Social determinants of songbird vocal activity and implications for the persistence of small populations

Conspecific attraction is an important aspect of animal behaviour and several avian studies have shown that vocalizations may be used as an inadvertent cue to locate areas of suitable habitat. By studying the metapopulation system of a territorial passerine, the Dupont's lark Chersophilus duponti, we analysed the demographic correlates of population vocal activity, and the relationships between the occurrence of immigration and the availability of social information (e.g. vocal activity, population size, density and productivity) in 22 local populations. We found that the proportion of active singing days in spring and territorial call advertisement after breeding were positively related to the number of males within local populations. In turn, the intensity of vocal activity was associated with the likelihood of receiving immigrants, better explaining immigration than other kinds of social or public information. Because of depressed signalling, small local populations could experience reduced rescuing from others, thus compromising population persistence. In such cases, habitat management alone may not be enough to overcome this behavioural constraint. Because we found that the occurrence of inter-patch movements also depended on the size of nearby local populations, understanding regional processes may be as important as controlling social and environmental factors for the maintenance of small populations. © Journal compilation © 2008 The Zoological Society of London.Peer Reviewe

Deep neural networks for automated detection of marine mammal species

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867).Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.Publisher PDFPeer reviewe

Combining observations with acoustic swath bathymetry and backscatter to map seabed sediment texture classes: the empirical best linear unbiased predictor

Seabed sediment texture can be mapped by geostatistical prediction from limited direct observations such as grab-samples. A geostatistical model can provide local estimates of the probability of each texture class so the most probable sediment class can be identified at any unsampled location, and the uncertainty of this prediction can be quantified. In this paper we show, in a case study off the northeast coast of England, how swath bathymetry and backscatter can be incorporated into a geostatistical linear mixed model (LMM) as fixed effects (covariates). Parameters of the LMM were estimated by maximum likelihood which allowed us to show that both covariates provided useful information. In a cross-validation, each observation was predicted from the rest using the LMMs with (i) no covariates, or (ii) bathymetry and backscatter as covariates. The proportion of cases in which the most probable class according to the prediction corresponded to the observed class was increased (from 58% to 65% of cases) by including the covariates which also increased the information content of the predictions, measured by the entropy of the class probabilities. A qualitative assessment of the geostatistical results shows that the model correctly predicts, for example, the occurrence of coarser sediment over discrete glacial sediment landforms, and muddier sediment in relatively quiescent, localized deep water environments. This demonstrates the potential for assimilating geophysical data with direct observations by the LMM, and could offer a basis for a routine mapping procedure which incorporates these and other ancillary information such as manually-interpreted geological and geomorphological maps

A robust sequential hypothesis testing method for brake squeal localisation

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)

\A Ro- bust Node Selection Strategy for Lifetime Extension in Wireless Sensor Networks

Distributed Wireless Sensor Networks (WSNs) consist of energy-constrained sensor nodes that may be deployed in large numbers in order to monitor a given area, track military targets, detect civilian targets or for other purposes. In such densely deployed environments, multiple transmissions can lead to collisions resulting in packet losses and network congestion. This can increase latency and reduce energy efficiency. These networks also feature significant redundancy since nodes close to each other often sense similar data. Therefore, it may be adequate to employ only a subset of the deployed nodes at any given time in the network. In this thesis, node subsets are selected in a manner that coverage and connectivity are consistently achieved. The working subsets are changed after predetermined durations. A framework using concepts from spatial statistics is developed as an approach to selecting the subset of sensor nodes. Proximal nodes negotiate with each other using energy information, to decide which nodes stay working while others go to sleep mode. The algorithm is executed autonomously by the network. The approach presented ensures that the selected subsets while not necessarily exclusive of previous selections covers the region of interest. Simulation results show that the algorithm is robust and retains some level of redundancy. The algorithm shows significant improvement in energy consumption compared with a network with no selection. The selected subset is shown to be able to withstand significant levels of fault in the network. Conclusions regarding the flexibility and application scenarios of the algorithm are drawn and opportunities for future work indicated

Leak Detection and Localization in Pressurized Space Structures Using Bayesian Inference: Theory and Practice

Impact from micrometeoroids and orbital debris (MMOD) can cause severe damage to space vehicles. The crew habitat can begin to leak precious oxygen, critical systems can be punctured causing fatal failures, and an accumulation of impacts by MMOD can decrease the lifetime of any and all devices in space. Due to these and other potential dangers, MMODs have been considered the third largest threat to spacecraft after launch and re-entry. Many satellites and other spacecraft face this very problem inherent in all space travel on a daily basis, but often times they can be repaired. A major hurdle is to first be able to identify the presence of a leak. Many times an impact and subsequent leak is not discovered until it has caused a problem. A complete system is needed to detect and localize the impact to improve longevity of the habitat or other pressurized space structures. In this work, a system for detection and localization of air leaks using air-borne acoustic waves is proposed. The system uses microelectromechanical systems (MEMS) microphone sensors to detect and record high frequency noise in an environment, angle of arrival (AOA) calculations to estimate possible leak locations, and a Bayesian tree-search filter to detect and more accurately localize a leak. This work includes proof of concept, simulations, and physical prototypes as steps to creation of a complete system. Data from deployed flight test using said prototypes are analyzed. Modeling the effects of environmental reflections on the accuracy of localization is also studied

MAREA PROJECT : MEDISEH (Mediterranean Sensitive Habitats) specific contract no 2 (SI2.600741)

Based on the following Terms of Reference (TOR) of the content of the European Commission DG MARE request Ares (2011)665688: “Compile information supporting the identification and location of nursery areas (juveniles in their first and, if appropriate, second year of life) and spawning aggregations. This information, which is to be collated and archived in formats adequate for GIS rendering, shall refer to all the demersal and small pelagic species in the Mediterranean included in Appendix VII of Council Regulation (EC) No 199/2008 as well as for the species subject to minimum size (Council Regulation (EC) No 1967/2006-Annex III). In addition, ecological characterisation of these areas, both in terms of biological community (assemblage) and habitats therein, must be provided.” The technical tender form of the Specific Contract 2 (MEDISEH) defined the following objectives: Review of historical and current data on the locations and the status of seagrass beds, coralligenous and mäerl beds in different GSAs (Geographical Sub-Areas amending amending the Resolution GFCM/31/2007/2) all over the Mediterranean basin. Transform the information into a digitilized format within the framework of a geodatabase Review and map of all existing specific Marine Protected Areas (MPAs) in the Mediterranean area as well as areas that are under any form of national or international regulation. Identify and map suitable areas for Posidonia, coralligenous and mäerl communities by developing habitat distribution models at different spatial scales. Review and map all existing information on historical and current data of nurseries and spawning grounds of certain small pelagic (i.e., Engraulis encrasicolus, Sardina pilchardus, Scomber spp., Trachurus spp.) and demersal species (i.e., Aristaeomorpha foliacea, Aristeus antennatus, Merluccius merluccius, Mullus barbatus, Mullus surmuletus, Nephrops norvegicus, Parapenaeus longirostris, Pagellus erythrinus, Galeus melastomus, Raja clavata, Illex coindetti, Eledone cirrosa) that are included in the Data Collection Framework for the Mediterranean and subjected to minimum landing size based on Council Regulation No 1967/2006-Annex II. Analyze existing survey data and apply spatial analysis techniques in order to identify locations that are more likely to be density hot spot areas or are being more suitable for fish nurseries and spawning grounds for Engraulis encrasicolus, Sardina pilchardus, Scomber spp., Trachurus trachurus, Aristaeomorpha foliacea, Aristeus antennatus, Merluccius merluccius, Mullus barbatus, Mullus surmuletus, Nephrops norvegicus, Parapenaeus longirostris, Pagellus erythrinus, Galeus melastomus, Raja clavata, Illex coindetti, Eledone cirrosa These areas will also be characterized from an environmental and ecological perspective upon data availability. Integrate and present the aforementioned information through a Web-based GIS viewer with an associated geo-referenced database that will operate as a consulting tool for spatial management and conservation planning. Following the revision of the knowledge base, to identify gaps and suggest future research priorities. In order to meet these objectives, an expert team was composed within the MAREA Consortium from scientists with established expertise in the different topics required, and working in different areas of the Mediterranean basin. The team formed to execute the project includes the main Institutes of EU countries in the Mediterranean, all having solid reputations in the fields covered. The participating Institutes/Entities operate in the Western, Central and Eastern parts of the Mediterranean basin, and this ensures familiarity with the geographical areas that are related to the specific tendering. Moreover, a large number of scientists outside of the MAREA Consortium collaborated on a volunteer basis with data and other input. Details on the list of experts and external collaborators can be found in each Work Package in the present report. For CV details, check the MAREA expert web-site http://www.mareaproject.net/.peer-reviewe

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on more suitable ML algorithms or because they were trained through superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. While much research has been performed about the privacy of the elements of training sets, in this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. This kind of information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights