Biodiversity Informatics in India & Way Ahead…

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Biodiversity Informatics in India & Way Ahead…. Indian Biodiversity Information System http://www.indianbiodiversity.org “Application of Open source Geospatial tools for biodiversity conservation and awareness in western ghats and other eco-regions of Gujarat” 11-08-2015

Arpit Deomurari Deputy Manager – Biodiversity Informatics & GIS Many of us have been asked some variant of these questions regarding species.

“What is it called?”

“What does it do?”

“What other species live around here?”

Are these the sorts of questions being addressed within biodiversity informatics today? History of “Biodiversity Informatics”

Canadian Biodiversity Informatics Consortium (1993)

John S. Whiting Biodiversity Informatics interoperability of scientific names, classifications

computerized handling of any biodiversity information It typically builds on a foundation of taxonomic, biogeographic, or ecological information stored in digital form, which, with the application of modern computer techniques, can yield new ways to view and analyze existing information, as well as predictive models for information that does not yet exist Global Biodiversity Informatics Outlook Biodiversity Information

• Species – Names – Biology – Interactions – Literature – Distribution – Molecular Data – Images and multimedia Challenges: Taxonomy

• 250-year history of seeking to interpret biodiversity • Multiple Taxonomic Authorities/Checklists • Many names for the same species – Synonyms – Species described more than once – Species moved to new genus – Split into multiple species concepts – Merge into one species concepts • Common names • Alternative opinions on higher classification • Result: – Related information found under different names Challenges: Standardizing data

• Need structured data for machine use • Need agreed standard data elements – ScientificName – DecimalLatitude, Decimal Longitude – CoordinatePrecision • Need standard formats for data values – India vs. Indian vs. IN – 2008-05-15 vs. 05/15/2008 vs. 15 May 2008 – Specimen vs. S vs. Voucher • Standards allow data to be combined and reused • Biodiversity Information Standards (TDWG) Challenges: Detecting errors • Misspellings: • Coordinate problems: – Positive values for South or West – Latitude / Longitude transposed – Coordinates not near Locality – Unknown precision • Other issues: – Same record shared through different routes – Unknown collecting strategy Biodiversity Informatics Initiatives

• Many Initiatives – Global, Regional, National • Specific vs. General – National vs. Global – GBIF (Global Biodiversity Information Facility) – WorldBirds – eBird – Xeno-Canto – BirdSpot (IN) – MigrantWatch (IN) – IBP (IN) – IBIS (IN) – EOL – ALA IBIS Indian Biodiversity Information System

One of the fundamental issues in the conservation of biological diversity is the non- availability of adequate and reliable information on a single platform which would help in developing conservation strategies. Literature Datasets

• Bibliography ( 685000 Citations) • Books Excerpts ( 98 Bird Books, 46245 Pages, 19600 Taxon) IBIS Objectives

• Provide a plethora of information on Indian Biodiversity with proper scientific background in an easily accessible format.

• Create a web based modular and searchable device for species conservation through public participation with clear attribution and credits to the contributed information.

• Emphasis to involve all stakeholders, ranging from amateur naturalists to serious researchers to photographers. Taxa Groups • Birds (1664 sp) • Corals • Mammals (428 sp) • Fishes • Reptiles (518 sp) • Dragonflies • Amphibians (314 sp) • Butterflies and Moths • Flora (23000 sp) • Crabs, Crayfish, Shrimps The main features

Dynamic Biological Classifications

Classification term oriented system Biological Non-biological classifications classifications Taxonomies Hierarchical controlled vocabularies

Linked Multiple Taxonomic Authority Manually entered or imported

Auto generated The main features

Taxon pages Overview of data related to taxon

Generated from tagged content The main features

Bibliography management

An inbuilt Bibliography manager

Faceted browsing

Taxon tagging and free keywords

Import from and export to all major formats The main features

Specimen/Observation data

Annotated full specimen/observation records

Linked to images and georeferenced The main features

Distribution maps Google maps based

Data layers

Occurrence data GBIF, MW, XC, eBird, BS, WorldBirds

Distribution data TDWG regions IUCN/Birdlife Range Maps

GBIF data Taxonomy Module We have incorporated following taxonomical checklists in AVIS-IBIS

• Clements Checklist of Birds of the World (Ver 6.8) • International Ornithological Congress (IOC) Checklist of the Birds of the World(Ver. 3.5) • A Synopsis of Birds of India and Pakistan by Ali • Handbooks of the Birds of Indian and Pakistan by Ali and Ripley • Birdlife International Checklist (Ver. 5.1) • IUCN Checklist (2012) • Oriental Bird Club Checklist Draft (Nov. 2009) • Howard and Moore Complete Checklist of the Birds of the World (4th ED.) • TiFF 2.87 (Taxonomy in Flux) Citations Vs Year

4500 4000 3500 3000 2500 2000 Citations 1500 Year 1000 500

1758 1815 1830 1840 1849 1859 1868 1877 1886 1895 1904 1913 1922 1931 1940 1949 1958 1967 1976 1985 1994 2003 Museum Collection & Sighting • Compiled Museum Collection Database with 1,45,761 Museum Records • Covering 32 Major Avian Collection Museums • Compiled Sightings Records from other sources • Total 5,62,608 Records GBIF 145761 BirdSpot 59400 WorldBirds 153126 eBird 173362 MigrantWatch 26283 Xeno-Canto 4676 Avian Diversity – India Biogeographic Zones

698 Species in Desert and Semi Arid Regions

949 873

763 693

595 578 572 495 401

166 98 98 95 97 93 83 88 86 82 55 24 25 22 25 26 23 25 25 25 18

ORDER FAMILY NO_SPP. Based on Birdlife 2012 Avian Diversity – Dry lands Deserts – Semi Arid 350

300

250

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SPECIES FAMILY GENUS

Based on Birdlife 2012 Threats

• Biodiversity is under serious threat as a result of human activities. The main dangers worldwide are population growth and resource consumption, climate change and global warming, habitat conversion and urbanisation, invasive alien species, over-exploitation of natural resources and environmental degradation. But….

Covering the arid, semi-arid and sub-humid regions, more than 60% of the most backward zone in India fall in the rainfed regions. These are characterized by mixed farming systems, marginal famers and forest dwellers, high concentration of poverty and backwardness, as most of these areas have remained outside the dominant development and growth paradigm. However, they are critical to the food and ecological security of the country. Thus species distribution data will be useful for guiding prioritization of dry land areas for development interventions and developing conservation action plans for species occurring in dry area. Kutch and Thar Deserts Data Gaps

250

200

150

100 Birdlife Observations

Based on Birdlife 2012 & Observed Records Order Birdlife Observations Gap ACCIPITRIFORMES 30 39 9 Global Conservation Tools and Data like PASSERIFORMES 170 220 50 CHARADRIIFORMES 72 82 10 IUCN/Birdlife Expert Range Maps have major CORACIIFORMES 7 13 6 data gaps…… GRUIFORMES 15 12 -3 ANSERIFORMES 21 29 8 CICONIIFORMES 8 8 0 …..These data are used in almost all SULIFORMES 5 5 0 conservation actions and plans. APODIFORMES 4 6 2 PELECANIFORMES 21 23 2 OTIDIFORMES 2 4 2 STRIGIFORMES 11 14 3 PROCELLARIIFORMES 5 0 -5 CAPRIMULGIFORMES 4 5 1 CUCULIFORMES 7 6 -1 COLUMBIFORMES 8 8 0 GALLIFORMES 11 13 2 PICIFORMES 8 12 4 FALCONIFORMES 10 9 -1 BUCEROTIFORMES 2 2 0 PHAETHONTIFORMES 1 0 -1 PHOENICOPTERIFORMES 2 2 0 PODICIPEDIFORMES 4 3 -1 PSITTACIFORMES 3 3 0 PTEROCLIFORMES 5 4 -1 436 522 86 Where Specie Occurs?

The Massive Location Database Results

Fig.1 Maxent Model for Great Indian Bustard (Ardeotis nigriceps) Fig.2 Maxent Model for Spotted Sandgrouse (Pterocles senegallus) with Historical and Current Observation Data and all environmental layers with Historical and Current Observation Data and all environmental layers Results

Fig.3 Maxent Model for Red-tailed Wheater (Oenanthe chrysopygia) Fig.4 Maxent Model for Cream-Coloured courser (Cursorius cursor) with Historical and Current Observation Data and all environmental layers with Historical and Current Observation Data and all environmental layers Application of SDMs

Field of application Use of species prediction Conservation biology Identify sites expected to hold important species using environmental data Identify sites for species reintroductions Guide site management by manipulating features known to favour species occurrence Identify gaps in distribution and diagnose their cause Identify locations at risk of species extinction Biological indication Identify major influences on species distribution, hence revealing indicator value

Discriminate effects of habitat and pollution on species distribution to diagnose which is responsible for absence

Predict site value for important species using other biota as predictors Nuisance species Predict sites at risk from outbreaks Guide site management by manipulating features known to reduce species occurrence Invasion ecology Predict sites sensitive to alien invasion Model negative effects of non-indigenous species on native biota All areas of applied ecology Predict distributional change in response to changing climate or land use

Manel, S., Williams, H. C. and Ormerod, S.J. (2001), Evaluating presence–absence models in ecology: the need to account for 40 prevalence. Journal of Applied Ecology, 38: 921–931. Indian Scenario in SDM

• Very Few Research work on SDMs • Some of them: – Jeganathan, P., Green, R.E., Norris, K., Vogiatzakis, I.N., Bartsch, A., Wotton, S.R., Bowden, C.G.R., Griffiths, G.H., Pain, D. & Rahmani, A.R. (2004) Modelling habitat selection and distribution of the critically endangered Jerdon's courser Rhinoptilus bitorquatus in scrub jungle: an application of a new tracking method. Journal of Applied Ecology, 41, 224–237. – Peterson, A. T., & Papeş, M. (2006). Potential geographic distribution of the Bugun Liocichla Liocichla bugunorum, a poorly-known species from north- eastern India. Indian Birds, 2(6), 146-149 – Manel, S., Dias, J. M., & Ormerod, S. J. (1999). Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling, 120(2), 337-347. – Kumara, H. N., Irfan-Ullah, M., & Kumar, S. (2009). Mapping potential distribution of slender loris subspecies in peninsular India. Endangered Species Research, 7, 29-38.

41 Limitations

• Samples – We need more areas and species coverage • Computing – Single Species Model takes 2-3 days to complete • More Contributors and Validators to data…. Acknowledgements

• GBIF, eBird, MigrantWatch, Xeno-Canto, WorldBirds, BHL, IBP • IUCN, Birdlife International, Cornell University, NHM, Google • L. Shyamal, Bikram Grewal, Aasheesh Pittie, Suhel Quader…… • ………………………………………………………..…………………………………..And Many Others References

Guisan, A. and Thuiller, W. (2005), Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8: 993–1009. doi: 10.1111/j.1461- 0248.2005.00792.x Austin, M.P. and Cunningham, R.B., 1981. Observational analysis of environmental gradients.Proc. Ecol. Soc. Aust,.11:109-119. Hirzel A.H. and Guisan A. (2002). “Which is the optimal sampling strategy for habitat suitability modelling?”. Ecological Modelling, 157, 331-341. Cawsey, E.M., Austin, M.P., Baker, B.L., 2002. Regional vegetation mapping in Australia: a case study in the practical use of statistical modeling. Biodiversity Conservation. Graham, C. H., Ferrier, S., Huettman, F., Moritz, C., & Peterson, A. T. (2004). New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution, 19(9), 497-503. Huettmann, F.. (2005). Databases and science-based management in the context of wildlife and habitat: towards a certified ISO standard for objective decision-making for the global community by using the internet. Journal of Wildlife Management. 69: 466–472. Soberón, J. M. and Peterson, A. T.. (2005). Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiversity Informatics. 2: 1–10. Jeganathan, P., Green, R.E., Norris, K., Vogiatzakis, I.N., Bartsch, A., Wotton, S.R., Bowden, C.G.R., Griffiths, G.H., Pain, D. & Rahmani, A.R. (2004) Modelling habitat selection and distribution of the critically endangered Jerdon's courser Rhinoptilus bitorquatus in scrub jungle: an application of a new tracking method. Journal of Applied Ecology, 41, 224–237. Peterson, A. T., & Papeş, M. (2006). Potential geographic distribution of the Bugun Liocichla Liocichla bugunorum, a poorly-known species from north-eastern India. Indian Birds, 2(6), 146-149 Manel, S., Dias, J. M., & Ormerod, S. J. (1999). Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecological Modelling, 120(2), 337-347. Kumara, H. N., Irfan-Ullah, M., & Kumar, S. (2009). Mapping potential distribution of slender loris subspecies in peninsular India. Endangered Species Research, 7, 29-38. Manel, S., Williams, H. C. and Ormerod, S.J. (2001), Evaluating presence–absence models in ecology: the need to account for prevalence. Journal of Applied Ecology, 38: 921– 931. Hijmans et al. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25:1965-1978. Hirzel A.H. and Guisan A. (2002). “Which is the optimal sampling strategy for habitat suitability modelling?”. Ecological Modelling, 157, 331-341. Cawsey, E.M., Austin, M.P., Baker, B.L., 2002. Regional vegetation mapping in Australia: a case study in the practical use of statistical modeling. Biodivers. Conserv. Graham, C. H., Ferrier, S., Huettman, F., Moritz, C., & Peterson, A. T. (2004). New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution, 19(9), 497-503. Huettmann, F.. 2005. Databases and science-based management in the context of wildlife and habitat: towards a certified ISO standard for objective decision-making for the global community by using the internet. J. Wildl. Manage. 69: 466–472. Soberón, J. M. and Peterson, A. T.. 2005. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodiv. Inform. 2: 1–10. Busby, J. R. 1991. BIOCLIM – a bioclimate analysis and prediction system. In: Margules, C.R. and Austin, M. P. (eds.), Nature conservation: Cost effective biological surveys and data analysis. CSIRO, pp. 64–68 Guisan et al. 2002. Generalized linear and additive models in studies of species distributions: Setting the scene. Ecological Modeling 157:89-100 Elith et al. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77:802-813. Phillips et al. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231-259. Phillips & Dudík. 2008. Modeling species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31:161-175. Elith et al. 2011. A statistical explanation of Maxent for ecologists. Diversity & Distributions 17:43- 57. Guo et al. 2005. Support vector machines for predicting distribution of Sudden Oak Death in California. Ecological Modelling 182:75-90. 44 Hirzel et al. 2002. Ecological-niche factor analysis: How to compute habitat- suitability maps without absence data? Ecology 83:2027-203 Parasharya, B. M.; Borad, C. K.; Rank, D. N. 2004. A checklist of the birds of Gujarat. Bird Conservation Society, Gujarat (Book) Thank You