期刊
ECOLOGICAL MONOGRAPHS
卷 88, 期 2, 页码 170-187出版社
WILEY
DOI: 10.1002/ecm.1284
关键词
biodiversity estimation; methods comparison; monitoring; spatial scale; species richness; species-area relationship; upscaling
类别
资金
- UKPopNet grant
- EU FP7 SCALES [ENV-2008-226852]
- EU BON (Building the European Biodiversity Observation Network) [ENV-2012-308454]
- Stellenbosch Institute for Advanced Studies
- National Research Foundation of South Africa [81825, 76912]
- Australian Research Council [DP150103017]
- Marie Curie BIOCONS (European Centre for Biodiversity and Conservation Research) EST programme [MEST-CT-2004-514350]
- Czech Science Foundation [14-36098G]
- Polish Science Committee [KBN 3 P04F 03422, KBN 2 P04F 039 29]
- BBSRC [BB/I000364/1] Funding Source: UKRI
- NERC [ceh020015] Funding Source: UKRI
The challenge of biodiversity upscaling, estimating the species richness of a large area from scattered local surveys within it, has attracted increasing interest in recent years, producing a wide range of competing approaches. Such methods, if successful, could have important applications to multi-scale biodiversity estimation and monitoring. Here we test 19 techniques using a high quality plant data set: the GB Countryside Survey 1999, detailed surveys of a stratified random sample of British landscapes. In addition to the full data set, a set of geographical and statistical subsets was created, allowing each method to be tested on multiple data sets with different characteristics. The predictions of the models were tested against the true species-area relationship for British plants, derived from contemporaneously surveyed national atlas data. This represents a far more ambitious test than is usually employed, requiring 5-10 orders of magnitude in upscaling. The methods differed greatly in their performance; while there are 2,326 focal plant taxa recorded in the focal region, up-scaled species richness estimates ranged from 62 to 11,593. Several models provided reasonably reliable results across the 16 test data sets: the Shen and He and the Ulrich and Ollik models provided the most robust estimates of total species richness, with the former generally providing estimates within 10% of the true value. The methods tested proved less accurate at estimating the shape of the species-area relationship (SAR) as a whole; the best single method was Hui's Occupancy Rank Curve approach, which erred on average by <20%. A hybrid method combining a total species richness estimate (from the Shen and He model) with a downscaling approach (the Sizling model) proved more accurate in predicting the SAR (mean relative error 15.5%) than any of the pure upscaling approaches tested. There remains substantial room for improvement in upscaling methods, but our results suggest that several existing methods have a high potential for practical application to estimating species richness at coarse spatial scales. The methods should greatly facilitate biodiversity estimation in poorly studied taxa and regions, and the monitoring of biodiversity change at multiple spatial scales.
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