Species undersampling in tropical bat surveys : effects on emerging biodiversity patterns

Meyer, CFJ; Aguiar, LMS; Aguirre, JF; Baumgarten, J; Clarke, FM; Cosson, J; Estrada Villegas, S; Fahr, J; Faria, D; Furey, N; Henry, M; Jenkins, RKB; Kunz, TH; Cristina MacSwiney González, M; Moya, I; Pons, J; Racey, PA; Rex, K; Sampaio, EM; Stoner, KE; Voigt, CC; von Staden, D; Weise, CD; Kalko, EKV

Abstract

Summary:

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.

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.

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.

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 nonrandom 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.

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.

• Journal Article Type: Article
• Acceptance Date: Jun 6, 2014
• Online Publication Date: Jun 18, 2014
• Publication Date: Jul 22, 2014
• Deposit Date: Jan 25, 2016
• Publicly Available Date: Apr 5, 2016
• Journal: Journal of Animal Ecology
• Print ISSN: 0021-8790
• Electronic ISSN: 1365-2656
• Publisher: Wiley
• Volume: 84
• Issue: 1
• Pages: 113-123
• DOI: https://doi.org/10.1111/1365-2656.12261
• Publisher URL: http://dx.doi.org/10.1111/1365-2656.12261
• Related Public URLs: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-2656
• Additional Information: Funders : Conservation International;The MacArthur Foundation;German Academic Exchange Service;German Science Foundation;The Leverhulme Trust;The National Geographic Society;German Federal Ministry of Education and Research;BIOTA program;The Center for Ecology and Conservation Biology, Boston University;Lubee Bat Conservancy;US National Science Foundation;Landesgraduiertenförderung Baden‐Württemberg;The Darwin Initiative;The Rufford Foundation;The University of Aberdeen;Consejo Nacional de Ciencia y Tecnología;Conselho Nacional de Pesquisa – CNPq;Electricité de France;Convention Muséum/EDF;Centro de Investigaciones en Ecosistemas, Universidad Nacional Autónoma de México; Grant Number: KA 1241/6‐1; Grant Number: Vo 890/7; Grant Number: 01LC0017; Grant Number: 01LC0411; Grant Number: 01LC0617E1; Grant Number: 168990; Grant Number: CQZH 1294