Ecological niche modelling of a critically endangered species Commiphora wightii (Arn.) Bhandari using bioclimatic and non-bioclimatic variables

Abstract

Background The aim of this study is to examine the effects of four different bioclimatic predictors (current, 2050, 2070, and 2090 under Shared Socioeconomic Pathways SSP2-4.5) and non-bioclimatic variables (soil, habitat heterogeneity index, land use, slope, and aspect) on the habitat suitability and niche dimensions of the critically endangered plant species Commiphora wightii in India. We also evaluate how niche modelling affects its extent of occurrence (EOO) and area of occupancy (AOO). Results The area under the receiver operating curve (AUC) values produced by the maximum entropy (Maxent) under various bioclimatic time frames were more than 0.94, indicating excellent model accuracy. Non-bioclimatic characteristics, with the exception of terrain slope and aspect, decreased the accuracy of our model. Additionally, Maxent accuracy was the lowest across all combinations of bioclimatic and non-bioclimatic variables (AUC = 0.75 to 0.78). With current, 2050, and 2070 bioclimatic projections, our modelling revealed the significance of water availability parameters (BC-12 to BC-19, i.e. annual and seasonal precipitation as well as precipitation of wettest, driest, and coldest months and quarters) on habitat suitability for this species. However, with 2090 projection, energy variables such as mean temperature of wettest quarter (BC-8) and isothermality (BC-3) were identified as governing factors. Excessive salt, rooting conditions, land use type (grassland), characteristics of the plant community, and slope were also noticed to have an impact on this species. Through distribution modelling of this species in both its native (western India) and exotic (North-east, Central Part of India, as well as northern and eastern Ghat) habitats, we were also able to simulate both its fundamental niche and its realized niche. Our EOO and AOO analysis reflects the possibility of many new areas in India where this species can be planted and grown. Conclusion According to the calculated area under the various suitability classes, we can conclude that C. wightii's potentially suitable bioclimatic distribution under the optimum and moderate classes would increase under all future bioclimatic scenarios (2090 > 2050 ≈ current), with the exception of 2070, demonstrating that there are more suitable habitats available for C. wightii artificial cultivation and will be available for future bioclimatic projections of 2050 and 2090. Predictive sites indicated that this species also favours various types of landforms outside rocky environments, such as sand dunes, sandy plains, young alluvial plains, saline areas, and so on. Our research also revealed crucial information regarding the community dispersion variable, notably the coefficient of variation that, when bioclimatic + non-bioclimatic variables were coupled, disguised the effects of bioclimatic factors across all time frames.

Predicting the potential distribution of Perina nuda under climate change in China

“…The present study represents the use of the MaxEnt model to evaluate the potential habitat for P. nuda under the influence of climate change. The MaxEnt model assumes a significant role in ecological research domains, including invasion biology, conservation biology, and evolutionary ecology (Dyderski et al., 2018; Mathur et al., 2023; Varol et al., 2021). To enhance the precision of the model, refinements in control multiplicity and FCs have been applied, effectively addressing the potential issues of overfitting commonly associated with the MaxEnt model.…”

Potential Range Map Dataset of Indian Birds

“…Numerous studies have used SDMs to estimate species ranges and inhabited areas for IUCN Red List evaluations [4,17,[27][28][29][30][31]. Several countries have published national-level biodiversity assessments using SDMs [32][33][34][35][36][37][38][39] with only a small number of species or groups in India have their distribution evaluated for various applications; including; for example for birds [4,[40][41][42][43][44][45][46][47][48][49][50][51][52], for plants [53][54][55][56][57][58][59][60][61][62][63][64], for mammals [65][66][67][68][69][70][71], for invasive and pest species…”

Ecological setting of phlebotomine sand flies in the Republic of Kosovo

“…Bioclimatic variables are commonly used in ecological analysis and niche modelling [ 43 ]. The involved bioclimatic factors were as follows: wind speed in m s-1, bio1: annual mean temperature, bio2: mean diurnal range, bio3: isothermality, bio4: temperature seasonality, bio5: max temperature of warmest month, bio6: min temperature of coldest month, bio7: temperature annual range, bio8: mean temperature of the wettest quarter, bio9: mean temperature of the driest quarter, bio10: mean temperature of the warmest quarter, bio11: mean temperature of the coldest quarter, bio12: annual precipitation, bio13: precipitation of the wettest month, bio14: precipitation of the driest month, bio15: precipitation seasonality, bio16: precipitation of the wettest quarter, bio17: precipitation of the driest quarter, bio18: precipitation of the warmest quarter, bio19: precipitation of the coldest quarter.…”