Statistics, Department of


Date of this Version

Spring 3-21-2014


Hefley, T.J., (2014). New statistical methods for analysis of historical data from wildlife populations. Doctoral dissertation. University of Nebraska‒Lincoln.


A DISSERTATION Presented to the Faculty of the Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Statistics and Natural Resource Sciences (Applied Ecology), Under the Supervision of Professors Erin E. Blankenship and Richard A.J. Tyre. Lincoln, Nebraska: March, 2014

Copyright (c) 2014 Trevor J. Hefley

Chapters 1-4 are published as follows:

Hefley, T.J., Baasch, D.M., Tyre, A.J. & Blankenship, E.E. (2014). Correction of location errors for presence-only species distribution models. Methods in Ecology and Evolution, 5, 207–214.

Hefley, T., Tyre, A., Baasch, D. & Blankenship, E. (2013a). Nondetection sampling bias in marked presence-only data. Ecology and Evolution, 3, 5225–5236.

Hefley, T.J., Tyre, A.J. & Blankenship, E.E. (2013b). Fitting population growth models in the presence of measurement and detection error. Ecological Modelling, 263, 244–250.

Hefley, T.J., Tyre, A.J. & Blankenship, E.E. (2013c). Statistical indicators and state–space population models predict extinction in a population of bobwhite quail. Theoretical Ecology, 6, 319–331.


Wildlife biologists, many times with the help of ordinary citizens, have developed and maintained long-term datasets for monitoring the status of wildlife populations. These datasets can range from a collection of citizen-reported sightings of a rare species, to datasets collected by biologists using standardized methods. The commonality is that these datasets span a temporal and spatial scale that is beyond the scope of most scientific studies. Ensuring the continued persistence of wildlife populations requires predictions of the impact of human actions. Regardless if the predictions are quantitative or qualitative, the best we can do is use the past data to predict the future.

Statistical methods are the main data analysis technique used for developing quantitative predictions in the life sciences, but these methods are rarely applied to long-term datasets because the methods are underdeveloped in most cases. This underdevelopment of statistical methods and applications was the motivation for my research. In Chapter 1, I develop a time series analysis method for populations that accounts for errors in detection. In Chapter 2, I develop and apply a variety of methods to predict an extinction threshold using long-term monitoring data from a population of bobwhite quail (Colinus virginianus). In Chapter 3, I link the unified framework of missing data developed in the statistical literature to species distribution modelling, which is a common method used to analyze historical location reports of a species. In Chapter 4 I introduce an example using location records of one of the rarest avian species in the world—the whooping crane (Grus americana). The whooping crane location records were imprecisely recorded, and in Chapter 4, I extend regression calibration methods to correct for the location error. In Chapter 5, I explore when a commonly used statistical estimation method will fail for analyses using historical location records; I then test several alternative estimation methods. Finally, in Chapter 6, I present an application by predicting the spatial and temporal distribution of whooping cranes using historical location records. This application was developed to determine what habitat is used by whooping cranes during migration and what habitat may require special protection to ensure survival of the species.

Advisors: Erin E. Blankenship and Richard A.J. Tyre