Despite abundant focus on responsible care of laboratory animals, we argue that inattention to the maltreatment of wildlife constitutes an ethical blind spot in contemporary animal research. We begin by reviewing significant shortcomings in legal and institutional oversight, arguing for the relatively rapid and transformational potential of editorial oversight at journals in preventing harm to vertebrates studied in the field and outside the direct supervision of institutions. Straightforward changes to animal care policies in journals, which our analysis of 206 journals suggests are either absent (34%), weak, incoherent, or neglected by researchers, could provide a practical, effective, and rapidly imposed safeguard against unnecessary suffering. The ARROW (Animal Research: Reporting on Wildlife) guidelines we propose here, coupled with strong enforcement, could result in significant changes to how animals involved in wildlife research are treated. The research process would also benefit. Sound science requires animal subjects to be physically, physiologically, and behaviorally unharmed. Accordingly, publication of methods that contravenes animal welfare principles risks perpetuating inhumane approaches and bad science.
Data preparation code
This is a data preparation file. The code incorporates external guidelines scores into journal scores to account for journal referral to external guidelines using hierarchical approach (see Fig B in S1 Text).
Logistic regression analysis and Poisson regression analysis
Code to conduct logistic regression analysis to test for associations between the presence of any animal care policy and journal characteristics (impact factor, open-access status, animal welfare legislation in country of the journals’ headquarters, and whether the journal was conservation-oriented). We centered all predictors (subtracted the mean from each observation) and scaled (divided by 2 SDs). This file also includes code for a Poisson regression analysis to test for associations with number of criteria detected among journals that had an animal care policy and the same journal characteristics. For the latter, criteria were considered fulfilled if a journal received any score other than “none”.
Plot proportion of journals that fulfill a given criterion
This file contains code to create Fig. 1 in the main text of the manuscript, which plots proportions of journals that fulfill a given criterion based on compliance language (i.e., requirement (3), recommendation (2), suggestion (1) or absent (0)).
Code for coefficient plot in main text (Fig 2.). Use 'model-rescale-coef-names.csv' to extract label names from external CSV.
Raw journal data
Scores assigned to journals based on compliance language used by journals. Use Script1_Data-preparation.R to incorporate external guideline scores into final journal score. See README file for more information.
External and publishing house guidelines
Scores assigned to external and publishing house guidelines based on compliance language. Use Script1_Data-preparation_KField.R to incorporate external guideline scores into final journal score. See README file associated with JournalData.csv for more information.
Label names for coefficient plot
This file contains label names, which are called into Script4_Coefficient-plot.R to create coefficient plot.