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A comparison of the engineering schools at UC Berkeley and Stanford during the 1940s and 1950s shows that having an excellent academic program is necessary but not sufficient to make a university entrepreneurial (an engine of economic development). Key factors that made Stanford more entrepreneurial than Cal during this period were superior leadership and a focused strategy. The broader institutional context mattered as well. Stanford did not have the same access to state funding as public universities (such as Cal in the period under consideration) and some private universities (such as the Massachusetts Institute of Technology and the Johns Hopkins University in their early histories). Therefore, in order to gather resources, Stanford was forced to become entrepreneurial first, developing "business" skills (engaging with high-tech industry) at the same time Cal was developing "political" skills (protecting and increasing its state appropriation). Stanford's early development of entrepreneurial business skills played a crucial role in the development of Silicon Valley.

Population monitoring is essential to know whether coastal California's Endangered Species Act-listed Chinook salmon , coho salmon , and steelhead stocks are progressing toward recovery. In coastal California, salmonids are at the southern edge of their range, and this one of many reasons they are not abundant. This provides unique challenges for monitoring, as different survey methods will result in estimates with different levels of accuracy and precision, which are important for evaluating population trends. For this study we intensively monitored three Mendocino County watersheds to evaluate the reliability of two-stage data for monitoring regional escapement. Under this scheme, regional spawning surveys (stage 1) were calibrated with data from intensively monitored watersheds (stage 2), where escapement was estimated using capture-recapture methods, redd counts, and fish counts. The objective of the study was to evaluate the quality of the stage 2 data for calibrating regional surveys. We evaluated the precision of live-fish capture-recapture estimates and compared these estimates with estimates derived from spawning survey data using carcass capture-recapture, area under the curve (AUC), and redd counts. Live-fish capture-recapture produced escapement estimates with narrower 95% confidence bounds where permanent structures were used to capture fish. Redd counts converted to fish numbers using spawner : redd ratios were chosen for the regional salmonid monitoring method because they were reliable, economical, and less intrusive. Converted redd counts were statistically and operationally similar to live-fish capture-recapture estimates but required fewer resources. The AUC estimates were less reliable than converted redd counts and live-fish capture-recapture methods due to the sensitivity of the estimates of residence time and observer efficiency. Finally, we found that carcass capture-recapture methods were operationally unsuccessful in coastal California streams. On the basis of our results, we recommend that annual spawner : redd ratios from intensively monitored watersheds be used to calibrate redd counts for regional status and trend monitoring of California's coastal salmonids. [ABSTRACT FROM AUTHOR]

In coastal California, many evolutionarily significant units (ESUs) of Chinook salmon , coho salmon , and steelhead are listed under the federal Endangered Species Act (ESA). Monitoring species status at the ESU or ESU subdivision scale requires specialized sampling. The purposes of this study were (1) to evaluate abundance estimated from a generalized random tessellation stratified (GRTS) design and compare the results with those from a more intensive stratified random monitoring program and (2) to evaluate the statistical power of the design to detect population trends. This 3-year pilot study considered five Mendocino County streams as an example region of coastal California to evaluate a two-stage sampling approach for monitoring regional escapement. Under this scheme, regional redd surveys (stage 1) were conducted in stream reaches in a GRTS sampling design. Ten percent of anadromous habitat was sampled in year 1 and 10-35% in years 2 and 3. Spawner : redd ratios were derived from smaller-scale census watersheds (stage 2) where 'true' escapement was estimated using capture-recapture methods. Regional escapement was then estimated from expanded redd counts, calibrated by spawner : redd ratios. As an alternative, more intensive method for estimating escapement, three survey streams were also sampled in a stratified random design. The results, added to counts from the census basins, produced more rigorous 'sum-of-streams' estimates for comparison with the GRTS sampling. Redd counts and the resulting escapement estimates were reliable for regional monitoring. The GRTS and sum-of-streams estimates overlapped, and the variation in the 95% confidence intervals did not change after 15%. Our results suggest that a sample size of 15% or 41 or more reaches (whichever results in fewer survey reaches) should have adequate precision and statistical power to detect regional trends in salmon populations. We recommend that this monitoring approach be applied at regional spatial scales consistent with ESA recovery planning efforts. [ABSTRACT FROM AUTHOR]

Abstract: At the southern end of its range, the Northern Spotted Owl (Strix occidentalis caurina) occurs in high densities and nests in a wide range of forest types and ages, exhibiting different foraging and nesting habits than in the northern part of its range. The intensive monitoring of this subspecies on public lands in Marin County, California, combined with the availability of fine-scale geographic information system (GIS) data, provided a unique opportunity to apply and evaluate a habitat-based species occurrence modeling approach at the scale most relevant to local land managers and planning agencies. We used 4 years of breeding owl survey data (1998–2001) and GIS layers representing topographic, anthropogenic, and vegetation-based landscape characteristics to build logistic regression models of owl nest-site occurrence. Models were used to develop spatial predictions of occurrence within the study area and in adjacent ecoregions, which were validated with an independent dataset. We also compared the predictive performance of two vegetation layers differing in their floristic detail and spatial accuracy. The model based on a local vegetation layer generally exhibited better model performance than the model based on the more generic regional layer. Model results indicated that forest connectivity and topographic conditions, rather than forest type or age, were the strongest predictors of nesting owl presence. Predicting outside the original study area was somewhat successful for a coastal ecoregion similar in vegetation and climate, but not better than random for a nearby inland ecoregion, suggesting that locally derived models are necessary to adequately predict nest-site occurrence. [Copyright &y& Elsevier]