Abstract
In animal ecology, inter-individual encounters are often investigated using automated proximity loggers. However, data acquired are typically spatially implicit, i.e. the question ‘Where did the contact occur?’ remains unanswered. To resolve this issue, recent advancements in Wireless Sensor Network technology have facilitated the geo-referencing of animal contacts. Among these, WildScope devices integrate GPS-based telemetry within fully distributed networks, allowing contact-triggered GPS location acquisition. In this way, the ecological context in which contacts occur can be assessed. We evaluated the performance of WildScope in close-to-real settings, whilst controlling for movement of loggers and obstacles, performing field trials that simulated: (1) different scenarios of encounters between individuals (mobile–mobile contacts) and (2) patterns of individual focal resource use (mobile–fixed contacts). Each scenario involved one to three mobile and two fixed loggers and was replicated at two different radio transmission powers. For each scenario, we performed and repeated a script of actions that corresponded to expected contact events and contact-triggered GPS locations. By comparing expected and observed events, we obtained the success rate of: (1) contact detection and (2) contact-triggered GPS location acquisition. We modelled these in dependence on radio power and number of loggers by means of generalized linear mixed models. Overall we found a high success rate of both contact detection (88–87%: power 3 and 7) and contact-triggered GPS location acquisition (85–97%: power 3 and 7). The majority of errors in contact detection were false negatives (66–69%: power 3 and 7). Number of loggers was positively correlated with contact success rate, whereas radio power had little effect on either variable. Our work provides an easily repeatable approach for exploring the potential and testing the performance of WildScope GPS-based geo-referencing proximity loggers, for studying both animal-to-animal encounters and animal use of focal resources. However, our finding that success rate did not equal 100%, and in particular that false negatives represent a non-negligible proportion, suggests that validation of proximity loggers should be undertaken in close-to-real settings prior to field deployment, as stochastic events affecting radio connectivity (e.g. obstacles, movement) can bias proximity patterns in real-life scenarios.
Highlights
In animal ecology, inter-individual encounters are often investigated using automated proximity log‐ gers
Proximity loggers can be used as both biologging units, i.e. deployed on animals for subsequent data retrieval via recapture [5], and as biotelemetry devices, i.e. equipped with a remote data retrieval system, such as a GSM modem [9, 19], or a Wireless Sensor Network (WSN) comprised of mobile proximity loggers and fixed base stations
Encounternet [12] and BATS [20] are WSN systems that infer spatial contextualization of proximity patterns by post-processing, and by estimating the distance between mobile loggers worn by individuals, and fixed loggers deployed in the study area
Summary
Inter-individual encounters are often investigated using automated proximity log‐ gers. Encounternet [12] and BATS [20] are WSN systems that infer spatial contextualization of proximity patterns by post-processing, and by estimating the distance between mobile loggers worn by individuals, and fixed loggers deployed in the study area In this scenario, the geo-referencing of proximity patterns essentially depends on the spatial overlap between a network of fixed loggers and animal movements, i.e. on the range of the target species. Where monitored animals move over wide areas (as is typical of medium to large mammals), coverage with fixed loggers becomes unfeasible In this case, geo-referencing of proximity patterns between individuals is possible via integration of a GPS sensor in the logger [13]. The fully distributed configuration characterizes the prototype WildScope [19] and has recently been proposed for some commercial loggers [27], so far empirical applications in animal ecology remain limited
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