Are all species necessary to reveal ecologically important patterns? |
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Authors: | Edwin Pos Juan Ernesto Guevara Andino Daniel Sabatier Jean‐François Molino Nigel Pitman Hugo Mogollón David Neill Carlos Cerón Gonzalo Rivas Anthony Di Fiore Raquel Thomas Milton Tirado Kenneth R Young Ophelia Wang Rodrigo Sierra Roosevelt García‐Villacorta Roderick Zagt Walter Palacios Milton Aulestia Hans ter Steege |
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Institution: | 1. Ecology and Biodiversity Group, Utrecht University, Utrecht, the Netherlands;2. Section Botany, Naturalis Biodiversity Center, Leiden, the Netherlands;3. Department of Integrative Biology, University of California, Berkeley, California;4. IRD, UMR AMAP, Montpellier, France;5. The Field Museum, Illinois;6. Center for Tropical Conservation, Nicholas School of the Environment, Duke University, Durham, North Carolina;7. Endangered Species Coalition, Silver Spring, Maryland;8. Universidad Estatal Amazónica, Puyo, Ecuador;9. Universidad Central Herbario Alfredo Paredes, Escuela de Biología Herbario Alfredo Paredes, Quito, Ecuador;10. Wildlife Ecology and Conservation & Quantitative Spatial Ecology, University of Florida, Gainesville, Florida;11. Department of Anthropology, University of Texas at Austin, Texas;12. Iwokrama International Programme for Rainforest Conservation, Georgetown, Guyana;13. GeoIS, Quito, Ecuador;14. Geography and the Environment, University of Texas, Austin, Texas;15. Northern Arizona University, Flagstaff, Arizona, 86011;16. Institute of Molecular Plant Sciences, University of Edinburgh, Edinburgh, UK;17. Royal Botanic Garden of Edinburgh, Edinburgh, UK;18. Tropenbos International, Wageningen, the Netherlands;19. Universidad Técnica del Norte, Herbario Nacional del Euador, Quito, Ecuador;20. Herbario Nacional del Ecuador, Quito, Ecuador |
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Abstract: | While studying ecological patterns at large scales, ecologists are often unable to identify all collections, forcing them to either omit these unidentified records entirely, without knowing the effect of this, or pursue very costly and time‐consuming efforts for identifying them. These “indets” may be of critical importance, but as yet, their impact on the reliability of ecological analyses is poorly known. We investigated the consequence of omitting the unidentified records and provide an explanation for the results. We used three large‐scale independent datasets, (Guyana/ Suriname, French Guiana, Ecuador) each consisting of records having been identified to a valid species name (identified morpho‐species – IMS) and a number of unidentified records (unidentified morpho‐species – UMS). A subset was created for each dataset containing only the IMS, which was compared with the complete dataset containing all morpho‐species (AMS: = IMS + UMS) for the following analyses: species diversity (Fisher's alpha), similarity of species composition, Mantel test and ordination (NMDS). In addition, we also simulated an even larger number of unidentified records for all three datasets and analyzed the agreement between similarities again with these simulated datasets. For all analyses, results were extremely similar when using the complete datasets or the truncated subsets. IMS predicted ≥91% of the variation in AMS in all tests/analyses. Even when simulating a larger fraction of UMS, IMS predicted the results for AMS rather well. Using only IMS also out‐performed using higher taxon data (genus‐level identification) for similarity analyses. Finding a high congruence for all analyses when using IMS rather than AMS suggests that patterns of similarity and composition are very robust. In other words, having a large number of unidentified species in a dataset may not affect our conclusions as much as is often thought. |
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Keywords: | Beta‐diversity Fisher's alpha indets large‐scale ecological patterns Mantel test morpho‐species nonmetric multidimensional scaling similarity of species composition spatial turnover |
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