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Juniper encroachment into shrub steppe and grassland systems is one of the most prominent changes occurring in rangelands of western North America. Most studies on juniper change are conducted over small areas, although encroachment is occurring across large regions. Development of image-based methods to assess juniper encroachment over large areas would facilitate rapid monitoring and identification of priority areas for juniper management. In this study, we fused Landsat 5 Thematic Mapper and Light Detection and Ranging (lidar)-based juniper classifications to evaluate juniper expansion patterns in the Reynolds Creek Experimental Watershed of southwestern Idaho. Lidar applications for characterizing juniper encroachment attributes at finer scales were also explored. The fusion-based juniper classification model performed well (83% overall accuracy). A comparison of the resulting juniper presence/absence map to a 1965 vegetation cover map indicated 85% juniper expansion, which was consistent with tree-ring data. Comparisons of current and previous canopy-cover estimates also indicated an increase in juniper density within the historically mapped juniper distribution. Percent canopy cover of juniper varied significantly with land-cover types highlighting areas where intensive juniper management might be prioritized. Resumen El avance del Juniperus sobre las estepas arbustivas y los ecosistemas graminosos es uno de los cambios más prominentes ocurriendo actualmente en pastizales del Oeste de Ame´rica del Norte. La mayoría de los estudios sobre cambios asociados al Juniperus se conducen en áreas pequeñ as a pesar de que la invasión de esta especie está ocurriendo a escala de grandes regiones. El desarrollo de métodos basados en imágenes para relevar el avance del Juniperus a escala de áreas extensas facilitaría el monitoreo expeditivo y la identificación de áreas de manejo de Juniperus. En este ensayo fusionamos clasificaciones de Juniperus de imágenes Landsat 5 TM y LIDAR para evaluar los patrones de expansión del Juniperus en la cuenca experimental de Reynolds Creek del sudoeste de Idaho. Aplicaciones del LIDAR para caracterizar atributos de la invasión de Juniperus a escalas más finas tambie´n fueron exploradas. El modelo basado en la fusión de la clasificación de Juniperus tuvo un buen desempeñ o (83% de exactitud general). Una comparación del mapa de presencia/ausencia de Juniperus obtenido con este análisis con un mapa de cobertura de vegetación de 1965 indicó un 85% de expansión de Juniperus, patrón que fue consistente con datos de anillos de crecimiento de los árboles. La comparación de estimaciones de cobertura de canopeo actuales y pasadas también indicó un aumento en la densidad de Juniperus dentro del área de distribución histórica de Juniperus mapeada. El porcentaje de cobertura del canopeo de Juniperus varió significativamente con el tipo de cobertura del terreno remarcando áreas en las que el manejo intensivo del Juniperus podría ser priorizada.

Probit models for estimating hydrothermal germination rate yield model parameters that have been associated with specific physiological processes. The desirability of linking germination response to seed physiology must be weighed against expectations of model fit and the relative accuracy of predicted germination response. Computationally efficient empirical models have been proposed that do not require a priori assumptions about model shape parameters, but the accuracy of these models has not been compared to the more common probit‐optimization procedure. Thirteen seedlots, representing six native perennial rangeland grasses and an invasive annual weed, were germinated over the constant temperature range of 3 to 36°C and water potential range of 0 to ‐2.5 MPa. Hydrothermal germination models were generated using probit optimization, optimized regression, and statistical gridding. These models were evaluated for the pattern and magnitude of residual model error and the relative magnitude of predictive errors under field‐simulated temperature and moisture conditions. Residual model errors in predictions of germination rate were greatest for the probit optimization procedure. Statistical gridding and optimized regression produced lower predictive model error, but the latter procedure could not resolve germination response for slower‐germinating seed populations. The more computationally efficient and accurate regression and statistical‐gridding procedures may be desirable for identifying germination strategies and syndromes that are based on predicted response to simulated conditions of field temperature and moisture.

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