Abstract
1 Introduction
The International Panel on Climate Change (IPCC) has reported that the global temperature is likely to increase by about 1.5℃ between 2030 and 2052 (
The changes in climate have exacerbated habitat loss and fragmentation in different ecological regions globally; and these changes have already impacted 54% of important terrestrial species during the 21st century (
The Himalayan region is a shelter for numerous species (
2 Materials and methods
2.1 Selection of the study area
GRB is located between 28.35°N and 29.33°N and between 82.87°E and 85.80°E. The basin covers around 32807 km2 of the central Himalayan region (
of mammals, birds, and reptiles are found (
Figure 1.Location of the GRB and species occurrences
2.2 Datasets
2.2.1 Species occurrence
The points of known HM occurrences were extracted from the Global Biodiversity Information Facility (GBIF) portal (http://www.gbif.org/), a platform which provides more than 280 million records of different species observations worldwide (
2.2.2 Bioclimatic variables
The bioclimatic variables (at 30 arc-second spatial resolution) were extracted from WorldClim: http://www.worldclim.org/ (
Variable | Description | Retained or | |
---|---|---|---|
Climate | Bio 1 | Annual mean temperature | √ |
Bio 2 | Mean diurnal range | √ | |
Bio 3 | Isothermality | √ | |
Bio 4 | Temperature seasonality | √ | |
Bio 5 | Max temperature of warmest month | √ | |
Bio 6 | Min temperature of coldest month | √ | |
Bio 7 | Temperature annual range (Bio 5, 6) | × | |
Bio 8 | Mean temperature of wettest quarter | √ | |
Bio 9 | Mean temperature of driest quarter | × | |
Bio 10 | Mean temperature of warmest quarter | √ | |
Bio 11 | Mean temperature of coldest quarter | × | |
Bio 12 | Annual precipitation | √ | |
Bio 13 | Precipitation of wettest month | × | |
Bio 14 | Precipitation of driest month | √ | |
Bio 15 | Precipitation seasonality | √ | |
Bio 16 | Precipitation of wettest quarter | √ | |
Bio 17 | Precipitation of driest quarter | √ | |
Bio 18 | Precipitation of warmest quarter | √ | |
Bio 19 | Precipitation of coldest quarter | √ | |
Topography | Elevation, slope, aspect | √ | |
Land cover | Land cover data at 30 m spatial resolution | √ |
Table 1.
Environmental variables and their descriptions
2.2.3 Land cover data
National land cover data for 1990 were download from the International Centre for Integrated Mountain Development (ICIMOD) (http://rds.icimod.org). Similarly, land cover data for 2010 were extracted from Uddin et al. (2015). Based on the 1990 and 2010 data, land cover data for 2050 were simulated using a Cellular Automata (CA) Markov model in TerraSet version 18.21. The Markov model is often used for projecting future land cover changes (
2.3 Maximum Entropy (MaxEnt) model
The freely available MaxEnt version 3.4.1 software was used in this study. This model identifies wild species’ environmental requirements and geographical distributions (
This study categorized habitat suitability by defining five classes: Very high suitability (greater than 70% probability); High suitability (from 50% to 70% probability); Medium suitability (from 30% to 50% probability); Low suitability (from 10% and 30% probability); and Very low suitability (less than 10% probability) (
3 Results
3.1 Land cover changes
Based on land cover data for 1990 and 2010, the CA Markov model was used to predict land cover data for 2050. This model predicts that agricultural land, shrubland and built-up areas are likely to increase in the future, while forest cover, grassland, barren land and water body area would decrease by 2050 (
LULC types | 2010 | 2050 | ||
---|---|---|---|---|
Area | Percentage (%) | Area | Percentage (%) | |
Forest | 13174 | 40.15 | 11344 | 34.58 |
Shrubland | 821 | 2.50 | 1072 | 3.27 |
Grassland | 3798 | 11.58 | 3366 | 10.26 |
Agricultural land | 7574 | 23.09 | 8379 | 25.54 |
Barren land | 3284 | 10.01 | 3147 | 9.59 |
Water body | 168 | 0.51 | 115 | 0.35 |
Snow/Glacier | 3887 | 11.85 | 5218 | 15.91 |
Built-up area | 101 | 0.31 | 166 | 0.51 |
Total | 32807 | 100 | 32807 | 100 |
Table 2.
Current (2010) and projected (2050) land use and land cover (LULC) types
The results show that the predominant forest cover is likely to decrease in each of the elevation ranges, while the shrubland is likely to increase at elevations up to 3750 m (
Figure 2.Current (a, 2010) and projected (b, 2050) LULC
Figure 3.Land cover changes along the altitudinal gradient
3.2 Model evaluation and analysis of variables
In setting-up the MaxEnt model, 25% of the occurrence locations were used for testing and the remaining randomly selected 75% were used to train the model. This process found the area under the Receiver Operating Characteristics (ROC) curve, or mean Area Under ROC (AUC) test set data, to be 0.920. The overall accuracy of the model performance was high, which indicates that the distributions generated provide close estimations for the real-world distribution probabilities.
The environmental variables are fundamental to MaxEnt modeling and each one contributes to the spatial distribution of habitat. Different environmental variables play important roles in explaining whether or not the species exists in a particular area. Thus, if any environmental layer provides a higher contribution, then it represents a higher impact of the variables on the habitat prediction. The result shows that climatic layer Bio 8 (mean temperature of wettest quarter) provided the highest contribution to the HM, corresponding to 29%. Two others, Bio 14 (precipitation of driest month) (12%) and elevation (9%) were also found to be important variables contributing to the habitat of HM (
Figure 4.Environmental variables and contributions Note: See
3.3 Habitat status and future distributions
Under the appropriate conditions of the climatic variables, topography and land cover, the modeling results reveal the suitable habitat across the GRB. The total area of HM habitat is projected to increase by 695 km2 in the future, with corresponding decreases and stable areas projected to be 3177 km2 and 995 km2, respectively, in the future (
Figure 5.Projected habitat changes of HM within the GRB in the future (by 2050)
This study assessed the habitat suitability according to five classes: very high suitability (啊啊啊70% probability); high suitability (50%-70% probability); medium suitability (30%-50% probability); low suitability (10%-30% probability); and very low suitability (嗯嗯嗯10% of probability) (
Habitat suitability | Current | Future |
---|---|---|
Very high | 748.50 | 560.54 |
High | 1532.99 | 846.76 |
Medium | 2625.70 | 1849.90 |
Low | 4520.88 | 9201.65 |
Very low | 23379.74 | 20349.06 |
Table 3.
The suitability status of current and predicted future habitat areas (km2)
Figure 6.Current (a) and predicted (b) habitat distributions in the GRB
3.4 Habitat changes along the altitudinal gradient
This study assessed the very high suitability area of greater than 70% prediction probability as it was mapped at different altitudes in the future. The habitat of HM is likely to decrease at elevations between 1750 m and 3750 m, and between 4500 m and 5500 m, while at elevation ranges from 3750 to 4500 m it will likely increase in the future. Overall, the projections found around 186 km2 of total area loss within these elevation ranges (
Figure 7.Changes in very high suitability habitat at different elevation ranges in the GRB
4 Discussion: The potential impact of climate and land cover changes on habitat of HM
Currently, the HM is estimated to occur within less than 20000 km2 area globally, including Nepal, India, Bhutan, Afghanistan, Pakistan, Tibet and Myanmar (
The habitat of HM is also projected to decrease at elevations between 1750 m and 3750 m, and to increase at elevations between 3750 m and 4500 m. Past studies also found the suitable habitat ranges between 2500 m and 4750 m in barren land and open forest of the Nepal Himalayan regions (
This species undergoes fluctuations with altitudinal and seasonal migrations (
5 Conclusions
This study assessed the current and future distributions of the habitat of the nationally protected bird, Himalayan Monal, within the GRB using the MaxEnt model. Under the conditions of changing climatic variables, land cover changes were found likely to impact the habitat of the Himalayan Monal in 2050. The highly suitable habitat of the Himalayan Monal is likely to decrease by around 188 km2 in the future, particularly in the northern and northwestern parts (i.e., Chhyo, Tatopani, Humde and Chame). For the most part, the suitable habitat would increase in the eastern parts such as Nyak, Langtang, Langtang National Park and Manaslu Conservation Area in 2050. This study highlights the fact that more attention should be given to HM, in terms of future habitat protection. This species is expected to experience considerable decreases in suitable habitat areas in the future. The northeast parts are particularly important regions and more attention should be paid to them, especially with regard to forest cover, grassland, and shrubland to protect the remaining habitat of HM.
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