Great Plains Flora? Plant Geography of Eastern Montana\u27s Lower Elevation Shrub-grass Dominated Vegetation

Montana plant geography at elevations below montane forests broadly includes open shrub-grass or ponderosa pine dominated dry sites and riparian-wetland systems. In contrast to conventional wisdom, the floristic composition of these settings in eastern Montana does not reflect a strong Great Plains influence. State and county geographical distribution patterns suggestive of an influence of the Great Plains flora on that of eastern Montana involve only 52 species of mostly uncommon and narrowly distributed species of dicot forbs that do not compose a common type of characteristic Great Plains plant community in the state. In addition, the floristic similarity of the grass family, Poaceae, which is very diverse in open dry vegetation in Montana, reveals that Montana shares many more grass species in common with Utah than with the adjacent Great Plains state of South Dakota. Instead of the Great Plains biome, the low elevation flora and vegetation of Montana appears to be part of or influenced by the Pacific Northwest, Boreal, and the Intermountain biome. From the perspective of plant identification, the volumes of the Intermountain Flora works as well as or better across the state of Montana at low elevation dry settings compared to the Flora of the Great Plains or the Flora of the Pacific Northwest. The flora and vegetation of the open dry settings of eastern Montana is generally characteristic of the Intermountain sagebrush steppe and this should be considered in “prairie” restoration programs, especially when large ungulates like bison are proposed for reintroduction

Aqua temporaria incognita

Invited commentary

Comprehensive space-time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping

Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e. return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realizations of hourly precipitation and temperature scenarios of 10 000 years each. These realizations were then run through a bucket-type hydrological model for 80 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping

Parrots Eat Nutritious Foods despite Toxins

Generalist herbivores are challenged not only by the low nitrogen and high indigestibility of their plant foods, but also by physical and chemical defenses of plants. This study investigated the foods of wild parrots in the Peruvian Amazon and asked whether these foods contain dietary components that are limiting for generalist herbivores (protein, lipids, minerals) and in what quantity; whether parrots chose foods based on nutrient content; and whether parrots avoid plants that are chemically defended.We made 224 field observations of free-ranging parrots of 17 species in 8 genera foraging on 102 species of trees in an undisturbed tropical rainforest, in two dry seasons (July-August 1992-1993) and one wet season (January-February1994). We performed laboratory analyses of parts of plants eaten and not eaten by parrots and brine shrimp assays of toxicity as a proxy for vertebrates. Parrots ate seeds, fruits, flowers, leaves, bark, and insect larvae, but up to 70% of their diet comprised seeds of many species of tropical trees, in various stages of ripeness. Plant parts eaten by parrots were rich in protein, lipid, and essential minerals, as well as potentially toxic chemicals. Seeds were higher than other plant materials in protein and lipid and lower in fiber. Large macaws of three species ate foods higher in protein and lipids and lower in fiber compared to plant parts available but not eaten. Macaws ate foods that were lower in phenolic compounds than foods they avoided. Nevertheless, foods eaten by macaws contained measurable levels of toxicity. Macaws did not appear to make dietary selections based on mineral content.Parrots represent a remarkable example of a generalist herbivore that consumes seeds destructively despite plant chemical defenses. With the ability to eat toxic foods, rainforest-dwelling parrots exploited a diversity of nutritious foods, even in the dry season when food was scarce for other frugivores and granivores

Polygenic hazard score is associated with prostate cancer in multi-ethnic populations

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score >= 7, stage T3-T4, PSA >= 10ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n=71,856), Asian (n=2,382), and African (n=6,253) genetic ancestries (p<10(-180)). Comparing the 80(th)/20(th) PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset. A polygenic hazard score (PHS1) improves prostate cancer screening accuracy in European patients. Here, the authors test the performance of a version compatible with OncoArray genotypes (PHS2) in a multi-ethnic dataset and find that it risk-stratifies men for any, aggressive, and fatal prostate cancer

Polygenic hazard score is associated with prostate cancer in multi-ethnic populations.

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10-180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset

Bivariate return periods and their importance for flood peak and volume estimation

Estimates of flood event magnitudes with a certain return period are required for the design of hydraulic structures. While the return period is clearly defined in a univariate context, its definition is more challenging when the problem at hand requires considering the dependence between two or more variables in a multivariate framework. Several ways of defining a multivariate return period have been proposed in the literature, which all rely on different probability concepts. Definitions use the conditional probability, the joint probability, or can be based on the Kendall’s distribution or survival function. In this study, we give a comprehensive overview on the tools that are available to define a return period in a multivariate context. We especially address engineers, practitioners, and people who are new to the topic and provide them with an accessible introduction to the topic. We outline the theoretical background that is needed when one is in a multivariate setting and present the reader with different definitions for a bivariate return period. Here, we focus on flood events and the different probability concepts are explained with a pedagogical, illustrative example of a flood event characterized by the two variables peak discharge and flood volume. The choice of the return period has an important effect on the magnitude of the design variable quantiles, which is illustrated with a case study in Switzerland. However, this choice is not arbitrary and depends on the problem at hand

Identification of flood reactivity regions via the functional clustering of hydrographs

Flood hydrograph shapes contain valuable information on the flood-generation mechanisms of a catchment. To make good use of this information, we express flood hydrograph shapes as continuous functions using a functional data approach. We propose a clustering approach based on functional data for flood hydrograph shapes to identify a set of representative hydrograph shapes on a catchment scale and use these catchment-specific sets of representative hydrographs to establish regions of catchments with similar flood reactivity on a regional scale. We applied this approach to flood samples of 163 medium-size Swiss catchments. The results indicate that three representative hydrograph shapes sufficiently describe the hydrograph shape variability within a catchment and therefore can be used as a proxy for the flood behavior of a catchment. These catchment-specific sets of three hydrographs were used to group the catchments into three reactivity regions of similar flood behavior. These regions were not only characterized by similar hydrograph shapes and reactivity but also by event magnitudes and triggering event conditions. We envision these regions to be useful in regionalization studies, regional flood frequency analyses, and to allow for the construction of synthetic design hydrographs in ungauged catchments. The clustering approach based on functional data which establishes these regions is very flexible and has the potential to be extended to other geographical regions or towards the use in climate impact studies

Flood volume estimation in Switzerland using synthetic design hydrographs - a multivariate statistical approach

Accurate estimations of flood peaks, volumes and hydrographs are needed to design safe and cost-effective hydraulic structures. In this study, we propose a statistical approach for the estimation of the design variables peak and volume by constructing a synthetic design hydrograph. Our approach is based on fitting probability density functions to observed flood hydrographs and takes the dependence between the two variables peak and volume into account. The method consists of the following six steps: sampling of flood events, baseflow separation, normalization of the hydrographs, fitting of the hydrography with statistical density functions, modeling of peak and volume considering their dependence, and construction of the synthetic design hydrograph. The method was developed and tested based on data from nine meso-scale catchments in Switzerland, and has been shown to provide reliable synthetic design hydrographs for all of these catchments. While the method has so far been applied to gauged catchments, it is foreseen to make it applicable to engaged catchments using regionalization approaches