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Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India
CHNAES-00682; No of Pages 6 Acta Ecologica Sinica xxx (2020) xxx
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Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India Kongkona Borborah, Kishor Deka, Debanjali Saikia, S.K. Borthakur, Bhaben Tanti ⁎ Department of Botany, Gauhati University, Guwahati 781014, Assam, India
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Article history: Received 4 August 2018 Accepted 3 February 2020 Available online xxxx Keywords: Musa flaviflora Taxonomy Distribution Ecological niche modelling (ENM)
a b s t r a c t Musa flaviflora Simmonds, a kind of typical species of banana grows wildly in North East Region of Assam. This species of Musa is confined only a few pockets of North East Region of India including Eastern part of Assam with a poor population size. Therefore, conservation of this plant through proper scientific investigation is urgently needed. The present study was conducted to find out the habited status of this species through ecological niche modelling (ENM), which helps to conserve this plant with proper scientific investigation in future. On the other hand, though earlier worker does the taxonomy of this species, it is not clear. So, in this present investigation a detailed taxonomic description of Musa flaviflora with its predicted location was find out through ecological niche modelling (ENM) for improving the conservation status of the species. © 2020 Published by Elsevier B.V. on behalf of Ecological Society of China.
1. Introduction The territory of Assam is rich in having a good number of wild species of Musa L., which could be the parent plants of all those present days cultivated bananas. Here sect. Musa is predominant with the occurrence of the progenitors like M. acuminata Colla (genome group AA) and M. balbisiana Colla (BB), which are considered as the parent plants of all the cultivated hybrids. So the wild Musa sp. in this region has the evolutionary significance. The occurrence of M. acuminate was claimed in Assam by earlier literature, but not any single species of wild M. acuminate has so far been recorded, although a similar wild species M. flaviflora has been found abundantly in many places of Assam, India. The distribution of M. acuminata in North Eastern states of India was cited by Hore et al. (1992) without specifically mentioning the localities of its occurrence in Assam [1]. The species M. flaviflora has been found to be restricted only a few locations of Assam in our present investigation. This species is found only few parts of Eastern Assam, whereas even a single species was not tracked in Western parts of Assam, although it was reported in earlier days. Because of habitat degradation, rapidly changing climate, invasion of alien species and over exploitation the population stock of M. flaviflora has been depleting very fast in its natural habitats, which hinder the sufficient propagation of the plant in its natural condition. Different environmental factors affecting species distribution are assessed by using habitat distribution modelling (HDM) or ecological niche modelling (ENM) [2–7]. Modelling of these ecological conditions takes into account temperature, precipitation, soil, vegetation and land cover; much of it from Geographic Information System ⁎ Corresponding author. E-mail address: [email protected] (B. Tanti).
(GIS) databases (www.worldclim.org and www.diva-gis.org.). High resolution satellite imageries coupled with environmental variables and spatial datasets on climate and vegetation enhance models accuracy. Since ENM predicts occurrence of a species, it can be inter-alia used to extrapolate species distribution across landscape in time and space [8,9]. Species distribution maps prepared using ENM have, therefore high level of statistical confidence and help to succinctly locate suitable areas for reintroduction of threatened species [10–12]. Predictive modelling of species distributions especially ENM is being increasingly favoured by conservation managers to study biogeography, evolution, ecology, conservation, and invasive-species management [13]. Species reintroduction/reinforcement is one of the successful ecological engineering techniques for restoration of the depleted species populations, and degraded habitats and ecosystems [14,15]. Reintroduction is a general term that describes the controlled placement of plant material into a natural or managed ecological area where as reinforcement is an effort to increase population size or diversity by adding individuals to an existing population [16,17]. This enables conservation planning for rescue and recovery of threatened species with assured success under the challenging situations of degradation and climate change [18]. Habitat distribution modelling therefore helps to identify the areas for species reserves, reintroduction, and in developing effective species conservation measures. It has been successfully used in restoring critical habitats and predicting the impact of environmental and climate change on species and ecosystems [19,20]. Long-term monitoring is necessary because initially high survival rates are often followed by reversals over time [21]. Considering the significance of the presence of wild M. flaviflora, a comprehensive account on the species with its predicted mapping
https://doi.org/10.1016/j.chnaes.2020.02.002 1872-2032/© 2020 Published by Elsevier B.V. on behalf of Ecological Society of China.
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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was carried out in the present investigation, for that further step to improve conservation in its natural habitat. 2. Material and methods 2.1. Survey and collection of the plant species It is difficult to finding the occurrence point of M. flaviflora because of its declining populations. The presence points of M. flaviflora were identified based on the field surveys in Assam of North East region of India. Finally, species occurrence point data was collected through frequentsurvey in different parts of Assam, during the year of 2014–2016 covering different seasons of the year to find out the species in its natural habitats and collected specimens are submitted to ASSAM and GUBH.
into replicate folds and each fold was used for test data. Model quality was evaluated based on Area Under Curve (AUC) value and the model was graded following as [26]: poor (AUC b 0.8), fair (0.8 b AUC b 0.9), good (0.9 b AUC b 0.95) and very good (0.95 b AUC b 1.0). 2.3.5. Assessment of habitat status and identification of areas for reintroduction The predicted potential areas were superimposed on Google Earth Ver. 6 (www.google.com/earth). The predicted suitability maps were exported in KMZ format using Diva GIS ver. 7.3 (www. diva-gis.org). The exported KMZ files were overlaid on satellite images in Google Earth to ascertain the actual habitat condition prevailing in the areas of occurrence [12]. 3. Result
2.2. Taxonomy
3.1. Botanical description of the plant
Morphological data was collected on the spot during field survey in different parts of Assam. Every parts of the plant including leaf, pseudo stem, flower and fruit were studied and recorded. Different parts of the plant use in different localities were also recorded from the local people.
Musa flaviflora Simmonds, Kew Bull. 471.1957; Hakkinen et al., Nordic J Bot. 000: 001–006.2013. Type: India, Assam, Mariani Hills, 1955, Simmonds 241, K! no: 18871.000 (in spirit), lectotype designated by Hakkinen and Vare (2008a). Kingdom: Plantae. Division: Angiospermae. Class: Scitaminae. Order: Zingiberales. Family: Musaceae. Genus: Musa. Species: M. flaviflora Simmonds. Plant slender, suckering freely, suckers 2–7, close to parent plant. Mature pseudostem 3.8–4 m tall, up to 20 cm in diam. at base, covered with dead remains of older sheaths. Sheaths light green with radish or blackish patches, underlying colour shiny green, devoid of wax, sap watery. Petiole 50–58 × 3.7–4 cm, petiole canal straight with erect margins, bases winged. Leaves intermediate, lamina elliptic, 241–279 × 57–63 cm, adaxially deep green, adaxially light green, shiny, midrib yellowish green with balck blotches base symmetric, apex truncate. Inflorescence arching downwards; peduncle glabrous, light green, not waxy; 2 sterile bracts. Carpellate bud lanceolate, 28–30 × 10–11 cm, bracts radish pink, convolute, waxy, revolute and deciduous, ovate, 28–35 cm long, 11–13 cm wide; basal flowers female; 18–20 flowers in two rows; ovary yellowish green; slightly curved,
2.3. Ecological niche modelling for recognizing the predicted habitats for reintroduction 2.3.1. Species occurrence data Primary distributional records of the species were collected from Lahorijan Reserve Forest, Karbi Anglong district of Assam, India. The coordinates of all the occurrence points were recorded to an accuracy of 10–40 m using a GPS (Garmin). The coordinates were then converted to decimal degrees for use in modelling the distribution of potential habitats of the species in its native range. These records were cleaned for spatial errors in Diva-GIS and duplicate records in the dataset were discarded using MS Excel (Broennimann & Guisan, 2008). 2.3.2. Species occurrence data Primary distributional records of the species were collected from Kamrup (Sonapur), Madhabpur (Jorhat), Diphu (Karbi Anglong) and Dibrugarh (Namrup) districts of Assam, India. The coordinates of all the occurrence points were recorded to an accuracy of 10–40 m using a GPS (Garmin). The coordinates were then converted to decimal degrees for use in modelling the distribution of potential habitats of the species in its native range. These records were cleaned for spatial errors in Diva-GIS and duplicate records in the dataset were discarded using MS Excel [22,23]. 2.3.3. Environmental data For modelling the current distributional range of the species, three types of variables viz., bioclimatic, remote sensing data (NDVI) and elevation were used in this study. Nineteen bioclimatic variables with 1 km resolution, were downloaded from World climate website (http:// www.worldclim.org.). This has a set of climate layers representing bioclimatic variables, derived from monthly temperatures and rainfall recorded worldwide. Normalized Difference Vegetation Index (NDVI) was obtained from http://glcf.umiacs.umd.edu/data/ndvi and Elevation (Digital Elevation Model-DEM) data was also obtained from the WorldClim website (Table 1) [12,24]. 2.3.4. Validation of model robustness To study the habitat modelling, the pixel dimension was 250 m × 250 m grid cell and the model was developed using maximum entropy modelling (MaxEnt version 3.3.3e, [25]). To validate the model robustness, we executed 20 replicated model runs for the species with a threshold rule of 10 percentile training presence. In the replicated runs, we employed cross validation technique where samples were divided
Table 1 List of Bioclimatic variables, NDVI and elevation used in the model. Variable
Description of the variable
Bio1 Bio2 Bio3 Bio4 Bio5 Bio6 Bio7 Bio8 Bio9 Bio10 Bio11 Bio12 Bio13 Bio14 Bio15 Bio16 Bio17 Bio18 Bio19 eu1- eu12 h_dem
Annual mean temp. (°C) Mean diurnal range - monthly (max temp – min temp) (°C) Isothermality (Bio2/Bio7 *100) (°C) Temperature Seasonality (standard deviation * 100) (°C) Maximum temperature of warmest month (°C) Minimum temperature of coldest month (°C) Temperature annual range (Bio5 - Bio6) (°C) Mean temperature of the wettest quarter (°C) Mean temperature of the driest quarter (°C) Mean temperature of the warmest quarter (°C) Mean temperature of the coldest quarter (°C) Annual precipitation (mm) Precipitation of the wettest month (mm) Precipitation of the driest month (mm) Precipitation seasonality (coefficient of variation) (mm) Precipitation of the wettest quarter (mm) Precipitation driest quarter (mm) Precipitation of the warmest quarter (mm) Precipitation of the coldest quarter (mm) Month of January–December Elevation (m)
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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trilocular with two rows of ovules in each locule; style to 3.6 cm long, creamy. Staminate bud lanceolate, 15–24 × 6.7–7 cm, bracts pink, waxy, imbricate, not revolute before falling; staminate flowers averaging 19–21 per bract in 2 rows, falling with bracts; compound tepal 4.8–5.0 cm long, 1.4 cm wide, without thickened keels, 5-toothed; 2 smaller, light orange in colour; free tepal to 1.9–2.25 cm long,
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1.1–1.5 cm wide, transluscent white, oblong with a short orange acumen; stamens 5, exerted; anthers yellowish green or light brown, 4.0–4.5 cm long; filament 1.8–2.0 cm long; anther lobe 2.3–2.6 cm long, pale brown. Fruit branch ascending, compact, slightly curved, ridged, 10.5–12.0 × 2.0–2.5 cm, apex pointed, pedicel 2 cm, slightly curved, glabrous; immature fruit light green, ripe one's yellowish
Fig. 1. Musa flaviflora Simmonds, A. Habitat, B. A female bud with female flower, C. A male bud with male flower, D. Upper surface of pseudostem, E. Inner pseudostem, F. Petiole canal, G. External surface of male bract, H. Internal surface of male bract, I. A complete female flower, J. A complete male flower, K. Anthers, L. Free tepal of male flower, M. Compound tepal of male flower, N. T.S. of ovary, O. Mature seeds, P. Mature fruit.
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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brown, not waxy; fruit pulp white, seeds up to 90 per fruit, angular, wrinkled, up to 3 mm long, 5–6 mm wide at apex [Fig. 1]. 3.1.1. Distribution BANLADESH, BHUTAN, INDIA; Assam, Jorhat District (Mariani, Titabor), Kamrup District (Hills of Jorabat), Dibrugarh District (Namrup, Dillighat), Karbi Anglong (Diphu) and Sonitpur District (Rangapara). Meghalaya, Arunachal Pradesh, Nagaland and Manipur. 3.1.2. Specimen examined India, Assam, Kamrup District, Sonapur, near Jorabat, April 5, 2014, K. Borborah 027 (26°10.564′ N, 91°86.350′ E); India, Assam, Dibrugarh District, Namrup, Dilli ghat, April 25, 2014, K. Borborah 037 (27°07.784′N, 95°22.037′E); India, Assam, Jorhat District, Titabor, Madhabpur, October 6, 2014, K. Borborah 068 (26°42.658′ N, 89°32.300′ E); India, Assam, Karbi Anglong District, Diphu, September 5, 2015, K. Borborah 094 (26°01.451′N, 93°23.334′E). 3.1.3. Uses Durring field work the present investigator recorded that the ripe fruits are kept in a glass of water overnight and the extract obtained in the next morning by seiving of the mixture, is taken as a health tonic juice. The inflorescence is taken as vegetables. 3.2. Calibration of models to predict potential habitat for reintroduction of M. flaviflora The model calibration test for Musa flaviflora through Maxent model yielded satisfactory results (AUC train = 0.97 ± 0.002 and AUC test = 0.95 ± 0.015). Estimation of relative contributions of the environmental variables to the Maxent model was mentioned in Table 2. To determine the first estimate, in each iteration of the training algorithm, the increase in regularized gain was added to the contribution of the corresponding variable, or subtracted from it if the change to the absolute value of lambda is negative. For the second estimate, for each environmental variable in turn, the values of that variable on training presence and background data were randomly permuted [27]. The model was revaluated on the permuted data, and the resulting drop in training AUC was normalized to percentages. The Jack knife results showed the test of variable importance. The environmental variable with highest gain when used in isolation was eu7_1_eur, which therefore appeared to have the most useful information by itself. However, dem_ne30 m (elevation) also revealed significantly higher gain as compared with the other NDVI and bioclimatic variables (Fig. 2). 3.3. Habitat status assessment and identification of areas for reintroduction Suitable habitat for reinforcement of M. flaviflora in the predicted potential areas revealed the seven different sites of both disturbed and undisturbed forest of Kamrup District, Dibrugarh District, Tinsukia district, Lakhimpur district, Jorhat District, Karbi Anglong and Sanitpurdistrict in Assam and therefore could be identified as suitable environment for persistence of the species. Superimposing the predicted potential habitat map of the species on Google Earth satellite images revealed a mosaic of habitats to be suitable for the species persistence. The areas with high to very high habitat suitability for the species were continuous patches of tropical and sub-tropical forests of Assam (India), and a mosaic of fragmented groves, settled cultivation areas and human settlements. The areas with medium to low habitat suitability were degraded open forest areas, settled cultivation areas, homestead gardens and human settlements. The areas with very low habitat suitability were grasslands, degraded open forests and human settlements. The superimposition of predicted potential habitat distribution map on Google Earth images identified these forest areas which would act as in situ
Table 2 Estimates of relative contributions and permutation importance of the predictor environmental and bioclimatic variables to the MaxEnt model. Variable
Percent contribution
Permutation importance
eu7_1_eur eu8_1_eur bio_18 eu1_1_eur dem_ne30m bio_2 bio_7 eu3_1_eur eu2_1_eur bio_6 bio_4 bio_15 bio_16 eu9_1_eur bio_11 bio_1 bio_10 bio_13 eu12_1_eur bio_14 bio_12 bio_17 eu5_1_eur bio_8 eu4_1_eur bio_9 bio_3 bio_19 bio_5 eu6_1_eur eu10_1_eur eu11_1_eur
24.9 16.2 14.1 11.9 11.7 12.7 11.9 7.3 8.5 1.2 0.8 0.9 1.0 0.7 0.6 0.6 0.4 0.3 1.8 1.6 1.7 1.2 1.2 1.0 0.5 0.4 0.5 0 0 0 0 0
14.3 12.8 12.9 11.6 9.4 8.9 8.9 9.2 7.6 4.9 5.9 5.1 4.8 4.6 4.6 3.9 2.6 2.6 0.2 0.2 0.1 0.6 0.2 0.3 0.8 0.3 0.2 0 0 0 0 0
conservation area for the species and could also be used for reintroduction of the species in the wild (Fig. 3). 4. Discussion In our present investigation, we have showed a clear-cut dissection of M. flaviflora, which will helpful to differentiate this species from M. acuminate. The population stock of M. flaviflora in its natural habitats have been depleting very fast due to certain factors such as habitat fragmentation, over-exploitation and other anthropogenic activities. M. flaviflora has been reproduce by succours only and different parts of this plant has been use locally in different purpose including food, ritual, and other local uses. Therefore, improvement of conservation status is urgently needed for this wild species of banana. Earlier records from secondary sources showed the occurrence of the M. flaviflora in different parts of both Eastern and western Assam [28]. However, the plant could not be recorded from Wester Assam. Extensive survey could have made in different parts of Assam. Surprisingly, the populations discovered so far revealed poor population strength with restricted areas. The cause for the smaller size in the number of individuals in each population is either directly or indirectly due to various anthropogenic activities. However, the population status could be changed through increasing its number of individuals in its suitable natural habitat either through reinforcement or reintroduction. For reintroduction, determining precise and suitable habitat of the plant is important and this is possible through the ecological modelling technique, which has been used in this study. The ENM in the present study showed good overall performance in its native range. The high AUC values for training and testing (N0.90) indicate that the niche model has a good ability to differentiate between presence and absence areas for the species. The niche model could successfully predict most of the validation points in the Indian
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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Fig. 2. Jackknife test of variable importance for M. flaviflora: individual variable contribution (blue bar), contribution when a given variable is excluded (green bar), whole set of variables (red bar). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3. Map showing sample collection sites of M. flaviflora, potential habitat distribution in seven different sites of both disturbed and undisturbed forest of Assam (India). The red patches in the map represent the highly predicted localities. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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subcontinent, showing its fair transferability. Nevertheless, it can be said that the species has conserved its original niche properties in the Northeastern part of the subcontinent. However, the lower degree of prediction probability for some of the validation points hints at the possibility of a niche shift for the species, which has to be studied further. This information would be a guideline for reintroduction of M. flaviflora and to increase the population status for conservation [29–32]. In the present study, some areas consisting of continuous and intact patches of tropical and sub-tropical forests of Assam provide sustained habitat for the species. The seven districts especially in Kamrup District, Dibrugarh District, Tinsukia district, Lakhimpur district, Jorhat District, Karbi Anglong and Sanitpur have been identified to offer suitable environmental conditions for potential habitats at higher levels of probability [33–36]. Hence, such forest areas could serve as habitats for in situ conservation and reintroduction. The present study demonstrated that habitat distribution modelling could be of great help in predicting the potential habitats of threatened species for reintroduction as well as reinforcement.
Acknowledgements Funding support received from Department of Biotechnology (DBT), Govt. of India for the research project entitled “Collection, evaluation, documentation and conservation of banana genetic resources from north eastern region” vide sanction No. 102/I.F.D/SAN/1765-1767/ 2017-2018 dated 20. 07. 2017 is greatly acknowledged. References [1] D.K. Hore, B.D. Sharma, G. Pandey, Status of banana in North-East India, J. Econ. Taxon. Bot. 16 (1992) 447–455. [2] A. Guisan, N.E. Zimmermann, Predictive habitat distribution models in ecology, Ecol. Model. 135 (2000) 147–186. [3] J. Elith, C.H. Graham, R.P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R.J. Hijmans, F. Huettmann, J.R. Leathwick, A. Lehmann, J. Li, L.G. Lohmann, B.A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J.M. Overton, A.T. Peterson, S.J. Phillips, K. Richardson, R. Scachetti-Pereira, R.E. Schapire, J. Soberón, S.E. Williams, M.S. Wisz, N.E. Zimmermann, Novel methods improve prediction of species distributions from occurrence data, Ecography. 29 (2006) 129–151. [4] D.H. Reed, R. Frankham, Correlation between population fitness and genetic diversity, Conserv. Boil. 17 (2003) 230–237. [5] M. Jusaitis, L.B. Polomka, Sorensen, habitat specificity, seed germination and experimental trans location of the endangered herb brachycome muelleri (Asteraceae), Biol. Conserv. 116 (2004) 251–266. [6] K. Deka, P.S. Baruah, B. Sarma, S.K. Borthakur, B. Tanti, Preventing extinction and improving conservation status of Vanilla borneensis Rolfe-a rare, endemic and threatened orchid of Assam, India, J. Nat. Conserv. 37 (2017) 39–46. [7] S.K. Borthakur, P.S. Baruah, K. Deka, P. Das, B. Sarma, D. Adhikari, B. Tanti, Habitat distribution modelling for improving conservation status of Brucea mollis Wall. ex Kurz. - An endangered potential medicinal plant of Northeast India, J. Nat. Conserv. 43 (2018) 104–110. [8] S. Schmitt, R. Pouteau, D. Justeau, F. Boissieu, De ssdm: an r package to predict distribution of species richness and composition based on stacked species distribution models, Methods Ecol. Evol. 8 (2017) 1795–1803. [9] M.E. Wittmann, G. Annis, A.M. Kramer, L. Mason, C. Riseng, E.S. Rutherford, W.L. Chadderton, N.E. Zimmermann, New trends in species distribution modelling, Ecography 33 (2010) 985–989. [10] S. Kumar, T.J. Stohlgren, Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia, J. Ecol. Nat. Environ. 1 (2009) 94–98. [11] H. Ren, H. Lu, W. Shen, C. Huang, Q. Guo, Z. Li, Sonneratia apetala Buch. Ham in the mangrove ecosystems of China: an invasive species restoration species? Ecol. Eng. 35 (2009) 1243–1248.
[12] D. Adhikari, S.K. Barik, K. Upadhaya, Habitat distribution modelling for reintroduction of Ilex khasiana Purk., a critically endangered tree species of northeastern India, Ecol. Eng. 40 (2012) 37–43. [13] K.B. Aubry, C.M. Raley, K.S. McKelvey, The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species, PLoS One 12 (2017), e0179152. [14] Y.A. Kuzovkina, T.A. Volk, The characterization of willow (Salix L.) varieties for use in ecological engineering applications: co-ordination of structure, function and autecology, Ecol. Eng. 3 (2009) 1178–1189. [15] T. Polak, D. Saltz, Reintroduction as an ecosystem restoration technique, Conserv. Biol. 25 (2011) 424–427. [16] S. Godefroid, C. Piazza, G. Rossi, S. Buord, A.D. Stevens, R. Aguraiuja, C. Cowell, C.W. Weekley, G. Vogg, J.M. Iriondo, I. Johnson, B.D. Dixon, S. Gordon, B. Magnanon, R. Valentin Bjureke, R. Koopman, M. Vicens, T. Vanderborght Virevaire, How successful are plant species reintroductions? Biol. Conserv. 6 (2011) 72–682. [17] R.P. Reading, T.W. Clark, B. Griffith, The influence of valuational and organizational consideration of the success of rare species translocation, Biol. Conserv. 79 (1997) 217–255. [18] S.K. Barik, D. Adhikari, Predicting geographic distribution of an invasive species Chromolaena odorata L. (King) & H. E. Robins in Indian subcontinent under climate change scenarios, in: J.R. Bhatt, J.S. Singh, R.S. Tripathi, S.P. Singh, R.K. Kohli (Eds.), Invasive Alien Plants- An Ecological Appraisal for the Indian Subcontinent, CABI, Oxford shire, UK, 2011. [19] T. Polak, D. Saltz, Reintroduction as an ecosystem restoration technique, Conserv. Biol. 25 (2011) 424–427. [20] A. Giriraj, M. Irfan-Ullah, B.R. Ramesh, P.V. Karunakaran, A. Jentsch, M.S.R. Murthy, Mapping the potential distribution of Rhododendron arboreum Sm. ssp. Nilagiricum (Zenker) Tagg (Ericaceae), an endemic plant using ecological niche modelling, Curr. Sci. 94 (2008) 1605–1612. [21] M.J. Hutchings, The population biology of the early spider orchid Ophrys sphegodes Mill. III. Demography over three decades, J. Ecol. 98 (2010) 867–878. [22] R.J. Hijmans, J.M. Cruz, E. Rojas, L. Guarino, DIVA-GIS, A geographic information system for the management and analysis of genetic resources data. Manual (Internet), International Potato Center and International Plant Genetic Resources Institute, Lima Peru, 2001. [23] O. Broennimann, A. Guisan, Predicting current and future biological invasions: both native and invaded ranges matter, Biol. Lett. 4 (2008) 585–589. [24] C.H. Graham, R.J. Hijmans, A comparison of methods for mapping species ranges and species richness, Glob. Ecol. Biogeogr. 15 (2006) 578–587. [25] S.J. Phillips, R.P. Anderson, R.E. Schapire, Maximum entropy modelling of species geographic distributions, Ecol. Model. 190 (2006) 231–259. [26] W. Thuiller, S. Lavorel, M.T. Sykes, M.B. Araujo, Using niche-based modelling to assess the impact of climate change on tree functional diversity in Europe, Divers. Distrib. 12 (2006) 49–60. [27] J. Elith, S.J. Phillips, T. Hastie, M. Dudík, Y.E. Chee, C.J. Yates, A statistical explanation of MaxEnt for ecologists, Divers. Distrib. 17 (2011) 43–57. [28] C. Barooah, I. Ahmed, Plant diversity of Assam-A checklist of Angiosperm and Gymnosperm, ASTEC, 2014. [29] P.M. Benham, E.J. Beckman, S.G. DuBay, L.M. Flores, A.B. Johnson, M.J. Lelevier, Satellite imagery reveals new critical habitat for endangered bird species in the high Andes of Peru, Endanger. Species Res. 13 (2011) 145–157. [30] K. Borborah, S.K. Borthakur, B. Tanti, A new variety of Musa balbisiana Colla from Assam, India, Bangladesh J. Plant Taxonomy. 23 (1) (2016) 75–78. [31] K. Borborah, S.K. Borthakur, B. Tanti, Ornamentally important species of Musa L. (Musaceae) in Assam, India, J. Econ. Taxon. Bot. 40 (1–2) (2016) 1–8. [32] K. Borborah, S.K. Borthakur, B. Tanti, Musa balbisiana Colla-Taxonomy, Traditional knowledge and economic potentialities of the plant in Assam, India, Indian J. Tradit. Knowl. 15 (1) (2016) 116–120. [33] K. Deka, S.K. Borthakur, B. Tanti, Habitat mapping, population size and preventing extinction through improving the conservation status of Calamus nambariensis Becc.-an endemic and threatened cane of Assam, India, Acta Ecol. Sin. (2018) https://doi.org/10.1016/j.chnaes.2018.03. [34] P.S. Baruah, K. Deka, L. Lahkar, B. Sarma, S.K. Borthakur, B. Tanti, Habitat distribution modelling and reinforcement of Elaeocarpus serratus L. - A threatened tree species of Assam, India for improvement of its conservation status, Acta Ecol. Sin. (2018) https://doi.org/10.1016/j.chnaes.2018.06. [35] P.S. Baruah, K. Deka, B. Sarma, P. Das, S.K. Borthakur, B. Tanti, Assessment of few unexplored RET plant wealth of Assam, India, J. Adv. Plant Sci. 9 (2) (2017) 10–15. [36] P.S. Baruah, S.K. Borthakur, B. Tanti, Conservation of Mesua assamica (King and Prain) Kosterm.-an endangered plant of Assam, NeBIO-An Int. J. Environ. and Biodiv. 7 (1) (2016) 17–22.
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India Kongkona Borborah, Kishor Deka, Debanjali Saikia, S.K. Borthakur, Bhaben Tanti ⁎ Department of Botany, Gauhati University, Guwahati 781014, Assam, India
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Article history: Received 4 August 2018 Accepted 3 February 2020 Available online xxxx Keywords: Musa flaviflora Taxonomy Distribution Ecological niche modelling (ENM)
a b s t r a c t Musa flaviflora Simmonds, a kind of typical species of banana grows wildly in North East Region of Assam. This species of Musa is confined only a few pockets of North East Region of India including Eastern part of Assam with a poor population size. Therefore, conservation of this plant through proper scientific investigation is urgently needed. The present study was conducted to find out the habited status of this species through ecological niche modelling (ENM), which helps to conserve this plant with proper scientific investigation in future. On the other hand, though earlier worker does the taxonomy of this species, it is not clear. So, in this present investigation a detailed taxonomic description of Musa flaviflora with its predicted location was find out through ecological niche modelling (ENM) for improving the conservation status of the species. © 2020 Published by Elsevier B.V. on behalf of Ecological Society of China.
1. Introduction The territory of Assam is rich in having a good number of wild species of Musa L., which could be the parent plants of all those present days cultivated bananas. Here sect. Musa is predominant with the occurrence of the progenitors like M. acuminata Colla (genome group AA) and M. balbisiana Colla (BB), which are considered as the parent plants of all the cultivated hybrids. So the wild Musa sp. in this region has the evolutionary significance. The occurrence of M. acuminate was claimed in Assam by earlier literature, but not any single species of wild M. acuminate has so far been recorded, although a similar wild species M. flaviflora has been found abundantly in many places of Assam, India. The distribution of M. acuminata in North Eastern states of India was cited by Hore et al. (1992) without specifically mentioning the localities of its occurrence in Assam [1]. The species M. flaviflora has been found to be restricted only a few locations of Assam in our present investigation. This species is found only few parts of Eastern Assam, whereas even a single species was not tracked in Western parts of Assam, although it was reported in earlier days. Because of habitat degradation, rapidly changing climate, invasion of alien species and over exploitation the population stock of M. flaviflora has been depleting very fast in its natural habitats, which hinder the sufficient propagation of the plant in its natural condition. Different environmental factors affecting species distribution are assessed by using habitat distribution modelling (HDM) or ecological niche modelling (ENM) [2–7]. Modelling of these ecological conditions takes into account temperature, precipitation, soil, vegetation and land cover; much of it from Geographic Information System ⁎ Corresponding author. E-mail address: [email protected] (B. Tanti).
(GIS) databases (www.worldclim.org and www.diva-gis.org.). High resolution satellite imageries coupled with environmental variables and spatial datasets on climate and vegetation enhance models accuracy. Since ENM predicts occurrence of a species, it can be inter-alia used to extrapolate species distribution across landscape in time and space [8,9]. Species distribution maps prepared using ENM have, therefore high level of statistical confidence and help to succinctly locate suitable areas for reintroduction of threatened species [10–12]. Predictive modelling of species distributions especially ENM is being increasingly favoured by conservation managers to study biogeography, evolution, ecology, conservation, and invasive-species management [13]. Species reintroduction/reinforcement is one of the successful ecological engineering techniques for restoration of the depleted species populations, and degraded habitats and ecosystems [14,15]. Reintroduction is a general term that describes the controlled placement of plant material into a natural or managed ecological area where as reinforcement is an effort to increase population size or diversity by adding individuals to an existing population [16,17]. This enables conservation planning for rescue and recovery of threatened species with assured success under the challenging situations of degradation and climate change [18]. Habitat distribution modelling therefore helps to identify the areas for species reserves, reintroduction, and in developing effective species conservation measures. It has been successfully used in restoring critical habitats and predicting the impact of environmental and climate change on species and ecosystems [19,20]. Long-term monitoring is necessary because initially high survival rates are often followed by reversals over time [21]. Considering the significance of the presence of wild M. flaviflora, a comprehensive account on the species with its predicted mapping
https://doi.org/10.1016/j.chnaes.2020.02.002 1872-2032/© 2020 Published by Elsevier B.V. on behalf of Ecological Society of China.
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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was carried out in the present investigation, for that further step to improve conservation in its natural habitat. 2. Material and methods 2.1. Survey and collection of the plant species It is difficult to finding the occurrence point of M. flaviflora because of its declining populations. The presence points of M. flaviflora were identified based on the field surveys in Assam of North East region of India. Finally, species occurrence point data was collected through frequentsurvey in different parts of Assam, during the year of 2014–2016 covering different seasons of the year to find out the species in its natural habitats and collected specimens are submitted to ASSAM and GUBH.
into replicate folds and each fold was used for test data. Model quality was evaluated based on Area Under Curve (AUC) value and the model was graded following as [26]: poor (AUC b 0.8), fair (0.8 b AUC b 0.9), good (0.9 b AUC b 0.95) and very good (0.95 b AUC b 1.0). 2.3.5. Assessment of habitat status and identification of areas for reintroduction The predicted potential areas were superimposed on Google Earth Ver. 6 (www.google.com/earth). The predicted suitability maps were exported in KMZ format using Diva GIS ver. 7.3 (www. diva-gis.org). The exported KMZ files were overlaid on satellite images in Google Earth to ascertain the actual habitat condition prevailing in the areas of occurrence [12]. 3. Result
2.2. Taxonomy
3.1. Botanical description of the plant
Morphological data was collected on the spot during field survey in different parts of Assam. Every parts of the plant including leaf, pseudo stem, flower and fruit were studied and recorded. Different parts of the plant use in different localities were also recorded from the local people.
Musa flaviflora Simmonds, Kew Bull. 471.1957; Hakkinen et al., Nordic J Bot. 000: 001–006.2013. Type: India, Assam, Mariani Hills, 1955, Simmonds 241, K! no: 18871.000 (in spirit), lectotype designated by Hakkinen and Vare (2008a). Kingdom: Plantae. Division: Angiospermae. Class: Scitaminae. Order: Zingiberales. Family: Musaceae. Genus: Musa. Species: M. flaviflora Simmonds. Plant slender, suckering freely, suckers 2–7, close to parent plant. Mature pseudostem 3.8–4 m tall, up to 20 cm in diam. at base, covered with dead remains of older sheaths. Sheaths light green with radish or blackish patches, underlying colour shiny green, devoid of wax, sap watery. Petiole 50–58 × 3.7–4 cm, petiole canal straight with erect margins, bases winged. Leaves intermediate, lamina elliptic, 241–279 × 57–63 cm, adaxially deep green, adaxially light green, shiny, midrib yellowish green with balck blotches base symmetric, apex truncate. Inflorescence arching downwards; peduncle glabrous, light green, not waxy; 2 sterile bracts. Carpellate bud lanceolate, 28–30 × 10–11 cm, bracts radish pink, convolute, waxy, revolute and deciduous, ovate, 28–35 cm long, 11–13 cm wide; basal flowers female; 18–20 flowers in two rows; ovary yellowish green; slightly curved,
2.3. Ecological niche modelling for recognizing the predicted habitats for reintroduction 2.3.1. Species occurrence data Primary distributional records of the species were collected from Lahorijan Reserve Forest, Karbi Anglong district of Assam, India. The coordinates of all the occurrence points were recorded to an accuracy of 10–40 m using a GPS (Garmin). The coordinates were then converted to decimal degrees for use in modelling the distribution of potential habitats of the species in its native range. These records were cleaned for spatial errors in Diva-GIS and duplicate records in the dataset were discarded using MS Excel (Broennimann & Guisan, 2008). 2.3.2. Species occurrence data Primary distributional records of the species were collected from Kamrup (Sonapur), Madhabpur (Jorhat), Diphu (Karbi Anglong) and Dibrugarh (Namrup) districts of Assam, India. The coordinates of all the occurrence points were recorded to an accuracy of 10–40 m using a GPS (Garmin). The coordinates were then converted to decimal degrees for use in modelling the distribution of potential habitats of the species in its native range. These records were cleaned for spatial errors in Diva-GIS and duplicate records in the dataset were discarded using MS Excel [22,23]. 2.3.3. Environmental data For modelling the current distributional range of the species, three types of variables viz., bioclimatic, remote sensing data (NDVI) and elevation were used in this study. Nineteen bioclimatic variables with 1 km resolution, were downloaded from World climate website (http:// www.worldclim.org.). This has a set of climate layers representing bioclimatic variables, derived from monthly temperatures and rainfall recorded worldwide. Normalized Difference Vegetation Index (NDVI) was obtained from http://glcf.umiacs.umd.edu/data/ndvi and Elevation (Digital Elevation Model-DEM) data was also obtained from the WorldClim website (Table 1) [12,24]. 2.3.4. Validation of model robustness To study the habitat modelling, the pixel dimension was 250 m × 250 m grid cell and the model was developed using maximum entropy modelling (MaxEnt version 3.3.3e, [25]). To validate the model robustness, we executed 20 replicated model runs for the species with a threshold rule of 10 percentile training presence. In the replicated runs, we employed cross validation technique where samples were divided
Table 1 List of Bioclimatic variables, NDVI and elevation used in the model. Variable
Description of the variable
Bio1 Bio2 Bio3 Bio4 Bio5 Bio6 Bio7 Bio8 Bio9 Bio10 Bio11 Bio12 Bio13 Bio14 Bio15 Bio16 Bio17 Bio18 Bio19 eu1- eu12 h_dem
Annual mean temp. (°C) Mean diurnal range - monthly (max temp – min temp) (°C) Isothermality (Bio2/Bio7 *100) (°C) Temperature Seasonality (standard deviation * 100) (°C) Maximum temperature of warmest month (°C) Minimum temperature of coldest month (°C) Temperature annual range (Bio5 - Bio6) (°C) Mean temperature of the wettest quarter (°C) Mean temperature of the driest quarter (°C) Mean temperature of the warmest quarter (°C) Mean temperature of the coldest quarter (°C) Annual precipitation (mm) Precipitation of the wettest month (mm) Precipitation of the driest month (mm) Precipitation seasonality (coefficient of variation) (mm) Precipitation of the wettest quarter (mm) Precipitation driest quarter (mm) Precipitation of the warmest quarter (mm) Precipitation of the coldest quarter (mm) Month of January–December Elevation (m)
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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trilocular with two rows of ovules in each locule; style to 3.6 cm long, creamy. Staminate bud lanceolate, 15–24 × 6.7–7 cm, bracts pink, waxy, imbricate, not revolute before falling; staminate flowers averaging 19–21 per bract in 2 rows, falling with bracts; compound tepal 4.8–5.0 cm long, 1.4 cm wide, without thickened keels, 5-toothed; 2 smaller, light orange in colour; free tepal to 1.9–2.25 cm long,
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1.1–1.5 cm wide, transluscent white, oblong with a short orange acumen; stamens 5, exerted; anthers yellowish green or light brown, 4.0–4.5 cm long; filament 1.8–2.0 cm long; anther lobe 2.3–2.6 cm long, pale brown. Fruit branch ascending, compact, slightly curved, ridged, 10.5–12.0 × 2.0–2.5 cm, apex pointed, pedicel 2 cm, slightly curved, glabrous; immature fruit light green, ripe one's yellowish
Fig. 1. Musa flaviflora Simmonds, A. Habitat, B. A female bud with female flower, C. A male bud with male flower, D. Upper surface of pseudostem, E. Inner pseudostem, F. Petiole canal, G. External surface of male bract, H. Internal surface of male bract, I. A complete female flower, J. A complete male flower, K. Anthers, L. Free tepal of male flower, M. Compound tepal of male flower, N. T.S. of ovary, O. Mature seeds, P. Mature fruit.
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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brown, not waxy; fruit pulp white, seeds up to 90 per fruit, angular, wrinkled, up to 3 mm long, 5–6 mm wide at apex [Fig. 1]. 3.1.1. Distribution BANLADESH, BHUTAN, INDIA; Assam, Jorhat District (Mariani, Titabor), Kamrup District (Hills of Jorabat), Dibrugarh District (Namrup, Dillighat), Karbi Anglong (Diphu) and Sonitpur District (Rangapara). Meghalaya, Arunachal Pradesh, Nagaland and Manipur. 3.1.2. Specimen examined India, Assam, Kamrup District, Sonapur, near Jorabat, April 5, 2014, K. Borborah 027 (26°10.564′ N, 91°86.350′ E); India, Assam, Dibrugarh District, Namrup, Dilli ghat, April 25, 2014, K. Borborah 037 (27°07.784′N, 95°22.037′E); India, Assam, Jorhat District, Titabor, Madhabpur, October 6, 2014, K. Borborah 068 (26°42.658′ N, 89°32.300′ E); India, Assam, Karbi Anglong District, Diphu, September 5, 2015, K. Borborah 094 (26°01.451′N, 93°23.334′E). 3.1.3. Uses Durring field work the present investigator recorded that the ripe fruits are kept in a glass of water overnight and the extract obtained in the next morning by seiving of the mixture, is taken as a health tonic juice. The inflorescence is taken as vegetables. 3.2. Calibration of models to predict potential habitat for reintroduction of M. flaviflora The model calibration test for Musa flaviflora through Maxent model yielded satisfactory results (AUC train = 0.97 ± 0.002 and AUC test = 0.95 ± 0.015). Estimation of relative contributions of the environmental variables to the Maxent model was mentioned in Table 2. To determine the first estimate, in each iteration of the training algorithm, the increase in regularized gain was added to the contribution of the corresponding variable, or subtracted from it if the change to the absolute value of lambda is negative. For the second estimate, for each environmental variable in turn, the values of that variable on training presence and background data were randomly permuted [27]. The model was revaluated on the permuted data, and the resulting drop in training AUC was normalized to percentages. The Jack knife results showed the test of variable importance. The environmental variable with highest gain when used in isolation was eu7_1_eur, which therefore appeared to have the most useful information by itself. However, dem_ne30 m (elevation) also revealed significantly higher gain as compared with the other NDVI and bioclimatic variables (Fig. 2). 3.3. Habitat status assessment and identification of areas for reintroduction Suitable habitat for reinforcement of M. flaviflora in the predicted potential areas revealed the seven different sites of both disturbed and undisturbed forest of Kamrup District, Dibrugarh District, Tinsukia district, Lakhimpur district, Jorhat District, Karbi Anglong and Sanitpurdistrict in Assam and therefore could be identified as suitable environment for persistence of the species. Superimposing the predicted potential habitat map of the species on Google Earth satellite images revealed a mosaic of habitats to be suitable for the species persistence. The areas with high to very high habitat suitability for the species were continuous patches of tropical and sub-tropical forests of Assam (India), and a mosaic of fragmented groves, settled cultivation areas and human settlements. The areas with medium to low habitat suitability were degraded open forest areas, settled cultivation areas, homestead gardens and human settlements. The areas with very low habitat suitability were grasslands, degraded open forests and human settlements. The superimposition of predicted potential habitat distribution map on Google Earth images identified these forest areas which would act as in situ
Table 2 Estimates of relative contributions and permutation importance of the predictor environmental and bioclimatic variables to the MaxEnt model. Variable
Percent contribution
Permutation importance
eu7_1_eur eu8_1_eur bio_18 eu1_1_eur dem_ne30m bio_2 bio_7 eu3_1_eur eu2_1_eur bio_6 bio_4 bio_15 bio_16 eu9_1_eur bio_11 bio_1 bio_10 bio_13 eu12_1_eur bio_14 bio_12 bio_17 eu5_1_eur bio_8 eu4_1_eur bio_9 bio_3 bio_19 bio_5 eu6_1_eur eu10_1_eur eu11_1_eur
24.9 16.2 14.1 11.9 11.7 12.7 11.9 7.3 8.5 1.2 0.8 0.9 1.0 0.7 0.6 0.6 0.4 0.3 1.8 1.6 1.7 1.2 1.2 1.0 0.5 0.4 0.5 0 0 0 0 0
14.3 12.8 12.9 11.6 9.4 8.9 8.9 9.2 7.6 4.9 5.9 5.1 4.8 4.6 4.6 3.9 2.6 2.6 0.2 0.2 0.1 0.6 0.2 0.3 0.8 0.3 0.2 0 0 0 0 0
conservation area for the species and could also be used for reintroduction of the species in the wild (Fig. 3). 4. Discussion In our present investigation, we have showed a clear-cut dissection of M. flaviflora, which will helpful to differentiate this species from M. acuminate. The population stock of M. flaviflora in its natural habitats have been depleting very fast due to certain factors such as habitat fragmentation, over-exploitation and other anthropogenic activities. M. flaviflora has been reproduce by succours only and different parts of this plant has been use locally in different purpose including food, ritual, and other local uses. Therefore, improvement of conservation status is urgently needed for this wild species of banana. Earlier records from secondary sources showed the occurrence of the M. flaviflora in different parts of both Eastern and western Assam [28]. However, the plant could not be recorded from Wester Assam. Extensive survey could have made in different parts of Assam. Surprisingly, the populations discovered so far revealed poor population strength with restricted areas. The cause for the smaller size in the number of individuals in each population is either directly or indirectly due to various anthropogenic activities. However, the population status could be changed through increasing its number of individuals in its suitable natural habitat either through reinforcement or reintroduction. For reintroduction, determining precise and suitable habitat of the plant is important and this is possible through the ecological modelling technique, which has been used in this study. The ENM in the present study showed good overall performance in its native range. The high AUC values for training and testing (N0.90) indicate that the niche model has a good ability to differentiate between presence and absence areas for the species. The niche model could successfully predict most of the validation points in the Indian
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002
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Fig. 2. Jackknife test of variable importance for M. flaviflora: individual variable contribution (blue bar), contribution when a given variable is excluded (green bar), whole set of variables (red bar). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3. Map showing sample collection sites of M. flaviflora, potential habitat distribution in seven different sites of both disturbed and undisturbed forest of Assam (India). The red patches in the map represent the highly predicted localities. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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subcontinent, showing its fair transferability. Nevertheless, it can be said that the species has conserved its original niche properties in the Northeastern part of the subcontinent. However, the lower degree of prediction probability for some of the validation points hints at the possibility of a niche shift for the species, which has to be studied further. This information would be a guideline for reintroduction of M. flaviflora and to increase the population status for conservation [29–32]. In the present study, some areas consisting of continuous and intact patches of tropical and sub-tropical forests of Assam provide sustained habitat for the species. The seven districts especially in Kamrup District, Dibrugarh District, Tinsukia district, Lakhimpur district, Jorhat District, Karbi Anglong and Sanitpur have been identified to offer suitable environmental conditions for potential habitats at higher levels of probability [33–36]. Hence, such forest areas could serve as habitats for in situ conservation and reintroduction. The present study demonstrated that habitat distribution modelling could be of great help in predicting the potential habitats of threatened species for reintroduction as well as reinforcement.
Acknowledgements Funding support received from Department of Biotechnology (DBT), Govt. of India for the research project entitled “Collection, evaluation, documentation and conservation of banana genetic resources from north eastern region” vide sanction No. 102/I.F.D/SAN/1765-1767/ 2017-2018 dated 20. 07. 2017 is greatly acknowledged. References [1] D.K. Hore, B.D. Sharma, G. Pandey, Status of banana in North-East India, J. Econ. Taxon. Bot. 16 (1992) 447–455. [2] A. Guisan, N.E. Zimmermann, Predictive habitat distribution models in ecology, Ecol. Model. 135 (2000) 147–186. [3] J. Elith, C.H. Graham, R.P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R.J. Hijmans, F. Huettmann, J.R. Leathwick, A. Lehmann, J. Li, L.G. Lohmann, B.A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J.M. Overton, A.T. Peterson, S.J. Phillips, K. Richardson, R. Scachetti-Pereira, R.E. Schapire, J. Soberón, S.E. Williams, M.S. Wisz, N.E. Zimmermann, Novel methods improve prediction of species distributions from occurrence data, Ecography. 29 (2006) 129–151. [4] D.H. Reed, R. Frankham, Correlation between population fitness and genetic diversity, Conserv. Boil. 17 (2003) 230–237. [5] M. Jusaitis, L.B. Polomka, Sorensen, habitat specificity, seed germination and experimental trans location of the endangered herb brachycome muelleri (Asteraceae), Biol. Conserv. 116 (2004) 251–266. [6] K. Deka, P.S. Baruah, B. Sarma, S.K. Borthakur, B. Tanti, Preventing extinction and improving conservation status of Vanilla borneensis Rolfe-a rare, endemic and threatened orchid of Assam, India, J. Nat. Conserv. 37 (2017) 39–46. [7] S.K. Borthakur, P.S. Baruah, K. Deka, P. Das, B. Sarma, D. Adhikari, B. Tanti, Habitat distribution modelling for improving conservation status of Brucea mollis Wall. ex Kurz. - An endangered potential medicinal plant of Northeast India, J. Nat. Conserv. 43 (2018) 104–110. [8] S. Schmitt, R. Pouteau, D. Justeau, F. Boissieu, De ssdm: an r package to predict distribution of species richness and composition based on stacked species distribution models, Methods Ecol. Evol. 8 (2017) 1795–1803. [9] M.E. Wittmann, G. Annis, A.M. Kramer, L. Mason, C. Riseng, E.S. Rutherford, W.L. Chadderton, N.E. Zimmermann, New trends in species distribution modelling, Ecography 33 (2010) 985–989. [10] S. Kumar, T.J. Stohlgren, Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia, J. Ecol. Nat. Environ. 1 (2009) 94–98. [11] H. Ren, H. Lu, W. Shen, C. Huang, Q. Guo, Z. Li, Sonneratia apetala Buch. Ham in the mangrove ecosystems of China: an invasive species restoration species? Ecol. Eng. 35 (2009) 1243–1248.
[12] D. Adhikari, S.K. Barik, K. Upadhaya, Habitat distribution modelling for reintroduction of Ilex khasiana Purk., a critically endangered tree species of northeastern India, Ecol. Eng. 40 (2012) 37–43. [13] K.B. Aubry, C.M. Raley, K.S. McKelvey, The importance of data quality for generating reliable distribution models for rare, elusive, and cryptic species, PLoS One 12 (2017), e0179152. [14] Y.A. Kuzovkina, T.A. Volk, The characterization of willow (Salix L.) varieties for use in ecological engineering applications: co-ordination of structure, function and autecology, Ecol. Eng. 3 (2009) 1178–1189. [15] T. Polak, D. Saltz, Reintroduction as an ecosystem restoration technique, Conserv. Biol. 25 (2011) 424–427. [16] S. Godefroid, C. Piazza, G. Rossi, S. Buord, A.D. Stevens, R. Aguraiuja, C. Cowell, C.W. Weekley, G. Vogg, J.M. Iriondo, I. Johnson, B.D. Dixon, S. Gordon, B. Magnanon, R. Valentin Bjureke, R. Koopman, M. Vicens, T. Vanderborght Virevaire, How successful are plant species reintroductions? Biol. Conserv. 6 (2011) 72–682. [17] R.P. Reading, T.W. Clark, B. Griffith, The influence of valuational and organizational consideration of the success of rare species translocation, Biol. Conserv. 79 (1997) 217–255. [18] S.K. Barik, D. Adhikari, Predicting geographic distribution of an invasive species Chromolaena odorata L. (King) & H. E. Robins in Indian subcontinent under climate change scenarios, in: J.R. Bhatt, J.S. Singh, R.S. Tripathi, S.P. Singh, R.K. Kohli (Eds.), Invasive Alien Plants- An Ecological Appraisal for the Indian Subcontinent, CABI, Oxford shire, UK, 2011. [19] T. Polak, D. Saltz, Reintroduction as an ecosystem restoration technique, Conserv. Biol. 25 (2011) 424–427. [20] A. Giriraj, M. Irfan-Ullah, B.R. Ramesh, P.V. Karunakaran, A. Jentsch, M.S.R. Murthy, Mapping the potential distribution of Rhododendron arboreum Sm. ssp. Nilagiricum (Zenker) Tagg (Ericaceae), an endemic plant using ecological niche modelling, Curr. Sci. 94 (2008) 1605–1612. [21] M.J. Hutchings, The population biology of the early spider orchid Ophrys sphegodes Mill. III. Demography over three decades, J. Ecol. 98 (2010) 867–878. [22] R.J. Hijmans, J.M. Cruz, E. Rojas, L. Guarino, DIVA-GIS, A geographic information system for the management and analysis of genetic resources data. Manual (Internet), International Potato Center and International Plant Genetic Resources Institute, Lima Peru, 2001. [23] O. Broennimann, A. Guisan, Predicting current and future biological invasions: both native and invaded ranges matter, Biol. Lett. 4 (2008) 585–589. [24] C.H. Graham, R.J. Hijmans, A comparison of methods for mapping species ranges and species richness, Glob. Ecol. Biogeogr. 15 (2006) 578–587. [25] S.J. Phillips, R.P. Anderson, R.E. Schapire, Maximum entropy modelling of species geographic distributions, Ecol. Model. 190 (2006) 231–259. [26] W. Thuiller, S. Lavorel, M.T. Sykes, M.B. Araujo, Using niche-based modelling to assess the impact of climate change on tree functional diversity in Europe, Divers. Distrib. 12 (2006) 49–60. [27] J. Elith, S.J. Phillips, T. Hastie, M. Dudík, Y.E. Chee, C.J. Yates, A statistical explanation of MaxEnt for ecologists, Divers. Distrib. 17 (2011) 43–57. [28] C. Barooah, I. Ahmed, Plant diversity of Assam-A checklist of Angiosperm and Gymnosperm, ASTEC, 2014. [29] P.M. Benham, E.J. Beckman, S.G. DuBay, L.M. Flores, A.B. Johnson, M.J. Lelevier, Satellite imagery reveals new critical habitat for endangered bird species in the high Andes of Peru, Endanger. Species Res. 13 (2011) 145–157. [30] K. Borborah, S.K. Borthakur, B. Tanti, A new variety of Musa balbisiana Colla from Assam, India, Bangladesh J. Plant Taxonomy. 23 (1) (2016) 75–78. [31] K. Borborah, S.K. Borthakur, B. Tanti, Ornamentally important species of Musa L. (Musaceae) in Assam, India, J. Econ. Taxon. Bot. 40 (1–2) (2016) 1–8. [32] K. Borborah, S.K. Borthakur, B. Tanti, Musa balbisiana Colla-Taxonomy, Traditional knowledge and economic potentialities of the plant in Assam, India, Indian J. Tradit. Knowl. 15 (1) (2016) 116–120. [33] K. Deka, S.K. Borthakur, B. Tanti, Habitat mapping, population size and preventing extinction through improving the conservation status of Calamus nambariensis Becc.-an endemic and threatened cane of Assam, India, Acta Ecol. Sin. (2018) https://doi.org/10.1016/j.chnaes.2018.03. [34] P.S. Baruah, K. Deka, L. Lahkar, B. Sarma, S.K. Borthakur, B. Tanti, Habitat distribution modelling and reinforcement of Elaeocarpus serratus L. - A threatened tree species of Assam, India for improvement of its conservation status, Acta Ecol. Sin. (2018) https://doi.org/10.1016/j.chnaes.2018.06. [35] P.S. Baruah, K. Deka, B. Sarma, P. Das, S.K. Borthakur, B. Tanti, Assessment of few unexplored RET plant wealth of Assam, India, J. Adv. Plant Sci. 9 (2) (2017) 10–15. [36] P.S. Baruah, S.K. Borthakur, B. Tanti, Conservation of Mesua assamica (King and Prain) Kosterm.-an endangered plant of Assam, NeBIO-An Int. J. Environ. and Biodiv. 7 (1) (2016) 17–22.
Please cite this article as: K. Borborah, K. Deka, D. Saikia, et al., Habitat distribution mapping of Musa flaviflora Simmonds - a wild banana in Assam, India, Acta Ecologica Sinica, https://doi.org/10.1016/j.chnaes.2020.02.002