The ABCs of nest survival: Theory and application from a biostatistical perspective
No abstract available.
Studies in Avian Biology↗
Geology topics
Dennis M. Heisey
Publications and source records attributed to Dennis M. Heisey.
A review of methods to estimate cause-specific mortality in presence of competing risks
Estimating cause-specific mortality is often of central importance for understanding the dynamics of wildlife populations. Despite such importance, methodology for estimating and analyzing cause-specific mortality has received little attention in wildlife ecology during the past 20 years. The issue of analyzing cause-specific, mutually exclusive events in time is not unique to wildlife. In fact, this general problem has received substantial attention in human biomedical applications within the context of biostatistical survival analysis. Here, we consider cause-specific mortality from a modern biostatistical perspective. This requires carefully defining what we mean by cause-specific mortality and then providing an appropriate hazard-based representation as a competing risks problem. This leads to the general solution of cause-specific mortality as the cumulative incidence function (CIF). We describe the appropriate generalization of the fully nonparametric staggered-entry Kaplan–Meier survival estimator to cause-specific mortality via the nonparametric CIF estimator (NPCIFE), which in many situations offers an attractive alternative to the Heisey–Fuller estimator. An advantage of the NPCIFE is that it lends itself readily to risk factors analysis with standard software for Cox proportional hazards model. The competing risks–based approach also clarifies issues regarding another intuitive but erroneous “cause-specific mortality” estimator based on the Kaplan–Meier survival estimator and commonly seen in the life sciences literature.
Journal of Wildlife Management↗
Present and Reference Concentrations and Yields of Suspended Sediment in Streams in the Great Lakes Region and Adjacent Areas
In-stream suspended sediment and siltation and downstream sedimentation are common problems in surface waters throughout the United States. The most effective way to improve surface waters impaired by sediments is to reduce the contributions from human activities rather than try to reduce loadings from natural sources. Total suspended sediment/solids (TSS) concentration data were obtained from 964 streams in the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River Basins from 1951 to 2002. These data were used to estimate median concentrations, loads, yields, and volumetrically (flow) weighted (VW) concentrations where streamflow data were available. SPAtial Regression-Tree Analysis (SPARTA) was applied to land-use-adjusted (residualized) TSS data and environmental-characteristic data to determine the natural factors that best described the distribution of median and VW TSS concentrations and yields and to delineate zones with similar natural factors affecting TSS, enabling reference or natural concentrations and yields to be estimated. Soil properties (clay and organic-matter content, erodibility, and permeability), basin slope, and land use (percentage of agriculture) were the factors most strongly related to the distribution of median and VW TSS concentrations. TSS yields were most strongly related to amount of precipitation and the resulting runoff, and secondarily to the factors related to high TSS concentrations. Reference median TSS concentrations ranged from 5 to 26 milligrams per liter (mg/L), reference median annual VW TSS concentrations ranged from 10 to 168 mg/L, and reference TSS yields ranged from about 980 to 90,000 kilograms per square kilometer per year. Independent streams (streams with no overlapping drainage areas) with TSS data were ranked by how much their water quality exceeded reference concentrations and yields. Most streams exceeding reference conditions were in the central part of the study area, where agricultural activities are the most intensive; however, other sites exceeding reference conditions were identified outside of this area. Whether concentrations or yields should be considered in guiding rehabilitation efforts depends on whether in-stream or downstream effects are more important. Although this study attempted to obtain all available water-quality data for the study area, any actual prioritization of sites for remediation would need to rely on more extensive data collection or numerical models that can accurately simulate the effects of various human activities in a range of environmental settings.
Scientific Investigations Report↗