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Literature compilation for: The rise of animal biotelemetry and genetics research data integration

Cite this dataset

Müller, Mara F.; Banks, Sam C.; Crewe, Tara L.; Campbell, Hamish A. (2023). Literature compilation for: The rise of animal biotelemetry and genetics research data integration [Dataset]. Dryad.


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: i) understanding how individual-based movements affect animal populations, ii) analyzing the relationship between genetic relatedness and social structuring, and iii) 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 advancing the field.


We carried out a literature search through January 2023. We used Web of Science, Scopus and Google Scholar to discover published manuscripts that reported the use of both animal telemetry techniques and genetics methods. We carried out one search for each taxon (i.e., amphibian, bird or avian, fish, mammal, reptile) using specific search terms for both telemetry and genetics (see Appendix 2). Since not all papers are openly available in a full-text format, we only searched in the title, keywords, and abstracts. This returned a total of 398 manuscripts.

To further refine the Web of Science and Scopus search, we used the 'revtools' package in R-project (v 4.0.2) (Westgate, 2019). We imported the 398 references into R studio (v 1.3.1056), removed duplicates, and used the screen_abstracts function to manually select only those papers where the title or abstract explicitly mentioned the use of both biotelemetry and genetics techniques. After the main author read the full text of the selected manuscripts, we kept 139 articles to be included in the analysis (Appendix 1).

To understand the scope of integrating biotelemetry with genetics, and whether the approach differs amongst taxa, we manually categorized these remaining articles into the following variables: aim of the study, duration of the study, sample size for telemetry and genetics, biotelemetry technology used, genetic markers used, genetic and biotelemetry statistics, global outcome of the study, and whether the study had a population-level or individual-level approach. Other information that we included was the publication year, the country of publication, and information on the study species (order, family, and environment). A previous perspectives paper was used as a guide for determining the overall aim of the study derived from the integration of telemetry and genetics (Shafer et al., 2016). More detailed information on the categorization and classification criteria for some of the most relevant variables can be found in Appendix 3. To reduce the risk of bias, the main author carried out the initial categorization of all the articles and the secondary authors selected a random subset of the data and double-checked the resulting classifications.


Australian Research Council, Award: LP1601101716

Charles Darwin University