With ecology’s embrace of the programming language R, we now have a plethora of open analysis tools that can be both a blessing and a curse: we have a world of technology at our disposal, yet we often have great difficulty navigating it. This rings particularly true for species distribution modeling (SDM), which has seen a sharp uptick in development of new methods recently due to the wealth of applications in ecology and conservation, with nearly 50 packages dedicated to SDM on CRAN, the main repository for R packages. Regardless of experience, researchers often find themselves asking, “Which should I use?” A team of package developers, we published a recent paper explaining why we should spice up our workflows with a variety of package tools because this helps us answer a greater variety of research questions, better address different kinds of uncertainty, and also achieve higher methodological standards. Using different sets of R packages, we composed three full analyses representing common SDM applications and scored them based on a new set of standards in the field, demonstrating that each one could achieve at least the “bronze” level for most categories only because of pluralistic package use. In this talk, I will provide an overview of how R packages for SDM relate to each other, review strategies we identified for combined tool use, discuss what functionalities were are currently missing, and introduce the new sdmverse metapackage to catalog the functionalities of R packages for SDM.