Implementing GitHub Actions Continuous Integration to Reduce Error Rates in Ecological Data Collection

Accurate field data are essential to understanding ecological systems and forecasting their responses to global change. Yet, data collection errors are common, and data analysis often lags far enough behind its collection that many errors can no longer be corrected, nor can anomalous observations be revisited. Needed is a system in which data quality assurance and control (QA/QC), along with the production of basic data summaries, can be automated immediately following data collection. Here, we implement and test a system to satisfy these needs. For two annual tree mortality censuses and a dendrometer band survey at two forest research sites, we used GitHub Actions continuous integration (CI) to automate data QA/QC and run routine data wrangling scripts to produce cleaned datasets ready for analysis. This system automation had numerous benefits, including (1) the production of near real-time information on data collection status and errors requiring correction, resulting in final datasets free of detectable errors, (2) an apparent learning effect among field technicians, wherein original error rates in field data collection declined significantly following implementation of the system, and (3) an assurance of computational reproducibility—that is, robustness of the system to changes in code, data and software. By implementing CI, researchers can ensure that datasets are free of any errors for which a test can be coded. The result is dramatically improved data quality, increased skill among field technicians, and reduced need for expert oversight. Furthermore, we view CI implementation as a first step towards a data collection and analysis pipeline that is also more responsive to rapidly changing ecological dynamics, making it better suited to study ecological systems in the current era of rapid environmental change

Scaling of Activity Space in Marine Organisms across Latitudinal Gradients

Unifying models have shown that the amount of spaceused by animals (e.g., activity space, home range) scales allometricallywith body mass for terrestrial taxa; however, such relationships arefar less clear for marine species. We compiled movement data from1,596 individuals across 79 taxa collected using a continental passiveacoustic telemetry network of acoustic receivers to assess allometric scal-ing of activity space. We found thatectothermic marine taxa do exhibitallometric scaling for activity space, with an overall scaling exponentof 0.64. However, body mass alone explained only 35% of the varia-tion, with the remaining variation best explained by trophic positionfor teleosts and latitude for sharks, rays, and marine reptiles. Taxon-specific allometric relationships highlighted weaker scaling exponentsamong teleostfish species (0.07) than sharks (0.96), rays (0.55), andmarine reptiles (0.57). The allometric scaling relationship and scalingexponents for the marine taxonomic groups examined were lowerthan those reported from studies that had collated both marine andterrestrial species data derived using various tracking methods. Wepropose that these disparities arise because previous work integratedsummarized data across many studies that used differing methods forcollecting and quantifying activity space, introducing considerableuncertainty into slope estimates. Ourfindings highlight the benefitof using large-scale, coordinated animal biotelemetry networks to ad-dress cross-taxa evolutionary and ecological questions