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

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 techniquebased biodiversity study for its amenability to incorporate any further change or modification, and utility of the geo-spatial biodiversity database for simulating various species

Diversity and distribution of amphibians in Romania

“…Several global scale initiatives have been started to compile the vast biodiversity datasets held in museums and herbaria, publications or data resulting from intensive field surveys e.g., GBIF, EOL (Franklin 2009). The increased amount of biodiversity data available is accompanied by progress in computation that allows not only the proper management of data (Matin et al 2012) but also its advanced and precise analysis (Reese et al 2005). Basic knowledge of the species distributions within a region is required for a proper management of biodiversity, e.g., to predict species extinction under habitat loss, to understand the potential impacts of climate change on biodiversity, to prioritize conservation efforts and design conservation areas (Margules and Pressey 2000, Primack 2010).…”

Assessing land transformation and associated degradation of the west part of Ganga River Basin using forest cover land use mapping and residual trend analysis

“…A class-wise comparison was carried out to estimate the changes in the area statistics during the period 1975-2010. The mapping accuracy was accessed using 1509 sample plots gathered through field sampling during the biodiversity characterization at landscape level project using a sample size of 20 m×20 m for trees, 5 m×5 m for shrubs and 1 m×1 m for herbs (Matin et al, 2012).…”

The charms and challenges of climate change biodiversity in a warming world

“…Species distribution models are static and probabilistic in nature as they statistically relate the geographical distribution of species or communities to their present environment. Matin et al (2012) utilized the GPS-based location information on Medicago sativa and Plantago annua to simulate their potential distribution in the year 2020 (SRES A1B scenario, IPCC) using the Maxent model in part of Ladakh Himalaya. The model suggested that the distribution of both the species would tend to move in the direction of shorter cold seasons.…”

“…Matin et al (2012) demonstrated a method to integrate the faunal component in a recently completed nationwide biodiversity study in India using the plants alone (Behera and Roy 2010). The study highlights the potential contribution of geoinformatics to biodiversity assessments .…”