Data from: Use of classical bird census transects as spatial replicates for hierarchical modeling of an avian community

New monitoring programs are often designed with some form of temporal
replication to deal with imperfect detection by means of occupancy models.
However, classical bird census data from earlier times often lack temporal
replication, precluding detection-corrected inferences about occupancy.
Historical data have a key role in many ecological studies intended to
document range shifts, and so need to be made comparable with present‐day
data by accounting for detection probability. We analyze a classical bird
census conducted in the region of Murcia (SE Spain) in 1991 and 1992 and
propose a solution to estimating detection probability for such historical
data when used in a community occupancy model: the spatial replication of
subplots nested within larger plots allows estimation of detection
probability. In our study, the basic sample units were 1‐km transects,
which were considered spatial replicates in two aggregation schemes. We
fit two Bayesian multispecies occupancy models, one for each aggregation
scheme, and evaluated the linear and quadratic effect of forest cover and
temperature, and a linear effect of precipitation on species occupancy
probabilities. Using spatial rather than temporal replicates allowed us to
obtain individual species occupancy probabilities and species richness
accounting for imperfect detection. Species‐specific occupancy and
community size decreased with increasing annual mean temperature. Both
aggregation schemes yielded estimates of occupancy and detectability that
were highly correlated for each species, so in the design of future
surveys ecological reasons and cost‐effective sampling designs should be
considered to select the most suitable aggregation scheme. In conclusion,
the use of spatial replication may often allow historical survey data to
be applied formally hierarchical occupancy models and be compared with
modern‐day data of the species community to analyze global change process.