Functional connectivity network between terrestrial and aquatic habitats by a generalist waterbird, and implications for biovectoring

Birds are vectors of dispersal of propagules of plants and other organisms including pathogens, as well as nutrients and contaminants. Thus, through their movements they create functional connectivity between habitat patches. Most studies on connectivity provided by animals to date have focused on movements within similar habitat types. However, some waterbirds regularly switch between terrestrial, coastal and freshwater habitats throughout their daily routines. Lesser black-backed gulls that overwinter in Andalusia use different habitat types for roosting and foraging. In order to reveal their potential role in biovectoring among habitats, we created an inter-habitat connectivity network based on GPS tracking data. We applied connectivity measures by considering frequently visited sites as nodes, and flights as links, to determine the strength of connections in the network between habitats, and identify functional units where connections are more likely to happen. We acquired data for 42 tagged individuals (from five breeding colonies), and identified 5676 direct flights that connected 37 nodes. These 37 sites were classified into seven habitat types: reservoirs, natural lakes, ports, coastal marshes, fish ponds, rubbish dumps and ricefields. The Donana ricefields acted as the central node in the network based on centrality measures. Furthermore, during the first half of winter when rice was harvested, ricefields were the most important habitat type in terms of total time spent. Overall, 90% of all direct flights between nodes were between rubbish dumps (for foraging) and roosts in other habitats, thereby connecting terrestrial and various wetland habitats. The strength of connections decreased between nodes as the distance between them increased, and was concentrated within ten independent spatial and functional units, especially between December and February. The pivotal role for ricefields and rubbish dumps in the network, and their high connectivity with aquatic habitats in general, have important implications for biovectoring into their surroundings. (C) 2019 The Authors. Published by Elsevier B.V

From agricultural benefits to aviation safety: Realizing the potential of continent-wide radar networks

Migratory animals provide a multitude of services and disservices—with benefits or costs in the order of billions of dollars annually. Monitoring, quantifying, and forecasting migrations across continents could assist diverse stakeholders in utilizing migrant services, reducing disservices, or mitigating human–wildlife conflicts. Radars are powerful tools for such monitoring as they can assess directional intensities, such as migration traffic rates, and biomass transported. Currently, however, most radar applications are local or small scale and therefore substantially limited in their ability to address large-scale phenomena. As weather radars are organized into continent-wide networks and also detect “biological targets,” they could routinely monitor aerial migrations over the relevant spatial scales and over the timescales required for detecting responses to environmental perturbations. To tap these unexploited resources, a concerted effort is needed among diverse fields of expertise and among stakeholders to recognize the value of the existing infrastructure and data beyond weather forecasting

Very rapid long-distance sea crossing by a migratory bird

Landbirds undertaking within-continent migrations have the possibility to stop en route, but most long-distance migrants must also undertake large non-stop sea crossings, the length of which can vary greatly. For shorebirds migrating from Iceland to West Africa, the shortest route would involve one of the longest continuous sea crossings while alternative, mostly overland, routes are available. Using geolocators to track the migration of Icelandic whimbrels (Numenius phaeopus), we show that they can complete a round-trip of 11,000 km making two non-stop sea crossings and flying at speeds of up to 24 m s-1; the fastest recorded for shorebirds flying over the ocean. Although wind support could reduce flight energetic costs, whimbrels faced headwinds up to twice their ground speed, indicating that unfavourable and potentially fatal weather conditions are not uncommon. Such apparently high risk migrations might be more common than previously thought, with potential fitness gains outweighing the costs

Effect of wind, thermal convection, and variation in flight strategies on the daily rhythm and flight paths of migrating raptors at Georgia's Black Sea coast

Every autumn, large numbers of raptors migrate through geographical convergence zones to avoid crossing large bodies of water. At coastal convergence zones, raptors may aggregate along coastlines because of convective or wind conditions. However, the effect of wind and thermal convection on migrating raptors may vary depending on local landscapes and weather, and on the flight strategies of different raptors. From 20 August to 14 October 2008 and 2009, we studied the effect of cloud development and crosswinds on the flight paths of raptors migrating through the eastern Black Sea convergence zone, where coastal lowlands at the foothills of the Pontic Mountains form a geographical bottleneck 5-km-wide near Batumi, the capital of the Independent Republic of Ajaria in southwestern Georgia. To identify key correlates of local aggregation, we examined diurnal variation in migration intensity and coastal aggregation of 11 species of raptors categorized based on size and flight strategies. As reported at other convergence zones, migration intensity of large obligate-soaring species peaked during the core period of thermal activity at mid-day. When clouds developed over interior mountains and limited thermal convection, these large obligate-soaring species aggregated near the coast. However, medium-sized soaring migrants that occasionally use flapping flight did not aggregate at the coast when clouds over the mountains weakened thermal convection. Numbers of alternate soaring-flapping harriers (Circus spp.) peaked during early morning, with these raptors depending more on flapping flight during a time of day with poor thermal convection. Small sparrowhawks (Accipiter spp.) aggregated at the coast during periods when winds blew offshore, suggesting aggregation caused by wind drift. Thus, weather conditions, including cloud cover and wind speed and direction, can influence the daily rhythm and flight paths of migrating raptors and, therefore, should be accounted for before inferring population trends from migration counts

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

Background Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. Automated behavioural classification has the potential to improve health and welfare monitoring processes as part of a Precision Livestock Farming approach. Recent studies have used accelerometers and pedometers to classify behavioural activities in dairy cows, but such approaches often cannot discriminate accurately between biologically important behaviours such as feeding, lying and standing or transition events between lying and standing. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. Results Data were collected from six dairy cows that were monitored continuously for 36 h. Direct visual observations of each cow were used to validate the algorithm. Results show that the decision-tree algorithm is able to accurately classify three types of biologically relevant behaviours: lying (77.42 % sensitivity, 98.63 % precision), standing (88.00 % sensitivity, 55.00 % precision), and feeding (98.78 % sensitivity, 93.10 % precision). Transitions between standing and lying were also detected accurately with an average sensitivity of 96.45 % and an average precision of 87.50 %. The sensitivity and precision of the decision-tree algorithm matches the performance of more computationally intensive algorithms such as hidden Markov models and support vector machines. Conclusions Biologically important behavioural activities in housed dairy cows can be classified accurately using a simple decision-tree algorithm applied to data collected from a neck-mounted tri-axial accelerometer. The algorithm could form part of a real-time behavioural monitoring system in order to automatically detect dairy cow health and welfare status

Continental-scale patterns in diel flight timing of high-altitude migratory insects

Many insects depend on high-altitude, migratory movements during part of their life cycle. The daily timing of these migratory movements is not random, e.g. many insect species show peak migratory flight activity at dawn, noon or dusk. These insects provide essential ecosystem services such as pollination but also contribute to crop damage. Quantifying the diel timing of their migratory flight and its geographical and seasonal variation, are hence key towards effective conservation and pest management. Vertical-looking radars provide continuous and automated measurements of insect migration, but large-scale application has not been possible because of limited availability of suitable devices. Here, we quantify patterns in diel flight periodicity of migratory insects between 50 and 500 m above ground level during March-October 2021 using a network of 17 vertical-looking radars across Europe. Independent of the overall daily migratory movements and location, peak migratory movements occur around noon, during crepuscular evening and occasionally the morning. Relative daily proportions of insect migration intensity and traffic during the diel phases of crepuscular-morning, day, crepuscular-evening and night remain largely equal throughout May-September and across Europe. These findings highlight, extend, and generalize previous regional-scale findings on diel migratory insect movement patterns to the whole of temperate Europe.</p

Identification of Behaviour in Freely Moving Dogs (Canis familiaris) Using Inertial Sensors

Monitoring and describing the physical movements and body postures of animals is one of the most fundamental tasks of ethology. The more precise the observations are the more sophisticated the interpretations can be about the biology of a certain individual or species. Animal-borne data loggers have recently contributed much to the collection of motion-data from individuals, however, the problem of translating these measurements to distinct behavioural categories to create an ethogram is not overcome yet. The objective of the present study was to develop a “behaviour tracker”: a system composed of a multiple sensor data-logger device (with a tri-axial accelerometer and a tri-axial gyroscope) and a supervised learning algorithm as means of automated identification of the behaviour of freely moving dogs. We collected parallel sensor measurements and video recordings of each of our subjects (Belgian Malinois, N=12; Labrador Retrievers, N=12) that were guided through a predetermined series of standard activities. Seven behavioural categories (lay, sit, stand, walk, trot, gallop, canter) were pre-defined and each video recording was tagged accordingly. Evaluation of the measurements was performed by support vector machine (SVM) classification. During the analysis we used different combinations of independent measurements for training and validation (belonging to the same or different individuals or using different training data size) to determine the robustness of the application. We reached an overall accuracy of above 90% perfect identification of all the defined seven categories of behaviour when both training and validation data belonged to the same individual, and over 80% perfect recognition rate using a generalized training data set of multiple subjects. Our results indicate that the present method provides a good model for an easily applicable, fast, automatic behaviour classification system that can be trained with arbitrary motion patterns and potentially be applied to a wide range of species and situations