Monitoring Bumble Bee Populations in the Twin Cities Metro – Lessons Learned

In a project funded by MNDOT, entomologists developed an innovative method for surveying bumble bee populations alongside roadways. The researchers have recently published an article in Biological Conversations, Vol 283 focusing on the lessons learned about sampling when surveying these quick-moving bees.


Cover of Biological Conservation journal.

Nearly one-quarter of bumble bee species assessed by the International Union for the Conservation of Nature are considered to be in decline, necessitating monitoring to support recovery planning. A key element of robust wildlife monitoring is accounting for imperfect detection in the observation process and efforts to understand sources of detection uncertainty in bumble bee surveys began recently in response to the listing of the rusty patched bumble bee, Bombus affinis, as federally endangered in the U.S. and Canada. The researchers investigated within-community variation in detection probabilities among eight species that vary in occupancy, including Baffinis. They sampled bumble bees along roadsides in the greater metropolitan area of Minneapolis-St. Paul, Minnesota in 2018, and generated estimates of occupancy, detection probability, and cumulative detection probability from a multispecies occupancy model using a Bayesian framework that included covariates on occupancy and detection. Covariates selected a priori included floral cover and impervious surface effects on occupancy, and date, time of day, and observer effects on detection. They found that Bombus species with low occurrence are not necessarily less detectable than common species with higher occurrence, and that species reach their peak detection probability at different dates. Detection was higher in the afternoon and varied among observers. Well-designed Bombus monitoring programs can reduce sampling effort and optimize detection by sampling on dates near species detection peaks, sampling in the afternoon, and using cumulative detection probabilities to determine optimum sampling effort.

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