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Article

Changing Climatic Scenarios Anticipate Dwindling of Suitable Habitats for Endemic Species of Himalaya—Predictions of Ensemble Modelling Using Aconitum heterophyllum as a Model Plant

by
Zishan Ahmad Wani
1,
Qamer Ridwan
1,
Sajid Khan
1,
Shreekar Pant
2,*,
Sazada Siddiqui
3,*,
Mahmoud Moustafa
3,
Ahmed Ezzat Ahmad
3,4 and
Habab M. Yassin
3
1
Department of Botany, Baba Ghulam Shah Badshah University, Rajouri 185234, Jammu and Kashmir, India
2
Centre for Biodiversity Studies, Baba Ghulam Shah Badshah University, Rajouri 185234, Jammu and Kashmir, India
3
Department of Biology, College of Science, King Khalid University, Abha 61413, Saudi Arabia
4
Department of Theriogenology, Faculty of Veterinary Medicine, South Valley University, Qena 83523, Egypt
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(14), 8491; https://doi.org/10.3390/su14148491
Submission received: 9 June 2022 / Revised: 1 July 2022 / Accepted: 7 July 2022 / Published: 11 July 2022
(This article belongs to the Section Sustainability, Biodiversity and Conservation)
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Abstract

:
In the changing climatic conditions, species distribution modelling is considered as a key strategy to estimate the probable influence of climatic variabilities on the habitat ranges of any species. The present study explores the potential distribution of Aconitum heterophyllum under current and future climatic scenarios. The results unfold that the distribution of this endemic species is governed significantly by bio12, i.e., Annual Precipitation. Ensemble modelling predicted that higher altitudes of Jammu, Kashmir and Ladakh are suitable habitats for A. heterophyllum. However, the future climatic modelling revealed that there will be a significant decrease in the suitable habitats for A. heterophyllum. Most of the shrinkage of habitats is predicted to occur within the time period of 2050, which seriously challenges their survival. The present study recommends an urgent need to frame a pertinent conservation and management policy for Aconitum heterophyllum and will act as a framework for planning such a policy.

1. Introduction

The climate of the Earth is changing quickly due to a biophysical phenomenon, i.e., global warming. Global warming is responsible for the change in temperature and precipitation patterns all over the world. The steady increase in greenhouse gas concentrations in the atmosphere is significantly connected to global warming. The substantial rise in the concentration of these gases can result in a cascade of effects at the atmospheric level, which leads to climate change. Climate change poses significant threats to the survival and growth of plant species because climate influences both their physiology and distribution [1,2], especially those with restricted habitats, and drives them to adapt to new conditions or relocate geographically [3,4]. In an attempt to reclaim adequate growth zones, their positions have begun to migrate towards the extremes and higher altitudes. Many endemic species might become extinct as a result of habitat degradation and migratory constraints brought on by climate change [5]. Species that depend on one other (co-existence) may be pushed to extinction if they no longer co-occur at the same time or in the same space. Species that have unique habitat needs or extended generation periods are more vulnerable to extinction [6]. Therefore, conservation strategies must be devised quickly in response to climate change, as any delay might result in the extinction of valuable endemic and threatened species [7]. In this scenario, species distribution modelling (SDM) is considered as a key strategy to estimate the probable influence of climatic variabilities on the habitat ranges of any species of interest using environmental facts and prospective global climate model (GCM) outputs [8,9,10]. Habitat model, niche-based model, habitat suitability model, climatic envelope, environmental niche model and ecological niche model are some of the terms used to describe the method [8,11,12,13]. The broad range of applications for SDM has stimulated the growth of implementations in several ecological and biogeographical research fields, particularly biodiversity preservation [8,14,15]. This interest has been recently amplified by the widespread prevalence of species distribution data and the advent of geographic information system (GIS) technologies to manage and interpret spatial data [16]. A greater understanding of species allocation gives significant ecological information and excellent prediction ability for determining species distribution and assessing rare and endangered species sampling tactics [17,18,19,20], to build natural conservation networks and define restoration priority areas [21,22,23], or to aid in the recovery of species and the rehabilitation of the ecosystem [24,25].
Aconitum heterophyllum is one of the most important medicinal, endemic and severely endangered species of Northwest Himalaya [26]. It is a herbaceous plant with a twining green stem and is found in the temperate alpine and sub-alpine Himalayan region from Kashmir to Sikkim and much further east to Myanmar, with an altitudinal range of 1830–4575 m asl [27,28]. The plant has biennial, paired roots with white or grey tubers. The leaves are spirally organized, heteromorphous, smooth, dark green and five-lobed [29,30]. It produces bluish-purple flowers that are complete, hermaphrodite and zygomorphic. The fruit is a follicle [31]. Among the 76 species of the Aconitum genus, A. heterophyllum is the most widely used for its therapeutic properties [32]. Some of the therapeutic properties are hepatoprotective, analgesic, diuretic, antipyretic, anodyne, antioxidant, alexipharmic, febrifuge, anti-atrabilious, anticancer, expectorant, immune-stimulant, antihelminthic, antidiarrheal, antiemetic, antidiabetic, anti-inflammatory, antiphlegmatic and antiflatulent effects [33]. However, overexploitation by humans for medicinal and other uses, overgrazing by livestock, landslides, soil erosion, illicit trade, etc., pose a significant threat to the survival of A. heterophyllum and endangers its natural habitat. Therefore, immediate conservation strategies should be devised for protecting it from further damage [34]. Keeping these facts in consideration, the present study was planned to predict suitable habitats for A. heterophyllum under current and future climatic scenarios using an ensemble modelling approach.

2. Materials and Methods

Intensive field surveys were conducted throughout Jammu and Kashmir from 2010 to 2021 for recording the occurrences of the target plant. The data was further supplemented from the Global Biodiversity Information Facility (GBIF) (http://www.gbif.org, assessed on 14 January 2022) using the ‘gbif’ function available in the ‘dismo’ package [35]. A total of 136 geo-referenced occurrence records were retrieved initially, which were later reduced to 56 after clipping for the study area (i.e., Jammu, Kashmir and Ladakh). In order to reduce the bias, these records were subjected to spatial rarefy (1 × 1 km grid), removing spatially autocorrelated occurrences. After spatial rarefying, 30 geo-referenced points formed the final dataset for modelling the distribution of A. heterophyllum. For modelling the current potential distribution of the selected species across the study region, the nineteen current climatic variables were retrieved from the WorldClim database, ver. 1.4 (http://www.worldclim.org, assessed on 18 January 2022) with a spatial resolution of 30 arc seconds. Pearson correlation analysis was used to test for autocorrelation (r > 0.7) among the climatic variables following the methodology described by Peterson et al. [36]. For the evaluation of the relative importance of each climatic variable in governing the current and future distribution of A. heterophyllum, permutation procedure was used [37]. Hadley Global Environment Model 2-Earth System (HADGEM2-ES) representing simulations for two representative concentration pathways (RCP4.5 and RCP8.5) for the years 2050 and 2070 was used for modelling the future potential distribution of the targeted species [38].
Current and future distribution modelling and forecasting were performed using the ‘biomod2’ package [39] within R statistical software [40], which is an ensemble of nine different algorithms. As these algorithms demand presence/absence datasets, 100 pseudoabsences within the study area were generated following the methodology described by Guisan et al. [41], and the procedure was replicated three times in order to reduce the sample bias in the generation of pseudoabsences. A total of 75% of the data was used for training and 25% was used for testing, and the entire procedure was repeated four times. Thus, 108 models (3 replicate pseudoabsences datasets × 9 algorithms × 4 replicates) for each climatic scenario and time period combination were obtained. Performance of the model was assessed by cross-validation using Kappa, true skills statistics (TSS) and area under the curve (AUC) [42,43]. Using committee averaging (CA) and weighted mean (WM) approaches separately, an ensemble model for RCP4.5 and RCP8.5 for the time periods of present day–2050 and present day–2070 was built from the individual modelling outputs [43]. Ensemble modelling was performed using all the repetitions and sets of pseudoabsences of the algorithm with best accuracy scores and models, with Kappa < 0.4, TSS < 0.8 and AUC < 0.9 excluded. Further, BIOMOD (RangeSize) function in the biomod2 package was used for quantifying and representing the range changes under changing climatic scenarios following the methodology described by Rather et al. [44].

3. Results

The final ensemble model obtained Kappa, TSS and AUC values of 0.52, 0.86 and 0.97 in terms of CA, and 0.53, 0.83 and 0.97, respectively, in terms of WM, signifying the robustness of the final model in forecasting the distribution of the target species. When compared at the individual algorithm level, the predictive accuracy was also excellent, with RF, GBM, MaxEnt and GLM performing fairly well, followed by FDA, MARS and ANN, whereas GAM, CTA and SRE displayed the lowest accuracy in comparison to the rest of the algorithms used for A. heterophyllum (Figure 1).
After Pearson’s correlation analysis, six variables were selected for modelling the distribution of A. heterophyllum under current climatic conditions (Supplementary Table S1). These variables include bio1 (Annual Mean Temperature), bio2 (Mean Diurnal Range), bio4 (Temperature Seasonality), bio8 (Mean Temperature of Wettest Quarter), bio12 (Annual Precipitation) and bio15 (Precipitation Seasonality). The importance of selected predictable variables showed greater variation across different algorithms. It was revealed that the distribution of A. heterophyllum is highly influenced by bio12, bio8 and bio4 (Table 1). The responses of the rest of the variables varied across different algorithms, revealing that their contribution in controlling the potential distribution of A. heterophyllum varied to a greater extent.
The results of the ensemble model showed that under current climatic conditions, eastern, central and western parts in the higher Pir-Panjal range of Jammu and Kashmir, entire parts of Kashmir Himalaya, western parts of Kargil and southern regions of Chilas possess highly suitable and optimal climatic conditions for the growth of A. heterophyllum (Figure 2). On the other hand, central, southern and western parts of Leh, southern and western regions of Baramulla and northern parts of Rajouri, Reasi and Doda districts are moderately suitable.
The prediction of the future ensemble models showed that there will be a decrease in the habitat suitability for A. heterophyllum under all the future climatic scenarios. However, some of the currently suitable areas will consistently remain suitable under future climates, which include higher altitudes of the Pir-Panjal range, western and southern parts of Kargil, eastern parts of Kupwara, eastern and southern parts of Anantnag, southern parts of Chilas and north and southern parts of Baramulla (Figure 3).
Results of the range change analysis showed that the target species will undergo significant range change under future climatic conditions. This range change will be governed mostly by the loss of suitable habitats in the future. More specifically, the suitable habitat for A. heterophyllum could be reduced by about 33.35% (under RCP4.5 2050), 52.70% (RCP4.5 2070), 52.86% (RCP8.5 2050) and by about 56.59% under RCP8.5 for the year 2070 when related to current habitat suitability (in terms of CA) (Table 2). In terms of WM, the results are quite similar; the suitable habitats could be reduced by about 37.53% (under RCP4.5 2050), 48.29% (RCP4.5 2070), 47.61% (RCP8.5 2050) and by about 50.32% under RCP8.5 for the year 2070 (Table 2). Thus, the ensemble modelling predicts the loss of suitable habitats for A. heterophyllum both in terms of CA and WM. The areas that are likely to become unsuitable in the future are mostly located towards lower altitudes of the Pir-Panjal range, north and western parts of the Doda district, northern parts of Rajouri, Poonch and Reasi districts, western parts of Anantnag and southern parts of the Leh district (Figure 4). In contrast, some of the currently unsuitable areas become suitable under future climate conditions, with a range expansion, and include mainly the southern parts of the Kupwara district, eastern parts of the Baramulla district, northern and western parts of Leh, southern parts of Kargil, southern and western parts of Gilgit and some western parts of the Doda district (Figure 4).

4. Discussion

Changing climatic scenarios tend to alter the distribution of species, and thus increase the risk of species extinction by anticipated major shifts [45]. However, species with a narrow distribution range are more prone to such climatic alterations [46]. A. heterophyllum is an endemic plant species with its distribution limited to Northwestern Himalaya [26]. Further, owing to its curative significance and soaring market value, unsustainable harvesting of its tubers coupled with other factors such as reproductive constraints and climate change has led to its categorization as critically endangered [47]. Thus, predicting its current suitable habitats along its range change under changing climatic scenarios is a prerequisite for its conservation. The present study provides potential distribution maps along with the strong statistical validation of the robustness of the model. The robustness of the model is evident from the values of Kappa, TSS and AUC. The model predicted a wide distribution of A. heterophyllum under current climatic conditions in Jammu, Kashmir and Ladakh; however, the previous ecological studies on A. heterophyllum carried out in the region are not satisfactory. Dad and Khan [48] recorded the lowest density of A. heterophyllum among the threatened plants in Gurez Valley, Kashmir Himalaya. Baig et al. [49] also recorded low density (0.56/m2) of A. heterophyllum in Menwarsar, Kashmir. Further, Jeelani et al. [50] revealed that populations of A. heterophyllum are not consistent as no subpopulations/pockets were expected to contain more than 250 mature individuals, and all the individuals of the species are in specific pockets in Kashmir Himalaya.
A. heterophyllum is predicted to be prone and susceptible to minute climatic changes, and thus acts as an indicator species [34]. The results of the present study provide a scientific validation to this hypothesis. The present study found that the distribution of A. heterophyllum is highly influenced by the bio12 (Annual Precipitation). Thus, any minor or major alteration in the climate may influence the distribution and survival of A. heterophyllum. The climatic alterations are linked with alterations in precipitation patterns, which in turn affect physiological as well as ecological processes of plant species at varying scales including plasticity responses of vegetative and reproductive growth [51,52]. The present study also revealed that the presently suitable areas may become unsuitable and vice versa under changing climatic scenarios, which may lead to the extinction of this species if it is unable to acclimatize. More tragic is that the percentage of suitable habitats will shrink under all the climatic scenarios and most of these range changes are predicted to occur by 2050. Coupled with this, poor seed germination, low seedling survival, low regeneration under natural conditions, overgrazing, prolonged seed dormancy, high seedling mortality and habitat fragmentation and degradation are the threats that are hindering the survival of the plant [34]; thus, the plant species deals with grave threats of extinction. Based on these findings, the present study shows that there is an urgent need to plan pertinent policies for the conservation and management of this valuable species. There is a need to identify, explore and monitor the natural populations of the plant and the present study will act as a footprint for such explorations. Further, more extensive research on its reproductive biology, standardization of mass multiplication protocols, identification of superior genotypes and on-site and ex situ cultivation measures are recommended. Promotion of its cultivation among farmers and strict legislative prohibition of its extraction from the wild may give a chance to the wild populations to flourish. The areas which will remain consistently suitable habitats for the target species (the Pir-Panjal range, western and southern parts of Kargil, eastern parts of Kupwara, eastern and southern parts of Anantnag, southern parts of Chilas and north and southern parts of Baramulla) should be prioritized for conservation and management. Further, such areas should also be prioritized for species reintroduction.

5. Conclusions

The present study is a first attempt to model the suitable habitats for A. heterophyllum under current and future climatic scenarios. Results of the current distribution maps will assist in exploring the natural populations of the target species. Further, the results of the ensemble modelling revealed that the target species will lose 50% of its suitable habitats by 2050, pointing out the possibility of extinction of this species in the future. As the earlier studies carried out on the target species have revealed, the species is undergoing several constraints and threats, making its survival difficult. Thus, the present study recommends that a strong policy for its conservation and management is required.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14148491/s1, Supplementary Table S1.

Author Contributions

Conceptualization, S.P. and Z.A.W.; methodology Z.A.W., Q.R. and S.K.; software, Z.A.W., Q.R., S.K. and S.P.; validation, Z.A.W.; formal analysis, Z.A.W., Q.R., S.K. and S.P.; investigation, S.P. and Z.A.W.; resources, S.P. and Z.A.W.; data curation, Z.A.W., Q.R., S.K. and S.P.; writing—original draft preparation, Z.A.W., Q.R., S.K. and S.P.; writing—review and editing, S.P., Z.A.W., S.S., M.M., A.E.A. and H.M.Y.; visualization, S.P. and Z.A.W.; supervision, S.P.; project administration, S.P.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.

Funding

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under grant number (R.G.P.1/360/43).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

KV Satish is acknowledged for his help during the preparation of the manuscript. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under grant number (R.G.P.1/360/43).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Mean evaluation values of model algorithms for Aconitum heterophyllum as per AUC and TSS.
Figure 1. Mean evaluation values of model algorithms for Aconitum heterophyllum as per AUC and TSS.
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Figure 2. Plot showing the geographic projections using the committee average (above) and weighted mean (below) ensemble models for Aconitum heterophyllum under current climatic conditions (0 represents no suitability and 1 represents highest suitability).
Figure 2. Plot showing the geographic projections using the committee average (above) and weighted mean (below) ensemble models for Aconitum heterophyllum under current climatic conditions (0 represents no suitability and 1 represents highest suitability).
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Figure 3. Plot of the predicted habitat suitability for Aconitum heterophyllum under future climate change scenarios (0 represents no suitability and 1 represents highest suitability).
Figure 3. Plot of the predicted habitat suitability for Aconitum heterophyllum under future climate change scenarios (0 represents no suitability and 1 represents highest suitability).
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Figure 4. Plot of the predicted range changes for Aconitum heterophyllum between current and future climatic conditions.
Figure 4. Plot of the predicted range changes for Aconitum heterophyllum between current and future climatic conditions.
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Table
Table 1. Importance values (algorithm-wise) of variables selected for modelling of Aconitum heterophyllum.
Table 1. Importance values (algorithm-wise) of variables selected for modelling of Aconitum heterophyllum.
BiovariablesGLMGBMGAMCTAANNSREFDARFMARSMaxEntMean
bio10.0730.0580.5020.1300.0900.2190.1000.1100.1650.0680.151
bio20.0430.0180.52100.0070.2120.0080.1530.0490.0450.105
bio40.0860.2260.5600.2010.1200.1090.4550.2590.3820.4060.280
bio80.1810.1010.4790.0410.0730.2130.1880.0500.2690.2310.282
bio120.9010.7060.8320.8630.0010.5560.9710.4570.9000.9490.713
bio150.0550.0120.42600.0780.2670.0030.0420.0340.0800.099
Table
Table 2. Range change statistics for Aconitum heterophyllum under changing climatic scenarios.
Table 2. Range change statistics for Aconitum heterophyllum under changing climatic scenarios.
ScenarioEnsemble TypeLossAbsentStableGainLoss (%)Gain (%)Range Change (%)
RCP4.5 2050Committee averaging14,495281,12916,475262243.808.46−38.33
RCP4.5 2070Committee averaging18,857281,21712,113253460.888.18−52.70
RCP8.5 2050Committee averaging18,312281,72512,658202659.126.54−52.58
RCP8.5 2070Committee averaging20,715280,56310,255318866.8810.29−56.59
RCP4.5 2050Weighted mean15,013281,51014,119407951.5314.02−37.53
RCP4.5 2070Weighted mean18,000281,65911,132393061.7813.49−48.29
RCP8.5 2050Weighted mean17,983281,47811,149411161.7214.11−47.61
RCP8.5 2070Weighted mean19,919280,3319213525868.3718.04−50.32
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Wani, Z.A.; Ridwan, Q.; Khan, S.; Pant, S.; Siddiqui, S.; Moustafa, M.; Ahmad, A.E.; Yassin, H.M. Changing Climatic Scenarios Anticipate Dwindling of Suitable Habitats for Endemic Species of Himalaya—Predictions of Ensemble Modelling Using Aconitum heterophyllum as a Model Plant. Sustainability 2022, 14, 8491. https://doi.org/10.3390/su14148491

AMA Style

Wani ZA, Ridwan Q, Khan S, Pant S, Siddiqui S, Moustafa M, Ahmad AE, Yassin HM. Changing Climatic Scenarios Anticipate Dwindling of Suitable Habitats for Endemic Species of Himalaya—Predictions of Ensemble Modelling Using Aconitum heterophyllum as a Model Plant. Sustainability. 2022; 14(14):8491. https://doi.org/10.3390/su14148491

Chicago/Turabian Style

Wani, Zishan Ahmad, Qamer Ridwan, Sajid Khan, Shreekar Pant, Sazada Siddiqui, Mahmoud Moustafa, Ahmed Ezzat Ahmad, and Habab M. Yassin. 2022. "Changing Climatic Scenarios Anticipate Dwindling of Suitable Habitats for Endemic Species of Himalaya—Predictions of Ensemble Modelling Using Aconitum heterophyllum as a Model Plant" Sustainability 14, no. 14: 8491. https://doi.org/10.3390/su14148491

APA Style

Wani, Z. A., Ridwan, Q., Khan, S., Pant, S., Siddiqui, S., Moustafa, M., Ahmad, A. E., & Yassin, H. M. (2022). Changing Climatic Scenarios Anticipate Dwindling of Suitable Habitats for Endemic Species of Himalaya—Predictions of Ensemble Modelling Using Aconitum heterophyllum as a Model Plant. Sustainability, 14(14), 8491. https://doi.org/10.3390/su14148491

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