From biologging to conservation: Tracking individual performance in changing environments

Abstract

Under an accelerating biodiversity crisis, increased urbanization, habitat fragmentation, and climate change require new approaches to assess conservation impact. We argue that animal biologging is a cost-effective method for monitoring biodiversity at its source, including tracked animals and the habitats they occupy. Biologging, or animal-mounted sensors to record data, can act as a reporting, measurement, and verification system, and deliver direct insights into environments of selection. Using a case study of migrating white storks (Ciconia ciconia), we show that biologging yields real-time measurements of individual performance, mechanistic insights into environments of selection, and the potential for gene flow across anthropogenically influenced habitats. These insights can inform the success of biodiversity targets and conservation initiatives and improve real-time species management. At the global scale, we further illustrate that biologging studies display substantial bias in the types of environments and human conditions sampled. Studies appear biased toward sparsely populated areas and remain particularly rare in highly urbanized areas, areas experiencing high rates of recent forest fragmentation, and key areas for global biodiversity conservation efforts. We highlight the need for equitable access to technology to leverage the biodiversity potential of biologging in the Global South. Advances in software-defined tracking technology will soon give real-time information on energy budgets, survival, reproduction, and ultimately demographic processes and population-level parameters. When deployed into areas most needed, biologging can operationalize access to key measures of biodiversity maintenance such as gene flow, especially in difficult-to-access areas that are key to the future persistence of a species.

Keywords: animal behavior; biodiversity; conservation; global conservation targets; movement ecology.

Fig. 1.

Movement (GPS and acceleration data) and remotely sensed data define species-specific selection landscapes. (A) The migratory path of a white stork (GPS data) overlays maps of remotely sensed data, e.g., human modification index (yellow = high, purple = low), known to influence: (B) breeding success in the northern, summer extent of migration, where daily foraging trips interact with human modification. One such daily foraging trip (white) required flight investment with (C) high vectoral dynamic body acceleration (VeDBA) derived from triaxial accelerometer data, throughout the day; (D–F) migration behavior over human modified landscapes, where increased climb rates in soaring allow for cheap flight with little activity; and (G and H) foraging behavior, where land types influence interactions with human landscapes and diet on migration—in this case, the stork GPS data show foraging on a garbage dump in Spain, and (H) relatively low expense of behavior.

Fig. 2.

A global overview on the distribution of animal telemetry studies on Movebank, and their sampling across a gradient of human modification, habitat fragmentation, forest fragmentation, remoteness, and biodiversity conservation priority areas. Most tracking data are located in areas that are (A) skewed toward northern latitudes (B) in areas with little human activities and infrastructures, and rarely in highly urban environments, (C) distributed in areas experiencing overall low degrees of forest fragmentation from 2000 to 2020, and (D) in areas with low travel times to the nearest city of 100,000 inhabitants, and (E) in locations of medium to high conservation priority areas (low values represent areas of high biodiversity importance).

Fig. 3.

Innovation in informed data collection on recognizing behavioral responses to environmental change in the immediate and longer-term scales, to capture the data required to assess individual and population level success in the face of landscape change, i.e., the selection landscape. Software-defined biologging and edge computing can facilitate dramatic shifts in real-time information delivery to conservation practitioners.