Cooperative Fish and Wildlife Research Units Program: all
Education, Research and Technical Assistance for Managing Our Natural Resources

Demographic studies of many bird species are challenging because their nests are cryptic, resulting in few nests being found. To maximize statistical power, methods are needed that minimize disturbance while yielding as much information per nest as possible. One way to meet these objectives is to use miniature thermal data loggers to precisely date nest fates. Our objectives, therefore, were to (1) examine the possible effect of thermal data loggers on nest success through hatching by grass- and shrub-nesting songbirds that differed in their parasite egg-accepting and -rejecting behavior, (2) examine the effect of using daily temperature data versus less frequent nest-visit data on statistical power, bias, and precision when estimating the daily survival rate (DSR) for nests, and (3) compare these two approaches using a simulation study and field data. We monitored the survival of nests located in agricultural landscapes and used a binomial logistic regression with main effects for data-loggers and parasite-accepting or -rejecting status and their interaction. We also compared maximum likelihood–derived DSR for differences in estimated rates, precision, and sample sizes with both data collected in the field and simulated with varying sample sizes and visit frequencies. We found no evidence that thermal data loggers had any effect on hatching rates either for all species or for parasite egg-accepting and -rejecting species, separately. Both our simulation and analysis of real nest data indicated that use of data loggers increased the statistical power from each nest studied by increasing effective sample sizes and precision of DSR estimates compared to in-person visits. We also found a negative bias in DSR estimates with longer visit intervals, which use of data-loggers removed. Both the results of simulated- and field-data analyses suggest that future studies of nest survival can be improved by automated nest monitoring by removing a source of bias and providing more time to find additional nests