Automated contact detection by means of proximity loggers permits the measurement of encounters between individuals (animal-animal contacts) and the time spent by individuals in the proximity of a focal resource of interest (animal-fixed logger contacts). The ecological inference derived from contact detection is intrinsically associated with the distance at which the contact occurred. But no proximity loggers currently exist that record this distance and therefore all distance estimations are associated with error. Here we applied a probabilistic approach to model the relationship between contact detection and inter-logger distance, and quantify the associated error, on free-ranging animals in semi-controlled settings. The probability of recording a contact declined with the distance between loggers, and this decline was steeper for weaker radio transmission powers. Even when proximity loggers were adjacent, contact detection was not guaranteed, irrespective of the radio transmission power. Accordingly, the precision and sensitivity of the system varied as a function of inter-logger distance, radio transmission power, and experimental setting (e.g., depending on animal body mass and fine-scale movements). By accounting for these relationships, we were able to estimate the probability that a detected contact occurred at a certain distance, and the probability that contacts were missed (i.e., false negatives). These calibration exercises have the potential to improve the predictability of the study and enhance the applicability of proximity loggers to key wildlife management issues such as disease transmission rates or wildlife use of landscape features and resources.

Quantifying the errors in animal contacts recorded by proximity loggers / Ossi, Federico; Focardi, Stefano; Tolhurst, Bryony A.; Picco, Gian Pietro; Murphy, Amy L.; Molteni, Davide; Giannini, Noemi; Gaillard, Jean‐michel; Cagnacci, Francesca. - In: JOURNAL OF WILDLIFE MANAGEMENT. - ISSN 0022-541X. - 2021:(2021), pp. 1-15. [10.1002/jwmg.22151]

Quantifying the errors in animal contacts recorded by proximity loggers

Ossi, Federico;Picco, Gian Pietro;Molteni, Davide;
2021-01-01

Abstract

Automated contact detection by means of proximity loggers permits the measurement of encounters between individuals (animal-animal contacts) and the time spent by individuals in the proximity of a focal resource of interest (animal-fixed logger contacts). The ecological inference derived from contact detection is intrinsically associated with the distance at which the contact occurred. But no proximity loggers currently exist that record this distance and therefore all distance estimations are associated with error. Here we applied a probabilistic approach to model the relationship between contact detection and inter-logger distance, and quantify the associated error, on free-ranging animals in semi-controlled settings. The probability of recording a contact declined with the distance between loggers, and this decline was steeper for weaker radio transmission powers. Even when proximity loggers were adjacent, contact detection was not guaranteed, irrespective of the radio transmission power. Accordingly, the precision and sensitivity of the system varied as a function of inter-logger distance, radio transmission power, and experimental setting (e.g., depending on animal body mass and fine-scale movements). By accounting for these relationships, we were able to estimate the probability that a detected contact occurred at a certain distance, and the probability that contacts were missed (i.e., false negatives). These calibration exercises have the potential to improve the predictability of the study and enhance the applicability of proximity loggers to key wildlife management issues such as disease transmission rates or wildlife use of landscape features and resources.
2021
Ossi, Federico; Focardi, Stefano; Tolhurst, Bryony A.; Picco, Gian Pietro; Murphy, Amy L.; Molteni, Davide; Giannini, Noemi; Gaillard, Jean‐michel; Ca...espandi
Quantifying the errors in animal contacts recorded by proximity loggers / Ossi, Federico; Focardi, Stefano; Tolhurst, Bryony A.; Picco, Gian Pietro; Murphy, Amy L.; Molteni, Davide; Giannini, Noemi; Gaillard, Jean‐michel; Cagnacci, Francesca. - In: JOURNAL OF WILDLIFE MANAGEMENT. - ISSN 0022-541X. - 2021:(2021), pp. 1-15. [10.1002/jwmg.22151]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/329891
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