Species- and individual-specific animal calls can be used in identification as verified in playback experiments and analyses of features extracted from these signals. The use of machine-learning methods and acoustic features borrowed from human speech recognition to identify animals at the species and individual level has increased recently. To date there have been few studies comparing the performances of these methods and features used for call-type-independent species and individual identification. We compared the performance of four machine-learning classifiers in the identification of ten passerine species, and individual identification for three passerines using two acoustic features. The methods did not require us to pre-categorize the component syllables in call-type-independent species and individual identification systems. The results of our experiment indicated that support vector machines (SVM) performed best generally, regardless of which acoustic feature was used, linear predictive coefficients (LPCs) increased the recognition accuracies of hidden Markov models (HMM) greatly, and the most appropriate classifiers for LPCs and Mel-frequency cepstral coefficients (MFCCs) were HMM and SVM respectively. This study will assist researchers in selecting classifiers and features to use in future species and individual recognition studies.

We describe an algorithm that helps to predict potential distributional areas for species using presence-only records. The Marble Algorithm is a density-based clustering program based on Hutchinson’s concept of ecological niches as multidimensional hypervolumes in environmental space. The algorithm characterizes this niche space using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. When MA is provided with a set of occurrence points in environmental space, the algorithm determines two parameters that allow the points to be grouped into several clusters. These clusters are used as reference sets describing the ecological niche, which can then be mapped onto geographic space and used as the potential distribution of the species. We used both virtual species and ten empirical datasets to compare MA with other distribution-modeling tools, including Bioclimate Analysis and Prediction System, Environmental Niche Factor Analysis, the Genetic Algorithm for Rule-set Production, Maximum Entropy Modeling, Artificial Neural Networks, Climate Space Models, Classification Tree Analysis, Generalised Additive Models, Generalised Boosted Models, Generalised Linear Models, Multivariate Adaptive Regression Splines and Random Forests. Results indicate that MA predicts potential distributional areas with high accuracy, moderate robustness and above-average transferability on all datasets, particularly when dealing with small numbers of occurrences.

A checklist of 805 spider species and subspecies belonging to 57 families described and/or reported from Kenya up to 31 December 2018 is provided. Species distribution within Kenya is given according to counties and specific localities. A historical survey is provided and each record is presented in its original combination. The list is dominated by members of the families Salticidae and Linyphiidae (160 and 110 species, respectively). Eighteen families are represented by a single species. About 300 species are known exclusively from Kenya and 158 species are sub-endemics. Two hundred and forty two species are described from a single sex (159 females and 83 males) and 24 from juveniles. Nairobi County has the greatest number of records, five counties had a frequency of one, while nine counties had no collection records. There are two fossil spiders known from Kenya belonging to the family Oonopidae. One new combination is proposed:Hypsosinga holzapfelae(Lessert, 1936),comb. nov.(ex.AraneusClerck, 1757).