One of the few important empirical generalizations regarding herbaceous plant systems has been the demonstration that species richness is related to standing crop with maximum richness occurring at moderate levels of standing crop. This relationship is normally demonstrated by comparing among vegetation types (i.e., vegetation with different dominants). We undertook this study to test whether the species richness-standing crop relationship was evident at a finer-groined level of organization, the within vegetation type level. Fifteen wetland sites were sampled in eastern Canada and species richness and standing crop determined in each of 224 0.25 m 2 quadrats. Each site was relatively homogeneous in terms of the dominant species present and were therefore categorized as single vegetation types. However, as a group, the sites comprised a wide range of vegetation types.A second order polynomial regression indicated a significant bitonic relationship between species richness and standing crop at the among-vegetation types scale, that is, when all 15 sites were combined. At the withinvegetation type level, however, no significant relationships were observed (p > 0.05). The results indicate that the model of species richness proposed by Grime has predictive power at a coarse-grained level of organization, among vegetation types, but does not survive the transition to a finer-grained level of organization, the within vegetation type level. Therefore, the higher level processes which structure species richness patterns among vegetation types are not the same processes which determine richness patterns within a vegetation type.

Abstract-The use of no-observed-effect levels (NOELs) and lowest-observed-effect levels (LOELs) as summary statistics for low toxic effects has been criticized recently. In this paper, we explore the regression-based approach for estimating low toxic effects. Key questions with this approach include: (1) is the approach practical with typical toxicity data sets, (2) can low toxic effects be estimated with confidence, (3) what models are appropriate for typical data sets and endpoints, and (4) how do point estimates of low toxic effects (e.g., EC10) compare with their corresponding LOELs and NOELs? Analyses of 198 toxicity data sets (all but 13 unpublished) revealed that: (1) even with a range of models to choose from (three models in the logistic family, probit model, Weibull model), Ͼ80% of the data sets did not produce a single adequate model fit; (2) estimates of low toxic effects were often model dependent when an extrapolation beyond the toxicity data was required; (3) confidence intervals can be quite large at 5% effect and lower; (4) of the five models, the three-parameter logistic equation with a steep slope parameter had the best model fit in the majority of the data sets; and (5) 76.9% of the NOELs and 100% of the LOELs were higher than their corresponding 10% effect point estimates from the best-fit model equations, suggesting that NOELs and LOELs are poor indicators of low toxic effects. We believe that the regression-based approach is a better tool than hypothesis testing for estimating low toxic effects. The approach, however, does not produce adequate model fits unless there is an obvious concentration-response relationship and several treatments with partial effects.

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Abstract-Some regulatory programs rely on quantitative structure-activity relationship (QSAR) models to predict toxic effects to biota. Many currently existing QSAR models can predict the effects of a wide range of substances to biota, particularly aquatic biota. The difficulty for regulatory programs is in choosing the appropriate QSAR model or models for application in their new and existing substances programs. We evaluated model performance of six QSAR modeling packages: Ecological Structure Activity Relationship (ECOSAR), TOPKAT, a Probabilistic Neural Network (PNN), a Computational Neural Network (CNN), the QSAR components of the Assessment Tools for the Evaluation of Risk (ASTER) system, and the Optimized Approach Based on Structural Indices Set (OASIS) system. Using a testing data set of 130 substances that had not been included in the training data sets of the QSAR models under consideration, we compared model predictions for 96-h median lethal concentrations (LC50s) to fathead minnows to the corresponding measured toxicity values available in the AQUIRE database. The testing data set was heavily weighted with neutral organics of low molecular weight and functionality. Many of the testing data set substances also had a nonpolar narcosis mode of action and/or were chlorinated. A variety of statistical measures (correlation coefficient, slope and intercept from a linear regression analysis, mean absolute and squared difference between log prediction and log measured toxicity, and the percentage of predictions within factors of 2, 5, 10, 100, and 1,000 of measured toxicity values) indicated that the PNN model had the best model performance for the full testing data set of 130 substances. The rank order of the remainder of the models depended on the statistical measure employed. TOPKAT also had excellent model performance for substances within its optimum prediction space. Only 37% of the substances in the testing data set, however, fell within this optimum prediction space.

Refined risk assessments for birds exposed to flowable and granular formulations ofCPY were conducted for a range of current use patterns in the United States. Overall,the collective evidence from the modeling and field study lines of evidence indicate that flowable and granular CPY do not pose significant risks to the bird communities foraging in agro-ecosystems in the United States. The available information indicates that avian incidents resulting from the legal, registered uses of CPY have been very infrequent since 2002 (see SI Appendix 3). The small number of recent incidents suggests that the current labels for CPY are generally protective of birds.However, incident data are uncertain because of the difficulties associated with finding dead birds in the field and linking any mortality observed to CPY.Plowable CPY is registered for a variety of crops in the United States including alfalfa, brassica vegetables, citrus, corn, cotton, grape, mint, onion, peanut, pome and stone fruits, soybean, sugar beet, sunflower, sweet potato, tree nuts, and wheat under the trade name Lorsban Advanced. The major routes of exposure for birds to flowable CPY were consumption of treated dietary items and drinking water. The Liquid Pesticide Avian Risk Assessment Model (Liquid PARAM) was used to simulate avian ingestion of CPY by these routes of exposure. For acute exposure,Liquid PARAM estimated the maximum retained dose in each of 20 birds on each of1,000 fields that were treated with CPY over the 60-d period following initial application.The model used a 1-h time step. For species lacking acceptable acute oral toxicity data (all focal species except northern bobwhite (C. virginianus) and redwinged blackbird (A. phoeniceus)), a species sensitivity distribution (SSD) approach was used to generate hypothetical dose-response curves assuming high, median and low sensitivity to CPY. For acute risk, risk curves were generated for each use pattern and exposure scenario. The risk curves show the relationship between exceedence probability and percent mortality. The results of the Liquid PARAM modeling exercise indicate that flow able CPY poses an acute risk to some bird species, particularly those species that are highly sensitive and that forage extensively in crops with high maximum application rates (e.g., grapefruit, orange). Overall, most bird species would not experience significant mortality as a result of exposure to flowable CPY.The results of a number of field studies conducted at application rates comparable to those on the Lorsban Advanced label indicate that flowable CPY rarely causes avian mortality. The results of the field studies suggest that Liquid PARAM is likely over-estimating acute risk to birds for flowable CPY.For chronic exposure, Liquid PARAM estimated the maximum total daily intake (TDI) over a user-specified exposure duration (28-d in the case of CPY).The maximum average TDI was compared to the chronic NOEL and LOEL from the most sensitive species tested for CPY, the mallard. This comparison was done for e...

Predicting the species density (number of species per unit area) of communities is a major goal of ecology. We present a regression model of species density on a local (0.25 m2) scale for the vegetation of freshwater shorelines in southwestern Quebec, Canada. Two attributes of the vegetation, the amount of aboveground biomass (in grams) and the proportion of the vegetation composed of obligate perennial species, predicted 76% of the variation in species density. The success of the predictor variables suggests that competitive intensity, as reflected in biomass levels, and the time elapsed since the last disturbance event, as reflected in the proportion of the vegetation composed of obligate perennials, are important determinants of local variation in species density. The model was then tested against independent data from shoreline vegetation in southeastern Ontario, Canada. There were no significant differences in the two data sets in their response to the two independent variables in the full model. However, only 42% of the variance in species density was explained in the combined data set.

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