High exposure rates of anticoagulant rodenticides in carnivorous birds and mammals in Danish landscapes

Elmeros, M., Christensen, T.K., Lassen, P

Open-source workflow approaches to passive acoustic monitoring of bats

The work was funded by grants to PTM from Carlsberg Semper Ardens Research Projects and the Independent Research Fund Denmark.The affordability, storage and power capacity of compact modern recording hardware have evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management particularly useful for bats and toothed whales that orient and forage using ultrasonic echolocation. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user-friendly and open access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats. We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open-source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst. Workflow performance depended strongly on the signal-to-noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large-scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both ~0.6), thus balancing computational speed and performance. We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open-source software such as PAMGuard and exemplify how detection choices, whether pre- or post-deployment, hardware or software-driven, affect the performance of deep learning classification and the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management.Publisher PDFPeer reviewe

Evolutionary history and species delimitations: a case study of the hazel dormouse, Muscardinus avellanarius

Robust identification of species and significant evolutionary units (ESUs) is essential to implement appropriate conservation strategies for endangered species. However, definitions of species or ESUs are numerous and sometimes controversial, which might lead to biased conclusions, with serious consequences for the management of endangered species. The hazel dormouse, an arboreal rodent of conservation concern throughout Europe is an ideal model species to investigate the relevance of species identification for conservation purposes. This species is a member of the Gliridae family, which is protected in Europe and seriously threatened in the northern part of its range. We assessed the extent of genetic subdivision in the hazel dormouse by sequencing one mitochondrial gene (cytb) and two nuclear genes (BFIBR, APOB) and genotyping 10 autosomal microsatellites. These data were analysed using a combination of phylogenetic analyses and species delimitation methods. Multilocus analyses revealed the presence of two genetically distinct lineages (approximately 11 % cytb genetic divergence, no nuclear alleles shared) for the hazel dormouse in Europe, which presumably diverged during the Late Miocene. The phylogenetic patterns suggests that Muscardinus avellanarius populations could be split into two cryptic species respectively distributed in western and central-eastern Europe and Anatolia. However, the comparison of several species definitions and methods estimated the number of species between 1 and 10. Our results revealed the difficulty in choosing and applying an appropriate criterion and markers to identify species and highlight the fact that consensus guidelines are essential for species delimitation in the future. In addition, this study contributes to a better knowledge about the evolutionary history of the species

Communicating complex ecological models to non-scientist end users

Complex computer models are used to predict how ecological systems respond to changing environ- mental conditions or management actions. Communicating these complex models to non-scientists is challenging, but necessary, because decision-makers and other end users need to understand, accept, and use the models and their predictions. Despite the importance of communicating effectively with end users, there is little guidance available as to how this may be achieved. Here, we review the challenges typically encountered by modellers attempting to communicate complex models and their outputs to managers and other non-scientist end users. We discuss the implications of failing to communicate effec- tively in each case. We then suggest a general approach for communicating with non-scientist end users. We detail the specific elements to be communicated using the example of individual-based models, which are widely used in ecology. We demonstrate that despite their complexity, individual-based models have characteristics that can facilitate communication with non-scientists. The approach we propose is based on our experiences and methods used in other fields, but which until now have not been synthesised or made broadly available to ecologists. Our aim is to facilitate the process of communicating with end users of complex models and encourage more modellers to engage in it by providing a structured approach to the communication process. We argue that developing measures of the effectiveness of communication with end users will help increase the impact of complex models in ecology