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Species undersampling in tropical bat surveys: effects on emerging biodiversity patterns
Author(s) -
Meyer Christoph F. J.,
Aguiar Ludmilla M. S.,
Aguirre Luis F.,
Baumgarten Julio,
Clarke Frank M.,
Cosson JeanFrançois,
Estrada Villegas Sergio,
Fahr Jakob,
Faria Deborah,
Furey Neil,
Henry Mickaël,
Jenkins Richard K. B.,
Kunz Thomas H.,
Cristina MacSwiney González M.,
Moya Isabel,
Pons JeanMarc,
Racey Paul A.,
Rex Katja,
Sampaio Erica M.,
Stoner Kathryn E.,
Voigt Christian C.,
Staden Dietrich,
Weise Christa D.,
Kalko Elisabeth K. V.
Publication year - 2015
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/1365-2656.12261
Subject(s) - undersampling , biodiversity , species richness , representativeness heuristic , global biodiversity , biology , sampling (signal processing) , tropics , ecology , sampling bias , species diversity , taxon , sample size determination , statistics , mathematics , filter (signal processing) , artificial intelligence , computer science , computer vision
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.