Questions: What is the optimum combination of image dates across a growing season for tree species differentiation in multi-spectral data and how does species composition affect overstorey canopy density? Location: Monks Wood, Cambridgeshire, eastern England, UK. Methods: Six overstorey tree species were mapped using five Airborne Thematic Mapper images acquired across the 2003 growing season (). After image pre-processing, supervised maximum likelihood classification was performed on the images and on all two-, three-, four-and five-date combinations. Relationships between tree species composition and canopy density were assessed using regression analyses. Results: The image with the greatest tree species discrimination was acquired on 27/10 when the overstorey species were in different stages of leaf tinting and fall. In this image, tree species were mapped with an overall classification accuracy (OCA) of 71% (kappa 0.63). A similar OCA was achieved from the other four images combined (OCA 72%, kappa 0.64). The highest classification accuracy was achieved by combining three images: 17 March, 16 July, 27 October. This achieved an OCA of 84% (kappa 0.79), increasing to 88% (kappa 0.85) after a post-classification clump and sieve procedure. Canopy height and percentage cover of oak explained 72% of variance in canopy density. Conclusions: The ability to discriminate and map temperate deciduous tree species in airborne multispectral imagery is increased using time-series data. An autumn image supplemented with an image from both the green-up and full-leaf phases was optimum. The derived tree species map provides a more powerful ecological tool for determining woodland structural/ compositional relationships than field-based measures.

A series of 6 daylight observations was made each summer and again each winter over 2 years to map cattle distribution on a California foothill pasture. Sixty animals were used in the study with no animals appearing in. 1 observation series. During daylight hours, small herds of cows containing between 14 and 16 animals were scan-sampled and videotaped every 15 minutes. A global positioning system was used to record the position of the camera to aid in accurately locating individual animals. Animal locations and individual identifications were then entered into a geographic information system (GIS) by on-screen digitizing using color orthophotographs. Animal positions were determined to be within 5 m of their true location. Association software, ASSOC1, was used to analyze animal positions to determine cattle subgroups and herd units. This position-based grouping was compared with observation-based grouping by researchers. Direct observation also identified dominant herd members. Older animals, up to 16 years of age, were generally dominant over younger animals, and subgroups tended to be composed of animals of similar age. The size of naturally occurring subgroups was between 3 and 6 animals. Some animals exhibited independence in their actions and behaviors compared with subgroup members. ASSOC1 produced grouping results consistent with direct observations. However, accurate interpretation of the ASSOC1 results depended on direct observational data. ASSOC1 identified close association patterns in 3 of the observations that defined the dominant animals in the herd. Forage availability and thermoregulatory needs influenced the distance between associated subgroup members. Distance between animals decreased when animals sought shade in summer or shelter in winter. Computer analysis of spatial data from GPS collars may be able to determine the social structure and identify dominant animals in herd situations. Incorporating knowledge of cattle social behavior should improve management of cattle on the range.

The objective of this study was to identify and model environmental and management factors associated with cattle feces deposition patterns across annual rangeland watersheds in the Sierra Nevada foothills. Daily cattle fecal load accumulation rates were calculated from seasonal fecal loads measured biannually on 40 m2 permanent transects distributed across a 150.5 ha pasture in Madera County, Calif. during the 4 year period from 1995 through 1998. Associations between daily fecal load per season, livestock management, and environmental factors measured for each transect were determined using a linear mixed effects model. Cattle feces distribution patterns were significantly associated with location of livestock attractants, slope percentage, slope aspect, hydrologic position, and season. Transects located in livestock concentration areas experienced a significantly higher daily fecal load compared to transects outside of these concentration areas (P < 0.001). Percent slope was negatively associated with daily fecal load, but this association had a significant interaction with slope aspect (P = 0.02). Daily fecal load was significantly lower during the wet season compared to the dry season (P = 0.002). Daily fecal loading rates across hydrologic positions were dependent upon season. Our results illustrate the opportunities to reduce the risk of water quality contamination by strategic placement of cattle attractants, and provide a means to predict cattle feces deposition based upon inherent watershed characteristics and management factors.

The state and transition model and the ball and cup analogy are used to organize the vegetation dynamics knowledge base for California's annual-dominated Mediterranean grasslands. These models help identify irreversible transitions and alternate stable states. Mechanisms that facilitate movement between successional stable states are categorized as demographic inertia, seedbank and germination, grazing impacts, establishment and competition, fue feedback, and irreversible changes in soil conditions. While theoretical work needs to continue to further describe states and transitions, managers can begin to use existing knowledge to develop management plans with realistic species composition objectives and to select the appropriate tools for reaching objectives.

Medusahead is among the most invasive grasses in the western United States. Selective control of this noxious winter annual grass is difficult in California grasslands, as many other desirable annual grasses and both native and nonnative broadleaf forbs are also important components of the rangeland system. Intensive grazing management using sheep is one control option. This study was designed to determine the optimal timing for sheep grazing on heavily infested medusahead sites, and to evaluate the changes in species composition with different grazing regimes. Midspring (April/May) grazing reduced medusahead cover by 86 to 100% relative to ungrazed plots, regardless of whether it was used in combination with early spring or fall grazing. Early spring (March) or fall (October to November) grazing, alone or in combination, was ineffective for control of medusahead. In addition, midspring grazing increased forb cover, native forb species richness, and overall plant diversity. At the midspring grazing timing, medusahead was in the “boot” stage, just prior to exposure of the inflorescences. The success of this timely grazing system required high animal densities for short periods. Although this approach may be effective in some areas, the timing window is fairly narrow and the animal stocking rates are high. Thus, sheep grazing is unlikely to be a practical solution for management of large medusahead infestations