Climate models predict a divergent future for the medicinal tree Boswellia serrata Roxb. in India

Habitat suitability of Opuntia ficus-indica (L.) MILL. (CACTACEAE): a comparative temporal evaluation using diverse bio-climatic earth system models and ensemble machine learning approach

A comprehensive evaluation of the habitat suitability across the India was conducted for the introduced species Opuntia ficus-indica. This assessment utilized a newly developed model called BioClimInd, takes into account five Earth System Models (ESMs). These ESMs consider two different emission scenarios known as Representative Concentration Pathways (RCP), specifically RCP 4.5 and RCP 8.5. Additionally, the assessment considered two future time frames: 2040-2079 (60) and 2060-2099 (80). Current study provided the threshold limit of different climatic variables in annual, quarter and monthly time slots like temperature annual range (26-30 °C), mean temperature of the driest quarter (25-28 °C); mean temperature of the coldest month (22-25 °C); minimum temperature of coldest month (13-17 °C); precipitation of the wettest month (250-500 mm); potential evapotranspiration Thronthwaite (1740-1800 mm). Predictive climatic habitat suitability posits that the introduction of this exotic species is deemed unsuitable in the Northern as well as the entirety of the cooler eastern areas of the country. The states of Rajasthan and Gujarat exhibit the highest degree of habitat suitability for this particular species. Niche hypervolumes and climatic variables affecting fundamental and realized niches were also assessed. This study proposes using multi-climatic exploration to evaluate habitats for introduced species to reduce modeling uncertainties.

Morpho-physiological and demographic responses of three threatened Ilex species to changing climate aligned with species distribution models in future climate scenarios

The success of a species in future climate change scenarios depends on its morphological, physiological, and demographic adaptive responses to changing climate. The existence of threatened species against climate adversaries is constrained due to their small population size, narrow genetic base, and narrow niche breadth. We examined if ecological niche model (ENM)-based distribution predictions of species align with their morpho-physiological and demographic responses to future climate change scenarios. We studied three threatened Ilex species, viz., Ilex khasiana Purkay., I. venulosa Hook. f., and I. embelioides Hook. F, with restricted distribution in Indo-Burma biodiversity hotspot. Demographic analysis of the natural populations of each species in Meghalaya, India revealed an upright pyramid suggesting a stable population under the present climate scenario. I. khasiana was confined to higher elevations only while I. venulosa and I. embelioides had wider altitudinal distribution ranges. The bio-climatic niche of I. khasiana was narrow, while the other two species had relatively broader niches. The ENM-predicted potential distribution areas under the current (2022) and future (2050) climatic scenarios (General Circulation Models (GCMs): IPSL-CM5A-LR and NIMR-HADGEM2-AO) revealed that the distribution of highly suitable areas for the most climate-sensitive I. khasiana got drastically reduced. In I. venulosa and I. embelioides, there was an increase in highly suitable areas under the future scenarios. The eco-physiological studies showed marked variation among the species, sites, and treatments (p < 0.05), indicating the differential responses of the three species to varied climate scenarios, but followed a similar trend in species performance aligning with the model predictions.

Bioclimatic modeling and FACE study forecast a bleak future for wheat production in India

Since the impact of future climate change on wheat productivity is inconsistent, we studied geographic distribution and yield of wheat using two global General Circulation Models (GCMs) and Free Air CO_2/O₃ Enrichment (FACE) experiments. The GCMs (IPSL-CM5A-LR and NIMR-HADGEM2-AO) with four Representative Concentration Pathways (RCPs) and 19 bioclimatic variables were used for distribution/ecological niche modeling (ENM). Currently cultivated eight wheat cultivars were exposed to individual treatment of (i) ambient CO₂, temperature, and ozone (ACO + AO + AT) representing the present climate scenario, and (ii) elevated CO₂ (550 ppm) (ECO), (iii) elevated temperature (+ 2 °C) (ET), (iv) elevated O₃ (ambient + 20 ppb) (EO), (v) elevated CO₂ + elevated O₃ (ECO + EO), and (vi) elevated CO₂ + elevated temperature + elevated O₃ (ECO + EO + ET) under FACE facility simulating the future climate change scenarios in 2050. The niche models predicted a reduction in climatically suitable areas for wheat, and identified "maximum temperature" as the most influencing factor for area reduction. The elevated CO₂, O₃, and temperature individually and in combinations had differential impacts on the yield of wheat cultivars. Only two cultivars, viz., DBW 184 and DBW 187 did not exhibit yield decline suggesting their suitability in the future climate change scenario. Since the performance of six out of eight cultivars significantly declined under simulated FACE experiment, and ENM predicted reduction in wheat cultivation area under RCP 8.5 in 2050, it was concluded that future of wheat cultivation in India is bleak. The study further indicates that coupling of bioclimatic modeling and FACE experiment can effectively predict the impact of climate change on different crops.

Realizing certainty in an uncertain future climate: modeling suitable areas for conserving wild Citrus species under different change scenarios in India

Citrus is an important horticultural crop of India and is often prone to diseases, particularly under increased temperature scenarios. For developing disease-resistant Citrus varieties, conservation of wild relatives is extremely important. However, our knowledge on temperature tolerance of these wild relatives of Citrus to varied climate change scenarios is extremely limited. Therefore, we determined the climatic niche of six wild relatives of cultivated Citrus species (C. indica Tanaka, C. karna Rafin., C. latipes (Swingle) Tanaka, C. macroptera Montrouz., C. medica L., and C. sinensis (L.) Osbeck.) and identified the geographical areas in India that would remain climatically stable in future through ecological niche modeling (ENM). Raster data on 19 bioclimatic variables with a resolution of 0.04° were used to generate niche models for each Citrus species that delineated their potential distribution areas. Future species distribution predictions for the year 2050 were made using the climate change scenarios from the most appropriate climate models, i.e., IPSL-CM5A-LR and NIMR-HADGEM2-AO with four Representative Concentration Pathways (RCPs). Ensemble of current and future projections was used to identify climatically stable areas for each species. Precipitation-related bioclimatic variables were the key climatic determinants for the modeled distribution pattern. The consensus of current and future projections suggests that most areas with stable climates for the species in the future would be available in the northeastern states of Arunachal Pradesh, Meghalaya, Mizoram, and Tripura. Efforts for in situ conservation and establishment of germplasm banks and citrus orchards may be encouraged in these identified areas.

Modeling the effect of climate change on the distribution of threatened medicinal orchid Satyrium nepalense D. Don in India

It is vital to understand the distribution area of a threatened plant species for its better conservation and management planning. Satyrium nepalense (family: Orchidaceae) is a threatened terrestrial orchid species with valuable medicinal and nutritional properties. The survival of S. nepalense in wild conditions has been challenged by increasing global surface temperature. Hence, understanding the impact of climate change on its potential distribution is crucial to conserve and restore this species. In present study, Maxent species distribution modeling algorithm was used to simulate the current distribution of S. nepalense in India and predict the possible range shift in projected future climate scenarios. A set of 19 bioclimatic variables from WorldClim database were used to predict the potential suitable habitats in current climatic condition and four Representative Concentration Pathway (RCP 2.6, 4.5, 6.0, and 8.5) scenarios by integrating five General Circulation Models (GCMs) for future distribution modeling of species for the years 2050 and 2070. Furthermore, change analysis was performed to identify the suitable habitat in current and future climate for delineating range expansion (gain), contraction (loss), and stable (no change) habitats of species. The Maxent model predicted that ~ 2.38% of the geographical area in India is presently climatically suitable for S. nepalense. The key bioclimatic variables affecting the distribution of studied species were the mean temperature of warmest quarter, mean temperature of wettest quarter, precipitation of warmest quarter, and temperature seasonality. Under future climate change scenarios, the total suitable habitat of S. nepalense will increase slightly in the Himalayan region and likely to migrate towards northward, but in the Western Ghats region, the suitable areas will be lost severely. The net habitat loss under four RCP scenarios was estimated from 26 to 39% for the year 2050, which could further increase from 47 to 60% by the year 2070. The finding of the predictive Maxent modeling approach indicates that warming climates could significantly affect the potential habitats of S. nepalense and hence suitable conservation measures need to be taken to protect this threatened orchid species in wild conditions.

Predicting the distributional range shifts of Rhizocarpon geographicum (L.) DC. in Indian Himalayan Region under future climate scenarios

Himalaya, the highest mountain system in the world and house of important biodiversity hotspot, is sensitive to projected warming by climate change. Rhizocarpon geographicum (map lichen), a crustose lichen, grows in high mountain ranges, is a potential indicator species of climate change. In the present study, MaxEnt species distribution modeling algorithm was used to predict the suitable habitat for R. geographicum in current and future climate scenarios. Nineteen bioclimatic variables from WorldClim database, along with elevation, were used to predict the current distribution and three representative concentration pathway (RCP) scenarios by integrating three general circulation models (GCMs) for future distribution of species covering years 2050 and 2070. Furthermore, we performed change analysis to identify the precise difference between the current and future distribution of suitable areas of the species for delineating habitat range expansion (gain), habitat contraction (loss), and stable habitats. The final ensemble model obtained had average test value 0.968, and its predicted ~ 27.5% of the geographical area in the Indian Himalayan Region is presently climatically suitable for the species. The predicted highly suitable area for R. geographicum is observed to be declining in Northwestern Himalaya, and it is shifting towards the higher elevation areas of the Eastern Himalaya. The projected distribution in future under the RCP scenarios (RCP 4.5, 6.0, and 8.5) showed the range expansion towards higher elevations, and it is more pronounced for the extreme future scenarios (RCP 8.5) than for the moderate and intermediate climate scenarios (RCP 4.5 and RCP 6.0). However, assuming that species can migrate to previously unoccupied areas, the model forecasts a habitat loss of 10.86-16.51% for R. geographicum, which is expected due to increase in mean annual temperature by 1.5-3.7 °C. The predictive MaxEnt modeling approach for mapping lichen will contribute significantly to the understanding of the impact of climate change in Himalayan ecosystems with wide implications for drawing suitable conservation plans and to take adaptation and mitigation measures.

Feature tuning improves MAXENT predictions of the potential distribution of Pedicularis longiflora Rudolph and its variant

Pedicularis longiflora Rudolph and its variant (P. longiflora var. tubiformis (Klotzsch) Tsoong) are alpine plants and traditional Chinese medicines with important medicinal value, and future climate changes may have an adverse impact on their geographic distribution. The maximum entropy (MAXENT) model has the outstanding ability to predict the potential distribution region of species under climate change. Therefore, given the importance of the parameter settings of feature classes (FCs) and the regularization multiplier (RM) of the MAXENT model and the importance of add indicators to evaluate model performance, we used ENMeval to improve the MAXENT niche model and conducted an in-depth study on the potential distributions of these two alpine medicinal plants. We adjusted the parameters of FC and RM in the MAXENT model, evaluated the adjusted MAXENT model using six indicators, determined the most important ecogeographical factors (EGFs) that affect the potential distributions of these plants, and compared their current potential distributions between the adjusted model and the default model. The adjusted model performed better; thus, we used the improved MAXENT model to predict their future potential distributions. The model predicted that P. longiflora Rudolph and its variant (P. longiflora var. tubiformis (Klotzsch) Tsoong) would move northward and showed a decrease in extent under future climate scenarios. This result is important to predict their potential distribution regions under changing climate scenarios to develop effective long-term resource conservation and management plans for these species.