4.4 Article

Environmental and geographical biases in plant specimen data from the Colombian Andes

Journal

BOTANICAL JOURNAL OF THE LINNEAN SOCIETY
Volume 200, Issue 4, Pages 451-464

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/botlinnean/boac035

Keywords

collecting bias; Colombia; flora; herbarium specimens; northern Andes; sampling completeness

Categories

Funding

  1. Colciencias Doctoral funding [727-2015]
  2. Universidad del Rosario

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Specimen records are an important source of species information for biodiversity research, but the currently available records may be biased in terms of geography and environment. This study focuses on geographical and environmental biases in online records of the flora of the Colombian Andes and finds significant sampling bias. The sampling is better in highland areas (>2000m) and poorer in mid- and lowland areas (e.g. montane and lowland forests). The median sampling completeness across the Colombian Andes is less than 75% at the scales studied.
Specimen records are a major source of species information for biodiversity research. However, specimen records currently available may be geographically or environmentally biased. Detailed knowledge of biases is useful for understanding and accounting for errors they introduce into analyses of biodiversity patterns. Here we study geographical and environmental biases in online records representing the flora of the Colombian Andes and explore their effect on sample completeness at different spatial scales. We found a strong geographical and environmental sampling bias. Plant records were concentrated close to cities where herbaria and researchers are located. The highlands > 2000 m are better sampled, whereas mid- and lowlands remain poorly sampled (i.e. montane and lowland forest). Sampling completeness (SC) median across the Colombian Andes is < 75% at the scales studied. We explore possible causes of sampling bias, identify critical gaps and priority areas for plant sampling and make recommendations for strategies to increase SC and reduce biases.

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