Predicting the potential distribution of Dactylorhiza hatagirea (D. Don) Soo-an important medicinal orchid in the West Himalaya, under multiple climate change scenarios.

Climate variability coupled with anthropogenic pressures is the most critical driver in the Himalayan region for forest ecosystem vulnerability. Dactylorhiza hatagirea (D.Don) Soo is an important yet highly threatened medicinal orchid from the Himalayan region. Poor regenerative power and growing demand have resulted in the steep decline of its natural habitats populations. The present study aims to identify the habitat suitability of D. hatagirea in the Western Himalaya using the maximum entropy model (MaxEnt). The community climate system model (CCSM ver. 4) based on representative concentration pathways (RCPs) was used to determine suitable future areas. Sixteen least correlated (< 0.8) bioclimatic, topographical and geomorphic variables were used to construct the species climatic niche. The dominant contributing variables were elevation (34.85%) followed by precipitation of the coldest quarter (23.04%), soil type (8.77%), land use land cover (8.26%), mean annual temperature (5.51%), and temperature seasonality (5.11%). Compared to the present distribution, habitat suitability under future projection, i.e., RCP 4.5 and RCP 8.5 (2050 and 2070), was found to shift to higher elevation towards the northwest direction, while lower altitudes will invariably be less suitable. Further, as compared to the current distribution, the climatic niche space of the species is expected to expand in between11.41-22.13% in the near future. High habitats suitability areas are mainly concentrated in the forest range like Dharchula and Munsyari range, Pindar valley, Kedarnath Wildlife Sanctuary, West of Nanda Devi Biosphere Reserve, and Uttarkashi forest division. The present study delineated the fundamental niche baseline map of D. hatagirea in the Western Himalayas and highlighted regions/areas where conservation and management strategies should be intensified in the next 50 years. In addition, as the species is commercially exploited illegally, the information gathered is essential for conservationists and planners who protect the species at the regional levels.

1. Introduction

Climate is an important ecological and abiotic factor affecting species potential geographic distribution and ecological niche space [1]. The minimal changes in species bioclimatic envelope are thought to have considerable impacts on the plant-pollinator relationship, seed set, and regeneration status. It is expected that species may no longer adapt to a set of environmental conditions to facilitate further expansion [2]. Therefore, the species must either cope with the prevailing ecological conditions or colonize to sustain or become extinct [3]. This has led to a growing interest in developing and scaling up prioritization strategies for such species to ensure the highest conservation gains [4].

The Himalayan regions are an assemblage of biodiversity hotspots [5]. However, the ongoing disturbance exacerbated by climate change, habitat fragmentation, invasions by alien species, grazing and trampling, overexploitation, and excessive consumption of natural resources has altered the structural and functional integrity of the various Himalayan ecosystems [6]. Besides these, climate variability, land-use change, and rural migration are key contributors to biodiversity loss in the region [7]. In the last few decades, the region saw cascading effects of climate variability mainly due to increased greenhouse gases concentration. It is believed that the rate of global warming in the Himalayas is much higher than the global average. For instance, in the last 100 years, the global average temperature rise was 0.74°C [8]. However, in the Himalayas, a 1.5°C temperature increase was documented in the final quarter (i.e., 1982–2006) of the twentieth century [9], with warming potentially reaching 5°C by the end of the twenty-first century [9, 10]. This rise is alarming because Himalayan floras are alienated to specific elevation gradient/microhabitat conditions [3, 11]. The shift in the climatic envelope is expected to bring significant change in the resident species habitat conditions, leading to changes in species richness, population structure, and those unable to cope are likely to face local extinction [1-3].

Furthermore, the recent upsurge in herbal or its derived products across the globe has led to uncontrolled abusive practice; thus, the natural stock of these plants is under tremendous pressure. In the case of the Indian Himalayan Region (IHR), a considerable number (1748 species) of medicinal and aromatic plants (MAPs) are reported, with 31% of them being native, whereas 15.5% are endemic and threatened [12]. The high potential instability and inherent vulnerability make the region one of the most ecologically fragile bio-geographic zones [13]. Other challenges on the MAPs include low population size, habitat specificity, genetic bottleneck effect, narrow distributional ranges, and heavy livestock grazing [14]. The literature on these threatened plants is fragmentary or limited to specific geographic pockets [15]. In the above context, it is obligatory to make a conservation framework encircling species habitat restoration and promote cultivation, thus, reducing pressure on the wild populations.

The development of statistical modelling and geospatial technology in predicting suitable habitat distribution has gained immense popularity. However, such information is at an initial phase for the Himalayan MAPs [6]. The use of geospatial technology could add an advantage as obtaining specific distribution maps for such species is difficult and often requires intensive surveys [16]. The difficulty level becomes amplified in the Himalayan region where the working conditions are not conducive for the survey, i.e., inaccessible and difficult topography perplexed with hostile conditions. Therefore, estimating current plants distribution and identifying important climatic refugia will help predict future distribution patterns and reveal regions with high extinction rates. At present, the common method to study potentially species distribution and environmentally suitable habitats is to use species distribution models (SDMs) [17]. SDM has made it possible to analyze the environmental drivers of species distributions and project a species realized niche into a geographic area [18]. Of many SDM algorithm methods, MaxEnt has proved decisive when modeling rare species with narrow ranges [19]. MaxEnt modelling is a robust computational algorithm that works on the backdrop of species presence points and rasterized environmental data. The probable ecological niche can be reconstructed using species presence data points and environmental variables/predictors [20]. Such model-based sampling would become an important benchmark for endemics and threatened species and is a well-recognized cost-efficient method [21].

In the present study, an effort has been made to model the potential habitat distribution and effect of future climate change on D. hatagirea, a critically endangered [22, 23] and endemic species of the Himalayan region. The species is a tall, slender, ground-dwelling, perennial herb with palmately lobed tuberoids that prefer to grow in a moist, mild, acidic soil environment (Fig 1A and 1B). The species has been reported from India, Afghanistan, Pakistan, Nepal, Tibet, and Bhutan [6]. In the IHR, the species is reported from Jammu and Kashmir, Ladakh, Himachal Pradesh, Uttarakhand, Sikkim, and Arunachal Pradesh at an altitudinal range of 2500 to 4500 m above sea level (asl). The estimated annual trade of the species is around 10–50 metric tons [24], with an economic value of US $ 68.88–89.54 (1US $ = Rs. 77.39) kg-1 of the dried tuber (Fig 1C). At present, the tuber of the species is destructively harvested and illegally traded; thus, it puts a stake on its future existence. Moreover, the species require specific microhabitat conditions for growth and perturbation, thus limiting the species widespread distribution. Therefore, to minimize the pressure on the wild populations, efforts are ongoing to develop and upscale the existing multiplication strategies for mass multiplication. Meanwhile, mapping and conserving the critical habitat is expected to offer a possible solution to species conservation and management. The study attempts to address the following scientific questions: (i) What is the present potential geographical distribution range of Dactylorhiza hatagirea in the Western Himalaya, India? (ii) What will be the impacts of climate variability on the future distribution of D. hatagirea using four Representative Concentration Pathways (RCPs)?, and (iii) Where are the high potential distributional areas of D. hatagirea that could be protected or could be suggested for cultivation, reintroduction/ recovery plans?. Answering these questions will help identify suitable habitats for the conservation of the species, which may help policy planers while developing strategies for its conservation.

Fig 1

Image showing (a) compact floral structure of D. hatagirea, (b) well developed palmately lobed tuber, and (c) tuber collected and processed as a marketed product.

2. Materials and methods

2.1. Study area and ecological significance

The study was undertaken in Uttarakhand state (28°43ˈ to 31°28ˈ N Latitude and 77°34ˈ to 80°03ˈ E Longitude) of IHR (Fig 2). The state has a total recorded forest area (RFA) of 38,000 km2 (71.05% of its total geographical area 53,485 km2), out of which 26,547 km2 is reserved forest, 9,885 km2 is protected forest, and 1,568 km2 is unclassed forests [25]. The state experience varied climates from warm dry to warm wet and with a latent cool, dry period. The state’s temperature ranges from sub-zero to 43°C [26], and average annual rainfall varies from 1093.8 mm to 1385.5 mm [27].

Fig 2

Field collected D. hatagirea (blue dots) points mounted on the elevation map of Uttarakhand state.

Maps in Fig 1 are generated with ArcGIS version 10.3 (ESRI, CA, USA).

The present study area harbors alpine vegetation, which covers 8,524 km2. Of these, 4,376 km2 is surmounted by permanent snow cover (i.e., corresponding to ca.24.11% statealpine geographical area) [28]. The alpine regions are well known for their high-value MAPs, including D. hatagirea. The region is experiencing major environmental transformation repercussions, and anthropogenic activities outnumber the natural eventualities, thus enforcing species to various threat categories [15].

2.2. Species point data

To predict species distribution, it is a prerequisite to have species presence points and environmental variables [29]. The data search was primarily made from online portals such as Global Biodiversity Information Facility [30], published literature, and herbarium consultation Botanical Survey of India, Dehradun (BSID). Data on the species were very limited, whereas herbarium records were not geo-referenced. Considering these limitations, an extensive field survey was conducted during 2016–19, and presence points were recorded. A total of 30 occurrences of the species were recorded during field surveys. A portable multi-channel Global Positioning System (Garmin) receiver with 10–20 m positional accuracy was used to record the species occurrence geo-coordinates. The coordinates were then converted to decimal degrees and used to model the species potential habitats distribution in its native range.

2.3. Data source

The climate data were downloaded from World Climate Database [31]. WorldClim provides current (baseline) and projected climate data for 2070 with a spatial resolution of 30 seconds (ca. 1 km) in GeoTIFF format. These climatic data are derivatives of maximum, minimum, and average values of monthly, quarterly, and annual temperatures and precipitation of the last 30 years, i.e., 1970–2000. Likewise, environmental variables such as soil type, soil moisture, and soil pH were downloaded from the International Soil Reference Information Centre [32], while land use land cover (LULC) from http://www.esa-landcover-cci.org/ [33] (Table 1). Besides these, non-climatic variables, i.e., altitude, aspect derived from NASA Shuttle Radar Topographic Mission (SRTM, version 4.1) [34]. The reason behind using both the climatic and non-climatic variables is to enhance the model’s predictive power as suggested for endemic plants [5, 35]. Further, for future prediction studies, we used Community Climate System Model (CCSM) ver. 4 (CCSM4) that is based on the Fifth Assessment Report (AR 5) of the Intergovernmental Panel on Climate Change [36] and two contrasting Representative Concentrations Pathways (RCP 4.5 and RCP8.5) for the years 2050 and 2070. Furthermore, we assumed that the edaphic properties are expected to remain stable in the next several decades, as soil properties should not change synchronously with sudden climate change; hence the same raster layer was used in future projections.

Table 1

Environmental variables and their percent contributions for predicting the potential distribution of D. hatagirea.

Type	Code	Variable name	Unit	% Contribution
Climatic	Bio1	Annual mean temperature	°C	5.51
	Bio2	Mean diurnal range (mean of monthly max. and min. temp.)	°C	0.07
	Bio3	Isothermality [(Bio2/Bio7) x 100]	-	0.98
	Bio4	Temperature seasonality (standard deviation x 100)	C of V	5.11
	Bio7	Temperature annual range (Bio5-Bio6)	°C	1.48
	Bio8	Mean temperature of wettest quarter	°C	0.08
	Bio12	Annual precipitation	mm	1.95
	Bio18	Precipitation of warmest quarter	mm	4.38
	Bio19	Precipitation of coldest quarter	mm	23.04
Geomorphologic	DEM	DEM	°	34.85
	SLP	SLOPE	°	3.06
		ASPECT		0.98
Pedologic		SOIL TYPE		8.77
		SOIL MOISTURE	mm	1.13
		SOIL pH		0.36
Land use land cover		LULC		8.26

2.3.1. Environmental layers and variable selection

The model’s output can be accurate, biologically meaningful, and generalized if built with predictor variables that directly impact species distribution. Strong collinearity between the variables in SDMs may cause model overfitting due to the high level of correlation among variables [37]. To avoid multi-collinearity among the 19 bioclimatic variables, highly correlated variables (r ≥ 0.80 Pearson correlation coefficient) were eliminated from further models using ENM Tools. This reduction of predictor variables resulted in the inclusion of nine bioclimatic variables and seven environmental variables for the prediction process (S1 Table). Further, using ArcGIS 10.3, all predictor variables layers were rasterized into the same bounds, cell sizes, and coordinate system as the layer of occurrence localities. Finally, these layers were converted to ASCII format for further processing in MaxEnt.

2.3.2. Model parameterization

MaxEnt algorithm (MaxEnt ver. 3.4.1) [38] for habitat distribution modelling was employed [29]. MaxEnt algorithm was chosen over other available machine learning tools owing to (i) presence of only data points of the species, (ii) works even relatively with a small number of occurrence locations and high predictive performance, (iii) can handle continuous and categorical environmental data simultaneously, (iv) analyze results in terms of percent contribution of environmental data through model output, (v) examine variables weight through jackknife method, and (vi) calibrate the model, run numerous replicates along with cross-validation, and bootstrapping to test model robustness [18, 39, 40]. We used 75% of the dataset for training and 25% dataset model testing in this study. For generating model robustness, the number of iterations was set to 5000, with 30 replicated model runs. The maximum background points10000 and ten percentile training presence with logistic threshold rule were applied, whereas other parameters were set to default.

2.3.3. Model performance and potential niche change

To calibrate the model and validate its robustness, threshold independent receiver-operating characteristic analysis (ROC) and area under the receiver-operating characteristic curve (AUC) were tested for model precision. The AUC value varies between 0 to1. The values close to +1 indicate conformity between observations and prediction, whereas zero or less values indicate a performance no better than random [41]. Statistically, AUC values near 1 indicate very good model performance, whereas AUC values close to 0 signify complete inaccurate prediction. Model performance based on AUC values are categorized as, very good (0.95 < AUC < 1.0), good (0.9 < AUC < 0.95), fair (0.8 < AUC < 0.9), and poor (AUC < 0.8) [42, 43]. In the past, several studies have suggested that the AUC values mislead the performance of predictive distribution models and reflect relative model performance [44]. Therefore, to assess the predictive success of models, sensitivity, specificity, overall accuracy, and True Skill Statistics (TSS) were calculated by a confusion matrix. Threshold-dependent TSS is considered an additional accuracy measurement that is not affected by prevalence as it does for the kappa coefficient and the size of the validation set [45]. It deals with sensitivity and specificity, values ranging from − 1 to + 1, where + 1 indicates perfect agreement, scores ranging from 0.6 to 0.9 specify fair to good model performance, and 0 represents a random fit [45]. For this, the output of the logistic layer derived from MaxEnt results was reclassified into a binary prediction map (unsuitable and suitable) with a threshold of 10 percentile training presence. All geographical plotting and suitable range-size estimation were conducted in ArcGIS software (version 10.3).

To identify the potential area of distribution, the distributional indices based on threshold interval classification (TIC) were categorized as highly suitable (TIC > 0.75), moderate suitable (0.50 < TIC < 0.75), least suitable (0.25 < TIC < 0.50), and unsuitable areas (TIC < 0.25). Changes in the potential niche of D. hatagirea between the current and future climatic scenarios were computed by converting ASCII output projections into raster format using ArcGIS 10.3. Simultaneously the number of cells (pixels) among the projected climatic extent was calculated using zonal statistics in spatial analyst tools in ArcGIS 10.3. The differences in the mean number of cells among four classes of potential habitats were converted to surface area (km2). Finally, MaxEnt predictive maps for the current and future scenarios were related to elevation classes. This would help map habitats and contribute to species-specific interventions/ reintroduction programs.

3. Results

3.1. Preliminary screening of model inputs variables

The credibility of any prediction model is dependent on input variables for species distribution modelling. Given this, sixteen predictor variables out of twenty-six variables; with correlation coefficients of ≤ 0.8 were retained after preliminary screening and selected for further modelling (Table 1).

3.2. Model performance and variable contributions

The results obtained by an ecological model are judged for their performance based on complex algorithm tests and model validation. The threshold-independent ROC showed that the average AUC yielded satisfactory results of 0.96 (Fig 3), which falls under ‘very good’ (0.95 < AUC < 1.0) model performance based on Thuiller et al. (2005) [42] classification. The confusion matrices for the current prediction model calculated the model’s sensitivity and specificity to be 0.79 and 0.95, respectively. With these matrices in place, the model performance (i.e., TSS) was calculated (sensitivity + specificity—1). The TSS for the current model was computed to be 0.74, which indicates that the model’s overall performance was good, based on Allouche et al. (2006) [45] criteria.

Fig 3

Receiver operating characteristic curve with area under the curve (AUC) signifying model robustness.

The variable contributions analysis highlights; elevation had the most (34.85%) influential effect followed by precipitation of coldest quarter (Bio 19; 23.04%), soil type (8.77%), LULC (8.26%), annual mean temperature (Bio 1; 5.51%), temperature seasonality (Bio 4; 5.11%), precipitation of warmest quarter (Bio 18; 4.38%) and Slope (3.06%) (Table 1). The variables mentioned above cumulative contributions stood at ~93% to the modeled potential climatic niche of D. hatagirea. Similarly, Jackknife analysis indicates annual mean temperature (Bio 1), elevation, mean temperature of the wettest quarter (Bio 8), and precipitation of coldest quarter (Bio 19) as most important predictor variables (Fig 4). These variables provide useful and distinctive information defining the D. hatagirea distributions when used in isolation. Variables like soil type, LULC, temperature seasonality (Bio 4), and annual temperature range (Bio 7) showed considerable change and showed moderate gain when used separately (Fig 4). Furthermore, the quantitative relationship between the logistic probability and input variables are depicted as response curves (Fig 5). A geomorphic variable, such as elevation, was one of the key variables that describe the present and future spatial distributions of D. hatagirea. Response curves analysis reveals average altitude ranged from 2800 m to 4500 m, precipitation of coldest quarter (Bio 19) ranged from (150 mm to 380 mm), annual mean temperature (Bio 1) ranged in between (0 – 25°C). Likewise, precipitation of the warmest quarter (Bio 18) ranged in between (200 mm-1100 mm), soil pH (5–6), and slope angle ranged from (5° - 45°). Thus, all the identified variables estimate the important climatic attributes that potentially influence the distribution of D. hatagirea in northwestern Himalaya, India.

Fig 4

Results of jackknife test highlighting the relative importance of each variable when used in isolation.

Fig 5

Response curves of six environmental predictors and their relationships with the probability of the target species suitability range.

3.3. Current predicted potential distribution of climatically suitable areas

Habitat suitability of D. hatagirea was determined based on threshold interval classification (TIC). Of these, maximum of 96.18% (51637 sq. km) of the geographic region was predicted to be unsuitable (TIC < 0.25), followed by 3.09% (1664 sq. km) with least habitat suitability (0.25 < TIC < 0.50), and moderate suitability being 0.47% (255 sq. km). The potential habitat with high suitability accounts for only 0.24% (131 sq. km) of the state’s total geographic area (Table 2). The areas with high habitats suitability (TIC > 0.75) are mainly concentrated in the forest range like Dharchula and Munsyari range, Pindar valley, Kedarnath Wildlife Sanctuary, West of Nanda Devi Biosphere reserve, and Uttarkashi forest division (Fig 6). Likewise, moderate habitat suitability was located in GovindVihar National Park, Uttarkashi forest division, Kedarnath Wildlife Sanctuary, Nanda Devi Biosphere Reserve, and National Park, Pindar valley and in the forest range of Dharchula and Munsyari range.

Table 2

Predicted habitat suitability area (km2) of D. hatagirea, in the present and future climate change scenarios (RCP 4.5–8.5; 2050 and 2070).

		Present	RCP 4.5 2050	RCP 8.5 2050	RCP 4.5 2070	RCP8.52070
Range	Area (km2)
0.00–0.25	Not Suitable	51637	51478	51526	51513	51564
0.25–0.50	Low Suitable	1664	1777	1734	1729	1684
0.50–0.75	Moderately Suitable	255	279	281	285	292
0.75–1.00	Highly Suitable	131	153	146	160	147
Total		53687	53687	53687	53687	53687

Fig 6

Potential geographic distributions of current and under RCP 4.5 (2050 and 2070) of D. hatagirea in the Uttarakhand state.

Maps in Fig 6 are generated with ArcGIS version 10.3 (ESRI, CA, USA).

3.4. Future projection of climatically suitable areas of D. hatagirea distribution

Future projection habitat suitability map under the CCSM4 model for RCP 4.5 and RCP 8.5 (2050 and 2070) is very similar to the current distribution (Table 2). The present study results depict the geographic distribution of the species would expand under predicted levels of climate change (RCP 4.5 and RCP 8.5) compared with the current potential distribution (Figs 6 and 7). High habitat suitability under the RCP 4.5 scenario; predicts an increase of 0.4% (22.10 sq. km) for 2050 and 0.3% (15 sq. km) for 2070. Under the RCP 8.5 projection, an increase of 0.5% (29 sq. km) is expected in 2050 and 0.27% in 2070. Although, the potential high suitability increases under both the scenarios (RCP 4.5 and 8.5) when compared with the current prediction, the rate of increase for the year 2050 is comparatively higher, after which (towards 2070) it showed a decreasing trend.

Fig 7

n-depth view of predicted future habitat for D. hatagirea under RCP 8.5 (2050 & 2070).

I Maps in Fig 6 are generated with ArcGIS version 10.3 (ESRI, CA, USA).

3.5. Shifts in habitat suitability under the climate change scenarios

The final output maps (current and future scenarios) were employed to find out habitats that will remain stable, gains in habitat area, habitat loss, and unsuitable habitat as part of computing change analysis (in sq. km) (Table 3) (S1 and S2 Figs). The change analysis highlights; only 1992 sq. km of stable habitat (maximum) under RCP 4.5 (2050) then under RCP 8.5 (2070) with a minimum stable habitat of 1928 sq. km. Stable habitats showed a decreasing trend with the climate projection model. Likewise, under RCP 4.5 (2050), an area of 107 sq. km was recorded as a gain in habitat, followed by 102 sq. km in 2050 (RCP 8.5) and 93 sq. km (RCP 8.5) in 2070. Habitat gain indicates the region becomes more suitable for the species under future climatic conditions. Besides these, habitat losses (contraction) were recorded. Of these, maximum (112 sq. km) contraction was seen in 2070 (RCP 4.5), followed by 110 sq. km in 2050 (RCP 4.5) and 105 sq. km in 2050 (RCP 8.5) (Table 3). The contraction in habitats was mainly recorded from the low habitat suitability class. A detailed shift in degrees in a different class is tabulated in Table 4.

Table 3

Habitat transformation (km2) as predicted by comparison with present distribution using RCP 4.5–8.5 (2050 and 2070).

	RCP 4.5 2050	RCP 4.5 2070	RCP 8.5 2050	RCP 8.5 2070
Gain	107	102	98	93
Loss	110	105	112	103
Stable	1992	1966	1951	1928
Unsuitable	51478	51514	51526	51564
Total area	53687	53687	53687	53687

Table 4

Change detection of suitability class by comparison with present distribution as computed in Km2 under RCP 4.5 and RCP 8.5.

	RCP 4.5 2050	RCP 4.5 2070	RCP 8.5 2050	RCP 8.5 2070
Suitability class	Shift in the area (km2)
Unsuitable	51478.4	51526	51513.8	51563.9
Unsuitable to low suitable	1099	1086	1079	1065.5
Moderate suitable	142	137	135	136
Low suitable	362	359	360	352.5
New low suitable	106.8	98.1	102	92.5
Contraction	109.7	112	105.3	102.5
Low to unsuitable	75.8	65.5	60.5	57.5
Low to moderate suitable	137	126	131	128
Moderate to low suitable	23.5	13.4	21.5	13.4
Moderate to highly suitable	52.9	54	56.4	50.1
Unsuitable to moderate suitable	0	18	19	27.5
Low to highly suitable	59.2	62	59.6	63.4
Unsuitable to highly suitable	41	30	44	33.5
Total area	53687	53687	53687	53687

4. Discussion

The susceptibility to climate change is now reflected in spatial distribution and forest ecosystem vulnerability across the globe [46]. Climate change is projected to be a ‘dominant stressor’ under different climate projection models in the latter half of the 21st century [47]. As there is no denial of the reality of climate change, attention is now being actively paid to formulate some mitigation measures to maintain the stability of an ecosystem. Species distribution models in this direction have contributed immensely by providing reliable information about the potentially suitable habitats of sensitive/ vulnerable species or communities that need priority attention. The present study investigates the habitat suitability of D. hatagirea under a changing climate scenario using a species distribution model. The study provides a detailed map of the current and future species distribution in light of climate changes (Figs 6 and 7). Dactylorhiza hatagirea distribution was mostly explained by topographical variables rather than bioclimatic variables (Table 1). Among the topographical factors, elevation (34.85%) was the most dominant contributing factor, followed by soil type (8.77%) and LULC (8.26%), while among the bioclimatic variables, precipitation of coldest quarter (Bio 19; 23.04%), annual mean temperature (Bio 1; 5.51%), and temperature seasonality (Bio 4; 5.11%) were most prevalent. These physiographic factors, along with topographical features (i.e., elevation, land use characteristics, slope angle, and aspect), and bioclimatic parameters, are reported to have pronounced effects on the pattern of species distribution and community structure in the alpine meadows [35, 48]. Such factors play a key role in the alpine biodiversity where the species are skewed more towards particular habitats (i.e., moist and marshy habitat) than open grasslands or rugged terrains [14, 49].

Previous findings are in line with this study, wherein altitude and bioclimatic variables (temperature and precipitation) were reported to play a major role in the distribution and population structure of D. hatagirea [50-53]. Among these, Thakur et al. (2021) [52] reported precipitation of the coldest quarter (Bio 19) as the most significant bioclimatic variable that influences D. hatagirea distribution as obtained in this study. The ecological relevance of Bio 19 in the target species distribution was accredited to its marshy habitat, for which precipitation (in the form of snow) during the winter season could have a direct role in recharging groundwater and maintaining desired soil moisture [52, 54]. Blinova (2008) [55] and Sletvold et al. (2010) [56] have shown that temperature (of the growing season) and precipitation are responsible for the persistence of Dactylorhiza populations. Further, a study by Shrestha et al. (2021) [53] from Nepal postulated a different stance where annual mean temperature (Bio1), precipitation seasonality (Bio15), and annual precipitation (Bio12) are the most significant variables in the target species distribution, while, precipitation in the coldest quarter (Bio19), precipitation in the driest quarter (Bio17), and the environmental layer were of intermediate significance. Similar to our findings, Rana et al. (2020) [3], using ensemble species distribution modelling (eSDM), reported elevation (30.97%) as the major dominant contributing variable, while amongst the bioclimatic variables, the mean temperature of the wettest quarter (Bio 8; 24.69%) and annual precipitation (Bio12; 21.11%) were the key contributing variables. Given the understanding from the above and several other studies on terrestrial orchids, other than biotic elements, temperature (of the growing season) and precipitation strongly modulate or affect their distribution. Thus, any significant climate change can be envisaged to have a magnified impact on these species overall distribution and growth performance. An observational study in the laboratory conditions noted maintaining the live germplasm of the species at controlled temperature chambers, i.e., 20°C, 30°C and in glasshouse condition (>35°C), revealed better growth performance and flower development at 20°C only. In comparison, growth ceased after the initial leaf development stage and later perished in temperature above 35°C, suggesting species narrow temperature tolerance regime for growth and perpetuation. Similar findings are reported elsewhere, where an excessive rise in temperatures was reported to affect the vegetative growth flower bud differentiation negatively and preclude regeneration of Paeonia delavayi [57], Camellia sinensis [37], Dalbergia cultrata [58], Rosa arabica [59], Hippophae salicifolia [60], Fritillaria cirrhosa [61], Rhododendron niveum [62], and Ilex khasiana [43].

In addition to the above, soil type, moisture ratio, and soil pH [49] are the other vital decisive variables that posture or play a role in shaping the distribution of D. hatagirea (Table 3). In a study by Thakur et al. (2021) [52] revealed that the populations of D. hatagirea flourished in soils rich in organic matter (i.e., loamy sand to sandy loam) and had adequate soil moisture (~ 63.23%). Also, Shrestha et al. (2021) [53] emphasized geological substrate and soil properties to be the key factors determining the distribution of the species. Similar observation was recorded during our ecological assessment surveys wherein the target species has luxuriant growth in moist sites along the hill slopes, dodged within narrow streams, and on open undulating meadows with high soil moisture [49]. Much of these habitats had slope steepness angles ranging from gentle (14°) to moderate slope (41°) angle, while north/ north-east aspects were most prevalent [49]. These variables were identified as factors of importance in our study, considering their role in posturing the vegetation reserve in the alpine habitats. Therefore, their role cannot be ignored in the Himalayas context, where the influence of the microclimatic conditions edaphic factors largely prevail [63].

In recent years, rapid climate changes in the Himalayas have resulted in distributional changes for a wide range of taxa [64]. The aftermath of these events has seriously impacted the geographical distribution of species, with reports of some species migrating to higher elevations [1, 57, 65–68]. The alpine species in the Himalayas are habitat-specific and have a narrow distributional range; therefore, they are more vulnerable to extinction. The disproportionate effects on these species migrating to high latitude or elevation are attributed to climate change [65]. Our prediction showed that the shift in suitable habitat distribution to high elevations would gradually become more significant using the climate projection model. Model predicted climatically suitable habitat would expand under the RCP 4.5 and RCP 8.5 climate scenario toward north/ northeast direction and would become invariably less suitable at the lower altitudes (Figs 6 and 7). Further, we speculate that the north/ northeastward shift observed in our study could also be attributed to; North-facing aspects in the mountains are associated with higher soil nutrient content, higher biomass, coverage, height, species diversity than south-facing slopes [49, 69, 70]. Climate warming and shifting of species towards north/ northeast in the Himalayas is reported [3, 35, 71, 72]. In Nepal, Shrestha et al. (2021) [53] projected that the target species would be elevated to 5000 m in the future, a substantial change when compared with the present distribution at 4000 m. A similar prediction for the species has been made elsewhere [3, 50].

Likewise, the change analysis, i.e., stable area, habitat loss, and habitat gain area, was carried out for RCP 4.5 and RCP 8.5 (Table 3). The area under stable habitat under RCP 4.5 (2050) was invariably higher RCP 8.5 (2050 and 2070); suggesting changes in climatic parameters and land use characteristics over higher emission rate would negatively impact the suitability of the habitat. Other than these, model prediction advocates climatic changes will also bring new areas (habitat gain) under habitat suitability class, with the highest gain observed under lower emission rate, i.e., RCP 4.5 than RCP 8.5. The study prospects the RCP4.5 scenario to be more favorable for D. hatagirea than RCP 8.5 in the northwestern part of IHR, thus providing an expansion scope. Meanwhile, warming of lower elevation or areas where the species is currently found explains the climatic niche loss in the future. The result obtained by overlaying the projected layer in the current and future climatic scenarios supports the projection made by Rana et al. (2021) [3] in the Nepal Himalaya using ensemble modelling (eSDM) approaches. Shreshta et al. (2021) [53] proposed a contrasting viewpoint with HadGEM2-ES, CCSM4, and BCC-CSM1-1 simulation model in a different study. Of the three models, BCC-CSM1-1 and CCSM4 simulations anticipated the species to lose 61–85% and 71–98% of its niches by 2070, whereas, with HadGEM2-ES, the species will lose all preferred niches by 2070 in RCP4.5 and RCP8.5 scenarios. In such conditions, parallel studies on community structure, biotic interactions, plant-pollinator or plant-mycorrhizal fungi interactions, population status, and habitat characteristics are also necessary. Hence, an integrated model with both the elements (biotic and abiotic) and their cumulative effects needs further investigation. This is notable because they play an important role in the persistence of D. hatagirea populations [52].

Along with climate change, irrational harvesting practices of MAPs as a result of increased market demand for herbal medicine and lack of knowledge/ awareness on sustainable harvesting led to serious habitat degradations in the Himalayan region [49]. Similarly, changes in LULC, anthropogenic interference (i.e., habitat degradation and fragmentation, unscientific collection, pre-mature tuber overharvesting, grazing), interspecies competition (i.e., mainly habitat engulfed by Persicaria wallichii), and poor seed germination has seriously impacted the relative distribution of D. hatagirea in the study area [49]. The present study provides an overview of habitat suitability and likely changes with projected climate scenarios in conjunction with changes in the species geomorphologic profile. In this context, the information provided in the present study could be beneficial in various conservation initiatives at the local and regional levels in West Himalaya. The study conducted has some limitations as the model developed to forecast the fundamental niche of the species rather than the realized niche. The species realized niche might not be the same as predicted in our model prediction results. Another limitation is that this study modeled the habitat suitability of D. hatagirea at different time scales (2050 & 2070) based on abiotic factors but did not consider biotic factors such as plant-mycorrhizal association or plant-pollinator interaction. As a result, potentially suitable area of the species might be overestimated, as biotic interaction, especially mycorrhizal association, play a crucial role in plant establishment in the family Orchidaceae [53, 73].

5. Conclusion

Dactylorhiza hatagirea, an endemic high-value Himalayan medicinal species, is under great stress and needs urgent attention. The perturbance of climate change has added extra pressure on the species allied with high degrees of anthropogenic stress. Therefore, the present study provided a clear overview of the habitat suitability for D. hatagirea and predicted the potential impacts of future climate on their distribution in Western Himalaya. The species potential distributions are explained mainly by elevation, precipitation of the coldest quarter, soil moisture, LULC, and mean annual temperature. The study reveals that D. hatagirea has approximately about 131 sq. km as its fundamental niche with high (>0.75) habitat suitability, which corroborates to about 0.24% of the total geographic area of the state. Under future projections RCP 4.5 and RCP 8.5, species distribution is projected to be very similar to the current distribution, except shifting species in a northward direction to higher elevation is expected. Furthermore, most predicted potential habitats fall within areas with anthropogenic encroachment leading to habitat degradation, unscientific and destructive harvesting practices, grazing, etc. Considering these, it is anticipated that the species may lose much of the habitat due to anthropogenic activities, while climate change impact will invariably be more at comparatively lower altitudes. Thus, it is imperative to undertake appropriate conservation steps and interventions like reintroduction/ augmentation programs. Besides these, the dependent communities awareness and sensitization curriculum holds utmost importance towards sustainable utilization. The model generated suitability maps can be of significant help to various policy prescribing National agencies, i.e., National Medicinal Plant Board (NMPB), Ministry of Environment Forest and Climate Change (MOEF &CC), Department of Science & Technology (DST). At the State level, the State Government has control over Forest Department and NGOs, thus ensuring and safeguarding the overall health of our forests and meadows.

Multi-collinearity test by using cross-correlations (Pearson correlation coefficients, r) among environmental variables using ENM Tools.

(DOCX)

Click here for additional data file.

Habitat transformations with respect to present distribution into different classes as depicted under RCP 4.5 (2050 and 2070).

Maps in S1 Fig are generated with ArcGIS version 10.3 (ESRI, CA, USA).

(TIF)

Click here for additional data file.

A comparative studies on habitat transformations between present distribution and under RCP 8.5 (2050 and 2070).

Maps in S2 Fig are generated with ArcGIS version 10.3 (ESRI, CA, USA).

(TIF)

Click here for additional data file.

2 Jul 2021

PONE-D-21-16347

Predicting the potential distribution of Dactylorhiza hatagirea (D. Don) Soo – a critically endangered medicinal orchid under multiple climate change scenarios

PLOS ONE

Dear Dr. Bhatt,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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PLOS ONE

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Additional Editor Comments (if provided):

Dr Bhatt et al.,

after two independent reviews, I believe your study may be accepted for publication in PLoS One after a significant improvement is performed to it. You will see that the reviewers had very contrasting, with one of them deciding for its rejection (but potential acceptance after deeply improved), whereas the other decided for a minor review. Considering this, I believe you should consider the issues raised by both of them, especially considering the fact that the distribution of the species was already discussed in a previous 2021 paper.

In case you decide to redo your MS, I suggest you to take special attention to the issues raised by reviewer #2.

Given all the required changes you will need to do, I will grant you a three-months period to perform the improvements (October 1st, 2021). In case you need more time, please let me know. Do not hesitate to resubmit earlier in case you are able to. In By the time you resubmit, please do not forget to prepare a rebuttal letter to inform to your reviewers of all the changes you accepted and implemented and those you did not agree with and with.

Sincerely,

Daniel Silva, Ph.D.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Comments to the Author:

Singh et al. have produced a study to discern the potential distribution of the orchid Dactylorhiza hantagirea in the Indian state of Uttarakhand. Using the common maximum entrophy algorithm implemented in the popular software Maxent, they are building a model with several climatic, edaphic and landscape variables to produce a map with the suitable areas for this species, that is further projected to two scenarios of climate change (moderate and extreme warming). Although the authors use a standard methodology, and the models seem to be well constructed, the study has a too narrow scope. It is hard to understand why the authors have limited the niche models to just a very small range of this species (Uttarakhand), as it is actually distributed not only to the Indian Himalayas (as the authors state in lines 91-92), but on a large strip from Pakistan to Mongolia and NE China (see e.g. http://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:626614-1, or http://www.efloras.org/florataxon.aspx?flora_id=2&taxon_id=242421813). The small study area, in addition to largely restrict the focus of the ms., also poses a methodological limitation to the built models: the potential occurrence in Uttarakhand is based on the fundamental niche derived from just 30 occurrences gathered by the authors. This could produce a biased model (and probably with less suitable area) if compared with a model produced with all species occurrences along its whole range. In other words, niche models, even when are produced for a small region, should be built ideally using all species’ occurrences.

Another large problem is that a very similar paper has been just published in Journal of Sustainable Forestry (https://doi.org/10.1080/10549811.2021.1923530), also using Maxent and focused in the same state (Uttarakhand). In addition, a second paper, also of 2021 and published in Journal of Applied Research on Medicinal and Aromatic Plants (https://doi.org/10.1016/j.jarmap.2020.100286), is also using Maxent to build a model for almost the whole species’ range (so, including Uttarakhand). Thus, I cannot recommend this ms. to be accepted in PLOS ONE. However, tacking the advantage that climate change models provided in the ms., and perhaps expanding its scope to more-focused conservation issues (e.g. to check whether the suitable areas fall within the present PAs in Uttarakhand), the paper could be published in a local journal or in a conservation journal.

A few specific comments:

1. Lines 89-93. The first time that the study species is mentioned, complete information about its distribution area should be provided. As I mentioned above, the species has a quite large distribution area. Some basic information about the morphology, taxonomy, ecology accross its range, and conservation status is also mandatory.

2. Lines 100-101. The authors should provide the reasons why the study area is restricted to Uttarakhand. As I mentioed above, including aims related to the local conservation issues in this state could make this paper stronger.

3. Lines 149-150. A way to improve the paper could be to incude other GCMs (such as MIROC and others).

Reviewer #2: I have read this paper with great interest. However, in its present form, there are some issues that would deserve some clarification before the paper is suitable for publication.

L36-38 I suggest removing all the Bio, and putting directly the variable name that is in parentheses.

L43-44 All these regions mentioned here should appear on the map in Figure 1. For readers, like me, who do not know the study region, they will not be able to identify and understand the results without this information in the figure.

L44-46 Considering that the title of the paper states that the study is about the evaluation of an endangered medicinal species, the conclusion of the paper should have this approach. From the title of the paper, the readers will look in the abstract for the conclusion of the study - what was found looking forward? The current conclusion is quite vague.

L48-49 These keywords (Dactylorhiza hatagirea, Habitatdistribution [space], Climate change) are already in the title. It is best to use different words

L52 Climate is an…

L63 Remove or inform which are the “etc.”

L71 Remove or inform which are the “etc.”

L74-75 I consider that before this paragraph, another paragraph with the main approach of the current manuscript needs to be developed. What are the future predictions for mountainous areas in the region? Mountain environments are quite sensitive to climate change, and small changes are already enough to alter the habitat suitability of species. I think the authors need to address this a bit, as well as changes in environmental conditions from climate change across elevational gradients. What are the expected impacts, and already known to science? What are the predicted or potential impacts on the species niche if changes occur? See this manuscript for a good approach of this: https://doi.org/10.1016/j.ecolind.2020.106435.

L100-104 Please add question marks to the questions.

L113-116 What is the range of altitude?

L258-262 Again, it is not possible to identify these regions in this figure.

L328-331 All this information is already available in the results. It is repetitive.

L421 The title of some REF are written in uppercase and others in lowercase. Standardize all of them to lowercase. Also, some REFs (4, 10, 43) are missing spaces between their names. Review REF 30.

I suggest that the authors revise the lack of space between words, because this occurred throughout the text (e.g., L41, L346, L354,....)

As the maps provided have low resolution, it was not possible to visualize the change of patterns for the different scenarios according to the changes in coloration. Anyway, besides increasing the font size and resolution of these figures, I suggest that the authors put the name of the axis with the biological variable (e.g., minimum temperature), and not the code given by WorldClim (e.g., Bio 2). But this code could come in parentheses.

**********

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Reviewer #2: No

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8 Nov 2021

Reviewer 1

1. Although the authors use a standard methodology, and the models seem to be well constructed, the study has a too narrow scope. It is hard to understand why the authors have limited the niche models to just a very small range of this species (Uttarakhand), as it is actually distributed not only to the Indian Himalayas (as the authors state in lines 91-92), but on a large strip from Pakistan to Mongolia and NE China

Response: As indicated by the reviewer, some insights on the potential distribution of the target species across the Himalayan region was given by Thakur et al. (2021), however, detailed investigation at the regional level was identified as gap area of research. Resource limitation to validate the model in other part of the Himalayan region, asserts the present study to the state of Uttarakhand. Further, on the realistic side, we have extensively surveyed the state of Uttarakhand for population estimation along the altitudinal gradient and in different habitat types [Singh et al. (2021); doi doi.org/10.1007/s10113-021-01762-6] and validated the model output with the field data. Needful revision on the manuscript, on the distribution of the target species is incorporated and revised.

2. Another large problem is that a very similar paper has been just published

Response: We believe that every study conducted has some merits and limitations. As indicated, previously two papers are published. In a study by Thakur et al. (2021), emphasized on the ecological factors affecting the occurrence of the species and ground truth for Himachal Pradesh (India) only, although they have model the presence of the species for the entire Himalayan region. In another study by Chandra et al. (2021), their work was concentrated specifically in the Uttarakhand state and ground truth. Likely impact of climate change on the species suitable habitat and transformation thereafter was the gap area of research. Thus, taking the learning’s from both the study, the present study envisaged to identify region/pockets where the target species habitat suitability was maximum, moderate, low or least. Further, we intended to identify, how the present distribution would be affected in the face of climate change using different climate projections parameters. Also, habitat transformation (i.e., stable, gain or loss) and change analysis was conducted to depict vulnerable areas and areas which will remain suitable in the next 50 years

3. Lines 89-93. The first time that the study species is mentioned, complete information about its distribution area should be provided. As I mentioned above, the species has a quite large distribution area. Some basic information about the morphology, taxonomy, ecology across its range, and conservation status is also mandatory.

Response: We thank the reviewer for the suggestion. The necessary information asked for is added into the manuscript.

4. Lines 100-101. The authors should provide the reasons why the study area is restricted to Uttarakhand. As I mentioned above, including aims related to the local conservation issues in this state could make this paper stronger.

Response: We agree with the reviewer viewpoint regarding elsewhere reported records of the target species. The state of Uttarakhand was extensively ground truth, which in others cases would not have been possible. Further, without validating a model with the field data would be biased in true sense.

5. Lines 149-150. A way to improve the paper could be to incude other GCMs (such as MIROC and others).

Response: We thank the reviewer for his/her remark. After extensive literature review on Himalayan species distribution modelling, the present model was selected. We believe there is prediction disparity among different models in different regions.

Reviewer 2:

1 L36-38 I suggest removing all the Bio, and putting directly the variable name that is in parentheses.

Response: We thank the reviewer for bringing up this correction. Needful actions has been taken in the revised manuscript

2 L43-44 All these regions mentioned here should appear on the map in Figure 1. For readers, like me, who do not know the study region, they will not be able to identify and understand the results without this information in the figure.

Response: Map of the Figure 1 has been revised extensively, with the region having high habitat suitability marked.

3 L44-46 Considering that the title of the paper states that the study is about the evaluation of an endangered medicinal species, the conclusion of the paper should have this approach. From the title of the paper, the readers will look in the abstract for the conclusion of the study - what was found looking forward? The current conclusion is quite vague.

Response: We thank the reviewer for his/her constructive remark. In the revised manuscript, abstract conclusion has been made more conclusive and drawn factual statement.

4 L48-49 These keywords (Dactylorhiza hatagirea, Habitat distribution [space], Climate change) are already in the title. It is best to use different words

Response: We thank the reviewer for his/her suggestions. Necessary actions has been undertaken and revised

5 L52 Climate is an…

Response: Incorporated as suggested

6 L63 Remove or inform which are the “etc.”

Response: In the revised manuscript “etc” has been removed as suggested

7 L71 Remove or inform which are the “etc.”

Response: In the revised manuscript “etc” has been removed as suggested

8 L74-75 I consider that before this paragraph, another paragraph with the main approach of the current manuscript needs to be developed. What are the future predictions for mountainous areas in the region? Mountain environments are quite sensitive to climate change, and small changes are already enough to alter the habitat suitability of species. I think the authors need to address this a bit, as well as changes in environmental conditions from climate change across elevational gradients. What are the expected impacts, and already known to science? What are the predicted or potential impacts on the species niche if changes occur?

Response: We thank the reviewer for the suggestions. In the revised manuscript, a separate paragraph has been added to address all the suggestion raised by the reviewer.

9 L100-104 Please add question marks to the questions.

Response: Necessary action is undertaken

10 L113-116 What is the range of altitude?

Response: In the Figure 1, altitudinal class across different region is provided, so as to depict highland and lowland areas within the state boundary

11 L258-262 Again, it is not possible to identify these regions in this figure.

Response: The regions mentioned in the manuscript has been outlined with respective boundary files and respectively labeled

12 L328-331 All this information is already available in the results. It is repetitive.

Response: The repetitive text has been deleted

13 L421 The title of some REF are written in uppercase and others in lowercase. Standardize all of them to lowercase. Also, some REFs (4, 10, 43) are missing spaces between their names. Review REF 30.

Response: Necessary actions has been undertaken and revised

14 I suggest that the authors revise the lack of space between words, because this occurred throughout the text (e.g., L41, L346, L354,....)

Response: Necessary actions has been undertaken and revised

15 As the maps provided have low resolution, it was not possible to visualize the change of patterns for the different scenarios according to the changes in coloration.

Response: We support the reviewer viewpoint in the revised manuscript; the resolution of the maps has been increased. Also, change-shift maps has been divided into separate maps of each, so as to make them more visible and meaningful

16 Anyway, besides increasing the font size and resolution of these figures, I suggest that the authors put the name of the axis with the biological variable (e.g., minimum temperature), and not the code given by WorldClim (e.g., Bio 2). But this code could come in parentheses.

Response: We thank the reviewer for the suggestion, the highlighted figures has been revised.

Submitted filename: Response to Reviewer.docx

Click here for additional data file.

13 Jan 2022

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Daniel de Paiva Silva, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments:

Dear Bhatt et al.,

In light of the reviews provided by both reviewers, I find your manuscript could be accepted to be published in PLoS One after the passes through a major review. Please resubmit until April 12, 2021. If you need more time, please let me know and do not hesitate in case you are able to resubmit earlier. Please take close attention to all the issues raised by both reviewers. Unfortunately, there are several points that you need to improve you manuscript yet.

By the time of resubmission, please do not forget to prepare a rebuttal letter for your reviewers, explaining each and every decision you made during the review process.

Sincerely,

Daniel Silva

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: (No Response)

Reviewer #4: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Partly

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #4: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: No

Reviewer #4: No

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: In their study, the authors predicted the potential distribution (current and future) of a medicinal orchid, Dactylorhiza hatagirea, which becomes even rarer due to the intense human activity in the Himalayan area. Specifically, they used Maxent algorithm to predict the potential distribution of D. hatagirea, a software classified among those with the highest predictive accuracy. From a methodological perspective, I disagree with the use of AUC value as a measure of model evaluation. Although this metric has been widely used so far, I believe that others are more important (e.g. AICc; see specific comment below). In general, authors did not use the findings of Thakur et al. (2021) and Shrestha et al. (2021) who also predicted the current and future potential distribution in the Himalayan are (India and Nepal). In the light of these two existing studies, the present study should highlight the novelty of their own study.

However, I believe that the most significant problem in the language! Although I am not a native English speaker, I feel that the manuscript would be greatly improved if it would be checked by someone with excellent skills in the English language.

Others suggestions

P.3, L.56: “ ……. and abiotic factor affecting species potential geographic…..”

P.3, L.71: “….. is much higher than the global average”. Delete the word “rate”.

P.4, L. 83: “….. with 31% of them being native, whereas 15.5 % endemic and threatened.”

P.4, L. 85: “Other challenges …..”

P.4, L.97: “Therefore, estimation current plants distribution and identifying important climatic refugia will help predict future distribution …..”

P.4, L.99: ecological niche modelling is a set of different techniques, whereas Maxent is just one of them. So, these two terms should not be treated as the same. In your case I would suggest to make a small introduction to the SDMs in general, and afterwards you could write one or two sentences about Maxent.

P.5, L. 101. “Using these input variables”. Which variables? You did not mention any kind of variables. You should say “Using environmental variables/predictors …”.

P5-6, L.105-127: I suggest stating other authors (Thakur et al. 2021; Shrestha et al. 2021) who predicted current and future potential distribution of D. hatagirea and after that you can add a part about the novelty of your study.

P.6, L.140: “…. harbors alpine vegetation, which covers …..”

P.6, L. 141: Replace the phrase “in totality it forms about” with “corresponding to c. 24.11%”

P.6, L.142: Either write “The alpine areas are well known for their high-value …” or “The alpine area is well known for its high-value ….”

P.7, L. 147: “presence points” instead of “presence point”

P7, L.150-151: “Data on the species were very limited, whereas herbarium records were not geo-referenced.”

P.7, L. 152: “presence points were recorded.”

P.7, L.155: “decimal degrees” instead of “degrees decimal”

P.8, L.168: can you please say a few words why you used CCSM4 instead of others?

P.8, L. 172: “edaphic properties are expected to remain stable”

P.8, L. 173: “hence the same raster layer was used in future projections.”

P.9, L.178-189. This part should be rewritten and should be checked by a native English speaker.

P.10, L. 232. I wouldn’t say that reduction in the number of predictors increases the predictive power of the model. Instead of that, if you use highly inter-correlated variables, then you will not be able to identify those that are highly important for the distribution of the studied species. And this is owned to the high correlation coefficient between different predictors.

Authors used AUC to assess the predictive accuracy of their model. Actually, I didn’t read that they used the average model prediction made after 30 runs. However, high AUC values, as in the case of this study, may be owned to the low number of species records. Instead of using AUC value, I would recommend of running the model a number of times (e.g. 10 or 30 runs) and then select the best model by using the Akaike information criterion (AICc) (Warren & Seifert 2011).

P. 13. L. 272: “The TIC value is the habitats suitability class of D. hatagirea on the maxent model.” What do you mean? Please rephrase that sentence.

In several parts of the manuscript, a semi-colon (;) was used instead of a comma.

p.17, L. 337: Authors used the term “Habitat distribution modelling”. This term is used here for the first time, instead of others that were used in other parts of the manuscript. I would suggest using the same terminology throughout the text.

p. 17, L.340: “under a changing climate scenario”

p.17, L.341-342: SDMs provide the potential distribution. However, species distributions are determined both by abiotic factors and biotic interactions. Orchids are characterized by strong biotic interactions which have to do with the mycorrhizal fungi that will help them germinate and keep feeding them, as well as by specific insects which will be their pollinators. Such biotic interactions are very important and, in many cases, can influence orchids’ potential distribution. Such limitations should be mentioned in the manuscript. You can read Evans et al (2021) and Tsiftsis & Djordjevic (2020).

P.18, L.349-350: I think that the term “species density” is wrong. Can you rewrite that part as this term is referring to a number of different species?

P.18, L. 354: add a comma after the word “variables”.

p.18, L.355-358: please rewrite that part. It is not clear what you want to say!

The findings of this study should be discussed in relation to the findings of Thakur et al. (2021) and Shrestha et al. (2021), which at this stage are ignored.

References

Evans, A., Janssens, S. & Jacquemyn, H. 2021. Impact of Climate Change on the Distribution of Four Closely Related Orchis (Orchidaceae) Species. Diversity 12(8): 312. DOI: 10.3390/d12080312

Shrestha, B., Tsiftsis, S., Chapagain, D.-J., Khadka, C., Bhattarai, P., Kayastha, N., Kolanowska, M. & Kindlmann, P. 2021. Dactylorhiza hatagirea in Nepal: Distribution prediction under current and future climate change context. Plants 10(3):467.

Tsiftsis, S. & Djordjević, V. 2020. Modelling sexually deceptive orchid species distributions under future climates: the importance of plant-pollinator interactions. Scientific Reports 10, 10623.

Warren, D. L., & Seifert, S. N. (2011). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21, 335–342.

Reviewer #4: Review of the manuscript “Prediction the potential distribution of Dactylorhiza hatagirea (D. Don) Soo – a critically endangered medicinal orchid under multiple climate change scenarios” by Singh et al.

In this study, Singh et al aims to identify habitat suitability of D. hatagirea in the Western Himalaya and determine the future geographical distribution under climate change scenarios.

Looking at the comments and suggestions from two previous reviewers and responses from authors, I see authors include suggestions from reviewers and I believe the manuscript has improve substantially compared to the previous version. However, I have some concerns on how analysis were conducted. I have some suggestions/comments which may help authors for a more clear analysis.

Abstract

L32. With these models, the authors are not determining the future geographical distribution, but the suitable areas (in terms of environmental variables).

L41. Most of the species loss habitat suitability in future scenarios. Especially when they move to higher altitude which by the conic shape of mountains, area is less available. Of course this is possible if species may disperse to different areas where is found in contemporary habitat.

Add in what percentage is expected to expand.

Introduction

L59. Not necessarily. Some species are more capable than others, not all species will show the same responses.

L69. The increased in greenhouse gases emission is not only for the region, but for the entire planet.

L106. The species is an orchid, right? Authors should say this here.

L116. Not clear what do the authors mean with “the species require…..conditions for growth and perturbation….”

L118. “The existing multiplication for mass multiplication” sounds odd to me.

L121. Delete “present”

Materials and methods

I´d suggest a section for Study species with a brief taxonomic description and more on the use and management. I´d include detailed pictures of the species (flowers, leaves, bulbs), individuals in the wild and also pictures of the products in trade.

Is the species included in the IUCN Red List? In case is not I´d highly recommend to make the IUCN assessment following the IUCN criteria and include this as part of results.

2.3. Climate data. I suggest a more general subtitle for this subsection. What the authors are using is not only climate data. They are using geomorphologic, pedologic and LULC.

The land use is an anthropogenic layer and as used, I believe is a categorical variable. This layer should not be included as a predictor variable in the model. Instead the resulting model can be clipped with this layer only as a polygon.

L212. AUC was the only statistical test used assess the models?

Since several years ago, AUC has been demonstrated its reliability as a comparative measure of accuracy. I suggest to include some other tests, (for example the binomial test, Partial ROC, Trus skill statistics). Lobo et al. 2008 (https://doi.org/10.1111/j.1466-8238.2007.00358.x) is a must read article for this.

L217. The used classification especially for comparison among RCPs and periods (Table 4; Figure 5, Figure 7 and 8) is quite confusing at least to me. I would use instead a binary classification (0/1, suitable/unsuitable) using only one threshold. This way would be much more clear to compare among RCPs and periods (see suggestions in the Results section).

Results

L235. Table 1 is in the Methods section, but it is a Result. I suggest to include it only here and not in Methods.

Table 1. I see the information on this table as a result. Why is it in methods?

Table 3. It is not clear to me how the Gain, Loss, Stable and Unsuitable areas were computed. Authors did use a classification of Unsuitalbe, low suitable, moderate suitable

Table 4. Are the authors presenting the same information in Table 4 than in the Maps (figure 7 and 8)? Highly recommended to include the information only once.

Figures

Fig. 1. I suggest to add (at least in a small inset) the Map of India.

Figures 2-4 are those generated by default by Maxent. I don´t see these figures essential to be included in the results. They could not be included or maybe only as supplementary.

Figures 7 and 8 and the colors used are not useful to explore what is the future of the species. The main problem from my point of view is the 11 categories resulting of comparisons between RCPs and years (2050 and 2070). I would not use this comparasion. I would do the comparison using the gain, loss, stable and unsuitable categories, considering only the binary classification.

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4 Apr 2022

1 I believe that the most significant problem in the language!

Although I am not a native English speaker, I feel that the manuscript would be greatly improved if it would be checked by someone with excellent skills in the English language. We thank the reviewer for the needful revision; in the revised manuscript, grammatical errors and other languages shortfalls has been significantly improved

2 P.3, L.56: “ ……. and abiotic factor affecting species potential geographic…..” Revised as suggested (L. 56-57)

3 P.3, L.71: “….. is much higher than the global average”. Delete the word “rate”. We thank the reviewer for the suggestion. Needful revisionsareundertakenin the manuscript (L. 71)

4 P.4, L. 83: “….. with 31% of them being native, whereas 15.5 % endemic and threatened.” Revised as suggested (L. 82-83)

5 P.4, L. 85: “Other challenges …..” We thank the reviewer for his/her remark. Needful revision is made in the manuscript (L. 84)

6 P.4, L.97: “Therefore, estimation current plants distribution and identifying important climatic refugia will help predict future distribution …..” Needful revision is undertaken in the revised manuscript (L. 96)

7 P.4, L.99: ecological niche modelling is a set of different techniques, whereas Maxent is just one of them. So, these two terms should not be treated as the same. In your case I would suggest to make a small introduction to the SDMs in general, and afterwards you could write one or two sentences about Maxent. We thank the reviewer for the needful correction. The revised manuscript differentiates between MaxEnt and other simulation tools. Also, as suggested, a small introduction about SDM is added.

(L. 99-104)

8 P.5, L. 101. “Using these input variables”. Which variables? You did not mention any kind of variables. You should say “Using environmental variables/predictors …”. We thank the reviewer for his/her remark.

Needful corrections are undertaken

(L. 104-105)

9 P5-6, L.105-127: I suggest stating other authors (Thakur et al. 2021; Shrestha et al. 2021) who predicted current and future potential distribution of D. hatagirea and after that you can add a part about the novelty of your study. With reference to Comment No. 31 (Sl. No), we have briefly discussed the finding of both authors and integrated them with the present study findings

(L. 352-472)

10 P.6, L.140: “…. harbors alpine vegetation, which covers …..” Revised as suggested (L. 145)

11 P.6, L. 141: Replace the phrase “in totality it forms about” with “corresponding to c. 24.11%” Revised as suggested (L. 146)

12 P.6, L.142: Either write “The alpine areas are well known for their high-value …” or “The alpine area is well known for its high-value ….” Revised as suggested (L. 147)

13 P.7, L. 147: “presence points” instead of “presence point” We thank the reviewer for the remark; needful correction is made in the revised manuscript (L. 152)

14 P7, L.150-151: “Data on the species were very limited, whereas herbarium records were not geo-referenced.” Needful correction is made

(L. 155-156)

15 P.7, L. 152: “presence points were recorded.” Revised as suggested (L. 157)

16 P.7, L.155: “decimal degrees” instead of “degrees decimal” We thank the reviewer for his/her remark; needful revision is made in the revised manuscript (L. 160)

17 P.8, L.168: can you please say a few words why you used CCSM4 instead of others? After extensive literature review and synthesis from the studies (mainly from the Hindu-Kush Himalayan region), we needed a balance model to fulfill our objective

18 P.8, L. 172: “edaphic properties are expected to remain stable” Revised as suggested (L. 176-177)

19 P.8, L. 173: “hence the same raster layer was used in future projections.” Revised as suggested (L. 178)

20 P.9, L.178-189. This part should be rewritten and should be checked by a native English speaker. We thank the reviewer for the needful revision; this section has been revised to develop a meaningful sentence.

(L. 182-191)

21 P.10, L. 232. I wouldn’t say that reduction in the number of predictors increases the predictive power of the model. Instead of that, if you use highly inter-correlated variables, then you will not be able to identify those that are highly important for the distribution of the studied species. And this is owned to the high correlation coefficient between different predictors. We thank the reviewer for the suggestion, and we are in line with/ agree with the reviewer's viewpoints. The sentence in the revised manuscript has beenrephrased

(L. 243-246)

22 Authors used AUC to assess the predictive accuracy of their model. Actually, I didn’t read that they used the average model prediction made after 30 runs. However, high AUC values, as in the case of this study, may be owned to the low number of species records. Instead of using AUC value, I would recommend of running the model a number of times (e.g. 10 or 30 runs) and then select the best model by using the Akaike information criterion (AICc) (Warren & Seifert 2011). We thank the reviewer for his/her remark. The needful revision sought is revised.

In the revised manuscript, other than relying on AUC for assessing the model accuracy, we also calculated sensitivity, specificity, and true skill statistics (TSS) (Lobo et al. 2008; https://doi.org/10.1111/j.1466-8238.2007.00358.x), which are widely used to access the model accuracy.

(L. 220-229 & L. 254-259)

23 P. 13. L. 272: “The TIC value is the habitats suitability class of D. hatagirea on the maxent model.” What do you mean? Please rephrase that sentence. We thank the reviewer for the needful revision; the sentence has been deleted as the convictionmade failed to make any fruitful argument in the preceding paragraph (L. 289-90)

24 In several parts of the manuscript, a semi-colon (;) was used instead of a comma. The manuscript has been thoroughly revised with all the parameters in consideration of the present comments

25 p.17, L. 337: Authors used the term “Habitat distribution modelling”. This term is used here for the first time, instead of others that were used in other parts of the manuscript. I would suggest using the same terminology throughout the text We thank the reviewer for his/her remark. In the revised manuscript, for the shake of uniformity, SDM is used throughout the manuscript

26 p. 17, L.340: “under a changing climate scenario” Revised as suggested (L. 360)

27 p.17, L.341-342: SDMs provide the potential distribution. However, species distributions are determined both by abiotic factors and biotic interactions. Orchids are characterized by strong biotic interactions which have to do with the mycorrhizal fungi that will help them germinate and keep feeding them, as well as by specific insects which will be their pollinators. Such biotic interactions are very important and, in many cases, can influence orchids’ potential distribution. Such limitations should be mentioned in the manuscript. You can read Evans et al (2021) and Tsiftsis&Djordjevic (2020). We thank the reviewer for the needful revision. The needful suggestion has been incorporated, and limitations of the study are adequately mentioned

(L. 466-473)

28 P.18, L.349-350: I think that the term “species density” is wrong. Can you rewrite that part as this term is referring to a number of different species? We thank the reviewer for the suggestion; needful action in the sentence is undertaken in the revised manuscript

(L. 409-412)

29 P.18, L. 354: add a comma after the word “variables”. Revised as suggested

30 p.18, L.355-358: please rewrite that part. It is not clear what you want to say! We thank the reviewer for the needful revision. This section has been re-written and revised accordingly (L. 409-417)

31 The findings of this study should be discussed in relation to the findings of Thakur et al. (2021) and Shrestha et al. (2021), which at this stage are ignored. We thank the reviewer for the suggestion. In the discussion section, both the publication and other studies conducted on the target species are adequately discussed with the present study's finding. (L. 353-373)

## Reviewer- 4

S. No. Comments/suggestions/ queries Response

1 L32. With these models, the authors are not determining the future geographical distribution, but the suitable areas (in terms of environmental variables). We thank the reviewer for bringing up this correction. Needful actions havebeen undertaken and revised accordingly (L. 32-34)

2 L41. Most of the species loss habitat suitability in future scenarios. Especially when they move to higher altitude which by the conic shape of mountains, area is less available. Of course this is possible if species may disperse to different areas where is found in contemporary habitat.

Add in what percentage is expected to expand. We thank the reviewer for the suggestion; needful expansion percent has been incorporated in the revised manuscript (L. 40-42)

3 L59. Not necessarily. Some species are more capable than others, not all species will show the same responses. We partially agree with the reviewer's viewpoint. Yes, all species may not reflect the same response, but here, we intend to emphasizeon those species (especially in the context of the Himalayan region) where such responses are adequately seen. Such responses are often among the Rare, Endangered, and Threatened (RET) species in the Himalayan region. Although we still believe more conclusive needs to be conducted to strengthen this argument elsewhere and in the Himalayas.

(Manish, 2022; https://doi.org/10.1016/j.ecoinf.2021.101546)

Rather et al. (2022) (https://doi.org/10.1016/j.ecoleng.2021.106534)

Hamid et al. (2018) (https://doi.org/10.1007/s10531-018-1641-8)

4 L69. The increased in greenhouse gases emission is not only for the region, but for the entire planet. We agree with the reviewer's viewpoint, but if we compare the impacts of climate change across the globe and the Himalayasin several reports, including IPPC and several other projection reports, the cascading impacts of climate change in this region arejust double than that of the global average.

https://report.ipcc.ch/ar6wg2/pdf/IPCC_AR6_WGII_CrossChapterPaper5.pdf

Sabin et al. (2020) (https://doi.org/10.1007/978-981-15-4327-2_11)

Liu J, Rasul G. Climate change, the Himalayan mountains, and ICIMOD. Sustainable mountain development. 2007; 53:11-4.

Rafiq et al. (2022) (https://doi.org/10.1007/978-3-030-89308-8_12)

5 L106. The species is an orchid, right? Authors should say this here. For kind consideration and also for better understanding of the readers, the identity of the species is added in the Title of the manuscript itself (L. 1-2; L. 29)

6 L116. Not clear what do the authors mean with “the species require…..conditions for growth and perturbation….” Other than being an endemic (i.e., Himalaya), the target species also have high habitat preferentially towards a particular set of conditions. In this region, local microclimatic conditions play an important role in shaping or determining the extent of such species' distribution.

The sentence has been re-phrased for better understanding (L. 118-119)

7 L118. “The existing multiplication for mass multiplication” sounds odd to me. We thank the reviewer for highlighting this; the sentence has been re-phrased in the revised manuscript (L. 119-122)

8 L121. Delete “present” Revised as suggested (L. 122-123)

9 I´d suggest a section for Study species with a brief taxonomic description and more on the use and management. I´d include detailed pictures of the species (flowers, leaves, bulbs), individuals in the wild and also pictures of the products in trade. We thank the reviewer for his/her remarks. In the introduction, a brief taxonomic description of characteristics is added. (L. 110-112)

A separate Figure 1 is added in the revised manuscript to address the follow-up question. (L. 131-132)

10 Is the species included in the IUCN Red List? In case is not I´d highly recommend to make the IUCN assessment following the IUCN criteria and include this as part of results? As per IUCN 2022 (https://www.iucnredlist.org/) the species is not included in the threatened category; however, at the regional level, the species is categorized as ‘Critically Endangered’ (CAMP; http://envis.frlht.org/camp.php).

As the species is not included in IUCN 2022 threat category, the Title of the manuscript is revised (L. 1-2).

In the subsequent publication, the threat assessment as per the IUCN criteria would be undertaken

11 2.3. Climate data. I suggest a more general subtitle for this subsection. What the authors are using is not only climate data. They are using geomorphologic, pedologic and LULC. We thank the reviewer for highlighting this. A general title as ‘Data source’ is included in the revised manuscript for better understanding(L.162)

12 L212. AUC was the only statistical test used assess the models?Since several years ago, AUC has been demonstrated its reliability as a comparative measure of accuracy. I suggest to include some other tests, (for example the binomial test, Partial ROC, Trus skill statistics). Lobo et al. 2008 (https://doi.org/10.1111/j.1466-8238.2007.00358.x) is a must read article for this. We thank the reviewer for the suggestion and agree with his/her viewpoint. Following this, in the revised manuscript, other than relying on AUC for assessing the model accuracy; we also calculated sensitivity, specificity, and true skill statistics (TSS) using confusion matrix preparation according to Alloche et al. (2006) (doi: 10.1111/j.1365-2664.2006.01214.x)

(L. 220-229 & L. 254-259)

13 L217. The used classification especially for comparison among RCPs and periods (Table 4; Figure 5, Figure 7 and 8) is quite confusing at least to me. I would use instead a binary classification (0/1, suitable/unsuitable) using only one threshold. This way would be much more clear to compare among RCPs and periods (see suggestions in the Results section). We thank the reviewer for the suggestion. As stated in Comments No. 6 (Sl. No) regarding high habitat preferentiality and contagious distribution (mostly) of the species, here we aimed to elucidate and identify pockets that are most, moderate, low, and least suitable habitat in the given study area. Doing this, it is envisaged to provide a clear-cut picture of where the species can be translocated or habitats that can be taken up for medicinal plant conservation and developmental area.

As Figures 7 & 8 giveaway the same information as given in Table 4, therefore in the revised manuscript, both the Figures are added as supplementary Figures 1 & 2. (L. 325)

The habitat suitability classification used in this study was prepared following the methodology of Adhikari et al. (2012) (doi:10.1016/j.ecoleng.2011.12.004), Yang et al. (2013)(http://dx.doi.org/10.1016/j.ecoleng.2012.12.004), and Zhang et al. (2019) (https://doi.org/10.1016/j.ecoinf.2019.01.004);

Arslan et al. (2020) (https://doi.org/10.1007/s10113-020-01695-6)

14 L235. Table 1 is in the Methods section, but it is a Result. I suggest to include it only here and not in Methods.

We thank the reviewer for the suggestion; needful revision is incorporated in the revised manuscript

(L. 247)

15 Table 1. I see the information on this table as a result. Why is it in methods?

16 Table 3. It is not clear to me how the Gain, Loss, Stable and Unsuitable areas were computed. Authors did use a classification of Unsuitalbe, low suitable, moderate suitable The geographical distribution of the species in terms of unsuitable, low-moderate suitable, and high suitability was initially determined using the classification of Adhikari et al. (2012) (doi:10.1016/j.ecoleng.2011.12.004), Yang et al. (2013)(http://dx.doi.org/10.1016/j.ecoleng.2012.12.004), and Zhang et al. (2019) (https://doi.org/10.1016/j.ecoinf.2019.01.004);

Arslan et al. (2020) (https://doi.org/10.1007/s10113-020-01695-6)

Subsequent to this, to determine gain, loss, or stable habitats, we took the pixels value of the current distribution (of the above-classified habitats) and subtracted them with the pixels values of the classified habitats of the two climatic scenarios (i.e., 2050 & 2070).

A similar study is undertaken in some of the previous studies:

Coban et al. (2010) Investigation on changes in complex

vegetation coverage using multi-temporal landsat data of WesternBlack sea region-a case study. J Environ Biol 31:169–178

Arslan et al. (2020): https://doi.org/10.1007/s10113-020-01695-6

17 Table 4. Are the authors presenting the same information in Table 4 than in the Maps (figure 7 and 8)? Highly recommended to include the information only once. We thank the reviewer for the suggestion, as Fig. 7 & Fig. 8 represent the same information as in Table 4; therefore, we decided to add both the Figures as supplementary figures in the revised manuscript

(L. 325)

18 Fig. 1. I suggest to add (at least in a small inset) the Map of India. Figure 1 has been revised as per suggestion, and now in the revised manuscript, Figure 1 is marked as Figure 2 due addition of a new Figure as Figure 1 in its place

19 Figures 2-4 are those generated by default by Maxent. I don´t see these figures essential to be included in the results. They could not be included or maybe only as supplementary. We thank the reviewer for the suggestions, but we believe retaining both the Figures might be helpful for knowledgeable readers and understanding parameters having a larger influence on the species distribution

20 Figures 7 and 8 and the colors used are not useful to explore what is the future of the species. The main problem from my point of view is the 11 categories resulting of comparisons between RCPs and years (2050 and 2070). I would not use this comparison. I would do the comparison using the gain, loss, stable and unsuitable categories, considering only the binary classification. We thank the reviewer for the suggestion, as Fig.7 and Fig.8 represent the same information as mentioned in Table 4; therefore, both the Figures are now added assupplementary datasets.

Yes, using 11 color categories can be quite confusing, but at the same time, our aim of the study was to pinpoint areas that are expected to see transformation in their habitat using the climate projection model for better advocacy of the management plan.

Submitted filename: Response to Reviewers Comments_2nd revision.docx

Click here for additional data file.

26 May 2022

Predicting the potential distribution of Dactylorhiza hatagirea (D. Don) Soo-an important medicinal orchid in the West Himalaya, under multiple climate change scenarios ​

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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear Dr. Bhatt,

It is a pleasure to inform you that your manuscript was formally accepted for publication in PLoS One. Congratulations!

Daniel Silva, PhD.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: (No Response)

Reviewer #4: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #4: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #3: No

Reviewer #4: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #3: Yes

Reviewer #4: Yes

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6. Review Comments to the Author

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Reviewer #3: (No Response)

Reviewer #4: Thanks to authors for taking into consideration and include most of the suggestions. I believe this new version make an important contribution for the conservation and sustainable management to Dactylorhiza hatagirea. Authors have included clearly most of suggestions and those they do not, they soundly justify and explain.

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Reviewer #3: No

Reviewer #4: Yes: Leonel Lopez Toledo

10 Jun 2022

PONE-D-21-16347R2

Predicting the potential distribution of Dactylorhiza hatagirea (D. Don) Soo-an important medicinal orchid in the West Himalaya, under multiple climate change scenarios

Dear Dr. Bhatt:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Daniel de Paiva Silva

Academic Editor

PLOS ONE