Wetlands Book Quotes

Today, cardinal-flower is a legally protected species and should never be picked or removed from the wild.

If you see a bird “feeding” on a cattail spike, observe closely: Is it delving for caterpillars or their cocoons? Or is it depositing or retrieving a food cache?

The Measure of America, a report of the Social Science Research Council, ranks every state in the United States on its “human development.” Each rank is based on life expectancy, school enrollment, educational degree attainment, and median personal earnings. Out of the 50 states, Louisiana ranked 49th and in overall health ranked last. According to the 2015 National Report Card, Louisiana ranked 48th out of 50 in eighth-grade reading and 49th out of 50 in eighth-grade math. Only eight out of ten Louisianans have graduated from high school, and only 7 percent have graduate or professional degrees. According to the Kids Count Data Book, compiled by the Annie E. Casey Foundation, Louisiana ranked 49th out of 50 states for child well-being. And the problem transcends race; an average black in Maryland lives four years longer, earns twice as much, and is twice as likely to have a college degree as a black in Louisiana. And whites in Louisiana are worse off than whites in Maryland or anywhere else outside Mississippi. Louisiana has suffered many environmental problems too: there are nearly 400 miles of low, flat, subsiding coastline, and the state loses a football field–size patch of wetland every hour. It is threatened by rising sea levels and severe hurricanes, which the world’s top scientists connect to climate change.

Beyond One-Way ANOVA The approach described in the preceding section is called one-way ANOVA. This scenario is easily generalized to accommodate more than one independent variable. These independent variables are either discrete (called factors) or continuous (called covariates). These approaches are called n-way ANOVA or ANCOVA (the “C” indicates the presence of covariates). Two way ANOVA, for example, allows for testing of the effect of two different independent variables on the dependent variable, as well as the interaction of these two independent variables. An interaction effect between two variables describes the way that variables “work together” to have an effect on the dependent variable. This is perhaps best illustrated by an example. Suppose that an analyst wants to know whether the number of health care information workshops attended, as well as a person’s education, are associated with healthy lifestyle behaviors. Although we can surely theorize how attending health care information workshops and a person’s education can each affect an individual’s healthy lifestyle behaviors, it is also easy to see that the level of education can affect a person’s propensity for attending health care information workshops, as well. Hence, an interaction effect could also exist between these two independent variables (factors). The effects of each independent variable on the dependent variable are called main effects (as distinct from interaction effects). To continue the earlier example, suppose that in addition to population, an analyst also wants to consider a measure of the watershed’s preexisting condition, such as the number of plant and animal species at risk in the watershed. Two-way ANOVA produces the results shown in Table 13.4, using the transformed variable mentioned earlier. The first row, labeled “model,” refers to the combined effects of all main and interaction effects in the model on the dependent variable. This is the global F-test. The “model” row shows that the two main effects and the single interaction effect, when considered together, are significantly associated with changes in the dependent variable (p < .000). However, the results also show a reduced significance level of “population” (now, p = .064), which seems related to the interaction effect (p = .076). Although neither effect is significant at conventional levels, the results do suggest that an interaction effect is present between population and watershed condition (of which the number of at-risk species is an indicator) on watershed wetland loss. Post-hoc tests are only provided separately for each of the independent variables (factors), and the results show the same homogeneous grouping for both of the independent variables. Table 13.4 Two-Way ANOVA Results As we noted earlier, ANOVA is a family of statistical techniques that allow for a broad range of rather complex experimental designs. Complete coverage of these techniques is well beyond the scope of this book, but in general, many of these techniques aim to discern the effect of variables in the presence of other (control) variables. ANOVA is but one approach for addressing control variables. A far more common approach in public policy, economics, political science, and public administration (as well as in many others fields) is multiple regression (see Chapter 15). Many analysts feel that ANOVA and regression are largely equivalent. Historically, the preference for ANOVA stems from its uses in medical and agricultural research, with applications in education and psychology. Finally, the ANOVA approach can be generalized to allow for testing on two or more dependent variables. This approach is called multiple analysis of variance, or MANOVA. Regression-based analysis can also be used for dealing with multiple dependent variables, as mentioned in Chapter 17.

had a visit from the EPA the day before Collins announced he was taking over. They wanted to inspect the farm for some wetland to see if there were any endangered animals that needed protecting. I told them we had nothing but dry land, but they threatened to return with a fly over. Haven’t heard a thing since.

the former head of Yale’s School of Forestry writes in the introduction to his latest book, The Bridge at the Edge of the World: Half the world’s tropical and temperate forests are now gone. The rate of deforestation in the tropics continues at about an acre a second. About half the wetlands and a third of the mangroves are gone. An estimated ninety percent of the large predator fish are gone, and 75 percent of marine fisheries are now over-fished or fished to capacity. Twenty percent of the corals are gone and another twenty percent severely threatened. Species are disappearing at rates about a thousand times faster than normal. The planet has not seen such a spasm of extinction in sixty-five million years, since the dinosaurs disappeared.