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Human memory retains information related to survival more effectively, termed survival processing effect (SPE). Adaptive memory should also more effectively store reliable information than unreliable information. In an experiment (n=107) we asked whether the SPE depends on the reliability of information encoded by linguistic markers. In Turkish, evidentiality markers encode whether information is gathered firsthand (-miş) or not (-di), and provide cues on source reliability. We found that sentences processed for their relevance to survival were better recalled than those processed for their relevance to moving. This survival processing effect was significant for both direct and indirect evidentiality markers, although it was stronger for direct evidentiality. That SPE persisted in the indirect evidentiality condition suggests survival related information is privileged in encoding even when potentially marked as unreliable by linguistic markers. Thus, the effect of language on memory is not profound but rather computed online.

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ABSTRACT Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step‐by‐step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field. [ABSTRACT FROM AUTHOR]

In this study, Scots pine (Pinus sylvestris L.) wood samples were treated with 3 different major wood preservatives (alkaline copper quaternary (ACQ), copper chrome boron (CCB) and copper azole (CuA)). Treated wood samples were subjected to 1 and 3 months of leaching in 3 different soil types and the amount of leached heavy metal Cu was determined on a laboratory scale. In addition, wood samples were exposed to white rot (Trametes versicolor) and brown rot (Neolentinus lepideus) fungi after leaching and weight losses were calculated. At the end of the 1 and 3 months of leaching period, the soil samples were taken from the pots and the amount of Cu was determined. The lowest Cu content (4.3 mg/kg) was detected in the samples treated with CCB among the wood preservatives, while the highest content (196.3 mg/kg) was determined in ACQ. The lowest Cu amount was determined in loamy sand soil type and the highest Cu amount was determined in clay loam soil type. According to the findings from the current study, leaching in soil did not affect weight losses caused by T. versicolor, while significant weight losses occurred by N. lepideus. This study indicated that loamy sand soil is more suitable for the treated wood if there is a risk regarding leaching. [ABSTRACT FROM AUTHOR]

Animal acoustic communication often takes the form of complex sequences, made up of multiple distinct acoustic units. Apart from the well-known example of birdsong, other animals such as insects, amphibians, and mammals (including bats, rodents, primates, and cetaceans) also generate complex acoustic sequences. Occasionally, such as with birdsong, the adaptive role of these sequences seems clear (e.g. mate attraction and territorial defence). More often however, researchers have only begun to characterise - let alone understand - the significance and meaning of acoustic sequences. Hypotheses abound, but there is little agreement as to how sequences should be defined and analysed. Our review aims to outline suitable methods for testing these hypotheses, and to describe the major limitations to our current and near-future knowledge on questions of acoustic sequences. This review and prospectus is the result of a collaborative effort between 43 scientists from the fields of animal behaviour, ecology and evolution, signal processing, machine learning, quantitative linguistics, and information theory, who gathered for a 2013 workshop entitled, 'Analysing vocal sequences in animals'. Our goal is to present not just a review of the state of the art, but to propose a methodological framework that summarises what we suggest are the best practices for research in this field, across taxa and across disciplines. We also provide a tutorial-style introduction to some of the most promising algorithmic approaches for analysing sequences. We divide our review into three sections: identifying the distinct units of an acoustic sequence, describing the different ways that information can be contained within a sequence, and analysing the structure of that sequence. Each of these sections is further subdivided to address the key questions and approaches in that area. We propose a uniform, systematic, and comprehensive approach to studying sequences, with the goal of clarifying research terms used in different fields, and facilitating collaboration and comparative studies. Allowing greater interdisciplinary collaboration will facilitate the investigation of many important questions in the evolution of communication and sociality.