Selective serotonin re-uptake inhibitors effects on hybrid striped bass predatory behavior and internal chemistry

Prescription drug use continues to increase across the United States. An important part of these medications are selective serotonin reuptake inhibitors (SSRIs) that function as anti-depressants and include drugs as citalopram (Celexa) and sertraline (Zoloft). SSRI\u27s main mode of action is the inhibition of the serotonin reuptake transporter, causing a buildup of extracellular serotonin, one of the neurotransmitters in the central and peripheral nervous system. SSRIs can be considered persistent pollutants due to their continuous release from wastewater treatment effluent, drug manufacturing effluent, and agricultural runoff. Aquatic organisms can become non-target organisms when subjected to sub-lethal concentrations (low ppb to high ppm) of antidepressants. Behavioral tests provide sensitive endpoints for determining whether aquatic organisms have been subjected to antidepressants, causing changes in their ecological fitness. The goal of this research was to determine whether SSRIs cause sublethal effects in fish populations through a change in feeding behavior, supported by brain and plasma chemistry and changes in serotonin-related gene expression in the intestine. We hypothesized a decrease in feeding behavior, a decrease in serotonin levels, and a change in gene expression after exposure to sertraline and citalopram. Hybrid striped bass (HSB) exposed to citalopram (6-day exposure at 50-150 µg/l) and sertraline (4-100 µg/l, 6 days exposure, 6 days recovery) were fed every three days to determine effects on behavior. Blood, brain, and intestine samples collected from euthanized fish every three days were analyzed for concentrations of citalopram, sertraline, and serotonin. Both sertraline and citalopram caused a change in predatory behavior during exposure, with sertraline having a more dramatic effect than citalopram. The sertraline recovery period showed that the bass was able to rapidly return to normal feeding behavior, even while the antidepressant was still located in the brain and plasma. Citalopram and sertraline were both detected in brain and plasma samples, but in different levels during the exposure and recovery period. Serotonin levels also differed between each SSRI treatment. Our results showed that SSRIs may cause an upregulation of both the serotonin reuptake transporter and cholecystokinin, a satiation signaling protein. From an ecological standpoint, an increased feeding time could make exposed bass populations less ecologically fit compared to other populations that are not as affected by antidepressants

Incorporating Human Effects in Quantifying Mechanisms of Stream Fish Community Structure Using Metacommunity Theory

Metacommunity theory incorporates local and regional factors to understand how biotic communities are structured across the landscape. Despite established knowledge of how humans impact aquatic systems, inclusion of anthropogenic factors in metacommunity studies have been largely ignored. Additionally, alpha, beta, and gamma diversity can all be explored at the metacommunity level to investigate mechanistic drivers of community structure. Beta diversity can be further partitioned into turnover and richness difference components, each with different mechanistic drivers. Streams provide an excellent study system for metacommunity research because of the dendritic structure of watersheds and the natural delineation that watershed boundaries provide. Large-extent datasets provide the ability to create multiple metacommunities serving as replicates for robust statistical analyses. As such, the overall goal of this dissertation was to use large datasets of stream fish community structure to investigate how anthropogenic variables affect stream fish beta diversity and metacommunity structure in conjunction with ‘traditionally investigated’ factors including natural landscape features and spatial distance among communities. This research uses two large extent datasets. The first covers 13 states on the eastern coast of the United States, and the second covers 350 sites throughout South Carolina. Three different approaches were taken to understand the factors affecting stream fish metacommunities across the landscape. First, we created a spatial scale continuum using nested watersheds identified by hydrologic unit codes (HUCs) to explore how beta diversity and its components change over three spatial scales, and (a) how land use, (b) climatic, and (c) anthropogenic factors affect beta diversity within and between spatial scales. We found increasing beta diversity with increasing spatial scale, and equal contribution between turnover and richness difference components. All three factors were related to beta diversity or its components depending on the spatial scale, but few scale-dependent relationships were found. These results suggest that while a diversity of factors affect beta diversity at a given spatial scale their effects on beta diversity do not change across spatial scales. These effects may be scale-invariant, although other cross-scale effects may arise at finer spatial scales. Second, we investigated how environmental and anthropogenic factors and aspects of study design affect coherence, turnover, and boundary clumping—the elements of metacommunity structure (EMS) at a single spatial scale. The EMS were affected by temperature, density of dams, and percentage of developed land, but also gamma diversity and number of sites sampled in a metacommunity. These results suggest that anthropogenic factors affect the elements of metacommunity structure and thus set the context for assigning metacommunities into archetypical processes across the landscape. Moreover, the EMS were affected by study design aspects such as the number of communities sampled and the distance between them within metacommunities. These results have important implications for both existing and future studies because they show that inference on spatial processes is contextualized by aspects of datasets that are inherent to datasets and are rarely considered in analyses. Including these factors in future analyses will allow researchers to better focus on the signal of key processes by accounting for the variability caused by aspects of study design. Third, we used variation partitioning to parse out the relative effects of anthropogenic, natural, and spatial factors on beta diversity as measured in three key dimensions: taxonomic, functional, and phylogenetic. These analyses were done at three spatial delineations representing artificial, geomorphic, and natural watershed metacommunities. These are commonly used spatial delineations in metacommunity analyses, but are rarely included in the same study. We explained 25-81% of beta diversity where different spatial, natural, and anthropogenic factors structured these metacommunities depending on the spatial delineation and diversity dimension. Geomorphic metacommunities had very different results compared to other spatial delineations suggesting that accounting for geomorphic differences leads to stronger anthropogenic signals. By conducting this work in different spatial delineations within the same dataset, we show for the first time that defining metacommunities has bearing on results of analyses—an issue that is rarely considered in metacommunity studies. Overall, this body of work suggests that anthropogenic factors have pervasive effects on stream fish beta diversity and metacommunity structure across the landscape—an aspect of metacommunity ecology that has until recently been largely ignored. This work suggests that considering anthropogenic effects in metacommunity studies will improve inference. Researchers must also consider important aspects of study design, including how metacommunities are defined and delineated, as well as how intensely and densely communities are sampled within those metacommunities. In all, this dissertation adds an important practical dimension to the field of metacommunity ecology, which up to this point has been largely theoretical. Considering these practicalities may improve our overall understanding of metacommunities in a variety of taxa and systems

A new assessment of graph construction competency for undergraduate biology students

With an increasing emphasis on teaching the skills and processes of science in the undergraduate biology classroom, working with and interpreting data has become an important part of the curriculum. Visual representations are a key tool when examining data, especially graphs. Undergraduate biology students notoriously have trouble both making good graphs and interpreting graphs. Yet, although there is an extensive literature on graph interpretation challenges amongst students, there has been much less work on the confusions students exhibit when constructing graphs. On the path to creating tools to help teach graphing to biology students, we have been building a new performance-based assessment of graph construction competence. The assessment presents students a research question and asks them to make graphs to test a hypothesis drawn from that question. The graphs are auto-scored for a number of practices associated with making good graphs. The digital nature and auto-scoring has allowed us to provide this assessment and analyze results at larger scales than previous assessments, gathering data that will help focus teaching tools on the areas of highest need. In this workshop, each participant will take one version of the graphing assessment themselves (about 20–30 minutes) and then we will discuss the experience. After talking about how well the assessment lines up to the graphing practices you look for in your students, the presenter will show data on where we find biology students struggle, drawn from students in a diverse set of classes and institutions. Bring your laptop (Mac or Windows only).Note: the creative commons license below is for the abstract and talk only, not the software