Environmental variables drive plant species composition and distribution in the moist temperate forests of Northwestern Himalaya, Pakistan.

By assessing plant species composition and distribution in biodiversity hotspots influenced by environmental gradients, we greatly advance our understanding of the local plant community and how environmental factors are affecting these communities. This is a proxy for determining how climate change influences plant communities in mountainous regions ("space-for-time" substitution). We evaluated plant species composition and distribution, and how and which environmental variables drive the plant communities in moist temperate zone of Manoor valley of Northwestern Himalaya, Pakistan. During four consecutive years (2015-2018), we sampled 30 sampling sites, measuring 21 environmental variables, and recording all plant species present in an altitudinal variable range of 1932-3168 m.a.s.l. We used different multivariate analyses to identify potential plant communities, and to evaluate the relative importance of each environmental variable in the species composition and distribution. Finally, we also evaluated diversity patterns, by comparing diversity indices and beta diversity processes. We found that (i) the moist temperate zone in this region can be divided in four different major plant communities; (ii) each plant community has a specific set of environmental drivers; (iii) there is a significant variation in plant species composition between communities, in which six species contributed most to the plant composition dissimilarity; (iv) there is a significant difference of the four diversity indices between communities; and (v) community structure is twice more influenced by the spatial turnover of species than by the species loss. Overall, we showed that altitudinal gradients offer an important range of different environmental variables, highlighting the existence of micro-climates that drive the structure and composition of plant species in each micro-region. Each plant community along the altitudinal gradient is influenced by a set of environmental variables, which lead to the presence of indicator species in each micro-region.

Introduction

Mountains are the most remarkable landforms on the earth, representing different vegetation zones based on environmental variations [1]. They offer habitat heterogeneity based on micro-environmental variation along the altitudinal gradient [2, 3], where environmental variables (including direct and indirect effects of abiotic and biotic effects) are important factors in determining altitudinal zone boundaries [4, 5]. It is well recognized that altitude is a dynamic gradient along which several environmental variables [6] and species diversity [7, 8] change concomitantly. Within this focus, plant biodiversity is strongly influenced by different environmental variables [9], and certain species can survive on the brink of extinction in high mountains across the world [10-12].

Many mountains across the globe are important hotspots of biodiversity [13-16], with roughly half of all plant species flourishing in hotspot areas [17, 18]. However, despite this high endemism of species greatly influenced by various environmental gradients, such as edaphic, climatic and physiographic variables [19], these areas suffer a major impact from climate change [20, 21]. Plant species present in these gradients have great adaptive power [22, 23]; however, the speed with which climate change is advancing might not be sufficiently achieved by plant species adaptation in these areas, leading to a strong impact on the biodiversity of these areas [24-26], ultimately leading to variations in the community structure [27].

In the face of the current climate change and considering the importance of phytosociological studies for the understanding of biodiversity and species distribution, the Himalayas represent a fundamental piece for these studies. This region is facing a marked increase in temperature [28], which is three times greater than the global average [29]. This unprecedented rise in temperature, and modification of various environmental variables as well, may lead to shifts in species composition [24-26] and variations in community structure [27]. Many researchers have explored the effect of altitude on species composition, diversity, and forests formation structure during the previous two decades, with around half of these studies indicating an inverse association between species richness and altitude. Rahbek [30], on the other hand, did a quantitative investigation of altitudinal gradients of species richness and discovered that among plants, hump-shaped patterns of species diversity with peaks at mid-elevation are the most typically recorded, followed by monotonically declining patterns. Kluge et al. [31] found a hump-shaped diversity pattern for seed plants in the Eastern Himalayas, even though endemic species richness peaks at higher elevations due to increasingly isolated habitats and smaller surface area in mountainous ecosystems, which promotes speciation [32]. Although there has been a considerable increase in the number of phytosociological studies in these altitudinal regions considered hotspots of biodiversity [7, 19, 22, 23, 33], there is still limited knowledge of how and which environmental variables drive the distribution and composition of plant species along altitudinal gradients in specific hotspots regions, such as the Northwestern Himalaya.

By assessing the patterns of composition and distribution of plant species in these biodiversity hotspots influenced by environmental gradients, we greatly advance our understanding of the local plant community and how environmental factors are affecting these communities, which is a proxy for assessing how impacts of climate change can affect plant communities located in mountainous regions [34]. In this context, we evaluated plant species composition and how and which environmental variables drive the plant species distribution of moist temperate zone of the Northwestern Himalaya, Pakistan. Briefly, we assessed (i) the potential plant communities present in the moist temperate zone; (ii) which are the environmental variables that most determine plant community structure in the moist temperate zone; (iii) whether there is variation in plant species composition between plant communities and which are the species that most contributed for species composition dissimilarity; (iv) whether there is variation of diversity indices among communities; and finally (v) which is the beta diversity process that most influence plant community structure in the moist temperate zone.

Materials and methods

Study area

The present study was carried out in the moist temperate zone of Manoor valley (District Mansehra, Khyber Pakhtunkhwa), which is a mountainous valley (34.68165 N to 34.83869 N latitude, and 73.57520 E to 73.73182 E longitude Fig 1) in the Northwestern Himalayan belt of Pakistan [35-39] along an altitudinal range of 1932–3168 m.a.s.l. Monsoon winds are the main source of precipitation as well as the primary force of controlling erosion, topography, climate and vegetation of the Northwestern Himalaya [1].

Fig 1

Map of the study area showing Pakistan, Khyber Pakhtunkhwa (KP) province, and sampling sites for data collection.

Points in the right figure represent stands of the four communities identified in the moist temperate zone, Northwestern Himalaya, Pakistan. CPI: Cedrus deodara-Pinus wallichiana-Isodon rugosus, IHC: Indigofera heterantha- Heracleum candicans-Cynodon dactylon, PCP: Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana, and VIP: Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana.

Ethics approval and consent to participate

This study was approved by the Board of Study (BoS), Committee, Department of Botany and Advanced Study Research Board (ASRB) of Hazara University, Mansehra 21300, KP, Pakistan.

Vegetation sampling and plant identification

In different growing seasons (from March to October), we evaluated 30 sampling sites per year, during four consecutive years, from 2015 to 2018. The line transect method (50 meters) we used for quantitative samplings [40-45], but we never repeated the same transects over years. The surveyed study area was subdivided into 30 stands and three points randomly selected were sampled within each sampling site along 50 meters transect (total = 90 transects). The distance between the sampling sites was kept at 200 meters, while the distance between the transects was kept at 100 meters. The individuals of plant species falling precisely on the line were recorded. The data from each sampling site was calculated using phytosociological characters (i.e., density, frequency, cover and their relative values, as well as importance value) [46-48]. The IV was further used to rank each plant species and species with the highest IV were considered as the dominant species [46, 47]. Similarly, each plant community was named based on three dominant species [49-52]. The slope angle, aspect and exposure were recorded using clinometer; and altitude, longitude and latitude were measured by geographical positioning system (GPS). Plants species collection, labelling, pressing and other herbarium work methodology was adopted following Ijaz [53], Ijaz et al. [54], Amjad et al. [55] and Stefanaki et al. [56]. Identification was done with the aid of Flora of Pakistan [57-59] and submitted to the Herbarium of Hazara University, Mansehra (Pakistan).

Environmental gradients

Soil samples weighting 200 grams were collected (15-30cm depth) randomly from each transect of sampled vegetation site [60, 61]. The replicated samples of each sampling site were thoroughly mixed to form a composite sample [62], which was placed in a sterile polythene bag and labelled accordingly. Raw materials such as rocks, and stones were sieved out and the samples were then shade dried. Each dried soil sample was processed for physicochemical tests [62, 63] to determine soil texture (i.e., clay, sand, silt, loam), pH [64], electrical conductivity (EC) [65], organic matter (OM) [66], nitrogen (N), phosphorus (P), potassium (K) and calcium carbonate (CaCO3) levels [60, 61, 67]. Other climatic variables such as barometric pressure, dew point, humidity, heat index, temperature, wet bulb (relative humidity and ambient air temperature) and wind speed were also determined using a small remote weather station (Kestrel weather tracker 4000) to record the data at each transect and then average values were calculated at sampling site level [19].

Statistical analyses

The recorded data of vegetation, edaphic, and other environmental variables of sampling sites were compiled in order to determine relationship among them [68, 69]. Matrixes of IV data of all the recorded plant species (244 species) from 30 sampling sites were used in the analyses. Analyses were conducted on PC-ORD [70] and R software 4.0.0 [71]. Packages used in R software are mentioned in each analysis. A georeferenced map was prepared to show the distribution pattern of distinct plant communities (Fig 1).

Sampling effort and cluster analyses

Species area curves (SAC) were used to check the efficiency of the sampling effort, where plant abundance data with Sørensen distance values were used to create species area curves [72]. For classification of the recorded plant species (244) and 30 sampling sites into different plant communities, we used three different cluster analyses: Two-way Indicator Species Analysis (TWINSPAN), and Cluster Analysis (CA). We identified and classified plant species and stands (sampling sites) into major plant communities [73], as well as assess the effects of various factors (such as environmental variables) on such communities by processing clustering method using TWINSPAN [19, 74].

Plant communities and associated environmental variables

Both species and stands (sampling sites) were constrained in relation with the environmental gradients [75, 76], which were divided into geographic, slope aspect, edaphic and climatic gradients. We used non-metric multidimensional scaling (NMDS) and Principal Component Analysis (PCA) ordination biplot to determine the relationship between vegetation communities and environmental gradients using the “vegan” package. In NMDS and PCA, arrows represent the environmental gradients, in which the length shows the strength of the gradient, and the direction represent the degree of correlation. The direction of gradients on the same axis reveals a positive correlation.

In addition, we performed canonical correspondence analysis (CCA) and variation partitioning tests (partial CCA) [77] to observe how explanatory variables (climatic, edaphic, geographic, and slope) drive the plant species distribution. First we built the best model with the lowest number of variables (those that most explain variance), through the step function with permutation using the “stats” package [71]. Next, we also evaluated multicollinearity between variables of the final model using Variance Inflation Factor (VIF), and we removed any variable with VIF>10, one at a time. Finally, with the final model, we carried out CCA and partial CCA to check how much each group of variables (geographic, edaphic, climatic, slope) explain in our model [77]. For these analysis, we used the “vegan” package [78].

Variation of plant species composition among plant communities

To compare whether there is difference in species composition between plant communities, we also used NMDS followed by a Permutational Multivariate Analysis of Variance (PERMANOVA) with Euclidean distance and 999 permutations. After PERMANOVA, we conducted pairwise comparisons between communities with corrections for multiple testing also using Euclidean distance and 999 permutations. We used false discovery rate (FDR) as p-value adjustment method. PERMANOVA and pairwise comparisons were conducted with “RVAideMemoire” package [79]. To observe the contribution of each plant species to overall dissimilarities, we used a similarity percentage analysis based on the decomposition of Bray-Curtis dissimilarity using the package “vegan” [78].

Variation of diversity indices among plant communities

To compare the diversity indices evaluated (species richness, Shannon, Simpson, and Pielou) among the four communities, we also conducted a GLM with Gaussian error distribution, except for species richness, in which we used Poisson distribution followed by Likelihood Ratio test. Pairwise comparisons were conducted with estimated marginal means using the package ‘emmeans’ [80].

Beta diversity

To evaluate which is the beta diversity process that most influence plant community structure in the moist temperate zone, we decomposed the Sørensen dissimilarity index (βsor), a measure of overall species replacement into two additive components: the spatial turnover (Simpson pairwise dissimilarity, βsim) and nestedness-resultant components (nestedness-fraction of Sorensen pairwise dissimilarity, βsne) [81-83]. Dissimilarity analysis was conducted in the package “betapart” [84].

Results

Sampling effort and plant communities

In total, 244 plant species belonging to 194 genera and 74 families (Table 1) were recorded in the moist temperate forests of the Manoor valley, Northwestern Himalaya, Pakistan. The moist temperate forests ranged from 1932.3m to 3168m. SAC analysis showed that the maximum number of plant species appeared up to stand 26 and the species curve became parallel after it, as no new species were recorded further (Fig 2).

Table 1

Species composition and IV according to each sampling site and community found along four years of collection in moist temperate forests of Manoor valley, Northwestern Himalaya, Pakistan.

Plant Species	Abbreviations	Family name	Plant Communities
			IHC	VIP	CPI	PCP
Acer caesium Wall. ex Brandis	Ace cae	Sapindaceae	0.00	0.00	0.26	0.56
Achyranthes aspera L.	Ach asp	Amaranthaceae	0.00	0.00	0.00	0.34
Achyranthes bidentata Blume	Ach bid	Amaranthaceae	0.00	0.00	0.00	0.32
Achillea millefolium L.	Ach mil	Asteraceae	0.87	0.50	0.00	0.00
Adiantum capillus-veneris L.	Adi cap-ven	Adiantaceae	0.00	0.00	0.67	1.75
Adiantum indicum J. Ghatak	Adi ind	Adiantaceae	0.00	0.00	0.96	1.84
Adiantum venustum D. Don	Adi ven	Adiantaceae	0.00	0.00	0.49	1.30
Aegopodium burttii Nasir	Aeg bur	Apiaceae	0.00	0.00	0.15	0.34
Ainsliaea aptera DC.	Ain apt	Asteraceae	0.00	0.00	0.00	0.26
Ajuga integrifolia Buch.- Ham.	Aju int	Lamiaceae	0.00	0.00	0.00	0.27
Alchemilla cashmeriana Rothum.	Alc cas	Rosaceae	0.43	0.00	0.51	0.12
Alcea rosea L.	Alc ros	Malvaceae	0.05	0.00	0.15	0.00
Alnus nitida (Spach) Endl.	Aln nit	Betulaceae	0.45	0.00	0.00	0.29
Amaranthus viridis L.	Ama vir	Amaranthaceae	0.00	0.00	0.00	0.24
Anagallis arvensis L.	Ana arv	Primulaceae	0.00	0.00	0.08	1.15
Anaphalis busua (Buch.-Ham.) DC.	Ana bus	Asteraceae	0.00	0.00	0.20	0.07
Androsace hazarica R.R. Stewart ex Y. Nasir	And haz	Primulaceae	0.00	0.00	0.30	0.39
Androsace rotundifolia Hardw.	And rot	Primulaceae	0.00	0.00	0.06	0.54
Arisaema flavum (Forssk.) Schott	Ari fla	Araceae	0.00	0.00	0.00	0.91
Arisaema jacquemontii Blume	Ari jac	Araceae	0.00	4.24	0.00	0.91
Artemisia absinthium L.	Art abs	Asteraceae	0.36	0.13	0.96	2.25
Asplenium adiantum-nigrum L.	Asp adi-nig	Adiantaceae	0.00	0.00	0.20	0.41
Asparagus fiicinus Buch.-Ham. ex D. Don	Asp fii	Asparagaceae	0.00	0.00	0.00	0.16
Avena sativa L.	Ave sat	Poaceae	0.04	0.09	0.00	0.00
Bauhinia variegata L.	Bau var	Caesalpiniaceae	0.05	0.00	0.00	0.00
Bergenia ciliata (Haw.) Sternb.	Ber cil	Saxifragaceae	0.00	0.00	0.09	0.12
Berberis lycium Royle	Ber lyc	Berberidaceae	0.00	1.42	1.55	0.49
Berberis parkeriana C.K. Schneid.	Ber pac	Berberidaceae	0.00	0.00	0.00	0.24
Bistorta amplexicaulis (D. Don) Greene	Bis amp	Polygonaceae	5.17	1.64	0.00	0.00
Brassica compestris L.	Bra com	Brassicaceae	0.52	0.00	0.00	0.00
Bromus diandrus Roth.	Bro dia	Poaceae	2.00	1.64	0.39	0.19
Bromus secalinus L.	Bro sec	Poaceae	1.83	1.49	0.67	0.59
Bromus tectorum L.	Bro tec	Poaceae	2.20	1.29	0.00	0.06
Bupleurum longicaule Wall. ex DC.	Bup lon	Apiaceae	0.00	0.00	0.10	0.32
Bupleurum nigrescens E. Nasir	Bup nig	Apiaceae	0.51	1.22	0.15	1.48
Caltha palustris var. alba (Cambess) Hook.f. & Thomson	Cal pal	Ranunculaceae	0.00	0.00	0.04	0.46
Calamintha umbrosa (M. Bieb.) Hedge	Cal umb	Lamiaceae	1.11	1.03	1.08	0.61
Campylotropis meeboldii (Schindl.) Schindl.	Cam mee	Papilionaceae	0.00	0.00	0.32	0.04
Cannabis sativa L.	Can sat	Cannabaceae	0.00	0.00	0.00	0.07
Capsella bursa-pastoris (L.) Medik.	Cap bur-pas	Brassicaceae	0.18	0.97	0.53	1.47
Castanea sativa Mill.	Cas sat	Fagaceae	0.00	0.00	0.12	0.14
Cedrus deodara (Roxb. ex Lamb.) G. Don	Ced deo	Pinaceae	0.00	5.16	22.50	16.07
Celosia argentea L.	Cel arg	Amaranthaceae	0.61	0.00	0.00	0.00
Chenopodium album L.	Che alb	Chenopodiaceae	0.75	0.41	0.00	0.24
Chrysanthemum indicum L.	Chr ind	Asteraceae	0.00	0.00	0.00	0.64
Cichorium intybus L.	Cic int	Asteraceae	0.00	0.00	0.23	0.11
Circaea alpina L.	Cir alp	Onagraceae	0.00	0.00	0.00	0.40
Cirsium arvense (L.) Scop.	Cir arv	Asteraceae	0.79	0.79	0.24	1.55
Circaea cordata Royle	Cir cor	Onagraceae	0.00	0.00	0.21	0.05
Cirsium falconeri (Hook.f.) Petr.	Cir fal	Asteraceae	0.00	0.00	0.00	0.07
Clematis grata Wall.	Cle gra	Ranunculaceae	0.00	0.00	0.96	2.55
Clinopodium vulgare L.	Cli vul	Lamiaceae	1.54	1.10	1.67	3.68
Colebrookea oppositifolia Sm.	Col opp	Lamiaceae	0.00	0.00	0.00	0.06
Commelina benghalensis L.	Com ben	Commelinaceae	0.00	0.65	0.00	0.13
Convolvulus arvensis L.	Con arv	Convolvulaceae	0.00	0.00	0.31	1.14
Conyza japonica (Thunb.) Less. ex Less.	Con jap	Asteraceae	0.00	0.18	0.09	0.40
Corydalis carinata Lidén and Z.Y.Su	Cor car	Papaveraceae	0.10	0.00	0.16	0.07
Corylus colurna L.	Cor col	Betulaceae	0.00	0.00	0.15	0.26
Corydalis cornuta Royle [Syn. Corydalis stewartii Fedde]	Cor cor	Papaveraceae	0.07	0.00	0.46	0.51
Cornus macrophylla Wall.	Cor mac	Cornaceae	0.05	0.00	0.00	0.23
Cornus oblonga Wall.	Cor obl	Cornaceae	0.00	0.00	0.00	0.19
Corydalis virginea Lidén and Z.Y.Su	Cor vir	Papaveraceae	0.00	0.00	0.13	0.18
Cotoneaster acuminatus Wall. ex Lindl.	Cot acu	Rosaceae	0.06	0.00	0.00	0.00
Cuscuta reflexa Roxb.	Cus ref	Cuscutaceae	0.00	0.00	0.08	0.34
Cyanthillium cinereum (L.)H.Rob.	Cya cin	Asteraceae	0.00	0.00	0.08	0.10
Cynoglossum apenninum L.	Cyn ape	Boraginaceae	0.08	0.18	0.00	0.00
Cynodon dactylon (L.) Pers.	Cyn dac	Poaceae	4.59	2.28	4.13	5.28
Cynoglossum glochidiatum Wall. ex Benth.	Cyn glo	Boraginaceae	1.58	0.25	0.33	1.52
Cynoglossum microglochin Benth.	Cyn mic	Boraginaceae	0.14	0.52	0.00	0.00
Cyperus odoratus L.	Cyp odo	Cyperaceae	0.15	0.89	0.08	0.20
Cyperus rotundus L.	Cyp rot	Cyperaceae	0.59	1.09	0.91	0.53
Dactylis glomerata L.	Dac glo	Poaceae	0.41	1.40	0.47	0.40
Daphne papyracea Wall. ex G. Don	Dap pap	Thymelaeaceae	0.00	0.00	0.06	0.25
Desmodium elegans DC.	Des ele	Papilionaceae	1.39	0.00	0.31	0.12
Dicliptera bupleuroides Nees	Dic bup	Acanthaceae	0.00	0.00	0.10	0.93
Dioscorea deltoidea Wall. ex Griseb.	Dio del	Dioscoreaceae	0.13	0.00	0.24	0.03
Diospyros lotus L.	Dio lot	Ebenaceae	0.00	0.00	0.16	0.00
Dipsacus inermis Wall. in Roxb.	Dip ine	Dipsacaceae	0.22	0.67	0.09	0.13
Dryopteris wallichiana (Spreng.) Hyl.	Dry wal	Dryopteridaceae	0.00	2.76	0.52	1.89
Duchesnea indica (Andx) Fake.	Duc ind	Rosaceae	0.00	0.00	0.00	0.54
Dysphania ambrosioides (L.) Mosyakin & Clemants	Dys amb	Chenopodiaceae	0.00	0.40	0.00	0.37
Elaeagnus umbellata Thunb.	Ela umb	Eleagnaceae	0.00	0.21	0.31	0.03
Elsholtzia ciliata (Thunb.) Hyl.	Els cil	Lamiaceae	0.00	0.16	0.18	0.04
Epilobium hirsutum L.	Epi hir	Onagraceae	0.04	0.00	0.17	0.12
Epilobium latifolium L.	Epi lat	Onagraceae	0.00	0.28	0.19	0.07
Epimedium elatum C.Morrenand Decne.	Epi ela	Berberidaceae	0.03	0.18	0.14	0.07
Equisetum arvense L.	Equ arv	Equisetaceae	0.40	0.00	0.00	0.00
Erigeron canadensis L.	Eri can	Asteraceae	0.87	0.00	0.55	0.39
Erysimum melicentae Dunn.	Ery mel	Brassicaceae	0.15	0.12	0.00	0.00
Euphorbia helioscopia L.	Eup hel	Euphorbiaceae	0.11	0.00	0.00	0.05
Euphrasia himalayica Wetts.	Eup him	Orobanchaceae	2.70	0.58	0.00	0.00
Euphorbia hirta L.	Eup hir	Euphorbiaceae	0.00	0.00	0.09	0.25
Euphorbia prostrata Ait.	Eup pro	Euphorbiaceae	0.00	0.00	0.00	0.07
Euphorbia serpens Kunth	Eup ser	Euphorbiaceae	0.00	0.00	0.08	0.30
Fagopyrum tataricum (L.) Gaertn.	Fag tat	Polygonaceae	0.00	0.21	0.00	0.06
Filipendula vestita (Wall. ex G. Don.) Maxim.	Fil ves	Rosaceae	0.58	2.22	1.26	0.32
Foeniculum vulgare Mill.	Foe vul	Apiaceae	0.70	1.00	0.00	0.68
Fragaria nubicola (Hook. f.) Lindl. ex Lacaita	Fra nub	Rosaceae	0.15	1.95	0.36	4.11
Fumaria indica (Hausskn) Pugsley	Fum ind	Fumaricaceae	0.00	0.00	0.00	0.13
Galium aparine L.	Gal apa	Rubiaceae	0.00	0.00	0.00	0.01
Galium asparagifolium Boiss. & Heldr.	Gal asp	Rubiaceae	0.00	0.00	0.04	0.02
Galium elagans Wall.	Gal ela	Rubiaceae	0.00	0.00	0.05	0.13
Galinsoga parviflora Cav.	Gal par	Asteraceae	0.00	0.00	0.04	0.02
Gentianodes clarkei (Kusn.) Omer	Gen cla	Gentianaceae	0.00	0.00	0.00	0.10
Gerbera gossypina (Royle) Beauverd	Ger gos	Asteraceae	0.00	0.00	0.00	0.21
Geranium nepalense Sweet.	Ger nep	Geraniaceae	1.83	0.28	1.04	2.16
Geranium wallichianum D. Don ex Sweet	Ger wal	Geraniaceae	2.94	1.04	0.66	3.18
Gymnosporia royleana Wall. ex M.A.Lawson	Gym roy	Celastraceae	0.00	0.46	0.00	0.00
Hackelia uncinata (Benth.) C.E.C. Fisch.	Hac unc	Boraginaceae	0.33	0.00	0.00	0.00
Hedera nepalensis K. Koch	Hed nep	Araliaceae	0.00	0.00	0.00	2.61
Helianthus annuus L.	Hel ann	Asteraceae	0.14	0.00	0.00	0.00
Heracleum candicans Wall. ex DC.	Her can	Apiaceae	5.89	1.79	0.00	0.00
Hyoscyamus niger L.	Hyo nig	Solanaceae	0.00	0.00	0.07	0.00
Hypericum perforatum L.	Hyp perf	Clusiaceae	0.00	0.00	0.06	0.22
Impatiens bicolor Royle.	Imp bic	Balsaminaceae	0.12	0.29	2.10	3.56
Impatiens brachycentra Kar. & Kir.	Imp bra	Balsaminaceae	3.98	0.16	0.00	0.00
Indigofera australis Willd.	Ind aus	Papilionaceae	1.15	1.27	0.00	0.00
Indigofera hebepetala Baker	Ind heb	Papilionaceae	0.78	1.83	0.00	0.00
Indigofera heterantha Brandis	Ind het	Papilionaceae	5.08	23.51	2.33	2.83
Inula cuspidata (Wall. ex DC.) C.B. Clarke	Inu cus	Asteraceae	0.00	0.00	0.00	0.24
Inula falconeri Hook.f.	Inu fal	Asteraceae	0.00	0.00	0.05	0.10
Ipomoea nil (L.) Roth	Ipo nil	Convolvulaceae	0.00	0.00	0.26	0.50
Isodon rugosus (Wall. ex Benth.) Codd	Iso rug	Lamiaceae	0.00	0.00	5.77	3.32
Juglans regia L.	Jug reg	Juglandaceae	4.41	0.56	0.00	0.00
Lactuca tatarica (L.) C.A. Mey	Lac tat	Asteraceae	0.35	0.00	0.48	0.41
Lamium album L.	Lam alb	Lamiaceae	0.00	0.00	0.10	0.06
Lamium amplexicaule L.	Lam amp	Lamiaceae	1.23	0.25	0.53	0.88
Lathyrus aphaca L.	Lat aph	Papilionaceae	4.17	0.70	1.65	1.61
Lathyrus odoratus L.	Lat odo	Papilionaceae	0.30	0.74	0.00	0.00
Lathyrus sativa L.	Lat sat	Papilionaceae	0.34	0.90	0.00	0.00
Launaea procumbens (Roxb.) Ramayya and Rajagopal	Lau pro	Asteraceae	0.00	0.00	0.91	0.40
Lavatera cachemiriana Camb. in Jacq.	Lav cac	Malvaceae	0.04	0.00	0.00	0.00
Leptodermis virgata Edgew. ex Hook.F.	Lep vir	Rubiaceae	1.36	0.63	1.24	1.67
Ligularia amplexicaulis DC.	Lig amp	Asteraceae	0.00	0.34	0.00	0.11
Lindelofia sp.	Lin sp	Boraginaceae	0.05	0.00	0.00	0.00
Lomatogonium spathulatum (A. Kern.) Fernald	Lom spa	Gentianaceae	0.00	0.00	0.00	0.22
Lonicera caerulea L.	Lon cae	Caprifoliaceae	0.05	0.28	0.07	0.17
Lotus corniculatus L.	Lot cor	Papilionaceae	0.00	0.14	0.03	0.07
Luffa sp.	Luf sp	Cucurbitaceae	0.00	0.00	0.39	0.37
Lyonia ovalifolia (Wall.) Drude	Lyo ova	Ericaceae	0.00	0.00	0.00	0.22
Malus domestica Borkh.	Mal dom	Rosaceae	0.27	0.35	0.05	0.22
Medicago sativa L.	Med sat	Papilionaceae	0.86	0.27	1.04	1.95
Mentha piperita L.	Men pip	Lamiaceae	0.00	0.00	0.00	0.64
Mentha royleana Wall. ex Benth.	Men roy	Lamiaceae	0.00	0.00	0.00	0.63
Micromeria biflora (Ham.) Bth.	Mic bif	Lamiaceae	0.00	0.00	2.17	0.88
Minuartia kashmirica (Edgew.) Mattf.	Min kas	Caryophyllaceae	0.00	0.00	0.00	0.12
Nepeta graciliflora Benth.	Nep gra	Lamiaceae	1.90	0.87	0.00	0.00
Nepeta laevigata (D. Don) Hand.- Mazz	Nep lae	Lamiaceae	1.00	2.07	0.00	0.00
Oenothera rosea L. Her ex Aiton	Oen ros	Onagraceae	1.36	0.46	0.26	0.44
Olea ferruginea Wall. ex Aitch.	Ole fer	Oleaceae	0.22	0.00	0.00	0.00
Onopordum acanthium L.	Ono aca	Asteraceae	0.00	1.57	0.00	0.00
Onychium contiguum C. Hope	Ony con	Pteridaceae	0.00	0.00	0.00	0.37
Origanum majorana L.	Ori maj	Lamiaceae	0.00	0.00	0.07	0.42
Origanum vulgare L.	Ori vul	Lamiaceae	0.00	0.00	0.74	1.06
Oxalis corniculata L.	Oxa cor	Oxalidaceae	1.28	0.24	1.89	4.94
Oxyria digyna (L.) Hill	Oxy dig	Polygonaceae	0.00	0.00	0.36	1.30
Parrotiopsis jacquemontiana (Decne.) Rehder	Par jac	Hamamelidaceae	0.43	0.00	4.69	10.33
Paspalum dilatatun Poir.	Pas dil	Poaceae	0.40	0.00	0.09	0.00
Pedicularis punctata Decne	Ped pun	Orobanchaceae	2.09	1.10	0.00	0.00
Pennisetum orientale Rich.	Pen ori	Poaceae	2.78	2.83	0.69	0.31
Periploca aphylla Decne.	Per aph	Asclepiadaceae	0.04	0.15	0.13	0.11
Persicaria capitata (Buch.-Ham. ex D.Don) H.Gross	Per cap	Polygonaceae	0.00	0.81	0.31	1.73
Pilea umbrosa Blume	Pil umb	Urticaceae	0.00	0.00	0.27	0.21
Pimpinella stewartii (Dunn) Nasir	Pim ste	Apiaceae	2.11	2.72	0.00	0.40
Pinus wallichiana A.B. Jacks	Pin wal	Pinaceae	0.00	5.30	20.28	16.24
Piptatherum aequiglume (Duthie ex Hook.f.) Roshev.	Pip aeq	Poaceae	0.13	0.00	0.00	0.00
Plantago lanceolata L.	Pla lan	Plantaginaceae	0.85	2.76	0.07	0.36
Plantago major L.	Pla maj	Plantaginaceae	2.06	4.64	0.71	0.79
Pleurospermum stellatum (D. Don) Benth. ex C.B. Clarke	Ple ste	Apiaceae	0.00	0.00	0.21	0.00
Pleurospermum stylosum C.B. Clarke	Ple sty	Apiaceae	0.00	0.00	0.24	0.04
Poa alpina L.	Poa alp	Poaceae	0.00	0.00	0.30	0.00
Poa annua L.	Poa ann	Poaceae	0.45	1.99	0.75	0.00
Poa infirma Kunth	Poa inf	Poaceae	3.08	3.03	1.11	0.03
Polygonum plebeium R.Br.	Pol ple	Convallariaceae	0.49	1.51	0.62	0.60
Polygonatum sp.	Pol sp.	Convallariaceae	0.00	0.00	0.00	0.13
Portulaca oleracea L.	Por ole	Portulacaceae	0.07	0.00	0.00	0.00
Potentilla anserina L.	Pot ans	Rosaceae	0.00	0.67	0.00	0.26
Potentilla nepalensis Hook.	Pot nep	Rosaceae	2.15	1.59	0.00	0.12
Prunus armeniaca L.	Pru arm	Rosaceae	0.07	0.00	0.00	0.00
Prunus cornuta (Wall.ex Royle) Steud	Pru cor	Rosaceae	0.25	0.00	0.00	0.00
Prunus domestica L.	Pru dom	Rosaceae	0.26	0.00	0.00	0.00
Prunella vulgaris L.	Pru vul	Lamiaceae	3.11	4.20	0.00	0.00
Pteridium aquilinum (L.) Kuhn	Pte aqu	Pteridaceae	0.29	0.00	0.00	0.00
Pteracanthus urticifolius (Wall. ex Kuntze) Bremek.	Pte urt	Verbenaceae	0.00	0.00	0.07	0.05
Pteris vittata L.	Pte vit	Pteridaceae	1.34	0.00	0.51	0.49
Pyrus pashia Buch.-Ham. ex D.Don	Pyr pas	Rosaceae	0.69	0.00	0.00	0.00
Ranunculus laetus Wall. ex Hook. f. and J.W. Thompson	Ran lae	Ranunculaceae	0.00	0.00	0.00	0.07
Ranunculus muricatus L.	Ran mur	Ranunculaceae	1.61	0.94	0.00	0.23
Reinwardtia trigyna Planch.	Rei tri	Linaceae	0.00	0.00	0.04	0.04
Rhamnus purpurea Edgew.	Rha pur	Rhamnaceae	0.00	0.00	0.09	0.16
Rhynchosia pseudo-cajan Cambess.	Rhy pse	Papilionaceae	0.23	0.00	0.00	0.06
Rosa brunonii Lindl.	Ros bru	Rosaceae	0.00	0.00	0.15	0.07
Rosa webbiana Wall. ex. Royle	Ros web	Rosaceae	0.00	0.00	0.04	0.00
Rubus fruticosus agg.	Rub fru	Rosaceae	0.00	0.00	0.24	0.16
Rubus sanctus Schreber	Rub san	Rosaceae	0.00	0.00	0.00	0.12
Rumex dentatus L.	Rum den	Polygonaceae	0.47	0.00	0.00	0.00
Rumex nepalensis Sprenge	Rum nep	Polygonaceae	0.42	0.77	0.00	0.02
Rydingia limbata (Benth.) Scheen & V.A. Albert [Syn. Otostegia limbata (Benth.) Boiss]	Ryd lim	Lamiaceae	0.00	1.24	0.28	0.00
Saccharum spontaneum L.	Sac spo	Poaceae	0.00	0.00	0.00	0.28
Salvia lanata Roxb.	Sal lan	Lamiaceae	0.15	0.00	0.00	0.00
Salvia nubicola Wall. ex Sweet	Sal nub	Lamiaceae	0.12	0.00	0.00	0.00
Sanicula elata Buch.-Ham. ex D.Don	San ela	Apiaceae	0.00	0.00	0.10	0.25
Sambucus wightiana Wall. ex Wight and Arn	Sam wig	Sambucaceae	1.34	0.00	0.54	0.00
Sarcococca saligna Müll.Arg.	Sar sal	Buxaceae	0.00	0.00	0.00	1.54
Sauromatum venosum (Dryand. ex Aiton) Kunth	Sau ven	Araceae	0.00	0.00	0.00	0.11
Schismus arabicus Nees.	Sch ara	Poaceae	0.00	0.00	0.46	1.24
Senecio analogous DC.	Sen ana	Asteraceae	0.00	0.00	0.05	0.03
Senecio chrysanthemoides DC.	Sen chr	Asteraceae	0.00	0.00	0.15	0.57
Seseli libanotis (L.) W.D.J. Koch .	Ses lib	Apiaceae	0.00	0.00	0.04	0.31
Sida cordata (Burm.f.) Borss..	Sid cor	Malvaceae	0.16	0.00	0.05	0.07
Silene conoidea L.	Sil con	Caryophyllaceae	0.37	0.00	0.00	0.05
Silene vulgaris (Moench) Garcke	Sil vul	Caryophyllaceae	0.37	0.23	0.00	0.17
Sisymbrium irio L.	Sis iri	Brassicaceae	0.03	0.32	0.05	0.00
Smilax glaucophylla Koltzsch	Smi glau	Smilacaceae	0.00	0.00	0.00	0.01
Solena amplexicaulis (Lam.) Gandhi	Sol amp	Cucurbitaceae	0.00	0.00	0.05	0.04
Sonchus asper (L.) Hill	Son asp	Asteraceae	0.00	0.00	0.00	0.00
Sorghum halepense (L.) Pers.	Sor hal	Poaceae	0.00	0.00	0.00	0.47
Sorbus tomentosa Hedl.	Sor tom	Rosaceae	0.00	0.00	0.00	0.06
Sorbaria tomentosa (Lindl.) Rehder	Sorb tom	Rosaceae	0.57	3.04	0.00	0.12
Spiraea affinis R.Parker	Spi aff	Rosaceae	0.00	0.00	0.00	0.12
Spiranthes sinensis (Pers.) Ames	Spi sin	Orchidaceae	0.00	0.00	0.00	0.02
Spiraea vaccinifolia D. Don	Spi vac	Rosaceae	0.00	0.00	0.09	0.02
Sporobolus diandrus (Retz.) P.Beauv.	Spo dia	Poaceae	2.48	0.00	0.75	0.26
Stellaria media (L.) Vill.	Ste med	Caryophyllaceae	0.11	0.00	0.00	0.18
Stellaria monosperma Buch.-Ham. ex D. Don	Ste mon	Caryophyllaceae	0.00	0.00	0.00	0.08
Swertia cordata (Wall. ex G. Don) C.B. Clarke	Swe cor	Gentianaceae	0.00	0.00	0.00	0.03
Tagetes minuta L.	Tag min	Asteraceae	0.00	0.00	0.70	2.22
Taraxacum officinale aggr. F.H. Wigg.	Tar off	Asteraceae	0.14	0.66	0.17	0.56
Thalictrum pedunculatum Edgew.	Tha ped	Ranunculaceae	0.00	0.00	0.13	0.05
Torilis japonica (Houtt.) DC.	Tor jap	Apiaceae	0.00	0.00	0.05	0.08
Trachyspermum amii (L.) Sprague	Tra ami	Apiaceae	0.00	0.00	0.05	0.18
Trifolium repens L.	Tri rep	Papilionaceae	1.55	0.31	0.14	0.69
Urochloa panicoides P. Beauv.	Uro pan	Poaceae	0.61	0.43	0.30	0.00
Urtica dioica L.	Urt dio	Urticaceae	1.52	0.00	1.21	0.53
Valeriana jatamansi Jones	Val jat	Caprifoliaceae	0.00	1.10	0.00	0.00
Verbascum thapsus L.	Ver tha	Scrophulariaceae	1.34	0.00	0.00	0.73
Veronica anagallis L.	Ver ana	Plantaginaceae	0.00	2.12	0.23	0.24
Viburnum grandiflorum Wall. ex DC.	Vib gra	Adoxaceae	0.00	24.43	0.00	1.45
Vicia sativa L.	Vic sat	Papilionaceae	0.36	0.99	0.17	0.00
Vincetoxicum petrense (Hemsl. & Lace) Rech. f.	Vinc pet	Asclepiadaceae	0.00	0.00	0.09	0.13
Viola odorata L.	Vio odo	Violaceae	0.42	0.70	0.15	0.51
Viola serpens Wall. Ex Ging	Vio ser	Violaceae	0.19	0.92	0.38	0.11
Vitex negundo L.	Vit neg	Vitaceae	0.00	0.80	0.00	0.12
Wulfenia amherstiana (Benth.) D.Y. Hong	Wul amh	Plantaginaceae	0.00	0.00	0.04	0.34

IHC: Indigofera heterantha- Heracleum candicans-Cynodon dactylon, VIP: Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI: Cedrus deodara-Pinus wallichiana-Isodon rugosus and PCP: Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana.

Fig 2

The Species-Area Curve (SAC) of 244 plant species distributed among 30 sampling sites.

The SAC was used to check the adequacy level of the sampling effort, where plant abundance data with Sørensen distance values were used to create the SAC.

Based on the TWINSPAN analysis (high cluster heterogeneity value–Lambda = 0.4045), we identified four different major plant communities (Fig 3), which were composed of different indicator species (IHC: Indigofera heterantha-Heracleum candicans-Cynodon dactylon; VIP: Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI: Cedrus deodara-Pinus wallichiana-Isodon rugosus, and PCP: Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana). The IHC community was primarily found in the lower mountainous ranges (1932.3–2338.4 m.a.s.l), where the dominating flora was a combination of shrub and herb species owing to the existence of a substantial herbaceous layer of Cynodon dactylon, which carpeted the landscape alongside Indigofera heterantha patches (Table 1). The VIP community was recognized mainly in the foothills and adjacent plains (2390.5–2437.8 m.a.s.l), where the predominant vegetation was shrubland with abundant patches of Viburnum grandiflorum and Indigofera heterantha, accompanied by the co-dominant Pinus wallichiana (tree species). Nonetheless, the other two plant communities, i.e., PCP and CPI, were significantly dominated by the tree species layer at the middle (2292–2947 m.a.s.l) and higher (2048.2–3168 m.a.s.l) altitudinal ranges alongside shrubby associates (Table 1).

Fig 3

Clustering analysis indicates the classification of 30 stands comprised of 244 plant species into four different plant communities.

IHC (red triangle): Indigofera heterantha-Heracleum candicans-Cynodon dactylon, VIP (blue circle): Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI (green square): Cedrus deodara-Pinus wallichiana-Isodon rugosus and PCP (yellow diamond): Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana. The plant communities are represented by the symbols in the illustration. Letters associated with numbers at the end of each branch of the dendrogram represent the stands evaluated.

Plant communities and associated environmental variables

NMDS and PCA were used to show the relationship between the plant communities of moist temperate forests and environmental variables (Fig 4A–4D) and PCA (Fig 4E). The ecological and environmental variables like geographic, slope, edaphic, and climatic variables were used to correlate communtities (Table 2). The most representative environmental variables that drive the community structure and diversity were altitude, slope angle and aspects (SE, NE, ES, WN), potassium (K), pH, organic matter, loam, silt, sand, clay, temperature, heat index, wind speed and barometric pressure. Environmental variables classify 30 sampling sites into four major plant communities, as shown by the cluster analysis (Fig 3). In constrained PCA ordination, the PC1 axis accounted for the most explanatory variance (20%), while the PC2 axis accounted for the least (14.2%). The profound influence of the environmental variables was revealed by classifying the moist temperate forests vegetation into four communities (Fig 4E), as also shown by CA, TWCA and NMDS.

Fig 4

Non-Multidimensional Scaling (NMDS) between plant communities in moist temperate forests and environmental gradients.

a) geographic, b) slope, c) edaphic and d) climatic. e). Principle Component Analysis (PCA) illustrating the relationship between various measured environmental variables and communities indicated by coloured circles. Large coloured circles show the centroid of each community. NMDS-PCA: Species contribution analysis for community ordination in NMDS is depicted in Table 2. IHC: Indigofera heterantha- Heracleum candicans-Cynodon dactylon, VIP: Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI: Cedrus deodara-Pinus wallichiana-Isodon rugosus and PCP: Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana.

Table 2

Mean (SD) of environmental variables and plant species richness per community found along four years of collection in moist temperate forests of Manoor valley, Northwestern Himalaya.

Communities	IHC	VIP	CPI	PCP
Species Richness	51(8)	53(10)	40(12)	68(13)
Altitude	2251.7(132.7)	2413(19.4)	2588.8(408.8)	2609(167.6)
Latitude	34.7(0)	34.8(0)	34.7(0)	34.7(0)
Longitude	73.6(0)	73.6(0)	73.6(0)	73.6(0)
Temp	23.4(2)	20.7(0.5)	20.8(3.2)	21(3)
Humidity	56.8(6)	54.6(3.7)	54.7(3.6)	56.7(3.7)
Heat index	23.9(2.2)	23.3(2.2)	22.6(2.9)	22.8(3.1)
Wind speed	1.6(0.3)	1.7(0.2)	1.7(0.5)	1.6(0.5)
Dew point	16(0.9)	16.3(0.5)	16.5(1.5)	16.6(2)
Wet bulb	18.2(1.3)	17.3(0.2)	18.2(1.5)	17.3(2.1)
Baro Press	770.2(12.8)	754.6(1.8)	750.4(31.2)	752.9(18.3)
Slope Angle	47.9(16.9)	35(4.1)	56.6(31.7)	46.7(22.2)
Slope ES	0(0)	0(0)	0.3(0.5)	0(0)
Slope N	0(0)	0(0)	0(0)	0.7(0.4)
Slope NW	0.1(0.3)	0(0)	0(0)	0(0)
Slope S	0(0)	0(0)	0.7(0.5)	0.3(0.4)
Slope SW	0.9(0.3)	0.7(0.5)	0(0)	0(0)
Slope W	0(0)	0.3(0.5)	0(0)	0(0)
pH	5.8(0.2)	5.6(0.2)	5.6(0.5)	5.4(0.5)
EC	2.4(1.1)	2(0.6)	1.7(0.8)	1.7(0.9)
OM	1.2(0.3)	1.3(0.3)	1.3(0.5)	1(0.4)
CaCO3	6.3(1.6)	9.3(1.9)	6.6(2.4)	5.6(2.4)
K	210.9(5.6)	220.3(5)	210.9(3.1)	216(5.2)
P	13.4(3.2)	11.7(0.5)	11.9(3.2)	10.5(3.8)
Sand	31.2(3.6)	27.6(2.8)	30.5(8.3)	35.2(6.9)
Silt	46.5(6.1)	46.7(3.5)	44.3(7.5)	41.7(7.6)
Clay	22.4(4.1)	25.7(1)	25.2(2.4)	23.2(4)
Loam	0.6(0.5)	0.3(0.5)	0.6(0.5)	0.5(0.5)
Sandy clay loam	0(0)	0(0)	0(0)	0.1(0.3)
Silt loam	0.4(0.5)	0.7(0.5)	0.4(0.5)	0.5(0.5)

IHC: Indigofera heterantha- Heracleum candicans-Cynodon dactylon, VIP: Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI: Cedrus deodara-Pinus wallichiana-Isodon rugosus and PCP: Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana.

The PCP community showed positive and significant correlation with northern aspect, silty loamy soil texture, humidity and altitude (Fig 4B and 4C). In contrast, CPI community showed positively significance with southern slope, wind speed, dew point and wet bulb. IHC community showed positive correlation with silty soil texture, electric conductivity and pH. And finally, VPI community revealed positively significant correlation with north-western slope, CaCO and pH. Thus, all the four communities were found separately in clumps with clear differences based on the environmental variables (Fig 4).

The CCA and variation partitioning tests showed that the total inertia results of CCA was 3.023, where our final variables (altitude, temperature, humidity, wind speed, slope angle, slope N, slope NW, slope SW, pH, EC, OM, CaCO3, K, P, sand, and loam) together explained 66.5% of variation (sum of canonical eigenvalues was 2.011). The first two canonical axes explained 37.1% of variation. CCA model was significant (χ2 = 2.010; pseudo-F value = 1.613; p<0.001; df = 16; permutations = 999). For the 16 explanatory variables, we tested simple term effects. Simple term effects showed that Altitude, Slope SW, Slope NW, Slope N, Slope Angle, K, and Humidity (decreasing order of importance) were significant (p<0.05; Table 3). The 16 explanatory variables were grouped into four classes: Climatic (Humidity, Temperature, Wind speed); Edaphic (pH, EC, OM, CaCO3, K, P, Sand, Loam); Geographic (Altitude); and Slope (Slope Angle, Slope N, Slope NW, Slope SW), and then, we performed variation partitioning tests (partial CCA) for all 15 possible classes (Table 4). Class [b] was the most explanatory variable (104.6%) followed by class [m] (7.2%) (Fig 5).

Table 3

The contribution and ranking of the studied variables in the variation partitioning tests (partial CCA model) to observe how explanatory variables (i.e., climatic, edaphic, geographic, and slope) drive the plant species distribution.

Variables	Df	ChiSquare	F	p-value
Altitude	1	0.251	3.229	0.001
Slope.SW	1	0.245	3.146	0.001
Slope.NW	1	0.178	2.297	0.001
Slope.N	1	0.214	2.748	0.002
Slope.Angle	1	0.163	2.097	0.009
K	1	0.140	1.798	0.018
Humidity	1	0.119	1.538	0.039
Wind.speed	1	0.114	1.472	0.069
CaCO3	1	0.098	1.270	0.151
Temp	1	0.081	1.049	0.376
OM	1	0.079	1.017	0.401
EC	1	0.076	0.976	0.474
P	1	0.070	0.907	0.578
Sand	1	0.069	0.895	0.590
Loam	1	0.056	0.724	0.864
pH	1	0.050	0.647	0.922
P	1	0.088	0.7295	0.820
K	1	0.075	0.6191	0.922

Significant variables are displayed in bold.

Table 4

Results of variation partitioning tests (partial CCA model) of four environmental variable groups studied (i.e., climatic, edaphic, geographic, and slope) that drives the plant species distribution.

For individual fraction letters code see Fig 5.

Individual Fraction	Adjusted R2	Variation explained (%)	% of all	Df
[a]	0.020	5.5	0.1	1
[b]	0.370	104.6	2.3	4
[c]	0.004	1.2	0.0	8
[d]	0.015	4.3	0.1	3
[e]	0.020	5.7	0.1	0
[f]	-0.091	-25.6	-0.6	0
[g]	-0.005	-1.5	0.0	0
[h]	-0.001	-0.3	0.0	0
[i]	-0.045	-12.9	-0.3	0
[j]	-0.002	-0.6	0.0	0
[k]	0.011	3.1	0.1	0
[l]	0.019	5.3	0.1	0
[m]	0.026	7.2	0.2	0
[n]	0.006	1.7	0.0	0
[o]	0.008	2.3	0.1	0
Total explained	0.354	100.0	2.2	18
All variation	15.835	/	100

Fig 5

The Venn diagram shows variation partitioning results (partial CCA model) and the contribution [77] of the four studied environmental variable groups (i.e., climatic, edaphic, geographic, and slope) that drive the plant species distribution.

Each letter code indicates the individual fraction.

Variation of plant species composition among plant communities and beta diversity

We found a significant variation in plant species composition among communities (Table 5; Fig 6), in which all communities showed a significant difference in species composition between each other (Table 6). Out of 244 species, six species greatly contributed to the variation in plant species composition between communities, namely Viburnum grandiflorum, Indigofera heterantha, Heracleum candicans, Cedrus deodara, Pinus wallichiana, and Parrotiopsis jacquemontiana (Table 6). Overall, the three species that most contributed for the variation in species composition between communities showed 13.7–29.7% of cumulative contribution (Table 6).

Table 5

PERMANOVA results comparing species composition between the four communities found in Moist temperate forest.

This analysis was made with Euclidean distance and 999 permutations. Pairwise comparisons between communities are depicted in Table 6.

	Df	Sums of Sqs	Mean Sqs	F	R2	Pr(>F)
Communities	3	9961.1	3320.4	13.324	0.6059	0.001
Residuals	26	6479.1	249.2		0.3941
Total	29	16440.2			1

Fig 6

NMDS with PERMANOVA analysis to compare species composition between communities of moist temperate forests.

IHC: Indigofera heterantha- Heracleum candicans-Cynodon dactylon, VIP: Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI: Cedrus deodara-Pinus wallichiana-Isodon rugosus and PCP: Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana.

Table 6

Pairwise comparisons with FDR p-value adjustment method of species composition and contrast results of the contribution of individual species to the overall Bray-Curtis dissimilarity of species composition between the four communities found in moist temperate forest.

We displayed only the three species that most contributed.

Communities	P-value	Species	Av dis	SD	Ratio	Av Com1	Av Com2	Cum	Cum %	Cont %
IHC-VIP	0.011	Vib.gra	0.1	0	5	0	24.4	0.1	12.2	12.2
		Ind.het	0.1	0	1.9	8.8	23.5	0.2	20.1	7.9
		Her.can	0	0	1.3	6.9	1.8	0.2	22.8	2.7
IHC-CPI	0.002	Ced.deo	0.1	0	3.8	0	22.5	0.1	10.9	10.9
		Pin.wal	0.1	0	5.3	0	20.3	0.2	20.8	9.9
		Ind.het	0	0	0.9	8.8	2.3	0.2	24.2	3.5
IHC-PCP	0.002	Pin.wal	0.1	0	2.7	0	16.2	0.1	6.9	6.9
		Ced.deo	0.1	0	3	0	16.1	0.1	13.8	6.9
		Par.jac	0	0	5.8	0.4	10.3	0.2	18	4.2
VIP-CPI	0.011	Vib.gra	0.1	0	5.1	24.4	0	0.1	11.6	11.6
		Ind.het	0.1	0	5.3	23.5	2.3	0.2	21.5	9.9
		Ced.deo	0.1	0	1.9	5.2	22.5	0.3	29.7	8.3
VIP-PCP	0.006	Vib.gra	0.1	0	3.3	24.4	1.4	0.1	9.5	9.5
		Ind.het	0.1	0	6.6	23.5	2.8	0.2	17.9	8.4
		Pin.wal	0	0	2.6	5.3	16.2	0.2	22.8	4.9
CPI-PCP	0.002	Ced.deo	0	0	1.2	22.5	16.1	0.1	5.4	5.4
		Par.jac	0	0	1.8	4.7	10.3	0.1	9.6	4.2
		Pin.wal	0	0	1.1	20.3	16.2	0.1	13.7	4

Av. dis.–Average dissimilarity; SD–Standard deviation; Av Com1 –Average Community 1; Av Com2 –Average community 2; Cum.–Cumulative; Cont.–Contribution. Vib.gra: Viburnum grandiflorum, Ind.het: Indigofera heterantha, Her.can: Heracleum candicans, Ced.deo: Cedrus deodara, Pin.wal: Pinus wallichiana, Par.Jac: Parrotiopsis jacquemontiana.

The total beta diversity (βsor) showed a value of 54.7% dissimilarity, of which spatial turnover (βsim) made up 40.5% and nestedness-resultant components (βsne) made up 14.2%. In βsim cluster, we observed 47.8% dissimilarity between PCP-CPI cluster and VIP-IHC cluster (Fig 7). PCP showed a dissimilarity of 9.4% with CPI, and VIP showed a dissimilarity of 21.5% with IHC (Fig 7). In βsne cluster, we found 24.5% dissimilarity between PCP and VIP-CPI-IHC cluster (Fig 7). VIP showed a dissimilarity of 11.3% with IHC-CPI, and IHC had 4.1% dissimilarity with CPI (Fig 7). Thus, plant community structure is twice more influenced by the spatial turnover of species (βsim) than by the species loss (nestedness-resultant, βsne).

Fig 7

Dissimilarity cluster based on spatial turnover (βsim) and nestedness-resultant components (βsne) of beta diversity components of species dissimilarity between four plant communities of moist temperate forests.

IHC: Indigofera heterantha-Heracleum candicans-Cynodon dactylon, VIP: Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI: Cedrus deodara-Pinus wallichiana-Isodon rugosus, and PCP: Pinus wallichiana-Cedrus deodara-Parrotiopsis jacquemontiana.

Variation of diversity indices among plant communities

We found a significant difference of four diversity indices, species richness (GLM χ2 = 73.113, df = 3, p<0.001; Fig 8A), Shannon (GLM χ2 = 35.797, df = 3, p<0.001; Fig 8B), Simpson (GLM χ2 = 46.465, df = 3, p<0.001; Fig 8C), and Pielou (GLM χ2 = 44.093, df = 3, p<0.001; Fig 8D), between the four communities. PCP showed the highest average number of species (68.1±4.2; mean±SE) followed by VIP (53.3±7.5) and IHC (51.1±3.5), and finally by CPI, with the lowest number of species (40.2±4.5) (Fig 8A). PCP showed a Shannon’ value of 3.62±0.08 (mean±SE), followed by IHC (3.53±0.07), VIP (3.29±0.1), and CPI (2.9±0.1) respectively (Fig 8B). IHC showed the highest Simpson’ value (0.959±0.01; mean±SE), followed by PCP (0.954±0.01), VIP (0.930±0.01), and CPI (0.898±0.01) respectively (Fig 8C). Finally, IHC showed the highest Pielou’ value (0.901±0.01; mean±SE), followed by PCP (0.862±0.01), VIP (0.830±0.01), and CPI (0.797±0.01) respectively (Fig 8D).

Fig 8

Variation of diversity indices between the four plant communities of moist temperate forests in the Northwestern Himalaya, Pakistan.

Figures represent ridgeline plots with raw data (black dots below each density distribution) and the first, second and third quartiles (vertical red lines). Lowercase letters on the left differ from each other by an estimated marginal mean. The Y-axis is displayed in an ascendant altitudinal gradient. IHC: Indigofera heterantha- Heracleum candicans-Cynodon dactylon, VIP: Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI: Cedrus deodara-Pinus wallichiana-Isodon rugosus, and PCP: Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana.

Discussion

Mountain ecosystems are characterized by dramatic changes in temperature and abiotic properties over short altitudinal and geographical distances, making them ideal natural laboratories for studying vegetation response to environmental changes [85]. In this study, we evaluated the plant species composition and distribution in a hotspot of biodiversity, the Northwestern Himalayan mountains, Pakistan, assessing how environmental gradients, source of habitat heterogeneity, influence plant community structure and diversity, which might be a proxy for assessing how climate change impacts on plant communities located in mountainous regions [34, 86, 87]. We found that (i) the moist temperate zone in this region can be divided in four different major plant communities; (ii) each plant community has a specific set of environmental drivers; (iii) there is a significant variation in plant species composition between communities, in which six species contributed most to the plant composition dissimilarity; (iv) there is a significant difference of the four diversity indices (species richness, Shannon, Simpson, Pielou) between communities; and finally (v) plant community structure is twice more influenced by the spatial turnover of species (βsim) than by the species loss (nestedness-resultant, βsne). Overall, we showed that altitudinal gradients offer an important range of different environmental variables, highlighting the existence of micro-climates that drive the structure and composition of plant species in each micro-region. In addition, each plant community along the altitudinal gradient has a set of environmental drivers, which lead to the presence of indicator species in each micro-region.

Mountain plant communities are thought to be sensitive to climate change and, thus, able to reveal its effects sooner than others [34, 88]. The four communities found showed a wide range of environmental drivers; however, altitude and temperature showed great prominence, probably making up the main environmental drivers in mountainous plant communities. Similar pattern was observed in the allied area (Nandiar catchment, Battagram) of Northwestern Himalaya by stating altitude and temperature as the governing gradient [74]. Such variables, which can be strongly correlated [89], modify the diversity and structure of plant communities by creating local micro-climates [90], directly influencing plant community composition and diversity [19, 26, 91, 92].

Indeed, there is no order of importance of environmental variables, but studies are unanimous in showing that there is a consensus on the explanations for the variables’ influences. For instance, in two recent studies we showed that the altitude-temperature relationship significantly influenced the physiological attributes of some plant species in the Northwestern Himalayan region [22, 23], which can be a proxy for understanding plant adaptation to climate change. Any change in soil parameters has a significant effect on the growth of plant communities [19]. The studies on mountain forests habitats around the world have also revealed the role of soil structure on species zonation [72, 93, 94]. Furthermore, both chemical and physical attributes of the soil are related to natural soil characteristics, with an impact on plant species composition and distribution of higher vascular plants [95-97]. For instance, some soil variables can have great influence on plant composition and distribution, such as pH. Some studies have shown that pH level on soil can influence nutrient availability, ultimately influencing nutrient uptake for growth [98-100]. However, the availability of some nutrients as a result of pH levels can be detrimental for some plants, since some nutrients are toxic to some plants [98, 101]. Considering that there is a great variability of pH levels and nutrient availability and concentration along altitudinal variables, it is expected a great variability of plant species composition which can be more or less related to specific soil parameters. Since plants are sensitive to small variations of soil characteristics such as pH, minerals, organic matter, among others, and these variables are constantly changing along altitudinal gradients directly and indirectly influencing the presence and availability of other organisms and resources, some plant species might have adapted to specific set of variables.

Variability in plant species diversity is an outcome of species interaction with particular set of environment variables either abiotic and biotic [102, 103], which can occur in both space and time [104, 105]. The concept of changing species composition and vegetation continuum along the ecological gradients emerged as an antithesis model for distinct units [106, 107]. In our study, the moist temperate forest of the studied Northwestern Himalayan region is comprised in an altitudinal gradient of approximately 1500 m. This gradient is subject to strong micro-climatic variation, which results in a set of micro-regions (better discussed above). Each micro-region has certain characteristics, which will influence the set of species that will inhabit these spaces [24-26]. In this sense, it is expected that the plant community structure is more influenced by the spatial turnover of species (βsim) than by the species loss (nestedness-resultant, βsne), i.e., that there are different plant communities along the altitudinal gradient, as shown by our results. The differentiation of species diversity was mainly a consequence of environmental variables which is due to soil factors [108]. Therefore, in addition to the influence of edaphic factors in space on species composition and vegetation continuum, as shown in our study, results from similar studies have shown that the altitude is also important in driving vegetation structure and diversity in plant communities.

We found that environmental heterogeneity among plant communities have significant effects on beta diversity, particularly the spatial turnover. These results indicate that there is not a significant loss of the number of species between the plant community, but a variation in the species composition. This variation may be closely linked to the environmental effects in the area, which induces the appearance of species adapted to environmental variables [109]. The local community composition replacement implied the simultaneous loss and gain of species due to immigration–extinction dynamics and trait‐based environmental filtering [110, 111]. This indicates the relationship among plant community types and among species based on multiple factors. Although we did not find a large variation in βsne (loss of species between plant communities), it is important to note that temporal analysis might be important to consider a notable variation in this component of beta diversity; and βsne variations will be better observed in long-term analysis in future studies. In this sense, it is expected that the plant community structure is more influenced by the spatial turnover of species (βsim) than by the species loss (nestedness-resultant, βsne), i.e., that there are different plant communities along the altitudinal gradient, as shown by our results. Similarly, results were report by Haq et al. [112] from forests of Kashmir Himalaya, India.

We observed a significant variation in plant species composition between communities, in which all communities showed a significant difference in species composition between each other. The measure of Bray-Curtis dissimilarity shows that species composition change that is influenced mainly by abundant species, in our study six species (Viburnum grandiflorum, Indigofera heterantha, Heracleum candicans, Cedrus deodara, Pinus wallichiana, and Parrotiopsis jacquemontiana) contributed most to the plant composition dissimilarity. These results suggest that the richness and turnover patterns we observed were driven primarily by rare species, which comprise most of the local species pools at these forest communities [113]. These findings are consistent with the idea that less abundant species are more sensitive to climate variability than longer lived and more abundant species [114]. The high level of turnover is common and is an important mechanism by which a large regional species pool buffers site level diversity from interannual variation in climate [115].

Current study provides the baseline and first insights of spatial distribution, vegetation pattern and species contribution in response to environmental gradients in a moist temperate forests, Northwestern Himalaya, Pakistan. Studies that evaluate the distribution and composition of the plant community are fundamental for a better understanding of the local plant community, the conservation status and protection of these communities, as well as providing support for mitigation measures. Especially in the case of Northwestern Himalaya, which represents a biodiversity hotspot, it is even more important that we conduct phytosociological studies in these areas to document and preserve the biodiversity there. In the face of current climate changes, these regions are being heavily impacted [28, 29], where the probability of species extinction may be higher than elsewhere, as these regions are rich in endemic species. Finally, we need to consider that phytosociological studies consider a general profile of the first trophic chains level, i.e., to evaluate the composition, distribution and diversity of plants is to indirectly assess the first level of trophic chains.

24 May 2021

PONE-D-21-11989

Environmental variables drive plant species composition and distribution in the moist temperate forests of Northwestern Himalaya, Pakistan

PLOS ONE

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The paper is well written with impressive datasets but lacks details in methodology, a robust discussion and conclusion. Please look into the comments given by all the three reviewers and address them well in the revised version of the MS. The reviewers have specifically commented on the methodology and discussion section which needs significant improvement. Additionally, please provide the list of plants along with details of their categories as supplementary materials. Although the introduction is well written but many important papers on elevational studies based on plants from the Himalaya (especially from Nepal and India) are not referred to. I suggest the authors to look into those literatures (minimum 10 such papers are are available) and try to give clear picture to what has already been done across the Himalaya in the subject.

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Reviewer #3: No

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Reviewer #1: The basic data collection on vegetation, soil, etc. and analysis are completely lacking. Basic ecological expert may be consulted before finalizing the manuscript.

Overall, the environmental observation of the study is very less (4 years, 2015-2018) to draw proper conclusion on impact of plant diversity.

Relation to calculating the vegetation diversity along with environmental parameters, in location specific data needs clarification / rectification.

Further, the following points / comments need clarification / improvement.

Line no. 24-31: The abstract needs little improvement.

Line no. 32 & 100-104: The location can be provided with district / provinces / state, etc. in Pakistan.

Line no. 106: What are the growing seasons targeted? The time period of the study also be specified.

Line no. 116-118: What is the deepness of soil sampling? Whether the soil has been collected for each stands / transect, please clarify.

Line no. 119-121: The duration, intervals, etc. of data obtained from the handheld weather station and position / place of the weather station for the study are not clear.

Line no. 177: Details of 244 species recorded may be supplemented.

Line no. 182-186: The community may be specified as Trees, Shrubs, Grass, etc. and can be analysed properly. For example, the presence of Indigofera is shrub and Cynodon is grass and the landscape is shrubland or grassland. On the other hand, the presence of Pinus and Cedrus are clearly representing tree communities. Whether the landscape is having patches of vegetation of trees / shrub / grass composition or uniform vegetation dominated by these group, need clarification.

Line no. 190-194: Any specific observation on Cedrus, Pinus, etc. Because, we could not find the all vegetation in all aspects, especially the studied tree species.

Discussion: Need revision in view of answering the above questions.

Reviewer #2: The topic is timely, and such a kind of study, especially from the underrepresented areas, is essential and requires our attention. The authors have come up with clear questions and have analyzed them well. My main concern is with the methodology and discussion section; I see much basic information missing, especially concerning the data collection and study site; please look at each of the comments provided below. Discussion lacks proper explanation, key findings of different communities and predictor variables associated with them are interesting, but these points are not discussed well. Currently, the discussion is too general, but there is much potential to improve it. I also felt that your questions 2 and 4 explain the same points, so instead of keeping them as standalone objectives, it will be nice to combine them. In objective two results, all the four communities were found separately in clumps with apparent differences based on the environmental gradients, so I dint understand the need to again look into the variation of environmental variables among plant communities (iv). Lastly, instead of just reporting six species names, it will be good to have a table (primary or supplementary table), including all details of plant species in each community, their abundance, elevation range, etc.

Reviewer #3: MS number: PONE-D-21-11989

Title: Environmental variables drive plant species composition and distribution in the moist temperate forests of Northwestern Himalaya, Pakistan

General comments:

In this research paper, the authors studied influence of environmental variables in shaping plant species composition and their distribution in the moist temperate forests of Northwestern Himalaya, Pakistan. Covering 30 sampling sites, they measured 21 environmental variables for four consecutive years. Generated data is analysed using different multivariate analyses in order to identify potential plant communities and influence of environmental variable in species composition, distribution and diversity patterns. Overall the present research is well-designed and presents interesting results of multivariate analysis in order to understand how the existence of micro-climates drive the structure and composition of plant species in studied area.

Major issue:

1. In Conclusions (line 46-48) author stated that overall, we showed that altitudinal gradients offer an important range of different environmental variables, highlighting the existence of micro-climates that drive the structure and composition of plant species in each micro-region. However there is no mention of elevation range of study area across the manuscript.

2. Kindly add some detailed information on sampling method such as: if quadrat method is opted during the study what was the size of these used quadrats. Furthermore it will be good for readers to understand the study if information regarding the number of sampling quadrates in each transect will be mentioned in the methodology section.

3. A detailed table describing four communities (IHC; VIP; CPI; PCP) needs to be added highlighting species composition, environmental variables, elevation range, dominant families etc.

4. Page 9 line 177 author mentioned “A total of 244 plant species were recorded 177 in moist temperate forest of Manoor valley, Himalaya, Pakistan”. Kindly add details of 244 plant species belonging to ……………genus and …….families respectively.

Other comments:

1. Kindly follow uniformity across the manuscript text to avoid confusion, such as spelling variations (in line 122 analyses and 123 analyzes) and such omissions needs to be checked throughout the manuscript.

2. Author has used the term Northwestern Himalaya (line 23); Himalaya (line 31): Northwestern Himalaya (line 102); Himalayan mountains (line 260) etc. thus it is suggested to use term Northwestern Himalaya to indicate study site throughout the manuscript.

3. For wider acceptability of the manuscript it is suggested to discuss similar studies carried out in other parts of Himalaya in discussion section.

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Reviewer #1: Yes: K. Chandra Sekar

Reviewer #2: No

Reviewer #3: Yes: Dr. Aseesh Pandey

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12 Aug 2021

Revision notes

Reviewer’s comments black font; author answers in blue font; responses are numbered (R#1, R#2, etc. for reference)

Reviewer(s)’ Comments to Author:

Reviewer #1

General comments:

In this research paper, the authors studied influence of environmental variables in shaping plant species composition and their distribution in the moist temperate forests of Northwestern Himalaya, Pakistan. Covering 30 sampling sites, they measured 21 environmental variables for four consecutive years. Generated data is analysed using different multivariate analyses in order to identify potential plant communities and influence of environmental variable in species composition, distribution and diversity patterns. Overall the present research is well-designed and presents interesting results of multivariate analysis in order to understand how the existence of micro-climates drive the structure and composition of plant species in studied area.

R#1 – We really appreciate the positive words.

Major issue:

1. In Conclusions (line 46-48) author stated that overall, we showed that altitudinal gradients offer an important range of different environmental variables, highlighting the existence of micro-climates that drive the structure and composition of plant species in each micro-region. However there is no mention of elevation range of study area across the manuscript.

R#2 – We now added this information in the manuscript (L.14-15,93,237-246). The altitudinal range of our data sampling ranged from 1932 to 3168 m.a.s.l.

2. Kindly add some detailed information on sampling method such as: if quadrat method is opted during the study what was the size of these used quadrats. Furthermore it will be good for readers to understand the study if information regarding the number of sampling quadrates in each transect will be mentioned in the methodology section.

R#3 – Required information regarding the vegetation sampling has been added. “The surveyed stands (study area) were subdivided into 30 stands and three points randomly selected within each stand were sampled along 50 meters transect (total = 90 transects). The interval distance kept between the stands was 200 meters and 100 meters between transects.” (L. 97-110)

3. A detailed table describing four communities (IHC; VIP; CPI; PCP) needs to be added highlighting species composition, environmental variables, elevation range, dominant families etc.

R#4 – We now added two new supplementary tables (Table S1 and S2) describing the communities. One table describes environmental variables and the other table the species composition for each plant community.

4. Page 9 line 177 author mentioned “A total of 244 plant species were recorded 177 in moist temperate forest of Manoor valley, Himalaya, Pakistan”. Kindly add details of 244 plant species belonging to ……………genus and …….families respectively.

R#5 – As per suggestion, the required details has been added i.e., 194 genera and 74 families. (L. 220)

Other comments:

1. Kindly follow uniformity across the manuscript text to avoid confusion, such as spelling variations (in line 122 analyses and 123 analyzes) and such omissions needs to be checked throughout the manuscript.

R#6 – Done. Thank you. (L. 15, 139, 146, 219, 294)

2. Author has used the term Northwestern Himalaya (line 23); Himalaya (line 31): Northwestern Himalaya (line 102); Himalayan mountains (line 260) etc. thus it is suggested to use term Northwestern Himalaya to indicate study site throughout the manuscript.

R#7 – We changed to Northwestern Himalaya in the places that we are talking about the specific region of Himalayas. Other parts are related to the Himalayas in a general perspective and then we can change it. We appreciate your understanding. (L. 10, 12, 40, 71, 80, 92, 95 and onwards)

3. For wider acceptability of the manuscript it is suggested to discuss similar studies carried out in other parts of Himalaya in discussion section.

R#8 – We added more citations about studies conducted in the Himalayan mountains. We have now discussed it in detail. We now also added more citations about plant community studies in the Himalayas. (L. 327-329, 367-369, 376-382, 398-432)

Reviewer #2

The topic is timely, and such a kind of study, especially from the underrepresented areas, is essential and requires our attention. The authors have come up with clear questions and have analyzed them well.

R#9 – We really appreciate your positive words.

My main concern is with the methodology and discussion section; I see much basic information missing, especially concerning the data collection and study site; please look at each of the comments provided below. Discussion lacks proper explanation, key findings of different communities and predictor variables associated with them are interesting, but these points are not discussed well. Currently, the discussion is too general, but there is much potential to improve it. I also felt that your questions 2 and 4 explain the same points, so instead of keeping them as standalone objectives, it will be nice to combine them. In objective two results, all the four communities were found separately in clumps with apparent differences based on the environmental gradients, so I dint understand the need to again look into the variation of environmental variables among plant communities (iv). Lastly, instead of just reporting six species names, it will be good to have a table (primary or supplementary table), including all details of plant species in each community, their abundance, elevation range, etc.

R#10 – We now improved our manuscript and cordially ask you to check our responses for each question raised. Overall, we added more information in methods (L. 93, 97-110, 112-114, 121-125, 136-138, 140-142, 144-145, 148-156) and discussion (L. 327-329, 367-369, 376-382, 398-432) sections. Following reviewer’s very important suggestion, two new supplementary tables with a summary of the environmental variables and the species composition based on the importance value were also added.

L39-40: ii) each plant community has a specific set of environmental drivers & L41-42 iv) most of the environmental variables were significantly different between communities. I feel both of these sentences highlight the same points; my advice would be to combine both and write them as a single point.

R#11 – Thank you for your suggestion. We agreed and combined accordingly. (L. 22, 84, 339)

L90-91- and L94: I am highlighting this point again; there is an overlap in your questions 2 and 4

R#12 – Please see R#11 for the answer about this question. (L. 22, 84, 339)

L106 What were the growing seasons? Please mention the seasons name

R#13 – We now added the months that represent the growing seasons in the area, which were from March to October. (L. 97)

L106 Was the same stand sampled for four years, meaning four times? Please note that many important information on sampling design is missing; for example, how were the individuals counted, those falling precisely on the line? Furthermore, was the line horizontal or vertical? Was it stratified random sampling? Also, what was the distance between the two lines transect?

R#14 – Yes. We repeated the stands over years, but not the same transects inside each stand. We now added more information about it the text. The surveyed stands (study area) were subdivided into 30 stands and three points randomly selected within each stand were sampled along 50 meters transect (total = 90 transects). The interval distance kept between the stands was 200 meters and 100 meters between transects. The individuals of plant species falling precisely on the line were noted. (L. 98-110)

L116 There is no clarity on how soil samples, Ph values were collected. Was it collected at a single location per stand, and what depth, slope, well-exposed sunlit areas or not?

Ph varies a lot, even at a short distance between north and south facing slopes, so it is better to provide all the sampling details.

R#15 – Basic details regarding the data collection of vegetation and soil sampling are now mentioned in Vegetation sampling (L. 98-110, 112-114), and Environmental gradients (L. 121-125, 136-138) respectively. A supplementary table S2 has also been included to show the variability of environmental gradients among the four plant communities.

L121 At what distance was the weather station located from those 30 stands? The environmental variable varies across the elevation, and authors have also mentioned the importance of microclimates, so I am curious to know how many weather stations were installed.

The overall sampling design will become more apparent if the authors explain how the line transects and weather stations are distributed along the elevation gradient.

R#16 – We used a small remote weather station and recorded the data at transect level and then averaged it to stand level. Required details regarding vegetation sampling using line transect are incorporated into the Vegetation sampling section (M & M).

Results:

I feel that apart from the given figures, it will be good to have a table where the species names, elevation range, abundance in each community are mentioned. A comprehensive table with all these details may give a clearer picture of your findings. You may decide how much information you would want to share in the main manuscript and supplementary information.

R#17 – Thank you for your comment. We now added two supplementary tables about plant species composition and environmental variables values in each community. Since we already have many tables and figures in the manuscript, and these other tables will be so long, we are adding them as supplementary tables (S1 and S2).

Fig 3: Legend need to be rewritten, the author has mentioned four communities' names and four colors corresponding to each community, but the link between each color and community is missing. Also, what do the alphabet and various numbers indicate on the left side of the graph?

R#18 – We now updated our legend. Numbers and letter are the stands.

“Figure 3. Clustering method using two way indicator species analysis (TWINSPAN) indicating four different plant communities. IHC (red triangle): Indigofera heterantha-Heracleum candicans-Cynodon dactylon, VIP (blue circle): Viburnum grandiflorum-Indigofera heterantha-Pinus wallichiana, CPI (green square): Cedrus deodara-Pinus wallichiana-Isodon rugosus and PCP (yellow diamond): Pinus wallichiana-Cedrus deodara- Parrotiopsis jacquemontiana. Letters associated to numbers at the end of each branch of the dendrogram represent the stands evaluated.

L209: What was the intention behind considering temperature and altitude both as the predictor variables? Because along the mountain gradient these variables are highly correlated.

R#19 – In this specific analysis, we did not consider any kind of multicollinearity. We plot all variables to see the overall relationship between them and communities. However, if you check the partial CCA and partitioning analyses, you will see that we removed the colinear variables. Therefore, in the first analysis (NMDS, PCA) we show the overall relationship of all variables with each community, while in the second analysis, we show which are the variables that most explain the patterns found in our study.

Discussion: In the result, section author has emphasized their findings of four distinct communities and the environmental variables associated with each one of them. However, in the discussion, the authors have failed to discuss their unique findings in detail. I feel that the discussion section is too general and poorly explained. There is a lot of scopes to improve the discussion considering the amount of analysis carried out. By highlighting just temperature and altitude (which is definitely important from the climate change aspect), you may lose other valuable details of your study.

R#20 – We totally agree. In discussion, we provided more details about how these other variables can influence plant community. We believe that discussing each variable and its relationship to the community will extend a lot the manuscript, turning it tiring for readers. In this context, we believe that the general patterns and discussion of the main variables should be the best approach. We appreciate your comment, and we cordially ask to check the lines mentioned, where we added information about the influence of other variables in the community structure. Some important edaphic variables like pH were considered and discussed accordingly. (L. 327-329, 367-369, 376-382, 398-432)

Reviewer #3

Comments on the manuscript entitled ‘Environmental variables drive plant species composition and distribution in the moist temperate forests of Northwestern Himalaya, Pakistan’.

The basic data collection on vegetation, soil, etc. and analysis are completely lacking.

Overall, the environmental observation of the study is very less (4 years, 2015-2018) to draw proper conclusion on impact of plant diversity.

Relation to calculating the vegetation diversity along with environmental parameters, in location specific data needs clarification / rectification.

R#21 – We agreed, basic information regarding the data collection of soil and vegetation sampling are now mentioned in detail as per suggestion. (L. 98-110, 112-114, 121-125, 136-138)

Further, the following points / comments need clarification / improvement.

Line no. 24-31: The abstract needs little improvement.

R#22 – Improved.

Line no. 32 & 100-104: The location can be provided with district / provinces / state, etc. in Pakistan.

R#23 – Required details provided in both the sections. (L. 11, 90-91)

Line no. 106: What are the growing seasons targeted? The time period of the study also be specified.

R#24 – We now added the months that represent the growing season in the area, which is from March to October. (L. 97)

Line no. 116-118: What is the deepness of soil sampling? Whether the soil has been collected for each stands / transect, please clarify.

R#25 – “Soil samples of 200-400grams were collected from three randomly selected transects (0-30cm depth) within each sampling stand of the studied vegetation area.” For more details, please see section: Environmental gradients. (L. 121-125)

Line no. 119-121: The duration, intervals, etc. of data obtained from the handheld weather station and position / place of the weather station for the study are not clear.

R#26 – We used a small remote weather station and recorded the data at transect level and then averaged it to stand level. (L. 136-138)

Line no. 177: Details of 244 species recorded may be supplemented.

R#27 – Two new supplementary tables have been with a summary of the environmental variables and the species composition based on the importance value index.

Line no. 182-186: The community may be specified as Trees, Shrubs, Grass, etc. and can be analysed properly. For example, the presence of Indigofera is shrub and Cynodon is grass and the landscape is shrubland or grassland. On the other hand, the presence of Pinus and Cedrus are clearly representing tree communities. Whether the landscape is having patches of vegetation of trees / shrub / grass composition or uniform vegetation dominated by these group, need clarification.

R#28 – As per suggestion, the required details have been mentioned following the communities. For instance, the IHC community was primarily found in the lower mountainous ranges (1932.3-2338.4 m.a.s.l), where the dominating flora was a combination of shrub and herb species owing to the existence of a substantial herbaceous layer of Cynodon dactylon, which carpeted the landscape alongside Indigofera heterantha patches……shrubby associates. (L. 237-246)

Line no. 190-194: Any specific observation on Cedrus, Pinus, etc. Because, we could not find the all vegetation in all aspects, especially the studied tree species.

R#29 – Please see R#28 for the answer about this question. (L. 237-246, 264-266)

Discussion: Need revision in view of answering the above questions.

R#30 – We provided more details about how the variables can influence plant community. We cordially ask to check the lines (L. 327-329, 367-369, 376-382, 398-432), where we added information about the influence of other variables in the community structure.

The manuscript holds excellent statistical analysis. Although, basic collection data on vegetation assessment and related environmental parameters are missing, and can be addressed.

We cordially appreciate your positive words, however, we have considered and addressed each of your suggestion accordingly.

Submitted filename: Response to reviewers.docx

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20 Sep 2021

Submitted filename: PONE-D-21-11989_R1_Editorial Comments.pdf

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12 Nov 2021

Revision notes

Reviewers and Editor’s comments; author responses.

Reviewer(s)’ Comments to Author:

Editor’s Comments to Author:

Comment: The authors have studied the variation of plant communities along environmental gradients in the Moist North Western Himalaya, and linked the plant communities with various environmental variables. The article is well written with clarity in introduction, methods (including data analysis), results and discussion. In the first round of review, three independent expert reviewers provided valuable comments which were mostly addressed by the authors. The revised version was sent to all the three same previous reviewers and all of them have positively commented on the MS. I have once again thoroughly evaluated the MS and provided some editorial comments. I suggest the authors to look into the comments (as sticky notes in the attached pdf manuscript files) and address them critically. I also suggest the authors to provide some more details in the sampling design and methodology section as pointed by reviewer 1. Once all these minor issues are resolved, the MS may be considered for publication. I request the authors to revise the MS quickly and submit the same. The MS may not be sent for further external review but will be evaluated by the academic editor before rendering the final decision. Looking forward to the revised MS at an early date.

Response: We really appreciate the reviewers for very constructive criticism and acceptance of our work. We are thankful to the Editor for highlighting such important points, which made the revised version much better. We have considered all the raised points and corrected the manuscript accordingly.

Comment: The Editor highlighted suggestions in the pdf file.

Response: We are thankful to the Editor for highlighting such important points, which made the revised version much better. We have considered all the raised points and corrected the manuscript accordingly. Each Table and Figure legend/caption were corrected with proper and complete details. The analysis and the linked results that were highlighted to be deleted have been done as suggested. We cordially ask you to check the track version for corrections.

Comment: Sorenson dissimilarly index (βsor) is an incidence based index which can be partitioned into turnover (βsim) and nestedness (βnes) components. Simpson is a different index. Again why authors have used only incidence based index? Why not abundance based index because abundance-based β-diversity can be estimated as Bray-Curtis dissimilarity index (dBC) and then partitioned into balanced variation (dBC-bal) and abundance gradient components (dBC-gra). Since authors have abundance data, both the approaches could give better result. Please see the following article for details:

Sharma, K., B.K. Acharya, G. Sharma, D. Valente, M.R Pasimeni, I. Petrosillo, and T. Selvan. 2020. Land use effect on butterfly alpha and beta diversity in the Eastern Himalaya, India. Ecological Indicators 110: 105605.

Response: We agree that when working with abundance, abundance-based B-diversity estimated as dBC is the best option. However, in all our analyses we used the importance value (IV), as described in the Vegetation Sampling and plant identification subtopic in Material and Methods, to standardize our analyses over the manuscript. IV takes in account relative frequency, relative density, and relative dominance. In this way, we chose to use the Bsor index based on the incidence to decrease potential confounding effects of using IV when calculating dBC. In few words, we used a more conservative Beta diversity analysis. For these reasons, we cordially ask you to keep the incidence-based Beta diversity analysis in our study.

Submitted filename: Response to reviewers.docx

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16 Nov 2021

Environmental variables drive plant species composition and distribution in the moist temperate forests of Northwestern Himalaya, Pakistan

PONE-D-21-11989R2

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14 Feb 2022

PONE-D-21-11989R2

Environmental variables drive plant species composition and distribution in the moist temperate forests of Northwestern Himalaya, Pakistan

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