Trevor J. Hefley

Correction of location errors for presence-only species distribution models

Summary 1. Species distribution models (SDMs) for presence-only data depend on accurate and precise measurements of geographical and environmental covariates that influence presence and abundance of the species. Some data sets, however, may contain both systematic and random errors in the recorded location of the species. Environmental covariates at the recorded location may differ from those at the true location and result in biased parameter estimates and predictions from SDMs. 2. Regression calibration is a well-developed statistical method that can be used to correct the bias in estimated coefficients and predictions from SDMs when the recorded geographical location differs from the true location for some, but not all locations. We expand the application of regression calibration methods to SDMs and provide illustrative examples using simulated data and opportunistic records of whooping cranes (Grus americana). 3. We found we were able to successfully correct the bias in our SDM parameters estimated from simulated data and opportunistic records of whooping cranes using regression calibration. 4. When modelling species distributions with data that have geographical location errors, we recommend researchers consider the effect of location errors. Correcting for location errors requires that at least a portion of the data have locations recorded without error. Bias correction can result in an increase in variance; this increase in variance should be considered when evaluating the utility of bias correction.

Evaluation of Fences for Containing Feral Swine under Simulated Depopulation Conditions

ABSTRACT Populations of feral swine (Sus scrofa) are estimated to include >2 million animals in the state of Texas, USA, alone. Feral swine damage to property, crops, and livestock exceeds

Assessing Resilience in Stressed Watersheds

50 million annually. These figures do not include the increased risks and costs associated with the potential for feral swine to spread disease to domestic livestock. Thus, effective bio-security measures will be needed to quickly isolate affected feral swine populations during disease outbreaks. We evaluated enclosures built of 0.86-m-tall traditional hog panels for containing feral swine during 35 trials, each involving 6 recently caught animals exposed to increasing levels of motivation. During trials, fences were 97% successful when enclosures were entered by humans for maintenance purposes; 83% effective when pursued by walking humans discharging paintball projectors; and in limited testing, 100% successful when pursued and removed by gunners in a helicopter. In addition to being effective in containing feral swine, enclosures constructed of hog panels required simple hand tools, took <5 min/m to erect, and were inexpensive (

On the existence of maximum likelihood estimates for presence-only data

5.73/m excluding labor) relative to other fencing options. As such, hog-panel fences are suitable for use by state and federal agencies for rapid deployment in disease response situations, but also exhibit utility for general control of other types of damage associated with feral swine.

A comparison of breeding population estimators using nest and brood monitoring data.

Although several frameworks for assessing the resilience of social-ecological systems (SESs) have been developed, some practitioners may not have sufficient time and information to conduct extensive resilience assessments. We have presented a simplified approach to resilience assessment that reviews the scientific, historical, and social literature to rate the resilience of an SES with respect to nine resilience properties: ecological variability, diversity, modularity, acknowledgement of slow variables, tight feedbacks, social capital, innovation, overlap in governance, and ecosystem services. We evaluated the effects of two large-scale projects, the construction of a major dam and the implementation of an ecosystem recovery program, on the resilience of the central Platte River SES (Nebraska, United States). We used this case study to identify the strengths and weaknesses of applying a simplified approach to resilience assessment. Although social resilience has increased steadily since the predam period for the central Platte River SES, ecological resilience was greatly reduced in the postdam period as compared to the predam and ecosystem recovery program time periods.

Statistical indicators and state-space population models predict extinction in a population of bobwhite quail

Summary Presence-only data can be used to determine resource selection and estimate a species’ distribution. Maximum likelihood is a common parameter estimation method used for species distribution models. Maximum likelihood estimates, however, do not always exist for a commonly used species distribution model – the Poisson point process. We demonstrate the issue with conventional maximum likelihood mathematically, using a data example, and a simulation experiment and show alternative estimation methods. We found that when habitat preferences are strong or the number of presence-only locations is small, by chance, maximum likelihood coefficient estimates for the Poisson point process model may not exist. We found that several alternative estimation methods can produce reliable estimates, but results will depend on the chosen method. It is important to identify conditions for which maximum likelihood estimates are unlikely to be identifiable from presence-only data. In data sets where the maximum likelihood estimates do not exist, penalized likelihood and Bayesian methods will produce coefficient estimates, but these are sensitive to the choice of estimation procedure and prior or penalty term. When sample size is small or it is thought that habitat preferences are strong, we propose a suite of estimation procedures researchers can consider using.

Nondetection sampling bias in marked presence-only data

Abstract For many species, breeding population size is an important metric for assessing population status. A variety of simple methods are often used to estimate this metric for ground‐nesting birds that nest in open habitats (e.g., beaches, riverine sandbars). The error and bias associated with estimates derived using these methods vary in relation to differing monitoring intensities and detection rates. However, these errors and biases are often difficult to obtain, poorly understood, and largely unreported. A method was developed to estimate the number of breeding pairs using counts of nests and broods from monitoring data where multiple surveys were made throughout a single breeding season (breeding pair estimator; BPE). The BPE method was compared to two commonly used estimation methods using simulated data from an individual‐based model that allowed for the comparison of biases and accuracy. The BPE method underestimated the number of breeding pairs, but generally performed better than the other two commonly used methods when detection rates were low and monitoring frequency was high. As detection rates and time between surveys increased, the maximum nest and brood count method performs similar to the BPE. The BPE was compared to four commonly used methods to estimate breeding pairs for empirically derived data sets on the Platte River. Based on our simulated data, we expect our BPE to be closest to the true number of breeding pairs as compared to other methods. The methods tested resulted in substantially different estimates of the numbers of breeding pairs; however, coefficients from trend analyses were not statistically different. When data from multiple nest and brood surveys are available, the BPE appears to result in reasonably precise estimates of numbers of breeding pairs. Regardless of the estimation method, investigators are encouraged to acknowledge whether the method employed is likely to over‐ or underestimate breeding pairs. This study provides a means to recognize the potential biases in breeding pair estimates.