Summary1. Species richness is a state variable of some interest in monitoring programmes but raw species counts are often biased due to imperfect species detectability. Therefore, monitoring programmes should quantify detectability for target taxa to assess whether it varies over temporal or spatial scales. We assessed the potential for tropical bat monitoring programmes to reliably estimate trends in species richness. 2. Using data from 25 bat assemblages from the Old and New World tropics, we estimated detectability for all species in an assemblage (mean proportion of species detected per sampling plot) and for individual species (species-specific detectability). We further assessed how these estimates of detectability were affected by external sources of variation relating to time, space, survey effort and biological traits. 3. The mean proportion of species detected across 96 sampling plots was estimated at 0AE76 (range 0AE57-1AE00) and was significantly greater for phytophagous than for animalivorous species. Species-*Correspondence author. E-mail: cmeyer@fc.ul.pt 1365-2664.2011.01976.x Ó 2011 The Authors. Journal of Applied Ecology Ó 2011 British Ecological Society averaged detectability for phytophagous species was influenced by the number of surveys and season, whereas the number of surveys and sampling methods [ground-or canopy-level mist nets, harp traps and acoustic sampling (AS)] most strongly affected estimates of detectability for animalivorous bats. Species-specific detectability averaged 0AE4 and was highly heterogeneous across 232 species, with estimates ranging from 0AE03 to 0AE84. Species-level detectability was influenced by a range of external factors such as location, season, or sampling method, suggesting that raw species counts may sometimes be strongly biased. 4. Synthesis and applications. Due to generally high species-specific detection probabilities, Neotropical aerial insectivorous bats proved to be well suited for monitoring using AS. However, for species with low detectability, such as most gleaning animalivores or nectarivores, count data obtained in bat monitoring surveys must be corrected for detection bias. Our results indicate that species-averaged detection probabilities will rarely approach 1 unless many surveys are conducted. Consequently, long-term bat monitoring programmes need to adopt an estimation scheme that corrects for variation in detectability when comparing species richness over time and when making regional comparisons. Similar corrections will be needed for other species-rich tropical taxa. Journal of AppliedEcology 2011, 48, 777-787 doi: 10.1111/j.

No abstract

Summary Monitoring global biodiversity is critical for understanding responses to anthropogenic change, but biodiversity monitoring is often biased away from tropical, megadiverse areas that are experiencing more rapid environmental change. Acoustic surveys are increasingly used to monitor biodiversity change, especially for bats as they are important indicator species and most use sound to detect, localise and classify objects. However, using bat acoustic surveys for monitoring poses several challenges, particularly in megadiverse regions. Many species lack reference recordings, some species have high call similarity or differ in call detectability, and quantitative classification tools, such as machine learning algorithms, have rarely been applied to data from these areas. Here, we collate a reference call library for bat species that occur in a megadiverse country, Mexico. We use 4685 search‐phase calls from 1378 individual sequences of 59 bat species to create automatic species identification tools generated by machine learning algorithms (Random Forest). We evaluate the improvement in species‐level classification rates gained by using hierarchical classifications, reflecting either taxonomic or ecological constraints (guilds) on call design, and examine how classification rate accuracy changes at different hierarchical levels (family, genus and guild). Species‐level classification of calls had a mean accuracy of 66%, and the use of hierarchies improved mean species‐level classification accuracy by up to 6% (species within families 72%, species within genera 71·2% and species within guilds 69·1%). Classification accuracy to family, genus and guild‐level was 91·7%, 77·8% and 82·5%, respectively. The bioacoustic identification tools we have developed are accurate for rapid biodiversity assessments in a megadiverse region and can also be used effectively to classify species at broader taxonomic or ecological levels. This flexibility increases their usefulness when there are incomplete species reference recordings and also offers the opportunity to characterise and track changes in bat community structure. Our results show that bat bioacoustic surveys in megadiverse countries have more potential than previously thought to monitor biodiversity changes and can be used to direct further developments of bioacoustic monitoring programs in Mexico.

Over the past 20 years, the biodiversity associated with shaded coffee plantations and the role of diverse agroforestry types in biodiversity conservation and environmental services have been topics of debate. Endophytic fungi, which are microorganisms that inhabit plant tissues in an asymptomatic manner, form a part of the biodiversity associated with coffee plants. Studies on the endophytic fungi communities of cultivable host plants have shown variability among farming regions; however, the variability in fungal endophytic communities of coffee plants among different coffee agroforestry systems is still poorly understood. As such, we analyzed the diversity and communities of foliar endophytic fungi inhabiting Coffea arabica plants growing in the rustic plantations and simple polycultures of two regions in the center of Veracruz, Mexico. The endophytic fungi isolates were identified by their morphological traits, and the majority of identified species correspond to species of fungi previously reported as endophytes of coffee leaves. We analyzed and compared the colonization rates, diversity, and communities of endophytes found in the different agroforestry systems and in the different regions. Although the endophytic diversity was not fully recovered, we found differences in the abundance and diversity of endophytes among the coffee regions and differences in richness between the two different agroforestry systems of each region. No consistent pattern of community similarity was found between the coffee agroforestry systems, but we found that rustic plantations shared the highest number of morphospecies. The results suggest that endophyte abundance, richness, diversity, and communities may be influenced predominantly by coffee region, and to a lesser extent, by the agroforestry system. Our results contribute to the knowledge of the relationships between agroforestry systems and biodiversity conservation and provide information regarding some endophytic fungi and their communities as potential management tools against coffee plant pests and pathogens.