Spatial Distribution of Trees and Landscapes of the Past: A Mixed Spatially Correlated Multinomial Logit Model Approach for the Analysis of the Public Land Survey Data

Public Land Survey (PLS) data have been widely used in landscape studies of forest and woodlands in the pre‐ and early‐European‐settled Midwestern and Western United States. We aim to reconstruct presettlement forest vegetation at a finer spatial resolution than available from the PLS data using environmental covariates (slope, aspect, geology, and soil type) and the spatially correlated structure of witness tree data. To accommodate various data obtained from multiple sources while explicitly taking into account their spatial structures, we adopt a mixed spatially correlated multinomial logit model within the framework of a generalized linear mixed model. The application of the proposed model is illustrated using the three most abundant tree taxa from PLS data in the Arbuckle Mountains of south‐central Oklahoma. To assess the influence of each source of information on the spatial prediction, we considered four variant multinomial/spatial models and evaluated their relative predictive power using a validation technique. The probabilistic information about the spatial distribution of tree species obtained from different models reveals the need to integrate information about witness tree data as well as environmental covariates, and the nature of tree species; that is, a tendency to cluster in space to share environmental conditions in the reconstruction of the presettlement forest vegetation surface. Los datos sobre el uso y cobertura de tierras del Public Land Survey (PLS) han sido utilizados ampliamente en estudios de paisaje de bosques y de bosques históricos para periodo previo al asentamiento de migrantes europeos en el medio oeste y oeste de los Estados Unidos. Nuestro objetivo es reconstruir la vegetación forestal previa al asentamiento europeo a una resolución espacial más fina que la disponible actualmente en base a datos del PLS, usando covariables ambientales (pendiente, orientación, geología y tipo de suelo) y la estructura de correlación espacial de los datos de los árboles testigos. Para dar cabida a los diversos datos obtenidos de fuentes múltiples, y a la vez teniendo en cuenta explícitamente sus estructuras espaciales, adoptamos un modelo logit multinomial espacial mixto dentro del marco de los modelos mixtos lineales generalizados (GLMM). La aplicación del modelo propuesto es ilustrada con los tres tipos más abundantes de árboles según los datos del PLS para las montañas de Arbuckle en el centro‐sur de Oklahoma, EEUU. Para evaluar la influencia de cada fuente de información sobre la predicción espacial, se consideraron cuatro variantes de los modelos multinomial y espaciales. El poder predictivo de dichos modelos fue evaluado en relación con una técnica de validación. La información probabilística acerca de la distribución espacial de las especies de árboles obtenidos a partir de los diferentes modelos revela que para la reconstrucción de la superficie de la vegetación forestal histórica, es necesario integrar la información sobre los datos de árboles testigos así como las covariables ambientales y la naturaleza de las especies de árboles: es decir, la tendencia de los arboles a agruparse en el espacio para compartir las mismas condiciones ambientales. 公共土地调查(PLS)数据在欧洲人定居美国中西部和西部地区之前以及早期的森林和林地景观研究中得到广泛应用。本文旨在利用环境协变量（坡度、坡向、地貌和土地类型）证据树数据的空间关联结构,重建比PLS数据中更有效的更精细空间分辨率的前殖民期森林植被。为集成多种来源的各类数据,并明确地考虑数据间的空间结构,本文在广义线性混合模型(GLMM)框架下提出了混合空间关联多项Logit模型。以俄克拉荷马州中南部的阿尔布克尔山脉为研究区,提取PLS数据中三种最丰富的树种对模型进行验证。为估计每种信息来源对模型空间预测准确性的影响,本文考虑了4种变异的多项/空间模型并运用验证技术评估它们的相对预测能力。从不同模型获得的树种空间分布的概率信息表明,需要对证据树数据、环境协变量和树种自然属性信息进行集成,也就是说,在重建前殖民期森林植被曲面时,空间上的集聚趋势共享了环境条件。     

Archaeology advances?

Michael B. Schiffer, ed. Advances in Archaeological Method and Theory, New York: Academic Press, 1981, 1982, 1983. Vol. IV, xii + 443 pp., $38.00; Vol. V, xiii + 478 pp., $42.00; Vol. VI, xiii + 359 pp., $37.50; Advances in Archaeological Method and Theory: Selections for Students from Volumes 1 through 4, New York: Academic Press, 1982, xiv + 690 pp., $19.95     

Measuring cost escalation in the formative era of U.S. higher education, 1875–1930

ABSTRACT

Cost escalation in higher education in the United States prior to 1930 has scarcely been studied, even though the period from the 1870s to the 1920s was formative for U.S. higher education. This article develops and explains a method to measure the cost during this period. The authors then compile more accurate cost data than have been available, calculate new cost indexes for higher education from 1875 to 1930, and compare these indexes to economy-wide indexes. The striking findings inform the two leading economic theories of cost escalation, advanced by economists Howard R. Bowen and William G. Bowen. Cost escalation in total expenses of higher education occurred consistently between 1875 and 1930, and exceeded the worrisome rate that economist Howard Bowen found for the period from 1930 to 1977. Cost escalation did not occur in the more salient per capita terms. This latter finding, combined with recent historical research, supports the “revenue theory of cost” of Howard Bowen and challenges the “cost disease theory” of William Bowen.     

CONSERVATION LAND AMENITIES AND REGIONAL ECONOMIES: A POSTMATCHING DIFFERENCE‐IN‐DIFFERENCES ANALYSIS OF THE NORTHWEST FOREST PLAN

The 1994 Northwest Forest Plan (NWFP) resulted in the protection of over 11 million acres of public forestland in the Pacific Northwest region of the United States. This paper quantifies the amenity effect arising from protected NWFP lands on long‐run community economic growth. Using community fixed effects and postmatching panel regression to control for many sources of bias, we find highly localized and positive amenity impacts on the growth in median income, population, and property values for small communities close to protected NWFP land, as compared to communities far from the NWFP. We find no effect on medium‐sized communities.     

Waivers,Federalism, and the Rule of Law

Waivers from legislation have been praised as the foundation for a new era of collaborative federalism. But these exceptions of law, often made without clear statutory authority, undermine the necessary basis for collaboration among governmental entities. That basis is law. Waivers in effect dispense from or even change laws in the interests of administrative flexibility. And, while some commentators have lauded this increased flexibility as empowering for state governments in particular, the real effect is to reduce relations based on law to a condition of perpetual bargaining in which federal administrators hold all the power. Able to change rules and even laws almost at will through new waiver programs and criteria, federal agencies gain arbitrary power, undermining the ability of states and people to plan for their futures on the basis of known rules—the most basic and necessary good provided by the rule of law.