GBIF_Ebbe_Nielsen_2018_CIAT_Asia_04092018.pdf
Creators: name: Dharani Dhar Burra
Publisher: name: figshare
Year: 2018
Subjects: subject: 50102 Ecosystem Function
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: Ecology
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 50199 Ecological Applications not elsewhere classified
subject: 50101 Ecological Impacts of Climate Change
subject: 50211 Wildlife and Habitat Management

description: Here we present a methodology and a prototype tool powered by machine learning algorithms that allows for near real time threat (and loss) monitoring of vulnerable plant species, using open data (ground-truthed and remotely sensed). Improvements in machine learning algorithms and access to high quality remotely sensed data (top down) has enabled “remote” monitoring of the earth’s status. The applicability of insights obtained can be further improved by incorporating ground truthed (bottom up) data. The proposed tool leverages upon the ease at which one can access GBIF’s high quality spatialized data in a standardized format at scale, further augments this with remote sensing data and machine learning algorithms in order to monitor threat status of plant species. This tool mashes multiple datasets, merges geospatial layers, to generate new insights, and provides a new use case of open data, further supporting the call for FAIR (Findable, Accessible, Interoperable and Reusable) data. Biodiversity monitoring is central to Sustainable Development Agenda, we propose to use this case-study as an example to promote spatialization and FAIR access of national biodiversity records currently held by various government agencies. <br>Details of how the monitoring tool works?(1) The prototype version of the tool pulls in and layers data from three varied open data sources: Red listed plant species list from the IUCN database, occurrence records of these species from GBIF repository, and data from near real time forest loss monitoring tool called Terra-I (powered by artificial neural networks; http://www.terra-i.org/terra-i.html) that was previously developed by CIAT.(2) Species distribution modelling, wherein a combination of random forests and Xtreme gradient boosting algorithms were used to model, predict and map species distribution as a function of bioclimatic variables across the continent wherein the species were originally located, based on the spatialized occurrence points obtained from the GBIF repository, combined with geospatial visualization frameworks ties in the three data sources together, into one monitoring tool.(3) The current version monitors threat and loss of three species (Pilgodendron <i>uviferum</i>, Podocarpus <i>salignus</i> and Prumnopitys <i>andina</i>) originally found in South America, and two species (Picea <i>asperata</i> and Taiwania <i>cryptomerioides</i>) originally identified in Asia, all of which are red listed, and currently face extinction threat primarily due to anthropogenic activities. For each continent (i.e. South America and Asia), we layer four types of raster data: The first is the most recent (updates occur once in every 15 days) raster of locations/points wherein deforestation was detected by the terra-i system. The next type of raster data is the predicted distribution of each species (output of the species distribution model), the third type of raster data displays the overlap between deforestation point detection and species distribution, separately for each species. <br>