Use of Community‐Composition Data to Predict the Fecundity and Abundance of Species

Abstract: Species distribution models are critical tools for the prediction of invasive species spread and conservation of biodiversity. The majority of species distribution models have been built with environmental data. Community ecology theory suggests that species co‐occurrence data could also be used to predict current and potential distributions of species. Species assemblages are the products of biotic and environmental constraints on the distribution of individual species and as a result may contain valuable information for niche modeling. We compared the predictive ability of distribution models of annual grassland plants derived from either environmental or community‐composition data. Composition‐based models were built with the presence or absence of species at a site as predictors of site quality, whereas environment‐based models were built with soil chemistry, moisture content, above‐ground biomass, and solar radiation as predictors. The reproductive output of experimentally seeded individuals of 4 species and the abundance of 100 species were used to evaluate the resulting models. Community‐composition data were the best predictors of both the site‐specific reproductive output of sown individuals and the site‐specific abundance of existing populations. Successful community‐based models were robust to omission of data on the occurrence of rare species, which suggests that even very basic survey data on the occurrence of common species may be adequate for generating such models. Our results highlight the need for increased public availability of ecological survey data to facilitate community‐based modeling at scales relevant to conservation.     

ESTIMATION OF COASTAL WATER CONTAMINATION LONG-TERM TREND USING SEASONAL LINEAR MODELS1

ABSTRACT: Bivalves are used as bioindicators to assess trends of the chemical quality of coastal and marine environments due to their ability to concentrate chemicals. These shellfish are subject to seasonal physiological changes influencing the chemical concentration. Using quarterly data, we model concentration via linear regression with a biologically based seasonal component. This was applied to cadmium concentration measured in the blue mussel (Mytilus edulis) at three sites in the Seine estuary (Normandy, France). In this case we have a high concentration season from January to June and a “low concentration” season from July to December. This season definition was checked a posteriori, using box-and-whisker plots and a statistical test of comparison of pair-wise adjusted least-squares mean differences, and it appears to be very reasonable. We averaged data by season and across sites. Our final model (R²= 0.846 with N= 27 observations) includes highly significant terms: a season effect, which accounts for 45% of the total variability, a linear and a quadratic time term. Outliers were identified by high Studentized residual values and attributed to bias in the temporal sampling schemes. The methodology developed will further be used with other shellfish and/or other trace elements and organic chemicals.     

ALTERNATIVE FUTURES USING THE WOLLMAN-BONEM MODELS1

Alternative futures are defined to be mutually consistent alternative combinations of assumed sets of future conditions. Alternative futures are employed in conjunction with the Wollman-Bonem model to project future water use. The model solutions under different alternative futures provide an indication of the direction and relative magnitude of changes in water use, both in quantity and quality, that may result from changes in policy-influenced variables, technology, and other data. The Wollman-Bonem model is employed in this paper as a tool to illustrate the alternative futures concept. The model can best be cast as an economic model. The model solutions are not given as a set of formal projections, but as various possible water-use-over-time curves. This should aid in disspelling the erroneous idea that it is possible to make distant projections of water use as a single curve. Multiple curves suggest that water use is really a function of many variables.     

EFFECTS OF THE TOPOGRAPHIC INDEX DISTRIBUTION ON PREDICTED RUNOFF USING GRASS1

ABSTRACT: The effects of digital elevation model resolutions and contour lengths on the distribution of the topographic index, a fundamental parameter for the hydrologic model, TOPMODEL, and their influence on the predicted peak flows are investigated in this paper. A small agricultural catchment (3.38 km²) is used to determine the catchment response modeled by TOPMODEL for three rainfall events.     

MASS BALANCE MODEL ESTIMATION OF PHOSPHORUS CONCENTRATIONS IN RESERVOIRS1

ABSTRACT: Mass balance models have been common tools in lake quality management for some years. However, verification for use on reservoirs, especially in the Western United States, has been seriously lacking, In this study, such a verification is attempted using data from the U.S EPA National Eutrophication Survey. Several models from the literature are compared for accuracy in application to the western reservoir data. Model standard error and correlation between estimated and observed reservoir phosphorus concentrations are the Criteria used for comparison. Standard errors am further used to calculate uncertainty of trophic state classification based on estimated phosphorus concentration. The model proposed by Dillon and Rigler (1974) proved most accurate, with a correlation coefficient of 0.86 and standard error of 0.2, based on logarithmic transformed values. Deficiencies in the other models appear to & from coefficients fit to lake data and from inappropriate model formulation.     

PROBLEMS IN FORECASTING WATER REQUIREMENTS1

ABSTRACT. Methodological problems associated with forecasting water requirements by use of regression analysis are examined. Problems occurring when long-range forecasts are based on linear and nonlinear extrapolation of time series models include possible changes in socioeconomic conditions, water allocation system structure, and limits to growth. Problems arising in forecasting based on multiple regression models are likely to involve serially dependent errors, multicollinear explanatory variables, and difficulties inherent to the presence of explanatory variables that must themselves be predicted.     

A REVIEW ON DECISION MODELS IN ECONOMICS OF REGIONAL WATER QUALITY MANAGEMENT1

ABSTRACT The literature on decision models in economics of regional water quality management is reviewed and classified.     

SPNM,A MODEL FOR PREDICTING SEDIMENT,PHOSPHORUS, AND NITROGEN YIELDS FROM AGRICULTURAL BASINS1

ABSTRACT: A model called SPNM from the words “sediment-phosphorus-nitrogen model” was developed for simulating agricultural contributions to water pollution. SPNM is designed to predict sediment, P, and N yields for individual storms on small basins and to route these yields through streams and valleys of large basins. Users need no computer programming experience because the model is a problem-oriented computer language. SPNM is useful in planning water resources projects and in research. Tests of the model on a watershed provided realistic results.     

MATHEMATICAL SIMULATIONS OF THE TOCCOA FALLS,GEORGIA, DAM-BREAK FLOOD1

ABSTRACT: Four dam-break models were selected for testing with an observed data set from the November 6, 1977, disaster at Toccoa Falls, Georgia. The Kelly Barnes Dam failure occurred with a 35-ft head of water and produced a peak discharge of 23,000 ft³/s. The selected models included: (1) Modified Puls (MP), (2) U. S. Army Corps of Engineers Gradually Varied Unsteady Flow Profiles (USTFLO), (3) National Weather Service's Dam-Break Flood Forecast (DBFF), and (4) U. S. Geological Survey's method of characteristics (MOC) coupled with a general purpose streamflow simulation (J879DB). Achieving a successful simulation was easiest with the MP model. The DBFF model required a moderate effort while the MOC-J879DB models required some data alterations and considerable effort. The USTFLO model failed to simulate this test case. In the stream segment near the dam, the computed peak stages were generally within 5 feet of the observed high water marks. Elsewhere, the peak stage results were much better, generally within 2 feet. The peak discharges computed by the models were generally within 20 percent of discharges estimated by slope area and contracted opening measurements, except near the dam where the MOC-J879DB model's results was 80 percent too high.     

DETERMINISTIC MODELS FOR PREDICTING RESIDENTIAL WATER CONSUMPTION1

ABSTRACT: Forecasts of future urban water demand traditionally have been made by the projection of historic trends in per capita consumption and population. This paper outlines the use of two deterministic models to forecast the residential component of urban water demand. The models incorporate specific representation of the activities which result in water consumption at each residence. Predictions of water use can then be made by modeling the changes expected in the number of these activities and the consumption for each such activity.