Species richness is an important state variable in the majority of biodiversity monitoring programmes (Yoccoz, Nichols & Boulinier 2001; Pollock et al. 2002). Studies investigating spatial or temporal trends in species richness or the effects of different environmental factors on local species occurrence are generally faced with the problem of imperfect and variable species detectability. Especially for species-rich taxa, it is unlikely that all species present at a particular place and time will be recorded during a typical survey, leading to a bias in species counts. Thus, comparisons of species richness over temporal or spatial scales will be distorted if based on simple counts of species (Boulinier et al. 1998; Pollock et al. 2002; Kéry & Royle 2008). Unbiased estimates of the relative difference in observed species richness require that mean species detectabilities are equal across assemblages (Kéry & Schmid 2006). Consequently, whenever species counts are used as a proxy for true species richness, tests of whether the expectation of the average species detection probability is constant over space and time are essential (Kéry & Plattner 2007; Kéry & Royle 2008). Knowledge about differences in detectability prior to implementation of a particular survey design may prove crucial. Moreover, the suitability of a taxon for a long-term monitoring programme depends on the baseline temporal and spatial variation in the state variables selected and how this inherent variability is influenced by extrinsic factors relating to options for survey design, sampling methodology or species traits.
Bats, with a cosmopolitan distribution and at least 1,232 extant species (N.B. Simmons, pers. comm.), are major contributors to mammalian biodiversity, comprising about 20% of mammalian species globally (IUCN 2009). Variation in bat species richness parallels that of mammals in general, showing a strong increase towards the equator (Patterson, Willig & Stevens 2003). In tropical forests, bats are important components of local mammal assemblages as they occupy a large variety of trophic niches and are often the most species-rich and abundant taxa (Kingston, Boo Liat & Zubaid 2006; Rex et al. 2008; Fahr & Kalko 2010). Tropical bats provide critical ecosystem services with respect to pollination, seed dispersal and control of arthropod populations (Patterson, Willig & Stevens 2003; Kalka, Smith & Kalko 2008; Lobova, Geiselman & Mori 2009). Bats are considered excellent bioindicators because they respond to a wide range of human-induced changes in habitat quality and climate, including urbanization, agricultural intensification, logging, habitat loss and fragmentation, global climate change and overhunting (Clarke, Rostant & Racey 2005; reviewed in Jones et al. 2009). Moreover, bats are reservoirs of a wide range of emerging infectious diseases whose spread may be related to habitat deterioration and climate change (Jones et al. 2009).
Globally, almost a quarter of all bat species are considered threatened (Schipper et al. 2008). Largely as a consequence of habitat loss, fragmentation, and degradation, bat populations have experienced world-wide declines in recent decades. In both the Neotropics and Paleotropics, bats have been projected to undergo considerable future declines as a result of continuing rampant deforestation and habitat fragmentation (Lane, Kingston & Lee 2006; IUCN 2009). Although the potential of bats as bioindicators and the urgent need for the implementation of a global network for monitoring bat populations have been recognized (Jones et al. 2009), existing monitoring programmes are largely limited to temperate regions (O’Shea & Bogan 2003). Long-term monitoring programmes for tropical bats are currently lacking, and so far bats have not been considered in established long-term monitoring programmes in the tropics [e.g. Conservation International’s Tropical Ecology, Assessment and Monitoring (TEAM) network, http://www.teamnetwork.org].
A standardized sampling scheme as part of a tropical bat monitoring programme should allow for consistent estimation of species richness at selected study sites so that robust conclusions can be made about temporal trends. Our primary objectives in the present study were to quantify detectability in tropical bat surveys and to identify important determinants of species detectability. Access to numerous empirical data sets from 25 locations across the Neotropics and Paleotropics constituted the basis for our assessment, providing us with the necessary reference data against which to calibrate a possible bat monitoring programme. Our analysis considered data from multiple sampling methods [ground and canopy mist nets, harp traps and acoustic sampling (AS)] as many bat species are usually missed in species inventories that employ only a single method (MacSwiney, Clarke & Racey 2008; Kunz & Parsons 2009; Fahr & Kalko 2010), and thus we judged sampling method to be a major factor affecting estimates of detectability.
Estimation of species richness as part of a bat monitoring programme would typically be based on repeated surveys conducted at various sampling plots at selected locations. Capture–recapture models permit estimation of species richness based on the pattern of detection/nondetection of species in replicated surveys. We employed the jackknife estimator associated with model Mh, which explicitly assumes heterogeneity in species encounter rates (Burnham & Overton 1979), and has been widely used for estimating species richness (Boulinier et al. 1998; Kéry & Schmid 2006; Husté & Boulinier 2007; Kéry & Plattner 2007; Rex et al. 2008). Using this analytical framework, we first estimated mean species detectability for each data set as the mean proportion of species detected for each plot-year combination (mean species inventory completeness). Following the approach of Kéry & Plattner (2007), we then modelled species-specific detectability as the probability of detecting a particular species during two successive surveys. We subsequently used the resulting estimates to assess how detectability varies in relation to potentially important sources of heterogeneity, particularly temporal and spatial variation and survey effort. We made the following predictions: (i) detection probability varies widely among bat species and between trophic groups or ensembles; (ii) sampling method is a strong determinant of detectability as suggested by a large body of empirical evidence; (iii) detectability is influenced by sampling interval, with higher detection probability for sampling on nonconsecutive vs. consecutive nights as a result of trap shyness of bats; (iv) we did not a priori expect detectability to vary between sampling years; however, several studies have noted seasonal differences in bat abundance (e.g. Stoner 2005; Meyer & Kalko 2008), and thus we predicted detection probability to differ among seasons; (v) mean species detectability is affected by sampling locality due to structural and compositional differences among local assemblages or habitat effects.