Block & Bridle Annual 1956-1957

Charles Adams (Faculty Advisor), Delbert Kuhlman, John Klingenberg, Ardyce Haring, Harvey Jorgensen, Roy Volzke, Billie Reed, Paul Yeutter Prof. Loeffel, Carolyn Hall, Byron Kort, Larry Paul, Elwin Mosier, Charles Corkle, Kay Robohm, Daniel Stilwell Duane Stokebrand, Gary Briggs, Walt Patterson, Wendell Mousel, Keith Smith, Darrel Zessin, Richard Bonne, Donald Kasbohm, Bruce Skinner Bob Discoe, Doyle Hulme, Jim Smith, Carl Lorenzen, Jay Cook, Gary Berke, Bob Volk, Roger Hild Donald Kuhl, Russell Person, Ray Cada, Ray DeBower, Bob Dannert, Phil Starck, Kay Knudsen, Jerry Brownfield, Allan McClure, Wally Bierman Morris Ochsner, Warren Mitchell, Ed McReynolds, Gerald Dart, Arza Snyder, Mervy Schliefert, Arley Waldo, Tom Hoffman, John Wink, Virgil Gellermann, Duane Neuman

Block & Bridle Annual 1957-1958

Gary Briggs, Paul Yeutter, Ray Cada, Darrel Zessin, Byron Kort. Louis Welch, Kay Robohm, Darrel Eberspacher, Elwin Mosier, Ardyce Haring, Carolyn Hall. Larry Lutz, Maurice Bonne, Max Waldo, Duane Stokebrand, Ted Klug, Prof. Richard B. Warren (Faculty Advisor). Eli Thomssen, Phil Starck, Ray DeBower, Gary Berke, Jay Cook, Roger Hild. Russell Person, Morris Ochsner, Del Kuhlman, John Wink, Jerry Dart, Tom Kraeger.

Block & Bridle Annual 1959-1960

President Roger Wehrbein Vice President Ted Klug Secretary George B. O'Neal Treasurer Ralph Hazen Marshal Bud Reece Historian Tom Kraeger Co-Historian John Zauha Ag. Executive Representative Larry Williams Faculty Advisor Dr. E. B. Peo, Jr. George Ahlschwede Richard Hahn Henry Beel Ralph Hazen Gary Briggs Gary Heineman Leslie Cook Max Hauser Richard Eberspacher Buce Jameson Russ Edeal Leon Janovy William Ehresman Alan Jorgensen Rolland Eubanks John Joyner Mickey Evertson Marshall Jurgens Jesse Felker Ron Kahle Mylon Filkins Donald Kavan Richard Frahm Max Keasling Roger French Ronald Kennedy Angus Garey Ted Klug Ed Gates Herb Kraeger Gerald Gogan Tom Kraeger Gerald Goold Fernando Lagos Jay Graf Gerald Lamberson Lloyd Langemeier Ralph Langemeier Gerald Loseke Donald Meiergerd Lowell Minert John Oeltjen George B. O'Neal Don Ormesher Larry Ott Bud Reece Ron Sabatka Keith Smith Ronald Smith Donn Simonson Daryl Starr Galen Stevens Eugene Turdy Ernest Thayer Charles Thompson Jerry Thompson Eli Thomssen William Watkins Allen Trumble Robert Weber Lawrence Turner Dan Wehrbein Reginald Turner Roger Wehrbein Vance Uden Dick White Max Waldo Billy Williams Blair Williams Larry Williams D. Patrick Wright John Zauha

Draft U.S. Pacific Marine Mammal Stock Assessments: 2009

Under the 1994 amendments to the Marine Mammal Protection Act (MMPA), the National Marine Fisheries Service (NMFS) and the U.S. Fish and Wildlife Service (USFWS) are required to publish Stock Assessment Reports for all stocks of marine mammals within U.S. waters, to review new information every year for strategic stocks and every three years for non-strategic stocks, and to update the stock assessment reports when significant new information becomes available. This report presents stock assessments for 13 Pacific marine mammal stocks under NMFS jurisdiction, including 8 “strategic” stocks and 5 “non-strategic” stocks (see summary table). A new stock assessment for humpback whales in American Samoa waters is included in the Pacific reports for the first time. New or revised abundance estimates are available for 9 stocks, including Eastern North Pacific blue whales, American Samoa humpback whales, five U.S. west coast harbor porpoise stocks, the Hawaiian monk seal, and southern resident killer whales. A change in the abundance estimate of Eastern North Pacific blue whales reflects a recommendation from the Pacific Scientific Review Group to utilize mark-recapture estimates for this population, which provide a better estimate of total population size than the average of recent line-transect and mark-recapture estimates. The ‘Northern Oregon/Washington Coast Stock’ harbor porpoise stock assessment includes a name change (‘Oregon’ is appended to ‘Northern Oregon’) to reflect recent stock boundary changes. Changes in abundance estimates for the two stocks of harbor porpoise that occur in Oregon waters are the result of these boundary changes, and do not reflect biological changes in the populations. Updated information on the three stocks of false killer whales in Hawaiian waters is also included in these reports. Information on the remaining 50 Pacific region stocks will be reprinted without revision in the final 2009 reports and currently appears in the 2008 reports (Carretta et al. 2009). Stock Assessments for Alaskan marine mammals are published by the National Marine Mammal Laboratory (NMML) in a separate report. Pacific region stock assessments include those studied by the Southwest Fisheries Science Center (SWFSC, La Jolla, California), the Pacific Islands Fisheries Science Center (PIFSC, Honolulu, Hawaii), the National Marine Mammal Laboratory (NMML, Seattle, Washington), and the Northwest Fisheries Science Center (NWFSC, Seattle, WA). Northwest Fisheries Science Center staff prepared the report on the Eastern North Pacific Southern Resident killer whale. National Marine Mammal Laboratory staff prepared the Northern Oregon/Washington coast harbor porpoise stock assessment. Pacific Islands Fisheries Science Center staff prepared the report on the Hawaiian monk seal. Southwest Fisheries Science Center staff prepared stock assessments for 9 stocks. The stock assessment for the American Samoa humpback whale was prepared by staff from the Center for Coastal Studies, Hawaiian Islands Humpback National Marine Sanctuary, the Smithsonian Institution, and the Southwest Fisheries Science Center. Draft versions of the stock assessment reports were reviewed by the Pacific Scientific Review Group at the November 2008, Maui meeting. The authors also wish to thank those who provided unpublished data, especially Robin Baird and Joseph Mobley, who provided valuable information on Hawaiian cetaceans. Any omissions or errors are the sole responsibility of the authors. This is a working document and individual stock assessment reports will be updated as new information on marine mammal stocks and fisheries becomes available. Background information and guidelines for preparing stock assessment reports are reviewed in Wade and Angliss (1997). The authors solicit any new information or comments which would improve future stock assessment reports. These Stock Assessment Reports summarize information from a wide range of sources and an extensive bibliography of all sources is given in each report. We strongly urge users of this document to refer to and cite original literature sources rather than citing this report or previous Stock Assessment Reports. If the original sources are not accessible, the citation should follow the format: [Original source], as cited in [this Stock Assessment Report citation].

U.S. Pacific Marine Mammal Stock Assessments

Under the 1994 amendments to the Marine Mammal Protection Act, the National Marine Fisheries Service (NMFS) and the U.S. Fish and Wildlife Service (USFWS) were required to produce stock assessment reports for all marine mammal stocks in waters within the U.S. Exclusive Economic Zone. This document contains the stock assessment reports for the U.S. Pacific marine mammal stocks under NMFS jurisdiction. Marine mammal species which are under the management jurisdiction of the USFWS are not included in this report. A separate report containing background, guidelines for preparation, and .a summary of all stock assessment reports is available from the NMFS Office of Protected Resources. This report was prepared by staff of the Southwest Fisheries Science Center, NMFS and the Alaska Fisheries Science Center, NMFS. The information presented here was compiled primarily from published sources, but additional unpublished information was included where it contributed to the assessments. The authors wish to thanks the members of the Pacific Scientific Review Group for their valuable contributions and constructive criticism: Hannah Bernard, Robin Brown, Mark Fraker, Doyle Hanan, John Heyning, Steve Jeffries, Katherine Ralls, Michael Scott, and Terry Wright. Their comments greatly improved the quality of these reports, We also thanks the Marine Mammal Commission, The Humane Society of the United States, The Marine Mammal Center, The Center for Marine Conservation, and Friends of the Sea Otter for their careful reviews and thoughtful comments. Special thanks to Paul Wade of the Office of Protected Resources for his exhaustive review and comments, which greatly enhanced the consistency and technical quality of the reports. Any ommissions or errors are the sole responsibility of the authors. This is a working document and individual stock assessment reports will be updated as new information becomes available and as changes to marine mammal stocks and fisheries occur; therefore, each stock assessment report is intended to be a stand alone document. The authors solicit any new information or comments which would improve future stock assessment reports. This is Southwest Fisheries Science Center Technical Memorandum NOAA-TM-NMFS-SWFSC- 219, July 1995. 111

Modeling Age-Specific Mortality For Marine Mammal Populations

A method is presented for estimating age-specific mortality based on minimal information: a model life table and an estimate of longevity. This approach uses expected patterns of mammalian survivorship to define a general model of age-specific mortality rates. One such model life table is based on data for northern fur seals (Callorhinus ursinus) using Siler’s (1979) 5-parameter competing risk model. Alternative model life tables are based on historical data for human females and on a published model for Old World monkeys. Survival rates for a marine mammal species are then calculated by scaling these models by the longevity of that species. By using a realistic model (instead of assuming constant mortality), one can see more easily the real biological limits to population growth. The mortality estimation procedure is illustrated with examples of spotted dolphins (Stenella attenuata) and harbor porpoise (Phocoena phocoena).

A New Birth-Interval Approach to Estimating Demographic Parameters of Humpback Whales

A demographic model is developed based on interbirth intervals and is applied to estimate the population growth rate of humpback whales (Megaptera novaeangliae) in the Gulf of Maine. Fecundity rates in this model are based on the probabilities of giving birth at time t after a previous birth and on the probabilities of giving birth first at age x. Maximum likelihood methods are used to estimate these probabilities using sighting data collected for individually identified whales. Female survival rates are estimated from these same sighting data using a modified Jolly–Seber method. The youngest age at first parturition is 5 yr, the estimated mean birth interval is 2.38 yr (SE = 0.10 yr), the estimated noncalf survival rate is 0.960 (SE = 0.008), and the estimated calf survival rate is 0.875 (SE = 0.047). The population growth rate (l) is estimated to be 1.065; its standard error is estimated as 0.012 using a Monte Carlo approach, which simulated sampling from a hypothetical population of whales. The simulation is also used to investigate the bias in estimating birth intervals by previous methods. The approach developed here is applicable to studies of other populations for which individual interbirth intervals can be measured.

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.