1. Jo-an Chen, Michael Chang jo-anchen@berkeley.edu, michanger@berkeley.edu Acknowledgements This poster template was made possible by Prof Radley's knowledge as well as the hard work of the two authors. Thank you USGS, NOAA, Databasin, and PRISM for detailed climate and spatial data that we could conduct our studies with. For more information on all of the papers used to inform our model, please refer to our PowerPoint. Conclusion We feel that we have come up with several competent models that predicts the habitat of ephemeroptea on a state-wide- to a local scale. It is interesting the unique factors that are in play for each level. Now that we have a good model for the current range and preferences under current climate conditions, we can potentially understand regional deficiencies of the mayfly as being caused by non-climate or spatial factors. For our climate-based suitability analysis, we have determined their ranges based on previous studies that show that the ideal temperature ranges for ephemeroptea is 8 to 16 degrees Celsius combined with a minimum annual precipitation of 500mm a year. This data gives a good idea of where the species can be found in a general sense. For our bay-area regional analysis, we have found out that there is no clear preference for slope in terms of population distribution. The ideal elevation for the Ephemeroptera is from 300-600 meters above sea level. The bay area is challenging because of multiple compounding factors- intense urbanization and farming activity can potentially explain the lack of mayflies in the delta or near sea level. The resulting regional suitability map for the bay area is empirically based- it analyses the locations in which bay area mayflies are found and then identifies all areas having similar properties. In this case mountainous areas 300-600 meters above sea level. The parameters may vary across different regions but may be a good predictive tool because it uses the properties of a population in a region to generate a habitable zone. For the San-Francisco scale model, we found that species located in the South-West and North-West will be more resilient. The south-west contains three large water bodies, while the northwest contains 2 large water bodies. The reason species will thrive in the west is due to its lower human population density as compared to that of the east. The west also is surrounded by water and is less heavily influenced by human disturbance such as pollution and transportation. We believe that our model has the ability to incorporate climate projections and stay a relevant tool to predict mayfly habitats even in coming decades as rainfall and temperature continue to shift. Aim We broke our analysis down into three parts representing the three scales of analysis- A Statewide climate-based suitability analysis to determine what current waterways, temperature, and precipitation conditions are conducive for current populations of Ephemeroptea A regionally based TIN approach to find out the how accurately aspect, slope, and elevation can predict Ephemeroptea locations. Proximity Analysis to study what sort of mobility options ephemeroptera have to accommodate a changing climate. We chose San Francisco due to the local scale and ample information. Introduction We will be studying the behaviour and habitat of Ephemeroptea in order to gain a better understanding into the capabilities of these water dwelling creatures. Commonly known as mayflies, the Ephemeroptea are used for biomonitoring on an ecosystem scale because their populations respond quickly to even trace amounts of pollutants in their environment. By collecting population data, scientists can easily access the potential health of the ecosystem. However, factors other than pollution are at play, namely a changing climate. As weather and precipitation patterns change, so will mayfly habitat. We hope to be able to determine which conditions are favourable to mayflies and hope to use the knowledge gained to understand how non-pollutant influences will change Ephemeroptea distributions. Data Collection We downloaded county line data from CalAtlas. The excel file of the locations of Ephermeroptea sightings in 2013, provided by CEDEN. NOAA provided a detailed regional model of the Bay Area. Climate data (precipitation and temperature) was from a Prism, a repository for climate and biology related GIS data. In addition to projected rainfall and temperature data. For the network analysis, data came from the US Census Bureau. Regional Analysis At the regional scale, there will be relatively uniform climate. (At least within the same elevation bands, the bay area tests the limits of this assumption with its microclimates). Mayflies were plotted and analyzed on a rastarized TIN of the bay area in maps representing elevation, slope, and aspect. There, the frequencies of mayflies found on each slope face and elevation could be counted. Mayflies were found to mostly exist between 300 and 600 meters from sea level in the bay area and were found in primarily mountainous areas (slope >15). There was a weak correlation with aspect, although this may be due to the TIN or the data being too granular. Aspect and Elevation data superimposed with the recorded range of Ephemeroptea recordings. This is the basis for our regional suitability analysis. This is the suitability map that was built for the state of California. It was derived from analysing which temperature and precipitation data matched existing locations of the ephemeroptea, and extrapolating to find the potential state-wide ranges. Studying the Impact of Changing Climate on Ephermeroptea Distribution State-wide Analysis We used climate, precipitation, and current mayfly locations and used other mayfly studies to inform which thresholds must be met for habitability. For example the Ephemeroptea do best in springtime temperatures between 8 and 16 degrees Celsius and in rainfall greater than 500mm a year. The resulting suitability map is shown below which matches well with current data. Citywide Analysis The network analysis used a combined sewage and existing stream data for the analysis. The lines were segmented so the distance could be accurately calculated. A new service area was created to represent the proximity of water bodies by time. This represents the ability for the macroinvertebrate species to migrate as a function of time. We assumed that the flow rate of the river network was constant, as was the mayfly’s speed. For the location allocation system. We chose to maximize attendance for large water bodies. This allowed us to predict which large water bodies had a tendency to migrate towards during reproduction season. This was made under the assumption that large water bodies could persist through a drought while smaller ones may dry up. This a graphic showing the correlation between water body demand and size. We can demonstrate that size of water body does not directly correlate to the population of Ephemeroptera in the area.