Luis F. Aguirre, Thomas H. Kunz, Erica M. Sampaio, Deborah Faria, Mickaël Henry, Sergio Estrada Villegas, Dietrich von Staden, Isabel Moya, Ludmilla M. S. Aguiar, Elisabeth K. V. Kalko, Paul A. Racey, Jean-Marc Pons, Neil M. Furey, M. Cristina Mac-Swiney González, Jean-François Cosson, Frank M. Clarke, Christa D. Weise, Christoph F. J. Meyer, Katja Rex, Julio Baumgarten, Christian C. Voigt, Jakob Fahr, Kathryn E. Stoner, Richard K. B. Jenkins, Universidade Nova de Lisboa = NOVA University Lisbon (NOVA), Institute of Experimental Ecology, Universität Ulm - Ulm University [Ulm, Allemagne], Universidade de Brasília (UnB), Universidad Mayor de San Simón [Cochabamba, Bolivie] (UMSS), Programa Conservac Murcielagos Bolivia, Partenaires INRAE, Universidade Estadual de Santa Cruz (UESC), Institute of Biological and Environmental Sciences, University of Aberdeen, Centre de Biologie pour la Gestion des Populations (UMR CBGP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), School of Freshwater Sciences, University of Wisconsin - Milwaukee, Department of Migration and Immuno-ecology, Max Planck Institute for Ornithology, Max-Planck-Gesellschaft-Max-Planck-Gesellschaft, Technical University Braunschweig, Abeilles & Environnement (UR 406 ), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Madagasikara Voakajy, School of Anthropology and Conservation, University of Kent [Canterbury], School of Environment, Natural Resources and Geography, Bangor University, Boston University [Boston] (BU), Universidad Veracruzana, Muséum national d'Histoire naturelle (MNHN), Centre for Ecology and Conservation, University of Exeter, Cornwall Campus, Leibniz Institute for Zoo and Wildlife Research (IZW), Leibniz Association, New Mexico State University, United States Fish and Wildlife Service (USFWS), and Smithsonian Tropical Research Institute
International audience; 1. Undersampling is commonplace in biodiversity surveys of species-rich tropical assemblages in which rare taxa abound, with possible repercussions for our ability to implement surveys and monitoring programmes in a cost-effective way. 2. We investigated the consequences of information loss due to species undersampling (missing subsets of species from the full species pool) in tropical bat surveys for the emerging patterns of species richness (SR) and compositional variation across sites. 3. For 27 bat assemblage data sets from across the tropics, we used correlations between original data sets and subsets with different numbers of species deleted either at random, or according to their rarity in the assemblage, to assess to what extent patterns in SR and composition in data subsets are congruent with those in the initial data set. We then examined to what degree high sample representativeness (r ≥ 0 8) was influenced by biogeographic region, sampling method, sampling effort or structural assemblage characteristics. 4. For SR, correlations between random subsets and original data sets were strong (r ≥ 0 8) with moderate (ca. 20%) species loss. Bias associated with information loss was greater for species composition; on average ca. 90% of species in random subsets had to be retained to adequately capture among-site variation. For non random subsets, removing only the rarest species (on average c. 10% of the full data set) yielded strong correlations (r > 0 95) for both SR and composition. Eliminating greater proportions of rare species resulted in weaker correlations and large variation in the magnitude of observed correlations among data sets. 5. Species subsets that comprised ca. 85% of the original set can be considered reliable surrogates, capable of adequately revealing patterns of SR and temporal or spatial turnover in many tropical bat assemblages. Our analyses thus demonstrate the potential as well as limitations for reducing survey effort and streamlining sampling protocols, and consequently for increasing the cost-effectiveness in tropical bat surveys or monitoring programmes. The dependence of the performance of species subsets on structural assemblage characteristics (total assemblage abundance, proportion of rare species), however, underscores the importance of adaptive monitoring schemes and of establishing surrogate performance on a site by site basis based on pilot surveys.