Processing and visualising association data from animal-borne proximity loggers
© Bettaney et al. 2015
Received: 25 March 2015
Accepted: 11 August 2015
Published: 29 August 2015
With increasing interest in animal social networks, field biologists have started exploring the use of advanced tracking technologies for mapping social encounters in free-ranging subjects. Proximity logging, which involves the use of animal-borne tags with the capacity for two-way communication, has attracted particular attention in recent years. While the basic rationale of proximity logging is straightforward, systems generate very large datasets which pose considerable challenges in terms of processing and visualisation. Technical aspects of data handling are crucial for the success of proximity-logging studies, yet are only rarely reported in full detail. Here, we describe the procedures we employed for mining the data generated by a recent deployment of a novel proximity-logging system, “Encounternet”, to study social-network dynamics in tool-using New Caledonian crows.
Our field deployment of an Encounternet system produced some 240,000 encounter logs for 33 crows over a 19-day study period. Using this dataset, we illustrate a range of procedures, including: examination of tag reciprocity (i.e. whether both tags participating in an encounter detected the encounter and, if so, whether their records differed); filtering of data according to a predetermined signal-strength criterion (to enable analyses that focus on encounters within a particular distance range); amalgamation of temporally clustered encounter logs (to remove data artefacts and to enable robust analysis of biological patterns); and visualisation of dynamic network data as timeline plots (which can be used, among other things, to visualise the simulated diffusion of information).
Researchers wishing to study animal social networks with proximity-logging systems should be aware of the complexities involved. Successful data analysis requires not only a sound understanding of hardware and software operation, but also bioinformatics expertise. Our paper aims to facilitate future projects by explaining in detail some of the subtleties that are easily overlooked in first-pass analyses, but are key for reaching valid biological conclusions. We hope that this work will prove useful to other researchers, especially when read in conjunction with three recently published companion papers that report aspects of system calibration and key results.
KeywordsAnimal social network Biologging Business card tag Contact network Corvus moneduloides Encounter mapping Encounternet Reality mining Transceiver tag Wireless sensor network
Animal social networks (ASN) are usually constructed from data on the spatiotemporal co-occurrence of identifiable subjects (reviews: [1–3]). Whenever two animals come within a pre-defined distance of each other, an ‘association’ (sometimes also called an ‘encounter’ or ‘contact’) is recorded for the dyad, which can be represented graphically as an ‘edge’ in a social network. Directly observing wild animals is often challenging, and in most study systems produces datasets that are biased (some subjects are easier to observe than others) and may be too sparse for robust statistical analyses (focal subjects are usually observed in the order of once per month, week, or day). With increasing interest in the dynamics and drivers of ASN topology [4–7], research areas that require particularly large amounts of high-quality data, field biologists have started exploring opportunities for automated data collection (review: ).
We have recently conducted the first full-scale deployment of a novel proximity-logging system, “Encounternet” (Encounternet LLC, Washington, Seattle, USA), to investigate the social networks of tool-using New Caledonian crows Corvus moneduloides. As explained in detail below, Encounternet is a fully digital proximity-logging technology, which unlike other commercially available terrestrial systems [17–22] enables tag-to-tag communication over distances well in excess of 10 m (other systems usually transmit over a few metres) and records raw signal-strength data for encounters [other systems record detections as binary (yes/no) data]. In earlier papers, we have described how we calibrated our system for field deployment  and reported the analysis of both time-aggregated  and dynamic network data . Here, we explain the basic procedures for processing and visualising proximity-logger data, focussing specifically on Encounternet-unique features (for an earlier study on tags developed by Sirtrack Ltd., see ) and on some subtleties that may be easily overlooked by first-time users. Taken together, our four papers [14, 15, 23, this study] provide a comprehensive description of how to use Encounternet and similar wireless sensor network (WSN) technology [25, 26], to study the social dynamics of free-ranging animals.
Sample encounter logs recorded by crow-mounted “Encounternet” proximity loggers
During an encounter, signal strength is recorded as a ‘received signal-strength indicator’ (RSSI) value, which is a measure of the power ratio (in dB) of the received signal and an arbitrary reference (for details, see ); the RSSI value is converted to an integer for recording and will henceforth be unitless. For each encounter log, which consists of (up to) a pre-programmed number of consecutively received radio pulses, the minimum, maximum and mean RSSI (RSSImin, RSSImax and RSSImean) values of the pulse sequence are recorded (Table 1). The proximity of the tags can later be estimated from RSSI values using an appropriate calibration curve [14, 27].
In the present study, we programmed tags to emit pulses every 20 s, which is significantly less than the timescales over which crows’ fission–fusion dynamics are expected to occur (minutes to tens of minutes; see ). Tags are unable to receive signals during the brief periods (several milliseconds) when they are transmitting, so although slight differences in on-board clock times (generated by tag-specific drift rates) ensured that phase synchrony was unlikely, the exact transmission times were jittered by multiples of 1/3 s up to ± 4/3 s to minimise this possibility.
In October 2011, we deployed Encounternet tags on 41 wild New Caledonian crows in one of our long-term study populations (for biological rationale of the study, see , and for background on the study species, see ); four tags failed after 4–11 days of transmission and a further four yielded no data, leaving 33 birds for analysis. Tags were attached to crows using weak-link harnesses which were designed to degrade over time, to release devices after the study. The data were collected via 45 basestations deployed in the study area. We have provided a full description of our field procedures elsewhere [15, 23].
Preliminary data processing and analysis
The distribution of encounter log durations is shown in Fig. 3b. The peaks at multiples of 20 s are a result of the tags’ programmed pulse rate (see above and Fig. 2). Tags created a single log for each encounter up to a maximum of 15 received pulses, giving a peak in recorded log durations at 300 s. Because pulses could occasionally be missed (for example, because of a temporary obstruction between the birds), tags did not ‘close’ encounter logs until no pulse had been received from the other tag for six consecutive pulse intervals (6 × 20 s = 120 s); when this occurred, the end time was recorded as the time of the last received pulse. There is thus a second peak at 320 s (one missed pulse during the encounter), a smaller one at 340 s (two missed pulses) and so on. If more than 15 pulses were received during an encounter, successive log files were created. Grouping encounter log durations by 10-point RSSImean bins reveals that long-distance encounters are much shorter than close-range ones (Fig. 3c).
Filtering and amalgamation of reciprocated encounter logs
Spatial proximity is a symmetric proxy for association; if crow A is 10 m from crow B, then crow B is also 10 m from crow A. The logs recorded by the tags, however, are not perfectly symmetrical; for example, there will be variation in the transmitting and receiving strength of tags. Details of the factors influencing signal strength can be found in . Here, we concentrate on the steps taken to clean the data, whatever the cause of the discrepancies.
To construct a symmetric set of encounters from the data, reciprocated signals must be amalgamated to produce a single timeline of encounters between each pair of crows. Since there were no calibration experiments performed to assess variation in tag performance (including output power and reception sensitivity; see [30, 31]), there is no way of reliably determining the ‘correct’ signal strength for encounters. The lack of tag-specific calibration also makes it impossible to know which tags are more accurately recording start and end times of encounters. In addition to these issues, nothing is known about the height of tags above the ground, the relative orientation of the two tags (and their antennae), or the habitat where the encounter took place, all of which affect RSSI (for details, see [14, 23]). We have therefore used a simple method of reconciling reciprocated encounter logs, which does not require any independent information on these factors.
The first step in amalgamating reciprocated encounter logs is to apply a filter criterion (FC), so that only logs that are likely to result from encounters of biological interest are retained for further analyses. In our study of social dynamics in New Caledonian crows, we were primarily interested in close-range encounters of birds , and after system calibration, settled on an FC of RSSImean ≥15; for single radio pulses, we estimated through simulation that 50 % of pulses of an RSSI ≥15 will result from an inter-tag distance of 4.74 m or less, while 95 % of pulses will originate from within 11.29 m (for details, see ). Over distances of a few metres, we would expect crows to be able to observe, and socially learn from, each other, which is key for the biological process we hoped to elucidate—the possible diffusion of foraging inventions across crow networks.
Temporal network visualisation
The complete temporal dataset of amalgamated encounters can be displayed on timeline plots for all crows (cf. ). Figure 7 shows such a plot for 1 day’s worth of encounters. Ordering crows according to ascending tag ID is not visually appealing, as many encounters (green shading) overlap with each other (Fig. 7a). One way to improve data visualisation is to place the timelines of frequently associating crows close together. An optimal ordering of the crows can be found by minimising the total area of green shading on each plot, as we have illustrated here for the first 7 days of our deployment (Fig. 7b; during which the population was not subjected to experimental manipulations; see ). It is easy to see that this layout makes the structure of the data much more apparent; for example, there are several pairs or triplets of crows (e.g. adults #81 and #68, and immature #74) which engage in close-range encounters with each other throughout the course of the day, suggesting that these crows have strong social bonds.
Research projects using proximity-logging systems proceed through three major stages: system preparation and calibration; field deployment and data collection; and data processing and analysis. Prospective users of this technology need to be aware that each of these steps will remain a major undertaking, until hardware, field procedures and analysis techniques have become more established. In this paper, we have offered some guidance on aspects of data processing and visualisation. Once deployed, proximity-logging systems can quickly generate vast amounts of data, which may take some users by surprise (especially, those that have no prior experience with biologging technologies). It is essential that research teams possess sufficient bioinformatics expertise as well as adequate infrastructure for data storage and handling.
While aspects of data cleaning and processing have been described previously (e.g. [18, 24, 30, 31]), these studies were concerned with proximity-logging systems that record encounters as binary detection data (such as the proximity tags by Sirtrack Ltd., New Zealand). In contrast, we provide the first description of techniques for a system that records raw signal-strength (i.e. RSSI) values and, therefore, enables post hoc data filtering by signal strength—and hence animal-to-animal distance—at the analysis stage. To allow further refinements of filtering procedures, we recommend that future studies quantify each tag’s transmission power before deployment , as such variation could cause animals to appear more or less sociable than they really are . Alternatively, field-recorded data could be used to assess the difference in RSSI values recorded by pairs of tags; comparison of RSSI frequency distributions may reveal differences in tag performance that could be taken into account in subsequent analyses. Our study also illustrated how certain data properties, such as encounter durations, are influenced by tag settings (such as pulse intervals; Fig. 3) and processing procedures (such as amalgamation and concatenation criteria; Fig. 8). When embarking on a proximity-logging project, it is important to recognise how this can potentially affect the biological conclusions that are being drawn from the data. Where possible, we encourage: pilot testing of parameter settings before field deployment, to ensure that they are suitable for mapping the biological processes of interest (e.g. ), and detailed sensitivity analyses at the data-mining stage, to confirm that key results are robust (e.g. ).
In many study contexts, well-established, indirect encounter mapping technologies (see “Background”; Fig. 1) will remain the method of choice; for example, for species living in open habitats, conventional GPS tracking systems can provide high-resolution datasets that are straightforward to analyse. Where proximity logging is the best option, however, its strengths should be recognised and fully exploited. First, being WSNs, data can be harvested remotely from roaming ‘nodes’ (animal-mounted tags) using fixed nodes (basestations) [25, 26], which create opportunities for near real-time analyses. In our study on New Caledonian crows, we used this feature to assess network parameters on a daily basis, to ascertain that a stable equilibrium state had been reached , before conducting experimental manipulations that were designed to disturb the network topology . Achieving this level of experimental control would be impossible with most other data collection techniques, but requires careful preparation of data-handling protocols and computer hard- and software resources, to enable ad hoc analyses under field conditions. Another strength of proximity-logging systems is the high temporal data resolution they can achieve. With encounter ‘checks’ several times per minute for all tagged subjects, sampling rates exceed those possible with unaided field observation by several orders of magnitude. This increase in data quality creates exciting opportunities for investigating social-network dynamics [4, 6–8, 15], but brings with it new challenges in terms of data visualisation. We have provided examples of a timeline procedure (cf. [4, 32]), which we have found useful in our own work, as it enabled us to examine our full dataset in an intuitive way and plan more elaborate diffusion simulations (; James et al. unpubl. manuscript).
Proximity logging promises unprecedented insights into the social organisation of wild animals. We hope that the present paper will help prospective users recognise some of the pitfalls inherent in basic data analyses, which must be avoided to reach valid biological conclusions.
animal social network
global positioning system
passive integrated transponder
received signal-strength indicator
very high frequency
wireless sensor network
All authors contributed to the development of ideas. CR and JSC conducted fieldwork, and RJ and EB conducted data analyses. EB and CR drafted the manuscript, and all authors contributed to the final version. All authors read and approved the final manuscript.
We thank: T. Mennesson and the late C. Lambert for logistical support in New Caledonia; the Province Sud, SEM Mwe Ara and the DENV for research permits, access to land and facilities, and other help; the field team for assistance; and the BBSRC (Grants BB/G023913/1 and/2 to CR) and the University of Bath (studentship to EMB) for funding.
Compliance with ethical guidelines
Competing interests The authors declare that they have no competing interests.
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