Data for: Occupancy–detection models with museum specimen data: Promise and pitfalls

Historical museum records provide potentially useful data for identifying
drivers of change in species occupancy. However, because museum records
are typically obtained via many collection methods, methodological
developments are needed in order to enable robust inferences.
Occupancy-detection models, a relatively new and powerful suite of
statistical methods, are a potentially promising avenue because they can
account for changes in collection effort through space and time. We use
simulated datasets to identify how and when patterns in data and/or
modelling decisions can bias inference. We focus primarily on the
consequences of contrasting methodological approaches for dealing with
species' ranges and inferring species' non-detections in both
space and time.  We find that not all datasets are suitable for
occupancy-detection analysis but, under the right conditions (namely,
datasets that are broken into more time periods for occupancy inference
and that contain a high fraction of community-wide collections, or
collection events that focus on communities of organisms), models can
accurately estimate trends. Finally, we present a case-study on eastern
North American odonates where we calculate long-term trends of occupancy
by using our most robust workflow.  These results indicate that
occupancy-detection models are a suitable framework for some research
cases and expand the suite of available tools for macroecological analysis
available to researchers, especially where structured datasets are
unavailable.