Mission Revival Jurisprudence: State Courts and Hispanic Water Law Since 1850

In this Article, the author argues that after the United States\u27 annexation of the Southwest, state judges in California, New Mexico, and Texas knowingly distorted the communal nature of applicable Spanish and Mexican water law. While previous scholars have acknowledged that courts misinterpreted municipal and riparian water rights originating in the Southwest\u27s Hispanic period, most historians have attributed the distortion to ignorance rather than design. Using archival sources, the author demonstrates that American judges created an historical fiction of Spanish absolute water control, and intentionally disregarded actual law and custom dictating water apportionment. The resulting doctrines of pueblo water rights and riparian irrigation rights facilitated water monopoly and accumulation by cities and large landowners. This intentional manipulation of Hispanic law bears implications for legal historical debates and contemporary water allocation problems

Litigating Property under the Guadalupe Hidalgo Treaty: The Sangre de Cristo Land Grant Case.

Abstract Forthcoming

Litigating Property under the Guadalupe Hidalgo Treaty: The Sangre de Cristo Land Grant Case.

Abstract Forthcoming

Wind and Fire: Rapid Shifts in Tree Community Composition Following Multiple Disturbances in the Southern Boreal Forest

Under a warming climate, the southern boreal forest of North America is expected to see a doubling in fire frequency and potential for increased wind disturbance over the next century. Although boreal forests are often considered fire-adapted, projected increases in disturbance frequency will likely result in novel combinations of disturbances with severities and impacts on community composition outside historic norms. Using a network of repeatedly measured vegetation monitoring plots, we followed changes in tree community composition in areas of the Boundary Waters Canoe Area Wilderness (BWCAW), in Minnesota, USA, experiencing disturbances ranging from severe windstorms or wildfires to areas affected by wind followed by fire or multiple fires within a short period of time. Using nonmetric multidimensional scaling ordination, hierarchical cluster analysis, and permutational analysis of variance, we compared successional pathways across different disturbance types and combinations to test whether multiple disturbances had altered successional pathways or caused greater convergence relative to single disturbances. We found that multiple disturbances often resulted in strong shifts toward wind-dispersed early-successional tree species, while single disturbances tended to have multiple successional pathways that favored both late- and early-successional species. All disturbances in our study resulted in significant shifts in composition, but we generally failed to find statistical evidence of changes in community dispersion. Although boreal forests appear to be somewhat resilient to multiple disturbance events, multiple disturbances resulted in post-disturbance tree communities that were heavily dominated by disturbance-adapted deciduous trees at the expense of conifers. Our results demonstrate that multiple disturbances are capable of altering successional pathways relative to single disturbance events and that increasingly frequent disturbances are likely to alter boreal forest structure and composition, perhaps leading to a forest region strikingly unlike that of today

Predicting the effects of climate change on water yield and forest production in the northeastern United States

Rapid and simultaneous changes in temperature, precipitation and the atmospheric concentration of CO2 are predicted to occur over the next century. Simple, well-validated models of ecosystem function are required to predict the effects of these changes. This paper describes an improved version of a forest carbon and water balance model (PnET-II) and the application of the model to predict stand- and regional-level effects of changes in temperature, precipitation and atmospheric CO2 concentration. PnET-II is a simple, generalized, monthly time-step model of water and carbon balances (gross and net) driven by nitrogen availability as expressed through foliar N concentration. Improvements from the original model include a complete carbon balance and improvements in the prediction of canopy phenology, as well as in the computation of canopy structure and photosynthesis. The model was parameterized and run for 4 forest/site combinations and validated against available data for water yield, gross and net carbon exchange and biomass production. The validation exercise suggests that the determination of actual water availability to stands and the occurrence or non-occurrence of soil-based water stress are critical to accurate modeling of forest net primary production (NPP) and net ecosystem production (NEP). The model was then run for the entire NewEngland/New York (USA) region using a 1 km resolution geographic information system. Predicted long-term NEP ranged from -85 to +275 g C m-2 yr-1 for the 4 forest/site combinations, and from -150 to 350 g C m-2 yr-1 for the region, with a regional average of 76 g C m-2 yr-1. A combination of increased temperature (+6*C), decreased precipitation (-15%) and increased water use efficiency (2x, due to doubling of CO2) resulted generally in increases in NPP and decreases in water yield over the region

Century-scale wood nitrogen isotope trajectories from an oak savanna with variable fire frequencies

Fire frequency exerts a fundamental control on productivity and nutrient cycling in savanna ecosystems. Individual fires often increase short-Term nitrogen (N) availability to plants, but repeated burning causes ecosystem N losses and can ultimately decrease soil organic matter and N availability. However, these effects remain poorly understood due to limited long-Term biogeochemical data. Here, we evaluate how fire frequency and changing vegetation composition influenced wood stable N isotopes (15N) across space and time at one of the longest running prescribed burn experiments in the world (established in 1964). We developed multiple 15N records across a burn frequency gradient from precisely dated Quercus macrocarpa tree rings in an oak savanna at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Sixteen trees were sampled across four treatment stands that varied with respect to the temporal onset of burning and burn frequency but were consistent in overstory species representation, soil characteristics, and topography. Burn frequency ranged from an unburned control stand to a high-fire-frequency stand that had burned in 4 of every 5 years during the past 55 years. Because N stocks and net N mineralization rates are currently lowest in frequently burned stands, we hypothesized that wood 15N trajectories would decline through time in all burned stands, but at a rate proportional to the fire frequency. We found that wood 15N records within each stand were remarkably coherent in their mean state and trend through time. A gradual decline in wood 15N occurred in the mid-20th century in the no-, low-, and medium-fire stands, whereas there was no trend in the highfire stand. The decline in the three stands did not systematically coincide with the onset of prescribed burning. Thus, we found limited evidence for variation in wood 15N that could be attributed directly to long-Term fire frequency in this prescribed burn experiment in temperate oak savanna. Our wood 15N results may instead reflect decadal-scale changes in vegetation composition and abundance due to early-to mid-20th-century fire suppression

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images