DOI: 10.5061/dryad.hx3ffbggs
title: Literature compilation for: The rise of animal biotelemetry and genetics research data integration

description: 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.
description: 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.