Accounting for both automated recording unit detection space and signal recognition performance in acoustic surveys: A protocol applied to the cryptic and critically endangered Night Parrot ( Pezoporus occidentalis )

What factors influence the rediscovery of lost tetrapod species?

We created a database of lost and rediscovered tetrapod species, identified patterns in their distribution and factors influencing rediscovery. Tetrapod species are being lost at a faster rate than they are being rediscovered, due to slowing rates of rediscovery for amphibians, birds and mammals, and rapid rates of loss for reptiles. Finding lost species and preventing future losses should therefore be a conservation priority. By comparing the taxonomic and spatial distribution of lost and rediscovered tetrapod species, we have identified regions and taxa with many lost species in comparison to those that have been rediscovered-our results may help to prioritise search effort to find them. By identifying factors that influence rediscovery, we have improved our ability to broadly distinguish the types of species that are likely to be found from those that are not (because they are likely to be extinct). Some lost species, particularly those that are small and perceived to be uncharismatic, may have been neglected in terms of conservation effort, and other lost species may be hard to find due to their intrinsic characteristics and the characteristics of the environments they occupy (e.g. nocturnal species, fossorial species and species occupying habitats that are more difficult to survey such as wetlands). These lost species may genuinely await rediscovery. However, other lost species that possess characteristics associated with rediscovery (e.g. large species) and that are also associated with factors that negatively influence rediscovery (e.g. those occupying small islands) are more likely to be extinct. Our results may foster pragmatic search protocols that prioritise lost species likely to still exist.

Effects of landscape and distance in automatic audio based bird species identification

The present work focuses on how the landscape and distance between a bird and an audio recording unit affect automatic species identification. Moreover, it is shown that automatic species identification can be improved by taking into account the effects of landscape and distance. The proposed method uses measurements of impulse responses between the sound source and the recorder. These impulse responses, characterizing the effect of a landscape, can be measured in the real environment, after which they can be convolved with any number of recorded bird sounds to modify an existing set of bird sound recordings. The method is demonstrated using autonomous recording units on an open field and in two different types of forests, varying the distance between the sound source and the recorder. Species identification accuracy improves significantly when the landscape and distance effect is taken into account when building the classification model. The method is demonstrated using bird sounds, but the approach is applicable to other animal and non-animal vocalizations as well.