We describe the design and implementation of Vildehaye, a family of versatile, widely-applicable, and field-proven tags for wildlife sensing and radio tracking. The family includes 6 distinct hard-ware designs for tags, 3 add-on boards, a programming adapter, and base stations; modular firmware for tags and base stations (both standalone low-power embedded base stations and base stations tethered to a computer running Linux or Windows); and desk-top software for programming and configuring tags, monitoring tags, and downloading and processing sensor data. The tags are versatile: they support multiple packet formats, data rates, and frequency bands; they can be configured for minimum mass (down to less than 1 g), making them applicable to a wide range of flying and terrestrial animals, or for inclusion of important sensors and large memories; they can transmit packets compatible with time-of-arrival transmitter-localization systems, tag identification and state packets, and they can reliably upload sensor data through their radio link. The system has been designed, upgraded, and main-tained as an academic research project, but it has been extensively used by 5 different groups of ecologists in 4 countries over a period of 5 years. More than 7100 tags have been produced and most of these have been deployed. Production used 41 manufacturing runs. The tags have been used in studies that so far resulted in 9 scientific publications in ecology (including in Science). The paper describes innovative design aspects of Vildehaye, field-use experiences, and lessons from the design, implementation, and maintenance of the system. Both the hardware and software of the system are open.

Agricultural activities cause rapid changes in vegetation development at local and regional scales. Those modifications affect the small-scale behavior of animals, like the foraging ground usage of breeding white storks. Only recently, a novel approach, that enables to quantify the relationship between mowing and harvesting activities and a prolonged foraging time of storks by combining remote sensing time series with GPS telemetry, has been proposed. This study examines the stability of this approach. We investigate two potential influencing factors: different vegetation indices and time lags over which vegetation dynamics were retrieved. Mostly independent from the vegetation index and time lag, we observed that storks spent large proportions of foraging time in areas characterized by a recent drop in vegetation indices, indicative for a preferred usage after harvesting and mowing events. This suggest that the proposed approach is relatively stable and hence, provides a reasonable basis to investigate the effects of anthropogenic vegetation alterations on animal behavior at small spatiotemporal scales.

Most species of birds and bats must be tracked with tracking tags weighing less than 10g and many require tags weighing less than 1g. Tags based on commodity internet-of-things system-on-chips (SoC) can be be mass produced at low-cost, hence allowing many individuals to be tracked. We report on the design and performance of two communication protocols that enable long-range communication with such tags. One is a unidirectional protocol, in which tags transmit unique codes that can be reliably detected from 15km away and that can be used for time-of-arrival and angle-of-arrival localization (tracking). The other is a bidirectional protocol that allows tags to transmit short data packets to low-power low-cost basestations and to receive commands from them. Data packets in this protocol can be reliably received from tags that are 8km away and sometimes from up to 15km, and commands packets can be received by tags from up to 4km away. These protocols have been implemented in low-cost tags that can weigh less than 1g (depending on the choice of battery) and using only about 60uJ per transmission. Our results have been gathered by tagging wild bats. The same tags have been used for time-of-arrival localization of wild bats and birds by several different research groups in 3 countries.

The field of Movement Ecology is experiencing a period of rapid growth in availability of data, and like many other fields is turning to data science for tools and methods to cope with the new challenges and opportunities that this presents. One rich and interesting source of data is the bio-logger. These small electronic devices are attached to animals free to roam in their natural habitats, and report back readings from multiple sensors, including GPS and accelerometer bursts. A common use of this accelerometer data is for supervised learning of behavioral modes. However, there is a need for unsupervised analysis tools as well, due to the inherent difficulties of obtaining a labeled dataset, which in some cases is either infeasible or does not successfully encompass the full repertoire of behavioral modes of interest. Here we present a matrix factorization based clustering method that allows either a soft or a hard partitioning of acceleration measurements, as well as a straight-forward way of drawing insight into the complex movements themselves. The method is validated by comparing the partitions with a labeled dataset, and is further compared to standard methods highlighting the advantages of the new method.