What are riparian ecosystems and why are we worried about them

Riparian areas represent less than 2 percent of all terrestrial ecosystems, but they are functionally on of the most important features within natural landscapes. They are characterized by high biotic production and diversity; they moderate flood intensity and store water; and they maintain high water quality by acting as nutrient and sediment sinks. These ecological functions make them valuable areas for a variety of human uses including agriculture, timber and livestock production, recreation, and housing. Human use, however, has resulted in severe degradation of the functional health of many riparian ecosystems. Recognition of the value of the systems and the magnitude of existing and continuing degradation has generated a concerted effort by natural resources managers and researchers to develop strategies to protect and restore riparian areas. Issues requiring particular attention are (1) development of a generally accepted definition of riparian ecosystems, (2) development of a functionally useful classification scheme of riparian areas, (3) quantification of the specific ways that human use causes ecological dysfunction, (4) collection of data from which we can objectively prioritize efforts to preserve extant systems, and (5) development of ecologically sound strategies for the restoration of degraded areas

Maintaining and restoring the ecological integrity of freshwater ecosystems: improving management by refining biological assessments

Water resources managers and conservation biologists need reliable, quantitative, and directly comparable methods for assessing the biological integrity of the world\u27s aquatic ecosystems. Large-scale assessments are constrained by the lack of consistency in the indicators used to assess biological integrity and our current inability to translate between indicators. In theory, assessments based on estimates of taxonomic completeness, i.e., the proportion of expected taxa that were observed (observed/expected, O/E) are directly comparable to one another and should therefore allow regionally and globally consistent summaries of the biological integrity of freshwater ecosystems. However, we know little about the true comparability of O/E assessments derived from different data sets or how well O/E assessments perform relative to other indicators in use. I compared the performance (precision, bias, and sensitivity to stressors) of O/E assessments based on five different data sets with the performance of the indicators previously applied to these data (three multimetric indices, a biotic index, and a hybrid method used by the state of Maine). Analyses were based on data collected from U.S. stream ecosystems in North Carolina, the Mid-Atlantic Highlands, Maine, and Ohio. O/E assessments resulted in very similar estimates of mean regional conditions compared with most other indicators once these indicators\u27 values were standardized relative to reference-site means. However, other indicators tended to be biased estimators of O/E, a consequence of differences in their response to natural environmental gradients and sensitivity to stressors. These results imply that, in some cases, it may be possible to compare assessments derived from different indicators by standardizing their values (a statistical approach to data harmonization). In situations where it is difficult to standardize or otherwise harmonize two or more indicators, O/E values can easily be derived from existing raw sample data. With some caveats, O/E should provide more directly comparable assessments of biological integrity across regions than is possible by harmonizing values of a mix of indicators. Read More: http://www.esajournals.org/doi/abs/10.1890/1051-0761(2006)016%5B1277%3AQBIBTC%5D2.0.CO%3B

Predicting Natural Base-Flow Stream Water Chemistry in the Western United States

Robust predictions of stream solute concentrations expected under natural (reference) conditions would help establish more realistic water quality standards and improve stream ecological assessments. Models predicting solute concentrations from environmental factors would also help identify the relative importance of different factors that influence water chemistry. Although data are available describing the major factors controlling water chemistry (i.e., geology, climate, atmospheric deposition, soils, vegetation, topography), geologic maps do not adequately convey how rocks vary in their chemical and physical properties. We addressed this issue by associating rock chemical and physical properties with geological map units to produce continuous maps of percentages of CaO, MgO, S, uniaxial compressive strength, and hydraulic conductivity for western United States lithologies. We used catchment summaries of these geologic properties and other environmental factors to develop multiple linear regression (LR) and random forest (RF) models to predict base flow electrical conductivity (EC), acid neutralization capacity (ANC), Ca, Mg, and SO4. Models were derived from observations at 1414 reference-quality streams. RF models were superior to LR models, explaining 71% of the variance in EC, 61% in ANC, 92% in Ca, 58% in Mg, and 74% in SO4 when assessed with independent observations. The root-mean-square error for predictions on validation sites were all \u3c11% of the range of observed values. The relative importance of different environmental factors in predicting stream chemistry varied among models, but on average rock chemistry \u3e temperature \u3e precipitation \u3e soil ¼ atmospheric deposition \u3e vegetation \u3e amount of rock/water contact \u3e topography

The inception of Symplectic Geometry: the works of Lagrange and Poisson during the years 1808-1810

The concept of a symplectic structure first appeared in the works of Lagrange on the so-called "method of variation of the constants". These works are presented, together with those of Poisson, who first defined the composition law called today the "Poisson bracket". The method of variation of the constants is presented using today's mathematical concepts and notations.Comment: Presented at the meeting "Poisson 2008" in Lausanne, July 2008. Published in Letters in Mathematical Physics. 22 page

Modeling Natural Environmental Gradients Improves the Accuracy and Precision of Diatom-Based Indicators

Diatom-based indicators can contribute significantly to comprehensive assessments of stream biological conditions. We used modeling to develop, evaluate, and compare 2 types of diatom-based indicators for Idaho streams: an observed/expected (O/E) ratio of taxon loss derived from a model similar to the River InVertebrate Prediction And Classification System (RIVPACS) and a multimetric index (MMI). Modeling the effects of natural environmental gradients on assemblage composition is a key component of RIVPACS, but modeling has seldom been used for MMI development. Diatom assemblage structure varied substantially among reference-site samples, but neither ecoregion nor bioregion accounted for a significant portion of that variation. Therefore, we used Classification and Regression Trees (CART) to model the variation of individual metrics with natural gradients. For both CART and RIVPACS modeling, we restricted predictors to natural variables unaffected by or resistant to human disturbances. On average, 46% of the total variance in 32 metrics could be explained by CART models, but the predictor variables differed among the metrics and often showed evidence of interacting with one another. The use of CART residuals (i.e., metric values adjusted for the effect of natural environmental gradients) affected whether or how strongly many metrics discriminated between reference and test sites. We used cluster analysis to examine redundancies among candidate metrics and then selected the metric with the highest discrimination efficiency from each cluster. This step was applied to both unadjusted and adjusted metrics and led to inclusion of 7 metrics in MMIs. Adjusted MMIs were more precise than unadjusted ones (coefficient of variation ;50% lower). Adjusted and unadjusted MMIs rated similar proportions of the test sites as being in nonreference condition but disagreed on the assessment of many individual test sites. Use of unadjusted MMIs probably resulted in higher rates of both Type I and Type II errors than use of adjusted metrics, a logical consequence of the inability of unadjusted metrics to distinguish the confounding effects of natural environmental factors from those associated with human-caused stress. The RIVPACS-type model for diatom assemblages performed similarly to models developed for invertebrate assemblages. The O/E ratio was as precise as the adjusted MMI, but rated a lower proportion of test sites as being in nonreference condition, implying that taxon loss was less severe than changes in overall diatom assemblage structure. As previously demonstrated for O/E measures, modeling appears to be an effective means of developing more accurate and precise MMIs. Furthermore, modeling enabled us to develop a single MMI for use throughout an environmentally heterogeneous region

Development of a RIVPACS-type predictive model for bioassessment of wadeable streams in Wyoming

RIVPACS models produce a community-level measure of biological condition known as O/E, which is derived from a comparison of the observed (O) biota with those expected (E) to occur in the absence of anthropogenic stress. We used benthic macroinvertebrate and environmental data collected at 925 stream monitoring stations, from 1993 to 2001, to develop, validate, and apply a RIVPACS model to assess the biological condition of wadeable streams in Wyoming. From this dataset, 296 samples were identified as reference, 157 of which were used to calibrate the model, 46 to validate it, and 93 to examine temporal variability in reference site O/E-values. We used cluster analyses to group the model development reference sites into biologically similar classes of streams and multiple discriminant function analysis to determine which environmental variables best discriminated among reference groups. A suite of 14 categorical and continuous environmental variables best discriminated among 15 reference groups and explained a large proportion of the natural variability in biota within the reference dataset. Eleven of the predictor variables were derived from GIS. As expected, mean O/E-values for reference sites used in model development and validation were near unity and statistically similar. Temporal variability in O/E-values for reference sites was low. Test site values ranged from 0 to 1.45 (mean = 0.73). The model was accurate in both space and time and precise enough (S.D. of O/E-values for calibration data = 0.17) to detect modest alteration in biota associated with anthropogenic stressors. Our model was comparable in performance to other RIVPACS models developed in the United States and can produce effective assessments of biological condition over a broad, ecologically diverse region. We also provide convincing evidence that RIVPACS models can be developed primarily with GIS-based predictor variables. This framework not only simplifies the extraction of predictor variable information while potentially reducing expenditures of time and money in the collection of predictor variable information, but opens the door for development and/or application of RIVPACS models in regions where there is a paucity of local-scale, abiotic information

Linking Land Use, In-Stream Stressors, and Biological Condition to Infer Causes of Regional Ecological Impairment in Streams

We used field-derived data from streams in Nevada, USA, to quantify relationships between stream biological condition, in-stream stressors, and potential sources of stress (land use). We used 2 freshwater macroinvertebrate-based indices to measure biological condition: a multimetric index (MMI) and an observed to expected (O/E) index of taxonomic completeness. We considered 4 categories of potential stressors: dissolved metals, total dissolved solids, nutrients, and flow alteration. For physicochemical factors that varied predictably across natural environmental gradients, we quantified potential stress as the site-specific difference between observed (O) and expected (E) levels of each factor (O–Estress). We then used 2 sets of Random Forest models to quantify relationships between: 1) biological condition and potential stressors, and 2) stressor values and land uses. The 2 indices of biological condition were differentially responsive to stressors, indicating that no single measure of biological condition could fully characterize assemblage response to stress. Total dissolved solids (as measured by electrical conductivity [EC]) and metal contamination were the stressors most strongly associated with biological degradation. The most likely sources of these stressors were agriculture, urban development, and mining. Our findings highlight the need to develop EC criteria for streams. Measures of biological condition and stress that account for natural variability should reduce errors of inference and increase confidence in causal analyses. This approach will require development of robust models capable of predicting physical and chemical reference conditions. Causal analyses for individual sites require appropriate hypotheses about which stressors and what levels of stress can cause biological degradation. Our study demonstrates the usefulness of field data collected from multiple sites within a region for developing these hypotheses

Reduction of Taxonomic Bias in Diatom Species Data

Inconsistency in taxonomic identification and analyst bias impede the effective use of diatom data in regional and national stream and lake surveys. In this study, we evaluated the effect of existing protocols and a revised protocol on the precision of diatom species counts. The revised protocol adjusts four elements of sample preparation, taxon identification and enumeration, and quality control (QC). We used six independent data sets to assess the effect of the adjustments on analytical outcomes. The first data set was produced by three laboratories with a total of five analysts following established protocols (Charles et al., Protocols for the analysis of algal samples collected as part of the U.S. Geological Survey National Water-Quality Assessment, 2002) or their slight variations. The remaining data sets were produced by one to three laboratories with a total of two to three analysts following a revised protocol. The revised protocol included the following modifications: (1) development of coordinated precount voucher floras based on morphological operational taxonomic units, (2) random assignment of samples to analysts, (3) postcount identification and documentation of taxa (as opposed to an approach in which analysts assign names while they enumerate), and (4) increased use of QC samples. The revised protocol reduced taxonomic bias, as measured by reduction in analyst signal, and improved similarity among QC samples. Reduced taxonomic bias improves the performance of biological assessments, facilitates transparency across studies, and refines estimates of diatom species distributions

Perceptual Context in Cognitive Hierarchies

Cognition does not only depend on bottom-up sensor feature abstraction, but also relies on contextual information being passed top-down. Context is higher level information that helps to predict belief states at lower levels. The main contribution of this paper is to provide a formalisation of perceptual context and its integration into a new process model for cognitive hierarchies. Several simple instantiations of a cognitive hierarchy are used to illustrate the role of context. Notably, we demonstrate the use context in a novel approach to visually track the pose of rigid objects with just a 2D camera

Reefs at Risk: A Map-Based Indicator of Threats to the Worlds Coral Reefs

This report presents the first-ever detailed, map-based assessment of potential threats to coral reef ecosystems around the world. "Reefs at Risk" draws on 14 data sets (including maps of land cover, ports, settle-ments, and shipping lanes), information on 800 sites known to be degraded by people, and scientific expertise to model areas where reef degradation is predicted to occur, given existing human pressures on these areas. Results are an indicator of potential threat (risk), not a measure of actual condition. In some places, particularly where good management is practiced, reefs may be at risk but remain relatively healthy. In others, this indicator underestimates the degree to which reefs are threatened and degraded.Our results indicate that:Fifty-eight percent of the world's reefs are poten-tially threatened by human activity -- ranging from coastal development and destructive fishing practices to overexploitation of resources, marine pollution, and runoff from inland deforestation and farming.Coral reefs of Asia (Southeastern); the most species-rich on earth, are the most threatened of any region. More than 80 percent are at risk (undermedium and high potential threat), and over half are at high risk, primarily from coastal development and fishing-related pressures.Overexploitation and coastal development pose the greatest potential threat of the four risk categories considered in this study. Each, individually, affects a third of all reefs.The Pacific, which houses more reef area than any other region, is also the least threatened. About 60 percent of reefs here are at low risk.Outside of the Pacific, 70 percent of all reefs are at risk.At least 11 percent of the world's coral reefs contain high levels of reef fish biodiversity and are under high threat from human activities. These "hot spot" areas include almost all Philippine reefs, and coral communities off the coasts of Asia, the Comoros, and the Lesser Antilles in the Caribbean.Almost half a billion people -- 8 percent of the total global population -- live within 100 kilometers of a coral reef.Globally, more than 400 marine parks, sanctuaries, and reserves (marine protected areas) contain coral reefs. Most of these sites are very small -- more than 150 are under one square kilometer in size. At least 40 countries lack any marine protected areas for conserving their coral reef systems