Wildlife crime, especially poaching, is globally one of the largest criminal industries in terms of yearly revenue. Poaching is listed as an important cause for the decline of many wildlife species, including charismatic species like elephants, rhinos and pangolins (Scheffers et al. 2019). Unfortunately, global solutions to lower the demand for and the trafficking of poached goods are far from effective. Thus, other innovative interventions are required.
The concept of animals as sentinels may be such an innovative intervention. Many animals, especially prey species, have evolved adaptations that increase their chance of preventing predation; either by lowering the chance of encounter predators and/or by lowering the chance of getting caught once a predator is encountered (Cooper and Blumstein 2015, Gaynor et al. 2019). The same principle likely applies to encounters with humans; upon encountering a human ‘predator’, prey animals are expected to show marked response behaviour. In other words, the presence of a human may disturb their behaviour.
The rapid increase of wireless sensor technology is nowadays enabling us to monitor animal movement behaviour (biologging) at high resolution for long periods of time, even in remote areas. Wireless sensor data, when coupled with data analytics, could thus potentially be used to detect behavioural change of animal sentinels. Furthermore, these analytics could potentially infer that certain behavioural patterns are in response to a human. Such signatures can possibly provide us with an early warning that the animals are disturbed, e.g., due to the presence of a poacher (O’Donoghue & Rutz 2016).
Detect behavioural signature patterns in biologging data of sensor-equipped sentinel animals that indicate the presence of a human disturbance. For a poacher early-warning system to be successful, we need to be able to recognize characteristic animal responses to human presence, before these signatures can be casted into a predictive algorithm. Because animal movement behaviour is also tightly linked to other variables than human presence alone, e.g., time of day, weather, vegetation, terrain etc., it is important to consider these variables in your analysis as well to be able to convincingly compare “normal” movement behaviour vs. behaviour near a human.
Multiple datasets will be supplied that have to be creatively merged and processed:
Scheffers, B. R., Oliveira, B. F., Lamb, I. & Edwards, D. P (2019). Global wildlife trade across the tree of life. Science 366, 71–76.
Cooper, W. E. & Blumstein, D. T (2015). Escaping From Predators: An Integrative View of Escape Decisions. Cambridge University Press.
Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S (2019). Landscapes of Fear: Spatial Patterns of Risk Perception and Response. Trends in Ecology & Evolution 34, 355–368.
O’Donoghue, P. & Rutz, C (2016). Real-time anti-poaching tags could help prevent imminent species extinctions. Journal of Applied Ecology 53, 5–10.
De Knegt, H. J., Eikelboom, J. A., Van Langevelde, F., Spruyt, W. F. & Prins, H. H. T (2021). Timely poacher detection and localization using sentinel animal movement. Scientific Reports 11, 4596.