Species Distribution Modeling of Variable Tawny Rajah (Charaxes bernardus hierax) in some unusual habitats of West Bengal, India: implications for conservation

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Species Distribution Modeling of Variable Tawny Rajah (Charaxes bernardus hierax) in some unusual habitats of West Bengal, India: implications for conservation

https://doi.org/10.21203/rs.3.rs-2954904/v1

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Lepidopteran species serve as one of the most important pollinators in ecosystem and this species also coevolved with their nectaring plants. They regarded as one of the good indicators of ecosystem health due to their high sensitivity to the changes in environmental variables. There are numerous butterfly and moth species are found in India whose possible distribution in the geographic areas yet to be known. In our present study we try to find out the predicted distribution area of Variable Tawny Rajah (Charaxes bernardus hierax) in West Bengal by the use MaxEnt 3.4.4 program. It was observed that northern part of West Bengal consists of greater areas that contain suitable environmental conditions (maximum and moderate) than southern part of West Bengal. The regions with > 304 mm and > 105 mm precipitation (bio 13 and bio 15) are considered to suitable for sustaining this species. This type of study may helps to provide an idea about predicted distribution of Charaxes bernardus hierax in West Bengal and selection of conservation strategies for this butterfly species in particular geographical region.

Information on the distribution of the species of the taxonomically diverse group like insects (Arthropoda: Mandibulata) is limited and often less detailed, partly due to the inadequate and incomplete surveys (Jiménez-Valverde & Hortal 2003) and insufficient sampling effort (Romo & García-Barros 2005). Besides, many of the techniques that are used for arthropod sampling not axiomatically standardized (Jiménez-Valverde &Lobo 2004) for one or more taxonomically similar groups. As a pre-requisite condition for framing conservation strategy for any species, a background knowledge of its biology, population structure, sensitivity to environmental perturbations and distribution of the species are necessary (New 2014), both at the local and regional scales. In many instances, the predictive models for the distribution of the species become a priority to supplement the information to project the future distribution pattern within the geographical limits (Rawat et al. 2021; Purohit & Rawat 2021).The prediction of the prospective distribution is more a necessity in view of the global climate change. Thus species distribution models (SDMs) that include a combination of the climatic and the environmental factor as the explanatory variables for projecting the distribution of a particular species are considered highly useful. In this context, the maximum entropy (MaxEnt) based model may serve as a valid approach to predict the distribution pattern of a reference species. In the maximum entropy modelling (Maxent), applied to presence-only data is thought to be resilient to the sparsely populated occurrence locations of species and is capable of establishing relationship that is non-linear between the predictor and response variables (Phillips et al. 2004, 2006; Elith et al. 2006; Benito de Pando & Peñas de Giles; 2007, Wisz et al. 2008; Gupta et al. 2022). As a consequence, several studies have utilized the MaxEnt model for the prediction of distribution of species deserving conservation priority and population management. Among the invertebrates the distribution of the butterflies (Jacinto-Padilla et al. 2017), snails (Barman et al. 2021) and slugs (Gupta et al. 2022) were predicted using the MaxEnt model of species distribution.

Although the taxonomic diversity (Mukherjee et al. 2015), adaptations (Kunte 2007) and morphological features (Mukherjee & Hossain 2021, 2022) have been studied from various parts of the world, the application of the SDMs for the predictive distribution of butterflies (Insecta: Lepidoptera) has been little emphasized (Schröder et al. 2009, Mainali et al. 2020 ). Butterflies execute significant role as pollinator and qualify as herbivores that coevolved with the host plants (Kunte 2000; Tiple et al. 2006). At the same time these are highly sensitive to the changes in environmental parameters and thus are often considered as important indicators for ecosystem health (Doré et al. 2021). Adult butterfly species mostly rely on flowers for their food, but some butterfly species never visit flowers. They mostly depend on other organic sources such as dung, urine, and overripe fruits (Kehimkar 2016; Mukherjee et al. 2022). Charaxes bernardus is a butterfly species that never depends on flowers and relies on dead- decaying materials for its food (Kehimkar 2016; Mukherjee et al. 2022). Charaxes bernardus is commonly known as variable tawny rajah and belongs to subfamily Charaxinae under the Nymphalidae family. Charaxes bernardus was first described by Fabricius (1793), later the subspecies Charaxes bernardus hierax was described by Felder and Felder (1867). The Himalayan subspecies of Charaxes bernardus hemana described by Butler (1870) (Lepidoptera: Nymphalidae) is legally protected under Schedule II of the wildlife (Protection) Act, 1972 of India (Das et al. 2018; Kunte et al. 2021). On global scale Charaxes bernardus hierax has fewer presence records from Myanmar, Cambodia and Yunnan, a province in southwestern China (Coene & Vis 2008, Kosterin 2020; Choi et al. 2021). In India, presence of the butterfly Charaxes bernardus hierax has been reported principally from Meghalaya, Assam, Mizoram, Arunachal Pradesh, Tripura, Nagaland, Sikkim, northern most part of West Bengal and some sparse distribution from Odisha and Chattisgarh (Kunte et al. 2021). Distribution of butterfly species can be affected by the changes in climatic conditions (Kehimkar 2016). The study aims to develop a map for potential distribution of C. bernardus hierax in West Bengal to determine the areas suitable for sustenance of this species along with highlighting the role of environmental variables for this species.

Study area

The survey was carried out in Purulia, West Bengal, India and during a survey on 8 august 2020 a single specimen of C. bernardus hierax was recorded and photographed from Garh Panchkot Hill (86.752 E, 23.614 N), located in the Purulia district. That is the first written literature about the presence of the species from Purulia, West Bengal, India (Mukherjee et al. 2022).

Data collection

A detailed literature review and internet survey revealed that the butterfly Charaxes bernardus hierax was also recorded from nearby areas like Baghmundi, Ajodhya Hill and Kamalpur (Kunte et al. 2021) localities of the study area. Occurrence of this butterfly species have not been reported from any other area of southern part of West Bengal so far. Further data on the occurrence points of Charaxes bernardus hierax throughout the West Bengal were collected from global and Indian data bases for butterfly species, namely, Global Biodiversity Information Facility (GBIF) (GBIF.org) and ifoundbutterflies.org (Kunte et al. 2021).

Environmental data

Information on the world's bioclimatic and topographic variables was obtained from WorldClim v2.1 database (http://www.worldclim.org) and the consensus land cover database (http://www.earthenv.org). The GeoTiff files for all variables were downloaded with the resolution of 30 arc second. Information on India's bioclimatic variables were extracted using QGIS (http://qgis.osgeo.org/). The bioclimatic variables that describe monthly mean temperature and precipitation trends include seasonality and extreme climatic features (Rawat et al. 2022). The variables serve as an important determining species habitat boundary and influence species geographical spreading (Rawat et al. 2022). The multicollinearity between variables can be a cause of model's overfitting (Rawat et al. 2022). To select the least correlated variables, we first run the model with all variables with default settings and remove the variables that have < 2% percent contribution and zero jackknife training gain. After that correlation analysis that was done by EnmTools package R v4.2.2 (R studio Team 2020) and when a pair of variables maintained a correlation coefficient > ± 0.8, one of the variables from such pair are selected based on their percent contribution (Cao et al. 2021; Purohit & Rawat 2021). Then final model was developed by using rest of the variables.

Modeling overview

Distribution of Charaxes bernardus hierax, commonly known as variable tawny rajah modeled by using maximum entropy modeling (MaxEnt) software 3.4.4 (https://www.cs.princeton.edu/~schapire/maxent/ ). MaxEnt predict the possibility of presence of a species in the scale ranging from 0 as the lowest to 1 as the highest distribution possibility in a particular area.

As variables are used with 30s resolution (1Km X 1Km) spatial thinning for the occurrence points (5km X 5Km) was done by using the spThin package in R v4.2.2 (R studio Team 2020)for removing the geographic biases (Gupta et al. 2022). After that 13 occurrence points were used to developed the model (Fig. 1). Out of 19 bioclimatic variables, 12 consensus landcover variables and one topographic variable five variables were used to interpret the distribution pattern of Charaxes bernardus hierax in the studied geographic location (Table 1). During modeling species distribution by MaxEnt, default settings were used (Gupta et al. 2022). Ten thousand background points were used to determine the MaxEnt distribution and all the occurrence points are used for training. The output was classified into colour gradient depending upon the model's prediction values, and the map was prepared using QGIS 3.28.

Table 1

List of environmental variables used during model building for distribution analysis of *Charaxes bernardus hierax.* Variables are marked bold that are used during final model development.
Variable	Abbreviation
Annual Mean Temperature	Bio 1
Mean Diurnal Range (Mean of monthly (max temp - min temp))	Bio 2
Isothermality (BIO2/BIO7) (×100)	Bio 3
Temperature Seasonality (standard deviation ×100)	Bio 4
Max Temperature of Warmest Month	Bio 5
Min Temperature of Coldest Month	Bio 6
Temperature Annual Range (BIO5-BIO6)	Bio 7
Mean Temperature of Wettest Quarter	Bio 8
Mean Temperature of Driest Quarter	Bio 9
Mean Temperature of Warmest Quarter	Bio 10
Mean Temperature of Coldest Quarter	Bio 11
Annual Precipitation	Bio 12
Precipitation of Wettest Month	Bio 13
Precipitation of Driest Month	Bio 14
Precipitation Seasonality (Coefficient of Variation)	Bio 15
Precipitation of Wettest Quarter	Bio 16
Precipitation of Driest Quarter	Bio 17
Precipitation of Warmest Quarter	Bio 18
Precipitation of Coldest Quarter	Bio 19
Elevation	Elev
Evergreen/Deciduous Needleleaf Trees	Con 1
Evergreen Broadleaf Trees	Con 2
Deciduous Broadleaf Trees	Con 3
Mixed/Other Trees	Con 4
Shrubs	Con 5
Herbaceous Vegetation	Con 6
Cultivated and Managed Vegetation	Con 7
Regularly Flooded Vegetation	Con 8
Urban/Built-up	Con 9
Snow/Ice	Con 10
Barren	Con 11
Open Water	Con 12

The butterfly species Charaxes bernardus hierax were recorded from three sites viz. Garh Panchkot Hill (86.752 E; 23.614 N), Baghmundi (86.0.5046 E; 23.19335 N) and Ajodhya Hill (86.12681 E; 23.20036 N) in Purulia district, West Bengal, India. Apart from Purulia, the butterfly species was also recorded from Bankura, Alipurduar, Kalimpong and Jalpaiguri districts of West Bengal. Apart from the record of the abundance of the specific butterfly, the species distribution model included 5 explanatory variables, namely, Precipitation of Wettest Month (bio 13), Precipitation Seasonality (bio 15), Mixed/ Other Trees (con 4), Urban / Built-up (con 9) and Open Water (con 12). Following the data input, the results were interpreted in terms of the Receiver Operating Characteristic Curve (ROC). The ROC was used to check the simulation accuracy of the model (Fielding & Bell, 1977) and the predictive precision of the model depended on the area under curve (AUC) value. In the present model for distribution of Charaxes bernardus hierax, the AUC value is 0.911 that signifies high model precision for the 5 variables that were used for build the model (Fig. 2). Based on the relative contributions, bio 13 with 60.6% contribution emerge as the most influential variable for the distribution of Charaxes bernardus hierax followed by bio 15, con 4, con 12, and con 9 with 22.4%, 11%, 3.3% and 2.7%contribution respectively (Table 2). Jackknife plot shows the precipitation of warmest quarter (bio 13) is best fit for training data (Fig. 3). The response curves showed the predicted probability presence of species changes as changes in each environmental variable (Fig. 4). The output from MaxEnt transferred in QGIS and prepared the map for West Bengal to interpret the predicted distribution of Charaxes bernardus hierax. In case of West Bengal 8,676 KM² and 8,012 KM² areas were regarded as most and moderately suitable to harbor this organism. It was observed that northern part of West Bengal consists of greater areas that contain suitable environmental conditions (maximum and moderate) than southern part of West Bengal (Fig. 5). It was observed that regions with > 304 mm and > 105 mm precipitation (bio 13 and bio 15) are considered to suitable for sustaining this species.

Table 2

The table gives estimates of relative contributions of the bioclimatic variables of MaxEnt model for distribution of *Charaxes bernardus hierax.*
Variables	Percent contribution	Permutation importance
Bio13	60.6	18.8
Bio 15	22.4	43.6
Con 4	11	4.4
Con 12	3.3	8.6
Con 9	2.7	24.7

The species distribution analysis is a prerequisite for identifying and predicting geographic areas where targeted species may be present or conservation strategies may be initiated further if conditions and resources are preferable for them (Rawat et al. 2022). Environmental conditions are pivotal for any living organism's survival in a new condition. The main idea behind species distribution using MaxEnt is to detect probability distribution that has been explained during model analysis. Environmental characteristics and species occurrence data are must for such types of distribution analysis. Previous studies demonstrate that climatic factors play a major role in lepidopteran abundance (Hernandez-Baz et al. 2022). As with other species deserving conservation management, information obtained on the distribution pattern of C. bernardus hierax will be useful in promoting the conditions that sustain the population. Of particular relevance will be the environmental and the climatic factors that favours the distribution and thus the population abundance of C. bernardus hierax. As discussed for the distribution modeling of the Smith’s Blue butterfly, Euphilotes enoptes smithi, the correspondence of the vegetation, edaphic factors and the environmental variables was useful in understanding and predicting the distribution pattern in the Santa Lucia Mountains along the Big Sur coast in Monterey County, California, USA (Arnold & Jensen 2022). A more elaborative approach for linking conservation management of butterflies and SDM is observed through the studies on the Ithomiini across the Neotropics (Doré et al. 2021). Apart from the vegetation and the climatic pattern predicting the distribution pattern of the entire Ithomiini tribe, the effects of the anthropogenic activities could be deduced from the studies. As a consequence, specific steps could be taken for conservation and sustenance of the taxonomic diversity of the Ithomiini tribe of butterflies (Doré et al. 2021). Similar approach of linking vegetation and climatic factors with the species occurrence was adopted for predicting the distribution pattern for Alpine endemic species Erebia calcaria in Slovenia and Austria (DeGroot et al. 2009). Since, the climatic, topographic and edaphic factors influence the availability of the host plant, so will the dependent taxa like the butterfly vary accordingly. For instance, the prediction of the current distributions for showy milkweed (A. speciosa), swamp milkweed (A. incarnata), and monarch (Danaus plexippus) butterflies corresponded with the distribution of the milkweed Asclepias spp., in Idaho, USA (Svancara et al. 2019). In Mexico, the SDM was applied to decipher the distribution pattern of several butterfly species like Pyrisitia proterpia, Danaus gilippus, Zerene cesonia, Archaeoprepona demophon, and Anteos maerula, where vegetation pattern and the climatic factors were the reasonable predictors for the observed pattern of the distribution of the species (Jacinto-Padilla et al. 2017). In China, the distribution and the habitat utilization pattern of the Gainville fritillary butterfly Melitaea cinxia was deduced through SDM (Zhou et al. 2012). The butterfly M. cinxia exhibited strong dependence on the distribution pattern fo the host plant Veronica spicata, justifying that plants, edaphic factors, tophography and the climatic conditions portrays the distribution pattern for the species with high specificity. Apparently, the distribution of the butterflies, like other dependent taxa, will vary with the availability of the host plants which on the other hand will depend on the climatic and edaphic factors. This applies for several other invertebrate species, which are linked with the abundance of the resources at the spatio-temporal scales for a concerned geographical region.

Consideration of bioclimatic variables for species distribution modeling has become more familiar (Franklin 2009). Predictions derived from species distribution modeling can be linked with other ecological parameters like productivity and species abundance (Pearce & Ferrier, 2001; Brambilla & Ficetola 2012; Huntley et al. 2012). The results of our study demonstrated that the regions where average precipitation in the warmest quarter (bio 13) and precipitation seasonality (bio 15) are > 304 mm and > 105 mm are suitable for sustaining this species. Empirical study demonstrates the increase in precipitation positively associated with butterfly diversity (Kehimkar 2008). The results also suggest that increase in mixed/ other trees (con 4) also associated with increase in probability of the occurrence of the species. The results also associated with increase in probability of occurrence with decrease in open water (con 12) and urban / built up (con 9). Empirical studies also showed that butterfly diversity decreases with increase in urban characteristics (Ruszczyk & De Araujo 1992; Blair & Launer 1997; Stefanescu et al. 2004; Dover & Settele 2009).The present study demonstrated that in West Bengal Darjeeling, Kalimpong, Jalpaiguri, Alipurduar, Cooch Behar, North Dinajpur, Purulia, Birbhum and Murshidabad has the suitable condition (maximum and moderate) to sustain this species. The study also indicate that our study area also contain suitable environmental conditions to sustain this species. This report for distribution of Charaxes bernardus hierax may be useful in selecting conservation strategies for the butterfly species in a particular area.

The distribution of butterfly Charaxes bernardus hierax was modeled by using MaxEnt throughout the India. Bioclimatic variables were used to predict the distribution of this butterfly. Results show precipitation Precipitation of Wettest Month (bio 13) is the most important variable to predict the distribution of Charaxes bernardus hierax. The results showed that in West Bengal Darjeeling, Kalimpong, Jalpaiguri, Alipurduar, Cooch Behar, North Dinajpur, Purulia, Birbhum and Murshidabad has the suitable condition to sustain this species. The present study helps to determine the suitable areas that are capable to sustaining this species and it also helps to find out the role of environmental variables for this species. This type of study may also be crucial for selecting proper conservation strategies for this species in right geographical location.

Acknowledgements

The authors thankfully acknowledge Head, Department of Zoology, The University of Burdwan, Golapbag, Burdwan, West Bengal, India, for the facilities provided.

Funding

SSM acknowledges financial assistance to UGC, Government of India, in the form of Senior Research Fellow [Ref. No. 657/(CSIR-UGC NET June 2018)].

Conflicts of interest

The authors declare that they have no conflict(s) of interest.
Availability of data and material

Data used in preparation of the MS is freely available on request to the corresponding author.
Code availability

Not applicable.

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Species Distribution Modeling of Variable Tawny Rajah (Charaxes bernardus hierax) in some unusual habitats of West Bengal, India: implications for conservation

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Abstract

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Introduction

Materials and methods

Study area

Data collection

Environmental data

Modeling overview

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Conclusion

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