NDLI: Similarity-based large-scale distribution mapping of orchids

Content Provider	Springer Nature Link
Author	Remm, Kalle Remm, Liina
Copyright Year	2008
Abstract	The expected presence/absence of a target species outside the area of actual observations is commonly estimated using statistical models or decision criteria. This investigation demonstrates a similarity-based solution for predictive mapping as an alternative to generalizing models. The maps of the expected distribution of 12 orchids were created using find sites from field observations and absence sites generated onto the observation track. The expected presence/absence of a species in a location was calculated according to the similarity between the location and selected examples of presence and absence sites. A machine learning system selected the best predictive sets for each species out of 161 cartographic and remote sensing features. The usefulness of the predictive distribution maps was expressed as the ratio of the density of find sites per track in the predicted presence area relative to the density per track in the predicted absence area. The predictive mapping was more efficient for Dactylorhiza incarnata, D. russowii, Gymnadenia conopsea, and Goodyera repens. Soil properties and the proportion of find sites for the other species in the vicinity were the most indicative site characteristics. The rarer species were found to be better indicators of the occurrence of the other species than were the more common orchids. The proposed approach—to direct subsequent field observations to sites where the occurrence of the target species was predicted but has not yet been recorded—helped discover new populations of orchids and enhance the representativeness of absence sites.
Starting Page	1629
Ending Page	1647
Page Count	19
File Format	PDF
ISSN	09603115
Journal	Biodiversity and Conservation
Volume Number	18
Issue Number	6
e-ISSN	15729710
Language	English
Publisher	Springer Netherlands
Publisher Date	2008-12-17
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Orchidaceae Similarity-based reasoning Predictive distribution mapping Indicator value of site characteristics Plant Sciences Tree Biology Evolutionary Biology
Content Type	Text
Resource Type	Article
Subject	Ecology Ecology, Evolution, Behavior and Systematics Nature and Landscape Conservation

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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