The rise of animal biotelemetry and genetics research data integration.

The advancement and availability of innovative animal biotelemetry and genomic technologies are improving our understanding of how the movements of individuals influence gene flow within and between populations and ultimately drive evolutionary and ecological processes. There is a growing body of work that is integrating what were once disparate fields of biology, and here, we reviewed the published literature up until January 2023 (139 papers) to better understand the drivers of this research and how it is improving our knowledge of animal biology. The review showed that the predominant drivers for this research were as follows: (1) understanding how individual-based movements affect animal populations, (2) analyzing the relationship between genetic relatedness and social structuring, and (3) studying how the landscape affects the flow of genes, and how this is impacted by environmental change. However, there was a divergence between taxa as to the most prevalent research aim and the methodologies applied. We also found that after 2010 there was an increase in studies that integrated the two data types using innovative statistical techniques instead of analyzing the data independently using traditional statistics from the respective fields. This new approach greatly improved our understanding of the link between the individual, the population, and the environment and is being used to better conserve and manage species. We discuss the challenges and limitations, as well as the potential for growth and diversification of this research approach. The paper provides a guide for researchers who wish to consider applying these disparate disciplines and advance the field.

Motivations and fears driving participation in collaborative research infrastructure for animal tracking.

Anthropogenic derived environmental change is challenging earth's biodiversity. To implement effective management, it is imperative to understand how organisms are responding over broad spatiotemporal scales. Collection of these data is generally beyond the budget of individual researchers and the integration and sharing of ecological data and associated infrastructure is becoming more common. However, user groups differ in their expectations, standards of performance, and desired outputs from research investment, and accommodating the motivations and fears of potential users from the outset may lead to higher levels of participation. Here we report upon a study of the Australian ornithology community, which was instigated to better understand perceptions around participation in nationally coordinated research infrastructure for detecting and tracking the movement of birds. The community was surveyed through a questionnaire and individuals were asked to score their motivations and fears around participation. Principal Components Analysis was used to reduce the dimensionality of the data and identify groups of questions where respondents behaved similarly. Linear regressions and model selection were then applied to the principal components to determine how career stage, employment role, and years of biotelemetry experience affected the respondent's motivations and fears for participation. The analysis showed that across all sectors (academic, government, NGO) there was strong motivation to participate and belief that national shared biotelemetry infrastructure would facilitate bird management and conservation. However, results did show that a cross-sector cohort of the Australian ornithology community were keen and ready to progress collaborative infrastructure for tracking birds, and measures including data-sharing agreements could increase participation. It also informed that securing initial funding would be a significant challenge, and a better option to proceed may be for independent groups to coordinate through existing database infrastructure to form the foundation from which a national network could grow.

Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts.

The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration) to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling) to reduce the probability that individuals will be detected on more than one occasion.