The participation of automated software agents known as social bots within online social network (OSN) engagements continues to grow at an immense pace. Choruses of concern speculate as to the impact social bots have within online communications as evidence shows that an increasing number of individuals are turning to OSNs as a primary source for information. This automated interaction proliferation within OSNs has led to the emergence of social bot detection efforts to better understand the extent and behavior of social bots. While rapidly evolving and continually improving, current social bot detection efforts are quite varied in their design and performance characteristics. Therefore, social bot research efforts that rely upon only a single bot detection source will produce very limited results. Our study expands beyond the limitation of current social bot detection research by introducing an ensemble bot detection coverage framework that harnesses the power of multiple detection sources to detect a wider variety of bots within a given OSN corpus of Twitter data. To test this framework, we focused on identifying social bot activity within OSN interactions taking place on Twitter related to the 2018 U.S. Midterm Election by using three available bot detection sources. This approach clearly showed that minimal overlap existed between the bot accounts detected within the same tweet corpus. Our findings suggest that social bot research efforts must incorporate multiple detection sources to account for the variety of social bots operating in OSNs, while incorporating improved or new detection methods to keep pace with the constant evolution of bot complexity.