Echolocation Calls of Bats from Madeira Island: Acoustic Characterization and Implications for Surveys

Unveiling the Potential Distribution of the Highly Threatened Madeira Pipistrelle (Pipistrellus maderensis): Do Different Evolutionary Significant Units Exist?

The isolation of islands has played a significant role in shaping the unique evolutionary histories of many species of flora and fauna, including bats. One notable example is the Madeira pipistrelle (Pipistrellus maderensis), which inhabits the Macaronesian archipelagos of the Azores, Madeira, and the Canary Islands. Despite the high biogeographic and conservation importance of this species, there is limited information on its ecology and evolutionary history across different archipelagos. In our study, we employed species distribution models (SDMs) to identify suitable habitats for the Madeira pipistrelle and determine the environmental factors influencing its distribution. Additionally, we conducted molecular comparisons using mitochondrial DNA data from various Macaronesian islands. Molecular analyses provided compelling evidence for the presence of distinct Evolutionary Significant Units on the different archipelagos. We identified distinct haplotypes in the populations of Madeira and the Canary Islands, with a genetic distance ranging from a minimum of 2.4% to a maximum of 3.3% between samples from different archipelagos. In support of this, SDMs highlighted relevant dissimilarities between the environmental requirements of the populations of the three archipelagos, particularly the climatic niche. Our research demonstrates that deeper investigations that combine ecological, morphological, and genetic areas are necessary to implement tailored conservation strategies.

Echolocation calls of some bats of Gujarat, India

Bats play an important role by providing ecosystem services including pollination, seed dispersal, forest regeneration and insect pest control and also serve as bio-indicators. In the present study, we present an acoustic guide to the calls of nine species of bats from Gujarat belonging to families Rhinopomatidae (Rhinopoma hardwickii, Rhinopoma microphyllum), Emballonuridae (Taphozous melanopogon, Taphozous longimanus and Taphozous nudiventris), Rhinolophidaea (Rhinolophus lepidus), Hipposideridae (Hipposideros galeritus) and Vespertilionidae (Scotophilus heathii, Pipistrellus ceylonicus). Discriminant function analysis was used to classify the bat calls to the species level using leave-one-out cross validation. Analysis was carried out separately for constant frequency (CF) calls and frequency-modulated (FM) calls. Bats echolocating with CF calls were classified with 100% success, while in the case of FM calls, the calls were classified with 66.7% accuracy. In species-rich communities, care should be taken while using echolocation calls to identify bats producing FM calls. More such call libraries of bats from other parts of India are needed for non-invasive documentation of chiropteran fauna in different biogeographic zones.

Quantitative acoustic differentiation of cryptic species illustrated with King and Clapper rails

Reliable species identification is vital for survey and monitoring programs. Recently, the development of digital technology for recording and analyzing vocalizations has assisted in acoustic surveying for cryptic, rare, or elusive species. However, the quantitative tools that exist for species differentiation are still being refined. Using vocalizations recorded in the course of ecological studies of a King Rail (Rallus elegans) and a Clapper Rail (Rallus crepitans) population, we assessed the accuracy and effectiveness of three parametric (logistic regression, discriminant function analysis, quadratic discriminant function analysis) and six nonparametric (support vector machine, CART, Random Forest, k-nearest neighbor, weighted k-nearest neighbor, and neural networks) statistical classification methods for differentiating these species by their kek mating call. We identified 480 kek notes of each species and quantitatively characterized them with five standardized acoustic parameters. Overall, nonparametric classification methods outperformed parametric classification methods for species differentiation (nonparametric tools were between 57% and 81% accurate, parametric tools were between 57% and 60% accurate). Of the nine classification methods, Random Forest was the most accurate and precise, resulting in 81.1% correct classification of kek notes to species. This suggests that the mating calls of these sister species are likely difficult for human observers to tell apart. However, it also implies that appropriate statistical tools may allow reasonable species-level classification accuracy of recorded calls and provide an alternative to species classification where other capture- or genotype-based survey techniques are not possible.