Relationship between water quality, watermilfoil frequency, and weevil distribution in the State of Washington

During the summer of 1997, we surveyed 50 waterbodies in Washington State to determine the distribution of the aquatic weevil Euhrychiopsis lecontei Dietz. We collected data on water quality and the frequency of occurrence of watermilfoil species within selected watermilfoil beds to compare the waterbodies and determine if they were related to the distribution E. lecontei . We found E. lecontei in 14 waterbodies, most of which were in eastern Washington. Only one lake with weevils was located in western Washington. Weevils were associated with both Eurasian ( Myriophyllum spicatum L.) and northern watermilfoil ( M. sibiricum K.). Waterbodies with E. lecontei had significantly higher ( P < 0.05) pH (8.7 ± 0.2) (mean ± 2SE), specific conductance (0.3 ± 0.08 mS cm -1 ) and total alkalinity (132.4 ± 30.8 mg CaCO 3 L -1 ). We also found that weevil presence was related to surface water temperature and waterbody location ( = 24.3, P ≤ 0.001) and of all the models tested, this model provided the best fit (Hosmer- Lemeshow goodness-of-fit = 4.0, P = 0.9). Our results suggest that in Washington State E. lecontei occurs primarily in eastern Washington in waterbodies with pH ≥ 8.2 and specific conductance ≥ 0.2 mS cm -1 . Furthermore, weevil distribution appears to be correlated with waterbody location (eastern versus western Washington) and surface water temperature.

Factors to consider when using native biological control organisms to manage exotic plants

Biological control of exotic plant populations with native organisms appears to be increasing, even though its success to date has been limited. Although many researchers and managers feel that native organisms are easier to use and present less risk to the environment this may not be true. Developing a successful management program with a native insect is dependent on a number of critical factors that need to be considered. Information is needed on the feeding preference of the agent, agent effectiveness, environmental regulation of the agent, unique requirements of the agent, population maintenance of the agent, and time to desired impact. By understanding these factors, researchers and managers can develop a detailed protocol for using the native biological control agent for a specific target plant. . We found E. lecontei in 14 waterbodies, most of which were in eastern Washington. Only one lake with weevils was located in western Washington. Weevils were associated with both Eurasian ( Myriophyllum spicatum L.) and northern watermilfoil ( M. sibiricum K.). Waterbodies with E. lecontei had significantly higher ( P < 0.05) pH (8.7 ± 0.2) (mean ± 2SE), specific conductance (0.3 ± 0.08 mS cm -1 ) and total alkalinity (132.4 ± 30.8 mg CaCO 3 L -1 ). We also found that weevil presence was related to surface water temperature and waterbody location ( = 24.3, P ≤ 0.001) and of all the models tested, this model provided the best fit (Hosmer- Lemeshow goodness-of-fit = 4.0, P = 0.9). Our results suggest that in Washington State E. lecontei occurs primarily in eastern Washington in waterbodies with pH ≥ 8.2 and specific conductance ≥ 0.2 mS cm -1 . Furthermore, weevil distribution appears to be correlated with waterbody location (eastern versus western Washington) and surface water temperature.

The distribution of Corixidae in relation to the water quality of British lakes: A monitoring model

This article is an attempt to devise a method of using certain species of Corixidae as a basis for the assessment of general water quality in lakes. An empirical graphical representation of the distribution of populations or communities of Corixidae in relation to conductivity, based mainly on English and Welsh lakes, is used as a predictive monitoring model to establish the "expected" normal community at a given conductivity, representing the total ionic concentration of the water body. A test sample from another lake of known conductivity is then compared with "expected" community. The "goodness of fit" is examined visually or by calculation of indices of similarity based on the relative proportions of the constituent species of each community. A computer programme has been devised for this purpose.

Measurement errors in body size of sea scallops (Placopecten magellanicus) and their effect on stock assessment models

Body-size measurement errors are usually ignored in stock assessments, but may be important when body-size data (e.g., from visual sur veys) are imprecise. We used experiments and models to quantify measurement errors and their effects on assessment models for sea scallops (Placopecten magellanicus). Errors in size data obscured modes from strong year classes and increased frequency and size of the largest and smallest sizes, potentially biasing growth, mortality, and biomass estimates. Modeling techniques for errors in age data proved useful for errors in size data. In terms of a goodness of model fit to the assessment data, it was more important to accommodate variance than bias. Models that accommodated size errors fitted size data substantially better. We recommend experimental quantification of errors along with a modeling approach that accommodates measurement errors because a direct algebraic approach was not robust and because error parameters were diff icult to estimate in our assessment model. The importance of measurement errors depends on many factors and should be evaluated on a case by case basis.

Modeling red sea urchin (Strongylocentrotus franciscanus) growth using six growth functions

The growth of red sea urchins (Strongylocentrotus franciscanus) was modeled by using tag-recapture data from northern California. Red sea urchins (n=211) ranging in test diameter from 7 to 131 mm were examined for changes in size over one year. We used the function Jt+1 = Jt + f(Jt) to model growth, in which Jt is the jaw size (mm) at tagging, and Jt+1 is the jaw size one year later. The function f(Jt), represents one of six deterministic models: logistic dose response, Gaussian, Tanaka, Ricker, Richards, and von Bertalanffy with 3, 3, 3, 2, 3, and 2 minimization parameters, respectively. We found that three measures of goodness of fi t ranked the models similarly, in the order given. The results from these six models indicate that red sea urchins are slow growing animals (mean of 7.2 ±1.3 years to enter the fishery). We show that poor model selection or data from a limited range of urchin sizes (or both) produces erroneous growth parameter estimates and years-to-fishery estimates. Individual variation in growth dominated spatial variation at shallow and deep sites (F=0.246, n=199, P=0.62). We summarize the six models using a composite growth curve of jaw size, J, as a function of time, t: J = A(B – e–Ct) + Dt, in which each model is distinguished by the constants A, B, C, and D. We suggest that this composite model has the flexibility of the other six models and could be broadly applied. Given the robustness of our results regarding the number of years to enter the fishery, this information could be incorporated into future fishery management plans for red sea urchins in northern California.