 My name is Brad Eichelberger and I'm a GIS analyst with the Natural Capital Project and I will be talking today about the Habitat Risk Assessment Model. The condition of a habitat is a key determinant of the environmental services it can provide. For example, multiple stressors including fishing, climate change, pollution, and coastal development threaten the ability of coastal ecosystems to provide the valuable goods and services that people want and need. The Habitat Risk Assessment Model is useful in helping identify which habitats are most at risk from human activities and where they occur, which activities pose the greatest risk to habitats or species, and which types of management are useful in reducing risk to habitats or species. Lastly, the Habitat Risk Assessment Model is applicable at both the habitat and species levels in both marine and terrestrial systems. It is important to note that both the Habitat Quality Model and the Habitat Risk Assessment Model, also known as HRA, can be used to examine how risks posed by human activities varies across the landscape or seascape and can identify the locations where risk is highest or lowest. The two models are mathematically similar, but different in a few key ways. Habitat Quality was developed with terrestrial ecosystems in mind, whereas HRA is applicable at both marine and terrestrial systems. Habitat Quality is a bit more simplified in the sense that it does not directly include factors such as mortality, recruitment, connectivity, and other exposure criteria, whereas the HRA model does. The risk assessment framework allows the HRA model to be interpreted along exposure and consequence axes in a manner that helps users explore which management strategies are likely to reduce risk. This schematic depicts the general overview of the model. The HRA model produces information about risk at two scales and with several types of outputs. We first determine the likelihood of exposure of the habitat to a stressor, such as the spatial overlap, temporal overlap, stressor intensity, and effectiveness of management, and the consequence of the exposure, such as a change in area, change in structure, frequency of natural disturbance, natural mortality, recruitment, agent maturity, recovery time, and connectivity. Overall exposure and consequence scores are calculated as weighted averages for each stressor habitat criterion. There are two options for calculating risk, either Euclidean or multiplicative. The Euclidean risk calculation calculates risk to a habitat caused by each stressor as the Euclidean distance from the origin and the exposure or consequence space. For multiplicative risk calculation, risk is calculated as the product of the summed exposure and consequence scores. Lastly, the model identifies areas of habitat that are considered risk hotspots. As with all models, there should be several limitations and caveats to be considered. Results should be interpreted on a relative scale. Due to the nature of the scoring process, results can be used to compare the risk of several human activities among several habitats within the study region, which can range in size from small local scales to a global scale. But these should not be used to compare risk calculations from separate analyses. Additionally, the user needs to define the thresholds for interpreting the severity of the risk. This may involve validating the model output with independent data on habitat quality or species abundance. Results do not reflect the effects of past human activities. Exposure of the human activities in the past may affect the consequences of human activities in the present and future. If users have historical data on the exposure of habitats in human activities, for example spatial and temporal extent, and information on how this affects current consequence scores, this may be included in the analysis for more accurate results. Results are based on equal weighting of criteria unless the user weights the criteria by importance or data quality. The model calculates the exposure and consequence scores assuming that the effect of each criterion, for example spatial overlap and recruitment pattern, is of equal importance in determining risk. The relative importance of each of the criteria is poorly understood, so we seem equal importance. However, the user has the option to weight the importance of each criterion determining overall risk. Next we'll discuss the inputs that are needed for the HRA model. First we need maps of relevant habitat or species distributions that we are interested in assessing risks to. Next we utilize maps of stressors that depict the spatial location of human activities or disturbances. And finally we look at the effects of stressors on habitat, which includes information regarding exposure and consequence and can be based on knowledge from scientific leisure or expert opinion. We have three main types of model outputs. First we have habitat risk maps. This shows risk scores per pixel for each habitat or species, as well as accumulative ecosystem risk. Next we have risk plots, which graphically illustrate the risk score for each habitat stressor interaction in the exposure consequence space. Finally we have a map of the recovery potential, which shows per pixel values for the recovery potential for a given habitat or species. In summary, this model can be used to help understand the spatial effects of stressors on habitat or species and which management practices contribute to or reduce risk. Resource managers can then use this model to better understand how management affects species habitat and analyze trade-offs with other ecosystem services.