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remarkable number of new vertebrate species have been discovered in Tanzania in the last 30 years. These discoveries have mainly been centred in the montane highlands, particularly the Eastern Arc Mountains. More recently, research in the often difficult to access interior of Tanzania has begun to reveal a unique avian community with associated endemic taxa. One such endemic taxon is the collaris race of the White-headed Black Chat Myrmecocichla arnotti. First described by Reichenow in 1882, collaris was found to be morphologically distinct from the nominate form, in that females possess a complete white collar. However, due to a lack of knowledge about the development of adult plumage, the distributional range, and the lack of diagnostic features among males of the different White-headed Black Chat races, collaris specimens were generally considered aberrant, and the name largely disappeared from the literature by 1910. Here, we report the results of morphological and genetic analyses, and detailed observations of this chat in what we now know to be the centre of its distributional range in Ruaha National Park, Tanzania. Our results suggest that all birds from west of the Eastern Arc and southern Tanzanian highlands are of the white-collared form, which we suggest should be accorded species rank and named Myrmecocichla collaris, the Ruaha Chat. [ABSTRACT FROM AUTHOR]

We describe the isolation of ten microsatellite loci from the Rwenzori Double-collared Sunbird using an enrichment protocol. All loci were variable with the number of alleles ranging from 5 to 13, and observed heterozygosity ranging from 0.500 to 0.929. Nine of these loci were screened on a panel of seven additional Sunbird species from three genera. All nine were successfully amplified and remained highly variable. Given 10–15% sequence divergence among these species in the mtDNA ND2 gene, our results suggest that these markers will work broadly across most members of the species rich bird family Nectariniidae. [ABSTRACT FROM AUTHOR]

We describe the isolation of 10 microsatellite loci from the Great White Pelican using an enrichment protocol. All loci were variable with the number of alleles ranging from 2 to 19, and observed heterozygosity ranging from 0.261 to 0.913. Two loci were out of Hardy-Weinberg equilibrium, although in each case this was restricted to one of the two populations screened. None of the loci were linked. Eight to 10 loci PCR-amplified in three related species: Brown, American White and Pink-backed Pelicans. These loci should prove to be widely applicable to studies of phylogeography and demography in pelicans globally. [ABSTRACT FROM AUTHOR]

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species "known" to our CNN in contrast to images of the species that were "unknown" to our CNN. [ABSTRACT FROM AUTHOR]