communities. The perennial lifespan (phanerophytes, chamaephytes, and hemicryotiphytes) was obviously symbolized. This may be due to the fact that perennial plants are adapted to the extreme habitat of the area [44].

The pruned tree suggests that Ni, in particular, plays a critical role in structuring species abundance across the dataset, though other variables contribute to subtle differences among subgroups. As Ni is an important micronutrient, it is needed in small amounts for plant growth [45]. The observed positive association between Ni and Pb may be interpreted by the fact that Ni offers a defense strategy to plants against stresses like Pb stress [46].

It is evident from both heatmaps that biotic and abiotic factors are dually influencing vegetation composition. Although the homogeneity in environmental composition among plots was noticed, there was some sort of variance among study locations. The Tameyah and Fayoum districts are closely correlated to each other, relying on abiotic variables; meanwhile, the Itsa and Youssef Al Seddik districts, as well as the Itsa and Fayoum districts, are similar in their species composition. Zea mays was a common indicator species between Itsa and Youssef Al Seddik, emphasizing their likeliness. The physiochemical qualities of soil are regulated by its pH, which also influences other soil factors [47]. A general glimpse at the Multivariate Regression Tree and NMDS analyses implies that Ni, Pb, CaCO₃, Ca, sand, silt, clay, organic matter, and pH are strongly controlling the species richness associated with Amaranthus. Ref. [48] highlighted the increase in soil organic matter as a result of plant debris decomposition. Because rainwater dissolved potassium and calcium decreased salt toxicity, vegetation diversification was enhanced. Analogous research has investigated the impact of various surface sediment size classes on the geographic distribution of soil moisture [49]. They have some features in common like urban sprawl and an increasing alkalinity with high CaCO₃ percentage. Ref. [16] stated that urban sprawl in both Itsa and Ibshaway was folded during the last decade (2003–2013). It may be concluded that that is why CaCO₃ and pH are strongly linked and discriminating in sites clustering.

Although the samples were collected from a narrow area in Egypt, the Fayoum Depression has a unique floristic structure complexity similar to the findings of [10] at the Fayoum Depression and [50] in Egyptian oases. Egypt is a phytogeographic intersection of floristic elements from at least four distinct regions: the Asiatic Irano Turanian, the Afro-Asiatic Saharo-Arabian, the Euro-Afro-Asiatic Mediterranean, and the African Sudano-Zambezian [51]. In the current situation, it is obvious from chorological analysis that the Pantropical element was the most prominent followed by Neotropical and Paleotropical, which are comparable to the cosmopolitan Amaranthus chorotype. Mediterranean taxa can adapt in mesic habitats. It could be why pluri-regional Mediterranean species were reasonably represented in the study area. The deduction is congruent with that of [15]. This confluence of various phytochoria belongs to bi- and tri-regional elements may be attributable to some environmental variables like farming history, entry of some invasive species, or oscillation of water supply [52]. Also, this remarkable variation may be a result of both topographic diversity and prolonged human interference [53]. From the aforementioned data, it can be concluded that Ibshaway, Youssef Al Seddik, and Itsa are the most corelated districts concerning pigweed distribution, relying on environmental variables besides indicator species.

5. Conclusions

The present endeavor shed light on two Amaranthus species’ distribution at the Fayoum Depression. The understanding of plant communities inhabited by Amaranth concluded the dominance of dicot families and therophyte, followed by the phanerophyte life form. Out of 56 species, 66% were herb growth forms, meanwhile 18% were trees and 16% were shrubs and sub-shrubs. The Pantropical chorotype, Mediterranean and Sudano-Zambezian, followed by Irano-Turanian, chorotypes, are frequent in monoregional and pluri-regional chorotypes, respectively. Gossypium barbadense, Corchorus olitorius, Sorghum bicolor, Sesamum indicum, and Zea mays are the indicator species most related to Amaranth occurrence. The heatmap proposes the positive coexistence of weeds Paspalum distichum, Echinochloa colonum, and Cyperus rotundus with Amaranthus. Floristic composition is alike in Itsa and Youssef Al Seddik, in addition to Itsa and Fayoum. The elevating percentages of pH and CaCO₃ were biased in Ibshaway, Itsa, and Youssef Al Seddik. The disparity in Ni concentrations among study sites made Ni the basis for district segmentation in pruned trees. NMDS analysis symbolized that the Ibshaway, Youssef Al Seddik, Itsas, and Fayoum districts are the most demonstrative districts for Amaranth existence on the basis of soil physicochemical properties. Finally, Amaranth flourished in both comfortable and harsh habitats with cultivated crops and orchards, as well as on the outskirts. The results show how topographic diversity and prolonged human interference induce rapid plant–environmental modifications, leading to alterations in plant variation distribution patterns and plant homogenization.