Michael Creel

ELK ALTER HABITAT SELECTION AS AN ANTIPREDATOR RESPONSE TO WOLVES

For elk (Cervus elaphus) in the Gallatin drainage of the Greater Yellowstone Ecosystem, Montana, USA, wolf movements caused local predation risk to vary substan- tially on a time scale of days. Spatially and temporally fine-scaled data from GPS radio collars show that elk moved into the protective cover of wooded areas when wolves were present, reducing their use of preferred grassland foraging habitats that had high predation risk. By constraining habitat selection, wolves may have greater effects on elk dynamics than would be predicted on the basis of direct predation alone. Based on changes in the woody vegetation following the reintroduction of wolves, it has been suggested that an- tipredator responses by elk may be driving a trophic cascade in the Yellowstone ecosystem. However, studies to date have been hampered by a lack of direct data on spatial variation in predation risk, and the ways in which elk respond to variation in risk. Our data support a central portion of the hypothesis that elk antipredator behavior could drive a trophic cascade, but changes in elk numbers are also likely to have affected elk-plant interactions.

Confidence intervals for evaluating benefits estimates from dichotomous choice contingent valuation studies.

I evaluate the potential usefulness of nonmarket valuation concepts and techniques from environmental economics for improving wildlife conservation. The concepts include distinguishing between on-site recreation use value and off-site passive use or ...

Recreation value of water to wetlands in the San Joaquin Valley: Linked multinomial logit and count data trip frequency models

The recreational benefits from providing increased quantities of water to wildlife and fisheries habitats is estimated using linked multinomial logit site selection models and count data trip frequency models. The study encompasses waterfowl hunting, fishing and wildlife viewing at 14 recreational resources in the San Joaquin Valley, including the National Wildlife Refuges, the State Wildlife Management Areas, and six river destinations. The economic benefits of increasing water supplies to wildlife refuges were also examined by using the estimated models to predict changing patterns of site selection and overall participation due to increases in water allocations. Estimates of the dollar value per acre foot of water are calculated for increases in water to refuges. The resulting model is a flexible and useful tool for estimating the economic benefits of alternative water allocation policies for wildlife habitat and rivers.

Confidence Intervals for Welfare Measures with Application to a Problem of Truncated Counts

Demand for deer hunting trips was estimated using statistical models based on the normal, Poisson, and negative binomial probability laws. Some of the models accounted for existing sampling truncation. Estimates of Marshallian and Hicksian welfare measures are presented, accompanied by 90 percent confidence intervals based on Krinsky and Robbs procedure. For each of the statistical models, the Hicksian measures are found to be very close to the Marshallian measures, with similar confidence intervals. Accounting for the truncation of the dependent variable has a statistically significant effect on the resulting estimates of welfare measures. Copyright 1991 by MIT Press.

Density dependence and climate effects in Rocky Mountain elk: an application of regression with instrumental variables for population time series with sampling error

1. Sampling error in annual estimates of population size creates two widely recognized problems for the analysis of population growth. First, if sampling error is mistakenly treated as process error, one obtains inflated estimates of the variation in true population trajectories (Staples, Taper & Dennis 2004). Second, treating sampling error as process error is thought to overestimate the importance of density dependence in population growth (Viljugrein et al. 2005; Dennis et al. 2006). 2. In ecology, state-space models are used to account for sampling error when estimating the effects of density and other variables on population growth (Staples et al. 2004; Dennis et al. 2006). In econometrics, regression with instrumental variables is a well-established method that addresses the problem of correlation between regressors and the error term, but requires fewer assumptions than state-space models (Davidson & MacKinnon 1993; Cameron & Trivedi 2005). 3. We used instrumental variables to account for sampling error and fit a generalized linear model to 472 annual observations of population size for 35 Elk Management Units in Montana, from 1928 to 2004. We compared this model with state-space models fit with the likelihood function of Dennis et al. (2006). We discuss the general advantages and disadvantages of each method. Briefly, regression with instrumental variables is valid with fewer distributional assumptions, but state-space models are more efficient when their distributional assumptions are met. 4. Both methods found that population growth was negatively related to population density and winter snow accumulation. Summer rainfall and wolf (Canis lupus) presence had much weaker effects on elk (Cervus elaphus) dynamics [though limitation by wolves is strong in some elk populations with well-established wolf populations (Creel et al. 2007; Creel & Christianson 2008)]. 5. Coupled with predictions for Montana from global and regional climate models, our results predict a substantial reduction in the limiting effect of snow accumulation on Montana elk populations in the coming decades. If other limiting factors do not operate with greater force, population growth rates would increase substantially.

Modeling hunting demand in the presence of a bag limit, with tests of alternative specifications

Abstract Bag limits complicate the measurement of consumer surplus from hunting because the point at which hunters cease taking trips is not necessarily the point at which they are unwilling to pay for additional hunting trips. We develop and apply an econometric model which accounts for bag limits to data of deer hunting in California, and we employ several alternative models that do not account for the bag limit. We conduct non-nested testing of the models, and estimate welfare measures at existing conditions and after a quality improvement. Confidence intervals are provided for the welfare measurements.

ECONOMIES OF SCALE IN THE US AIRLINE INDUSTRY AFTER DEREGULATION: A FOURIER SERIES APPROXIMATION

Abstract This paper analyzes the cost structure of the US airline industry after deregulation, finding that there exist economies of scale at moderate levels of output but that they die off at high levels. The cost–share system is estimated using the Fourier functional form. The global approximation property of the Fourier form allows consistent inference throughout the range of the data. The existence of economies of scale suggests that airlines will try to grow to the efficient size. This conclusion is consistent with the evolution of the US airline industry in the last decade.

Welfare Estimation Using The Fourier Form: Simulation Evidence For The Recreation Demand Case

The paper considers the estimation of welfare measures when the functional form of demand is unknown. An adaptation of an argument of Gallant (1987) is used to show that welfare estimators based on a Fourier functional form for demand will be consistent under weak assumptions. Simulation evidence is presented for equivalent variation. True demand is a generalized BoxCox function, estimated demand is a Fourier form, and equivalent variation is estimated by applying Vartias (1983) algorithm to the estimated demand function. The estimator of equivalent variation has small asymptotic bias in the case of the assumed family of data generating processes.

Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.

High Performance Implementation of an Econometrics and Financial Application on GPUs

In this paper, we describe a GPU based implementation for an estimator based on an indirect likelihood inference method. This method relies on simulations from a model and on nonparametric density or regression function computations. The estimation application arises in various domains such as econometrics and finance, when the model is fully specified, but too complex for estimation by maximum likelihood. We implemented the estimator on a machine with two 2.67GHz Intel Xeon X5650 processors and four NVIDIA M2090 GPU devices. We optimized the GPU code by efficient use of shared memory and registers available on the GPU devices. We compared the optimized GPU code performance with a C based sequential version of the code that was executed on the host machine. We observed a speed up factor of up to 242 with four GPU devices.