Historical museum records provide potentially useful data for
identifying drivers of temporal trends in species occupancy, however, because
these records were not explicitly collected for this purpose, methodological
developments are needed in order to enable robust inferences. Occupancy-detection models, a relatively new and
powerful suite of methods, are potentially useful here, because these models
allow us to account for changes in collection effort through space and time.
Applying such occupancy-detection models to historical museum records is not a
straightforward process, as these models have strict data requirements that
museum data usually do not meet. Here I will present a
methodological road-map for using occupancy models to analyze historical museum
records. I use simulated data-sets to identify how and when modeling decisions
and patterns in data can bias inferences. I will focus primarily on the
consequences of contrasting methodological approaches for dealing with species
ranges and non-detections in both space and time. Finally, I will present an
application of these methods to bees in North America and will present drivers
of change for these species in the past 30 years.
