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Abstract Detail


Pender, Jocelyn [1], Starr, Julian [2], Hahn, Marlene [3], Hipp, Andrew L. [3].

How sensitive are climatic niche inferences to distribution data sampling?

Ecological niche modeling is an important tool used in modern ecology, evolutionary biology and conservation planning. Frequently, museum-based occurrence records are used to model species’ distributions and ecological niches. Despite the acknowledgement of data quality issues in these occurrence datasets, little information exists on the sensitivity of niche estimates to inaccuracies and uncertainties in distribution data. The following research evaluates how Global Biodiversity Information Facility (GBIF) data perform against known county-level species distributions when used to estimate climatic niche for the largest angiosperm genus in the temperate world, Carex (Cyperaceae). Specifically, we quantified climatic niches for 480 North American Carex species using GBIF occurrence data and data from the Biota of North American Program (BONAP; The WorldClim BIOCLIM database was used to obtain climatic estimates from distribution data ( For each species, we performed a principal components (PC) analysis and calculated distance of GBIF and BONAP climatic niche estimates in PC space. We further conducted an analysis stratifying species according to elevation niche, to investigate whether high-elevation species are more sensitive to underlying sampling artefacts. The results are presented and discussed in the context of understanding the limits and applications of GBIF and BONAP data.

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1 - University of Ottawa, Department of Biology, Room 160, Gendron Hall, 30 Marie-Curie, Ottawa, ON, K1N6N5, Canada
2 - University of Ottawa, Biology, Gendron Hall, Room 160, 30 Marie Curie, Ottawa, ON, K1N 6N5, Canada
3 - The Morton Arboretum, 4100 Illinois Route 53, Lisle, IL, 60532, USA

Species distribution

Presentation Type: Poster:Posters for Topics
Session: P
Location: Hall D/The Shaw Conference Centre
Date: Monday, July 27th, 2015
Time: 5:30 PM
Number: PBG003
Abstract ID:621
Candidate for Awards:Ian and Syvia Taylor Award

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