Interactions among environmental drivers: Community responses to changing nutrients and dissolved organic carbon

Biological communities are frequently exposed to environmental changes that cause measurable responses in properties of the community (hereafter called environmental drivers). Predicting how communities respond to changing environmental drivers is a fundamental goal of ecology. Making predictions, however, can be very difficult, particularly when multiple environmental drivers change simultaneously and there are interactions among the drivers. We investigated the effects of the interaction between changes in nutrient loading and changes in colored dissolved organic matter (measured as dissolved organic carbon, DOC) on the dynamics of phytoplankton communities over a 7-yr period. In 1991, Long Lake, a small seepage lake in northern Michigan, was divided vertically, from sediment surface to water surface, with plastic curtains as part of a whole-lake experiment. The accompanying changes in hydrology led to increases in DOC concentration in one of the basins. Nutrients were added to both basins from 1993 to 1997, causing dramatic changes in phytoplankton community composition. We used multivariate autoregressive models to help interpret the patterns of phytoplankton community composition observed during the experiment. DOC and nutrient addition had diverse effects on phytoplankton: some taxonomic and morphological groups were directly affected by the changes in DOC and nutrients, whereas other groups experienced indirect effects via their interactions with groups that were directly affected. Model results suggest that there was an interaction between the effects of DOC and nutrients for many groups of phytoplankton, such that differences in DOC concentration accounted for differences between basins in response to nutrient addition. The effects of DOC can be explained by changes in physical structure (e.g., thermocline depth and transparency) and water chemistry (e.g., pH) that accompanied changes in DOC concentration. The interaction between DOC and nutrients suggests that predicting community responses to multiple drivers cannot be achieved by simply adding up the effects of single drivers

Interactions Among Environmental Drivers: Community Responses to Changing Nutrients and Dissolved Organic Carbon

Biological communities are frequently exposed to environmental changes that cause measurable responses in properties of the community (hereafter called environmental drivers). Predicting how communities respond to changing environmental drivers is a fundamental goal of ecology. Making predictions, however, can be very difficult, particularly when multiple environmental drivers change simultaneously and there are interactions among the drivers. We investigated the effects of the interaction between changes in nutrient loading and changes in colored dissolved organic matter (measured as dissolved organic carbon, DOC) on the dynamics of phytoplankton communities over a 7‐yr period. In 1991, Long Lake, a small seepage lake in northern Michigan, was divided vertically, from sediment surface to water surface, with plastic curtains as part of a whole‐lake experiment. The accompanying changes in hydrology led to increases in DOC concentration in one of the basins. Nutrients were added to both basins from 1993 to 1997, causing dramatic changes in phytoplankton community composition. We used multivariate autoregressive models to help interpret the patterns of phytoplankton community composition observed during the experiment. DOC and nutrient addition had diverse effects on phytoplankton: some taxonomic and morphological groups were directly affected by the changes in DOC and nutrients, whereas other groups experienced indirect effects via their interactions with groups that were directly affected. Model results suggest that there was an interaction between the effects of DOC and nutrients for many groups of phytoplankton, such that differences in DOC concentration accounted for differences between basins in response to nutrient addition. The effects of DOC can be explained by changes in physical structure (e.g., thermocline depth and transparency) and water chemistry (e.g., pH) that accompanied changes in DOC concentration. The interaction between DOC and nutrients suggests that predicting community responses to multiple drivers cannot be achieved by simply adding up the effects of single drivers

Perceptions of Athletes in Disabled and Non-Disabled Sport Contexts: A Descriptive Qualitative Research Study

The term supercrip suggests extraordinary feats but is sometimes applied to any proficiency demonstrated by an athlete with a disability. This use of the term potentially undermines spectator appreciation for achievements of the upper echelon of disability sports participants. Prior disability sport researchers have suggested that a comparison of individuals’ perceptions of athletic heroes with and without disabilities has potential to help disability sport marketers counter the supercrip stereotype. The purpose of this research was to explore differences in perceptions by comparing participant descriptions of role models with and without disabilities. Research participants, who consisted of undergraduate students at a large southwestern university, viewed video recorded scenarios of athletes with and without disabilities and participated in focus group interviews. Our findings supported those of prior researchers who suggested that participants were most likely to describe relatable circumstances as inspiring. Our participants also critiqued the depth of the stories presented in the scenarios based on their prior exposure to disability narratives. Disability sport marketers might benefit from emphasizing multiple elements of athletes’ backgrounds to appeal to potential consumers. Marketers should also consider that consumers might have developed preexisting expectations about style of presentation of athlete interest stories

Knowing When to Draw the Line: Designing More Informative Ecological Experiments

Linear regression and analysis of variance (ANOVA) are two of the most widely used statistical techniques in ecology. Regression quantitatively describes the relationship between a response variable and one or more continuous independent variables, while ANOVA determines whether a response variable differs among discrete values of the independent variable(s). Designing experiments with discrete factors is straightforward because ANOVA is the only option, but what is the best way to design experiments involving continuous factors? Should ecologists prefer experiments with few treatments and many replicates analyzed with ANOVA, or experiments with many treatments and few replicates per treatment analyzed with regression? We recommend that ecologists choose regression, especially replicated regression, over ANOVA when dealing with continuous factors for two reasons: (1) regression is generally a more powerful approach than ANOVA and (2) regression provides quantitative output that can be incorporated into ecological models more effectively than ANOVA output