Performance of proximity loggers under controlled field conditions: an assessment from a wildlife ecological and epidemiological perspective

Abstract

Abstract Background Ecological sciences have, in recent decades, benefited from the ability of proximity loggers (PLs)—i.e. devices that transmit and receive radio signals (UHF)—to quantify intra- and inter-specific interactions. These are used to estimate the frequency of contacts according to a predefined distance between individuals or between individuals and environmental features. The performance of these devices may, however, be potentially affected by several factors, signifying that they require accurate calibration under field conditions in order to correctly interpret the information obtained. We assessed the effect of four relevant factors in ecological and epidemiological studies over the attenuation of radio waves in terms of the received signal strength indicator (RSSI) and contact success rate at a short (3 m) and medium distance (up to 20 m). The factors considered were: height above the ground (0–1 m), the presence/absence of vegetation, the presence/absence of live body mass around the devices, the distance between devices and the overlaid effects of all of them. Results The RSSI was found to be an accurate measure of distance, although its precision decreased over greater distances (up to 100 m), with the loss being sharper with vegetation, with body mass and when the devices were located on the ground. The success rate at up to 20 m decreased with distance and was also affected by body mass and vegetation. A probability of contact success of 81% was obtained in the best conditions (without vegetation and body mass) at a distance of 3 m, whereas it was of 56% in the worst conditions. Conclusions Our study shows the potential synergistic effects of external factors on the performance of PLs, even when they are used to infer near-contacts. We, therefore, highlight the importance of assessing, for each particular study, the combined effect of non-controllable external factors on the performance of PLs in order to estimate the minimum (best scenario) and maximum (worst scenario) level of underestimation in the field data. The sampling design described here is a cost-effective protocol suitable for this purpose.