Antler Size of Alaskan Moose Alces alces gigas: Effects of Population Density, Hunter Harvest and Use of Guides

Moose Alces alces gigas in Alaska, USA, exhibit extreme sexual dimorphism, with adult males possessing large, elaborate antlers. Antler size and conformation are influenced by age, nutrition and genetics, and these bony structures serve to establish social rank and affect mating success. Population density, combined with anthropogenic effects such as harvest, is thought to influence antler size. Antler size increased as densities of moose decreased, ostensibly a density-dependent response related to enhanced nutrition at low densities. The vegetation type where moose were harvested also affected antler size, with the largest-antlered males occupying more open habitats. Hunts with guides occurred in areas with low moose density, minimized hunter interference and increased rates of success. Such hunts harvested moose with larger antler spreads than did non-guided hunts. Knowledge and abilities allowed guides to satisfy demands of trophy hunters, who are an integral part of the Alaskan economy. Heavy harvest by humans was also associated with decreased antler size of moose, probably via a downward shift in the age structure of the population resulting in younger males with smaller antlers. Nevertheless, density-dependence was more influential than effects of harvest on age structure in determining antler size of male moose. Indeed, antlers are likely under strong sexual selection, but we demonstrate that resource availability influenced the distribution of these sexually selected characters across the landscape. We argue that understanding population density in relation to carrying capacity (K) and the age structure of males is necessary to interpret potential consequences of harvest on the genetics of moose and other large herbivores. Our results provide researchers and managers with a better understanding of variables that affect the physical condition, antler size, and perhaps the genetic composition of populations, which may be useful in managing and modeling moose populations.

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.

Space–time zero-inflated count models of Harbor seals

Environmental data are spatial, temporal, and often come with many zeros. In this paper, we included space–time random effects in zero-inflated Poisson (ZIP) and ‘hurdle’ models to investigate haulout patterns of harbor seals on glacial ice. The data consisted of counts, for 18 dates on a lattice grid of samples, of harbor seals hauled out on glacial ice in Disenchantment Bay, near Yakutat, Alaska. A hurdle model is similar to a ZIP model except it does not mix zeros from the binary and count processes. Both models can be used for zero-inflated data, and we compared space–time ZIP and hurdle models in a Bayesian hierarchical model. Space–time ZIP and hurdle models were constructed by using spatial conditional autoregressive (CAR) models and temporal first-order autoregressive (AR(1)) models as random effects in ZIP and hurdle regression models. We created maps of smoothed predictions for harbor seal counts based on ice density, other covariates, and spatio-temporal random effects. For both models predictions around the edges appeared to be positively biased. The linex loss function is an asymmetric loss function that penalizes overprediction more than underprediction, and we used it to correct for prediction bias to get the best map for space–time ZIP and hurdle models.

Spatial methods for plot-based sampling of wildlife populations

Classical sampling methods can be used to estimate the mean of a finite or infinite population. Block kriging also estimates the mean, but of an infinite population in a continuous spatial domain. In this paper, I consider a finite population version of block kriging (FPBK) for plot-based sampling. The data are assumed to come from a spatial stochastic process. Minimizing mean-squared-prediction errors yields best linear unbiased predictions that are a finite population version of block kriging. FPBK has versions comparable to simple random sampling and stratified sampling, and includes the general linear model. This method has been tested for several years for moose surveys in Alaska, and an example is given where results are compared to stratified random sampling. In general, assuming a spatial model gives three main advantages over classical sampling: (1) FPBK is usually more precise than simple or stratified random sampling, (2) FPBK allows small area estimation, and (3) FPBK allows nonrandom sampling designs.

An animal movement model incorporating home range and habitat selection

Wildlife biologists are often interested in how an animal uses space and the habitat resources within that space. We propose a single model that estimates an animal’s home range and habitat selection parameters within that range while accounting for the inherent autocorrelation in frequently sampled telemetry data. The model is applied to brown bear telemetry data in southeast Alaska.

Spatial statistical models that use flow and stream distance

We develop spatial statistical models for stream networks that can estimate relationships between a response variable and other covariates, make predictions at unsampled locations, and predict an average or total for a stream or a stream segment. There have been very few attempts to develop valid spatial covariance models that incorporate flow, stream distance, or both. The application of typical spatial autocovariance functions based on Euclidean distance, such as the spherical covariance model, are not valid when using stream distance. In this paper we develop a large class of valid models that incorporate flow and stream distance by using spatial moving averages. These methods integrate a moving average function, or kernel, against a white noise process. By running the moving average function upstream from a location, we develop models that use flow, and by construction they are valid models based on stream distance. We show that with proper weighting, many of the usual spatial models based on Euclidean distance have a counterpart for stream networks. Using sulfate concentrations from an example data set, the Maryland Biological Stream Survey (MBSS), we show that models using flow may be more appropriate than models that only use stream distance. For the MBSS data set, we use restricted maximum likelihood to fit a valid covariance matrix that uses flow and stream distance, and then we use this covariance matrix to estimate fixed effects and make kriging and block kriging predictions.

Long-term recovery patterns of arctic tundra after winter seismic exploration

In response to the increasing global demand for energy, oil exploration and development are expanding into frontier areas of the Arctic, where slow-growing tundra vegetation and the underlying permafrost soils are very sensitive to disturbance. The creation of vehicle trails on the tundra from seismic exploration for oil has accelerated in the past decade, and the cumulative impact represents a geographic footprint that covers a greater extent of Alaska’s North Slope tundra than all other direct human impacts combined. Seismic exploration for oil and gas was conducted on the coastal plain of the Arctic National Wildlife Refuge, Alaska, USA, in the winters of 1984 and 1985. This study documents recovery of vegetation and permafrost soils over a two-decade period after vehicle traffic on snow-covered tundra. Paired permanent vegetation plots (disturbed vs. reference) were monitored six times from 1984 to 2002. Data were collected on percent vegetative cover by plant species and on soil and ground ice characteristics. We developed Bayesian hierarchical models, with temporally and spatially autocorrelated errors, to analyze the effects of vegetation type and initial disturbance levels on recovery patterns of the different plant growth forms as well as soil thaw depth. Plant community composition was altered on the trails by species-specific responses to initial disturbance and subsequent changes in substrate. Long-term changes included increased cover of graminoids and decreased cover of evergreen shrubs and mosses. Trails with low levels of initial disturbance usually improved well over time, whereas those with medium to high levels of initial disturbance recovered slowly. Trails on ice-poor, gravel substrates of riparian areas recovered better than those on ice-rich loamy soils of the uplands, even after severe initial damage. Recovery to pre-disturbance communities was not possible where trail subsidence occurred due to thawing of ground ice. Previous studies of disturbance from winter seismic vehicles in the Arctic predicted short-term and mostly aesthetic impacts, but we found that severe impacts to tundra vegetation persisted for two decades after disturbance under some conditions. We recommend management approaches that should be used to prevent persistent tundra damage.

Review of Applied Spatial Data Analysis with R by R. S. Bivand, E. J. Pebesma, and V. Gomez-Rubio

Most authors struggle to pick a title that adequately conveys all of the material covered in a book. When I first saw Applied Spatial Data Analysis with R, I expected a review of spatial statistical models and their applications in packages (libraries) from the CRAN site of R. The authors’ title is not misleading, but I was very pleasantly surprised by how deep the word “applied” is here. The first half of the book essentially covers how R handles spatial data. To some statisticians this may be boring. Do you want, or need, to know the difference between S3 and S4 classes, how spatial objects in R are organized, and how various methods work on the spatial objects? A few years ago I would have said “no,” especially to the “want” part. Just let me slap my EXCEL spreadsheet into R and run some spatial functions on it. Unfortunately, the world is not so simple, and ultimately we want to minimize effort to get all of our spatial analyses accomplished. The first half of this book certainly convinced me that some extra effort in organizing my data into certain spatial class structures makes the analysis easier and less subject to mistakes. I also admit that I found it very interesting and I learned a lot.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.

A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly auto-correlated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

Benefit Cost Scenarios of Potential Oral Rabies Vaccination for Skunks in California

Scenario-based analyses were computed for benefits and costs linked with hypothetical oral rabies vaccination (ORV) campaigns to contain or eliminate skunk-variant rabies in skunks (Mephitis mephitis) in California, USA. Scenario 1 assumed baiting eight zones (43,388 km2 total) that comprised 73% of known skunk rabies locations in the state. Scenario 2 also assumed baiting these eight zones, but further assumed that added benefits would result from preventing the spread of skunk-variant rabies into Los Angeles County, USA. Scenarios assumed a fixed bait cost ($1.24 each) but varied campaigns (one, two and three annual ORV applications), densities of baits (37.5/km2, 75/km2 and 150/km2), levels of prevention (50%, 75%, and 100%), and contingency expenditures if rabies recurred (20%, 40%, and 60% of campaign costs). Prorating potential annual benefits during a 12-yr time horizon yielded benefit-cost ratios (BCRs) between 0.16 and 2.91 and between 0.34 and 6.35 for Scenarios 1 and 2, respectively. Economic issues relevant to potentially managing skunk-variant rabies with ORV are discussed.

SCWDS BRIEFS: Volume 20, Number 2 (July 2004)

National Veterinary Services Laboratories in Ames, Iowa, confirmed vesicular stomatitis (VS) in horses at one premises in Texas. As of July 21, 2004, infected animals were identified on a total of 45 premises in Colorado (11), New Mexico (21), and Texas (13). These are the first reports of VS in livestock in the United States since the 1998 epizootic. SCWDS VS Studies Chronic Wasting Disease Developments: nearly 118,000 wild white-tailed deer, mule deer, and elk were tested in the United States from October 2002 to September 2003, with 592 animals testing positive for the CWD prion. More than $38,000,000 was spent by federal and state wildlife and animal health agencies on CWD-related activities during this same period. The Second International Chronic Wasting Disease Symposium, hosted by the Wisconsin Department of Natural Resources, will be held in Madison, Wisconsin, July 12-14, 2005. Crow Decoys Used in West Nile Virus Study Although Lyme disease caused by a spirochete bacterium, Borrelia burgdorferi, is relatively rare in the southeastern United States, a Lyme disease-like infection referred to as Southern Tick-Associated Rash Illness (STARI) and thought to be caused by Borrelia lonestari, has been recognized in people in this region. The Ohio Division of Wildlife joined SCWDS as an associate member beginning July 1, 2004. The Final Report of the 2003 Hemorrhagic Disease (HD) Surveillance project has been completed and distributed to all cooperators. New of Caroline Duffie, Robbie Edalgo and wife Jen, Clay George, Darrell Kavanaugh, Lynn Lewis-Weiss’s husband, Dr. Kevin Weiss, and Nate Mechlin. New SCWDS staff include Brian Chandler, Jay Cumbee, Ginger Goekjian, Bill Hamrick, Sabrina McGraw, Kerri Pedersen, and Ben Wilcox. Recent SCWDS Publications Available

International Consensus Guidelines for Nutrition Therapy in Pancreatitis

Guidelines for nutrition support in pancreatitis have been inconsistently adapted to clinical practice. The International Consensus Guideline Committee (ICGC) established a pancreatitis task force to review published guidelines for pancreatitis in nutrition support. A PubMed search using the terms pancreatitis, acute pancreatitis, chronic pancreatitis, nutrition support, parenteral nutrition, enteral nutrition, and guidelines was conducted for the period from January 1999 to May 2011. Eleven guidelines were identified for review. The ICGC used the following process to develop unified guideline statements: summarize the strength of evidence (grading) of the guidelines; establish level of evidence for ICGC statements as high, intermediate, and low; assign published guideline levels of evidence; and define an ICGC grading system. International Pancreatitis Guideline Grades were established as follows: platinum-high level of evidence and consistent agreement among the guidelines; gold-acceptable level of evidence and no conflicting statements in guidelines; and silver-single existing guideline statement with no conflict in other guidelines. Eighteen ICGC statements were derived from the 11 published pancreatitis guidelines. Uniform agreement from widely disparate groups (United States, Europe, Japan, and China) resulted in 4 platinum-level guideline statements for nutrition in pancreatitis: nutrition support therapy (NST) is generally not needed for mild to moderate disease, NST is needed for severe disease, enteral nutrition (EN) is preferred over parenteral nutrition (PN), and use PN when EN is contraindicated or not feasible. This methodology provides a template for future ICGC nutrition guideline development. (JPEN J Parenter Enteral Nutr. 2012;36:284-291)

THE ASSOCIATION BETWEEN CHILDHOOD ADVERSITY AND COMPONENTS OF METABOLIC SYNDROME IN ADULTS WITH MOOD DISORDERS: RESULTS FROM THE INTERNATIONAL MOOD DISORDERS COLLABORATIVE PROJECT

Objective: We sought to determine whether a reported history of childhood adversity is associated with components of the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP-III)-defined metabolic syndrome in adults with mood disorders. Method: This was a cross-sectional analysis of adult outpatients (N = 373; n = 230 female, n = 143 male; mean age [SD] = 42.86 [14.43]) from the International Mood Disorders Collaborative Project (University of Toronto and Cleveland Clinic) with DSM-IV-defined major depressive disorder and bipolar I/II disorder. Childhood adversity was measured with the Klein Trauma & Abuse-Neglect self-report scale. The groups with and without childhood adversity were compared to determine possible differences in the rates of metabolic syndrome and its components. Logistic and linear regressions adjusted for age, sex, education, employment status, and smoking were used to evaluate the association between childhood adversity and components of metabolic syndrome. Results: For the full sample, 83 subjects (22.25%) met criteria for metabolic syndrome. Individuals reporting a history of any childhood adversity had higher systolic and diastolic blood pressure (systolic: p = 0.040; diastolic: p = 0.038). Among subjects with a history of sexual abuse, a significant proportion met criteria for obesity (45.28% vs. 32.88%; p = 0.010); a trend toward overweight was found for subjects with a history of physical abuse (76.32% vs. 63.33%; p = 0.074), although this relationship did not remain significant after adjusting for potential confounders. There was no statistically significant difference in the overall rate of dyslipidemia and/or metabolic syndrome between subjects with and without childhood adversity. Conclusion: The results herein provide preliminary evidence suggesting that childhood adversity is associated with metabolic syndrome components in individuals with mood disorders. Int'l. J. Psychiatry in Medicine 2012;43:165-177)