Skip to main content

Advertisement

SpringerLink
Log in

Fauna data integration and species distribution modelling as two major advantages of geoinformatics-based phytobiodiversity study in today’s fast changing climate

  • Original Paper
  • Published:
Biodiversity and Conservation Aims and scope Submit manuscript

Abstract

The development and growth of geospatial techniques offer many advantages and challenges to the study of biodiversity, especially in the present era of climate change. We are now at the beginning of the international decade for biodiversity and by the time we travel through the decade, there would be sea-changes in the measurement and monitoring approaches, database management options, and inter-linked studies on biodiversity. With the onset of geoinformatics techniques comprising remote sensing, global positioning system (GPS), integrative tools, such as GIS, is realized as a complimentary system to ground-based biodiversity studies. Recently, a nationwide biodiversity study at landscape level using geoinformatics modeling techniques for India has been completed. The study has assessed plant diversity using a three-tier approach, wherein six biodiversity attributes (i.e., spatial, phytosociological, social, physical, economical, and ecological) were linked together based on their relative importance to stratify biological richness of forest vegetation (non-agricultural) of India. It has enumerated 7,964 plant species from 20,000 nested quadrate sampling plots of 0.04 ha each, delineated and mapped 120 vegetation classes; and organized the geo-spatial database on bisindia web portal. Here, we have (i) proposed a method to incorporate the fauna component in-line up with the existing methodology and (ii) utilized the GPS-gathered positional information on the distribution of two species (i.e., Medicago sativa and Poa annua to simulate their distribution for the year 2020 (SRES A1-B scenario, IPCC) using Maxent model. The study conducted in a test site of western Himalayas estimated (i) 24% increase in the overall biologically rich areas on supplement of fauna data and (ii) distribution of both the species would tend to increase in favor of shorter cold season. The study highlights the importance of geoinformatics technique-based biodiversity study for its amenability to incorporate any further change or modification, and utility of the geo-spatial biodiversity database for simulating various species distribution scenarios to understand their ecology in today’s fast changing climate for effective conservation prescriptions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Anonymous (2009) Ground water information booklet of Leh district. Jammu and Kashmir state, North western Himalayan region central ground water board, Jammu India

  • Anonymous (2011a) Biodiversity characterization at landscape level for Kargil district using remote sensing and GIS techniques. Project report submitted to IIRS, Dehradun (document no. IIT-CORAL/SAML/PR-1/LLS-2011)

  • Anonymous (2011b) Mammals of Ladakh; Field Guide. WWF-India

  • Aspinall R (1992) An inductive modeling procedure based on Bayes’ theorem for analysis of pattern in spatial data. Int J Geogr Inf Syst 6(2):105–121

    Article  Google Scholar 

  • Behera MD, Roy PS (2010) Assessment and validation of biological richness at landscape level in part of the Himalayas and Indo-Burma hotspots using geospatial modeling approach. J Indian Soc Remote Sens 38(3):415–429

    Article  Google Scholar 

  • Behera MD, Kushwaha SPS, Roy PS (2001) Forest vegetation characterization and mapping using IRS-1C satellite images in Eastern Himalayan region. Geocarto Int 16(3):53–62

    Article  Google Scholar 

  • Behera MD, Kushwaha SPS, Roy PS (2005) Geo-spatial modeling for rapid biodiversity assessment in Eastern Himalayan region. For Ecol Manag 207:363–384

    Article  Google Scholar 

  • Belal A, Springuel I (1996) Economic value of plant diversity in arid environments. Nat Resour 31(1):33–39

    Google Scholar 

  • Boitani L, Sinibaldi I, Corsi F, Biase A, Carranza ID, Ravagli M, Reggiani G, Rondinini C, Trapanese P (2007) Distribution of medium- to large-sized African mammals based on habitat suitability models. Biodivers Conserv 17:605–621

    Article  Google Scholar 

  • Bonn A, Rodrigues ASL, Gaston KJ (2002) Threatened and endemic species: Are they good indicators of patterns of biodiversity on a national scale? Ecol Lett 5:733–741

    Article  Google Scholar 

  • Brown JH (1995) Macroecology. The University of Chicago Press, Chicago

    Google Scholar 

  • Buckland ST, Elston A (1993) Empirical models for the spatial distribution of wildlife. J Appl Ecol 30:478–495

    Article  Google Scholar 

  • Butchart SHM, Walpole M, Collen B, Strien A, Scharlemann JPW, REA Almond, Baillie JEM, Bomhard B, Brown C, Bruno J, Carpenter KE, Carr GM, Chanson J, Chenery AM, Csirke J, Davidson NC, Dentener F, Foster M, Galli A, Galloway JN, Genovesi P, Gregory RD, Hockings M, Kapos V, Lamarque JF, Leverington F, Loh J, McGeoch, McRae L, Minasyan A, Morcillo MH, Oldfield TEE, Pauly D, Quader S, Revenga C, Sauer JR, Skolnik B, Spear D, Stanwell-Smith D, Stuart SN, Symes A, Tierney M, Tyrrell TD, Vié J, Watson R (2010) Global biodiversity: indicators of recent declines. Science 328:1164. doi:10.1126/science.1187512 (published online 29 April 2010)

  • Carpenter G, Gillison AN, Winter J (1993) DOMAIN: a flexible modeling procedure for mapping potential distributions of plants, animals. Biodivers Conserv 2:667–680

    Article  Google Scholar 

  • Chundawat RS, Rawat GS (1994) Indian cold deserts: a status report on biodiversity. Wildlife Institute of India, Dehradun

    Google Scholar 

  • Coops NC, Catling PC (1997) Predicting the complexity of habitat in forests from airborne videography for wildlife management. Int J Remote Sens 18:2677–2782

    Article  Google Scholar 

  • Corsi F, Dupré E, Boitani L (1999) A large-scale model of wolf distribution in Italy for conservation planning. Conserv Biol 13:150–159

    Article  Google Scholar 

  • Debinski DM, Jakubauskas ME, Kindscher K (1999) A remote sensing and GIS-based model of habitats and biodiversity in the Greater Yellowstone Ecosystem. Int J Remote Sens 20:3281–3292

    Article  Google Scholar 

  • Diamond J (1988) Factors controlling species diversity: overview and synthesis. Ann Mo Bot Gard 75:117–129

    Article  Google Scholar 

  • Dobrović I, Nikolić T, Jelaska SD, Plazibat M, Hršak V, Šoštarić R (2006) An evaluation of floristic diversity in Medvednica Nature Park (northwestern Croatia). Plant Biosyst 140:234–244

    Article  Google Scholar 

  • Duro DC, Coops NC, Wulder MA, Han T (2007) Development of a large area biodiversity monitoring system driven by remote sensing. Prog Phys Geogr 31(3):235–260

    Article  Google Scholar 

  • Ehrlich P, Raven P (1964) Butterflies and plants: a study in coevolution. Evolution 18:586–608

    Article  Google Scholar 

  • Forman RTT, Godron M (1986) Landscape ecology. Wiley, New York

    Google Scholar 

  • Gallagher RV, Hughes L, Leishman MR (2009) Phenological trends among Australian alpine species: using herbarium records to identify climate-change indicators. Aust J Bot 57(1):1–9

    Article  Google Scholar 

  • Gaston KJ (2003) The structure and dynamics of geographic ranges. Oxford University Press, New York

    Google Scholar 

  • Gaston KJ, Blackburn TM (2000) Pattern and process in macroecology. Blackwell, Oxford

    Book  Google Scholar 

  • Giles RH (1978) Wildlife management. Freeman & Co., San Francisco

    Google Scholar 

  • Grabherr G, Gottfried M, Pauli H (1994) Climate effects on mountain plants. Nature 369:448

    Article  Google Scholar 

  • Graham CH, Smith TB, Languy M (2005) Current and historical factors influencing patterns of species richness and turnover of birds in the Gulf of Guinea highlands. J Biogeogr 32:1371–1384

    Article  Google Scholar 

  • Groom G, Mucher CA, Ihse M, Wrbka T (2006) Remote sensing in landscape ecology: experiences and perspectives in European context. Landsc Ecol 21:391–408

    Article  Google Scholar 

  • Guisan A, Zimmerman NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186

    Article  Google Scholar 

  • Heide OM (2001) Flowering responses of contrasting ecotypes of Poa annua and their putative ancestors Poa infirma and Poa supine. Ann Bot 87:795–804

    Article  Google Scholar 

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978

    Article  Google Scholar 

  • Howard PC, Viskanic P, Davenport TRB, Kigenyi FW, Baltzer M, Dickinson CJ, Lwanga JS, Matthews RA, Balmford A (1998) Complementarity and the use of indicator groups for reserve selection in Uganda. Nature 394:472–475

    Article  CAS  Google Scholar 

  • Hršak V, Nikolić T, Plazibat M, Jelaska SD, Bukovec D (1999) Orchids of Medvednica Natural Park, Croatia. Acta Biol Slovenica 42(4):13–37

    Google Scholar 

  • Humbert-Droz B, Dawa S (2004) Biodiversity of Ladakh strategy and action plan. Sampark Publication, Calcutta

    Google Scholar 

  • Humboldt VA, Bonpland A (1807) Essai sur la géographie des plantes. Paris. In: Humboldt AV, Bonpland A, Jackson ST, Romanowski S (2008) Humboldt Alexander von, 1769-1859; Essay on the geography of plants. University of Chicago Press, Chicago

  • Huntley B, Berry PM, Cramer W, McDonald AP (1995) Modeling present and potential future ranges of some European higher plants using climate response surfaces. J Biogeor 22:967–1001

    Article  Google Scholar 

  • Indian Meteorological Department (IMD) (2010). http://www.imdpune.gov.in/. Accessed 14 December 2010

  • IUCN (1993) Parks for life. Report of the IVth world congress on national parks and protected areas. Gland, Switzerland

  • IUCN (2011) IUCN Red list of threatened species. IUCN, Gland, Switzerland. http://www.redlist.org. Accessed November 15, 2011

  • Jelaska SD, Kušan V, Peternel H, Grgurić Z, Mihulja A, Major Z (2005) Vegetation mapping of “Žumberak-Samoborsko gorje” Nature Park, Croatia, using Landsat 7 and field data. Acta Bot Croat 64:303–311

    Google Scholar 

  • Joshi PK, Rawat GS, Padaliya H, Roy PS (2005) Land use/land cover identification in an alpine and arid region (Nubra valley, Ladakh) using satellite remote sensing. J Ind Soc Remote Sens 33(3):371–380

    Article  Google Scholar 

  • Justus J, Sarkar S (2002) The principle of complementarity in the design of reserve networks to conserve biodiversity: a preliminary history. J Biosci 27:421–435

    Article  PubMed  Google Scholar 

  • Kadmon R, Farber O, Danni A (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol Appl 14:401–413

    Article  Google Scholar 

  • Kamat DS (1986) An integrated approach to remote sensing studies for wildlife habitat evaluation. In: Kamat DS and Panwar HS (eds) Proceedings of seminar-cum-workshop on wildlife habitat evaluation using remote sensing techniques, Dehradun, pp 165–182

  • Keller F, Kienast F, Beniston M (2000) Evidence of response of vegetation to environmental change on high-elevation sites in the Swiss Alps. Reg Environ Change 1:70–77

    Article  Google Scholar 

  • Kodric-Brown A, Brown JH (1993) Incomplete data sets in community ecology and biogeography: a cautionary tale. Ecol Appl 3(4):736–742

    Article  Google Scholar 

  • Kotwal PC, Parihar JS (1988) Management plan of Kanha national park and project Tiger, Kanha for the period 1989–90 to 1998–99 Report, Mandla

  • Kumar S, Stohlgren TJ, Chong GW (2006) Spatial heterogeneity influences native and nonnative plant species richness. Ecology 87:3186–3199

    Article  PubMed  Google Scholar 

  • Kumar KK, Kulkarni A, Patwardhan S, Deshpande N, Kamala K, Rao K (2010) Climate Change Scenarios 2030s, IITM-Pune, National Workshop India: Climate Change & India—A 4 × 4 Assessment, New Delhi

  • Kushwaha SPS, Roy PS (2002) Geospatial technology for wildlife habitat evaluation. Trop Ecol 43(1):137–150

    Google Scholar 

  • Lamprey HF (1963) Ecological separation of large mammal species in the Tarangire Game Reserve, Tanganyika. East African Wildlife J 5:151–166

    Google Scholar 

  • Lawler JJ, White D, Sifneos JC, Master LL (2003) Rare species and the use of indicator groups for conservation planning. Conserv Biol 17:875–882

    Article  Google Scholar 

  • Leyequien E, Verrelst J, Slot M, Schaepman-Strub G, Heitko¨nig IMA, Skidmore A (2007) Capturing the fugitive: applying remote sensing to terrestrial animal distribution and diversity. Int J Appl Ear Obs Geoinf 9:1–20

    Article  Google Scholar 

  • Lidicker WZ Jr (1995) Landscape approaches in mammalian ecology and conservation. University of Minnesota Press, Minneapolis, pp 3–19

    Google Scholar 

  • Mac-Nally R, Fleishman E, Bulluck LP, Betrus CJ (2004) Comparative influence of spatial scale on beta diversity within regional assemblages of birds and butterflies. J Biogeogr 31:917–992

    Article  Google Scholar 

  • Manel S, Dias JM, Ormerod SJ (1999) Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling 120:337–347

    Article  Google Scholar 

  • Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253

    Article  PubMed  CAS  Google Scholar 

  • Margules C, Usher MB (1981) Criteria used in assessing wildlife conservation potential: a review. Biol Conserv 21:79–109

    Article  Google Scholar 

  • Margules CR, Pressey RL, Williams PH (2002) Representing biodiversity: data and procedures for identifying priority areas for conservation. J Biol Sci 27(4):309–326

    CAS  Google Scholar 

  • Massot M, Clobert J, Ferrière R (2008) Climate warming, dispersal inhibition and extinction risk. Glob Change Biol 14:461–469

    Article  Google Scholar 

  • Miller J, Rogan J (2007) Using GIS and remote sensing for ecological mapping and monitoring. Integration of GIS and Remote Sensing. Wiley, New York, pp 233–268

  • Mladenoff DJ, Sickley TA, Wydeven AP (1999) Predicting gray wolf landscape recolonization: logistic regression models vs. new field data. Ecol Appl 9:37–44

    Article  Google Scholar 

  • Myers N, Mittermeier RA, Mittermeier CG, Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858

    Article  PubMed  CAS  Google Scholar 

  • Nikolić T, Fadljević D, Bukovec D (2001) Club mosses (class Lycopsida) distribution in Croatia. Nat Croat 10:33–52

    Google Scholar 

  • Noss RF (1990) Indicators for monitoring biodiversity: a hierarchical approach. Conserv Biol 4(4):355–364

    Article  Google Scholar 

  • Olson DM, Dinerstein E (1998) The Global 200: a representation approach to conserving the earth’s most biologically valuable ecoregions. Conserv Biol 2(3):502–515

    Article  Google Scholar 

  • Oreskes N, Shrader-Frechette K, Belitz K (1994) Verification, validation and confirmation of numerical models in the earth sciences. Science 263:641–646

    Article  PubMed  CAS  Google Scholar 

  • Orians GH (1993) Endangered at what level? Ecol Appl 31:206–220

    Article  Google Scholar 

  • Panwar HS (1986) Forest cover mapping for planning tiger corridors between Kanha and Bandhavgarh—a proposed project. In: Kamat DS and Panwar HS (eds) Proceedings of seminar-cum-workshop wildlife habitat evaluation using remote sensing techniques, IIRS Dehradun, pp 209–212

  • Parmesan C (1996) Climate and species range. Nature 382:765–766

    Article  CAS  Google Scholar 

  • Parry ML, Canziani OF, Palutikof JP, Van der Linden PJ, Hanson CE (2007) Climate Change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the IPCC. Cambridge University Press, Cambridge

  • Pearson RG (2007) Species distribution modeling for conservation educators and practitioners. American Museum of Natural History http://ncep.amnh.org

  • Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259

    Article  Google Scholar 

  • Podolsky R (1994) Ecological hot spots. A method for estimating biodiversity directly from digital Earth imagery. Earth Observation Magazine, June, 30–36

  • Podolsky R, Freilich J, Knehr R (1992) Predicting plant species richness from remotely sensed data in a high desert ecosystem. Technical Papers of the 1992 Annual Meeting of ASPRS/ACSM/RT, Washington, DC, 3–8 August 1992. V-4, Remote sensing and data acquisition. American Society for Photogrammetry and Remote Sensing, Bethesda, pp 57–63

  • Pressey RL, Logan VS (1998) Size of selection units for future reserves and its influence on actual vs targeted representation of features: a case study in western New South Wales. Biol Conserv 85:305–319

    Article  Google Scholar 

  • Pressey RL, Humphries CJ, Margules CR, Vane-Wright RI, Williams PH (1993) Beyond opportunism: key principles for systematic reserve selection. Trends Ecol Evol 8(4):124–128

    Article  PubMed  CAS  Google Scholar 

  • Rey-Benayas JM, de la Montańa E (2003) Identifying areas of high value vertebrate diversity for strengthening conservation. Biol Conserv 114:357–370

    Article  Google Scholar 

  • Rocchini D, Balkenhol N, Carter GA, Foody GM, Gillespie TW, He KS, Kark S, Levin N, Lucas K, Luoto M, Nagendra H, Oldeland J, Ricotta C, Southworth J, Neteler M (2010) Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol Informatics 5:318–329

    Article  Google Scholar 

  • Romme WH, Knight DH (1982) Landscape diversity: the concept applied to Yellowstone Park. BioScience 32:664–670

    Article  Google Scholar 

  • Rondinini C, Boitani L, Grantham H, Wilson KA, Possingham HP (2006) Tradeoffs of different species data types for use in systematic conservation planning. Ecol Lett 9:1136–1145

    Article  PubMed  Google Scholar 

  • Roy PS, Tomar S (2000) Biodiversity characterization at landscape level using geospatial modeling technique. Biol Conserv 95(1):95–109

    Article  Google Scholar 

  • Sætersdal M, Gjerde I, Blom HH, Ihlen PG, Myrseth EW, Pommeresche R, Skartveit J, Solhøy T, Aaas O (2003) Vascular plants as a surrogate species group in complementary site selection for bryophytes, macrolichens, spiders, carabids, staphylinids, snails, and wood living polypore fungi in a northern forest. Biol Conserv 115:21–23

    Article  Google Scholar 

  • Sarakinos H, Nicholls AO, Tubert A, Aggarwal A, Margules CR, Sarkar S (2001) Area prioritization for biodiversity consideration in Quebec on basis of species distributions: a preliminary analysis. Biol Conserv 9:1419–1472

    Google Scholar 

  • Sarkar S, Aggarwal A, Garson J, Margules CR, Zeidler J (2002) Place prioritization for biodiversity content. J Biosci 27:339–346

    Article  PubMed  Google Scholar 

  • Sarkar S, Pappas C, Garson J, Aggarwal A, Cameron S (2004) Place prioritization for biodiversity conservation using probabilistic surrogate distribution data. Divers Distrib 10:125–133

    Article  Google Scholar 

  • Scholes RJ, Biggs R (2005) A biodiversity intactness index. Nature. 434:45–49

    Article  PubMed  CAS  Google Scholar 

  • Scott JM, Davis F, Csuti B, Noss R, Butterfield B, Groves C, Anderson H, Caicco S, D’erchia F, Edwards TC Jr, Ulliman J, Wright RG (1993) Gap analysis: a geographic approach to protection of biological diversity. Wildlife Monogr 123:1–41

    Google Scholar 

  • Serić JL, Vujčić-Karlo S, Durbešic′ P (2004) Notes on the taxonomy of the Genus Carabus L. (Coleoptera: Carabidae) in Croatia. Acta Entomol Slovenica 12:129–138

    Google Scholar 

  • Smith RCG, Stephens M (1975) Importance of soil moisture and temperature on the growth of improved pasture on the northern tablelands of New South Wales. Aus J Agri Res 27:63–70

    Article  CAS  Google Scholar 

  • Stoms DM (1992) Effects of habitat map generalization in biodiversity assessment. Photogramm Eng Remote Sens 58:1587–1591

    Google Scholar 

  • Temunović M, Šerić Jelaska L, Durbešić P (2008) Diversity of water beetles (Hydradephaga, Coleoptera) in temporary ponds of Nature park “Lonjsko polje”, Croatia. Entomol Croat 8:1–15

    Google Scholar 

  • Tews J, Brose U, Grimm V, Tielborger K, Wichmann MC, Schwager M, Jeltsch F (2004) Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr 31:79–92

    Article  Google Scholar 

  • Thuiler W (2000) Climate change and the ecologist. Nature 448(2):550–552

    Google Scholar 

  • Treitz PM, Howarth PJ, Shuffling RC, Smith P (1992) Application of detailed ground information to vegetation mapping with high spatial resolution digital imagery. Remote Sens Environ 42:65–82

    Article  Google Scholar 

  • Turner MG (1989) Landscape ecology: the effect of pattern on process. Ann Rev Ecol Syst 20:171–197

    Article  Google Scholar 

  • Williams PH, Margules CR, Hilbert DW (2002) Data requirements and data sources for biodiversity priority area selection. J Biosci 27(suppl. 2):327–338

    Article  PubMed  CAS  Google Scholar 

  • Wolfgang B (2003) Biodiversity and agri-environmental indicators—general scopes and skills with special reference to the habitat level. Agric Ecosyst Environ 98:35–78

    Article  Google Scholar 

Download references

Acknowledgments

The study is a part of the joint DOS-DBT program on “Biodiversity characterization at landscape level using remote sensing and GIS” with financial assistance from NRSC (ISRO), Hyderabad and collaboration with Kashmir University, Srinagar and State Forest Dept., J&K. The authors are thankful to Mr. D. Gupta and Ms. P. Tripathy, CORAL, IIT Kharagpur for their kind help during analysis.

Author information

Authors and Affiliations

  1. Centre for Oceans, Rivers, Atmosphere and Land Sciences, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India

    Shafique Matin, Vishwas Sudhir Chitale, Mukunda Dev Behera & Birupakshya Mishra

  2. IIRS, 4-Kalidas Road, Dehradun, 248001, India

    Partha Sarathi Roy

Authors
  1. Shafique Matin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vishwas Sudhir Chitale
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mukunda Dev Behera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Birupakshya Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Partha Sarathi Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shafique Matin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Matin, S., Chitale, V.S., Behera, M.D. et al. Fauna data integration and species distribution modelling as two major advantages of geoinformatics-based phytobiodiversity study in today’s fast changing climate. Biodivers Conserv 21, 1229–1250 (2012). https://doi.org/10.1007/s10531-012-0233-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10531-012-0233-2

Keywords

Search

Navigation