Both the biodiversity model and the HRA model can be used to identify areas on a landscape or seascape where the risk posed by human activities is highest. The risk of human activities to coastal and nearshore habitats is a function of the habitat’s exposure to the activity and the consequence of exposure. The HRA model combines information about the exposure of habitats to each stressor with information about the consequence of that exposure for each habitat to estimate and produce maps of risk to habitats and habitat quality at both a grid cell and a subregional scale. The model allows for any number of criteria to be used when evaluating the risk to habitat areas. 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 should not be used to compare risk calculations from separate analyses. The CSVs contained within the habitat_stressor_ratings folder will provide all criteria information for the run of the Habitat Risk Assessment.
A raster representing the Consequence portion of the final risk calculations for the overlap of the given habitat and stressor. This raster layer depicts the sum of all cumulative risk scores for all habitats in each grid cell.
Plotting exposure and consequence data in this plot allows users to visualize risk, and to assess which types of risk are more effectively mitigated by human intervention (risks driven by exogenous human factors, top right region of the risk space) and which types of risk are better addressed through monitoring and preparedness (risks driven by endogenous habitat-specific factors). Habitats with high natural mortality rates are generally more productive and more capable of recovery. Using high quality data such as those from local assessments replicated at several sites within the study region for the species in question within the last ten years will yield more accurate results than using lower quality data that are collected at a distant location with limited spatial or temporal coverage.
The HRA model does not explicitly account for the effects of historical human activities on the current risk. These are vector layer files which would provide more explicit detail for a specific criteria in the assessment. This will be used in order to make determinations of low, medium, and high risk for a given habitat. For every pairing of habitat and stressor, the table provides average exposure, consequence, risk, and risk percentage (as a portion of total potential risk). Tables and risk plots (ie., Figure 1) show the contribution of different activities to the risk posed to each habitat at a subregional scale within the study area and among future scenarios.
Because indirect impacts are poorly understood and difficult to trace, we only model the risk of stressors that directly impact habitat by overlapping in space.
Habitats that lose a high percentage of their areal extent when exposed to a given stressor are highly sensitive, while those habitats that lose little area are less sensitive and more resistant.
Sensitivity criteria are also applied to a specific habitat-stressor pairing, but will be applied to the consequence portion of the risk equation. The stressor buffer CSV will be a single file containing the desired buffer for all stressors included in the assessment. In addition, if the Risk Equation chosen is Euclidean, the model will also generate a series of figures which clearly display the exposure-consequence ratings and the resulting risk results for each habitat-stressor combination by subregion.
In the context of marine ecosystem-based management, risk assessment evaluates the probability that human activities will impede the achievement of desired marine management objectives.
As a result, the model will help managers prioritize and evaluate management strategies with regards to their effectiveness of reducing risks to nearshore habitats and maintaining the delivery of desired ecosystem services. Thus, users may choose to consider these habitats to be functionally absent in inputs to other InVEST ecosystem service models (see the Interpreting Results section for guidance on how to use risk hotspots to identify trade-offs among human activities under alternative scenarios).
We hope that by including the option to rate data quality in the model, users will be aware of some sources of uncertainty in the risk assessment, and will therefore be cautious when using results derived from low quality data.
However, high storm frequency may increase resistance to destructive fishing, because both stressors impact habitats in similar ways. Evaluating impacts of fishing on benthic habitats: A risk assessment framework applied to Australian fisheries.
There is one habitat risk shapefile for each vector file originally used within the assessment. To conduct a risk-based assessment of the cloud computing environment, there are generic risk frameworks such as the Committee of Sponsoring Organizations of the Treadway Commission (COSO) Enterprise Risk Management—Integrated Framework. The IS auditor of Company A chose the Risk IT framework, supplemented with an understanding of the Cloud Controls Matrix, ENISA’s cloud computing risk assessment and the NIST guidelines. Risk IT provides a list of 36 generic high-level risk scenarios, which can be adapted for each organization.
Leveraging Risk IT in conjunction with a widely accepted IT governance and controls framework such as COBIT makes the risk identification robust and the risk assessment process effective and efficient. Once the risks and COBIT control objectives were defined, they were used by the IS auditor to develop a risk-based audit program. Due to competing resources, the prioritization of risks related to cloud computing needs to occur, and appropriate action should be taken based on the risk appetite of the company.
Once the company aligns IT risk with the organization’s overall business risk and remediates unacceptable security controls, the company is better prepared to harness the power of cloud computing. Figure 1: Habitats with high exposure to human activities and high consequence are at high risk. The HRA model produces information about risk at two scales and with several types of outputs. For Multiplicative Risk calculation, risk to habitat i caused by stressor j is calculated as the product of the summed exposure and consequence scores.


The risk of a habitat being affected by a stressor depends in part on the exposure of the habitat to that stressor.
The main computation portion of the HRA model will be done by the Habitat Risk Assessment executable.
A raster representing the Exposure portion of the final risk calculations for the overlap of the given habitat and stressor.
This raster layer depicts the cumulative risk for all the stressors in a grid cell on a habitat-by-habitat basis. Maps display variation at a grid cell scale in the relative risk of human activities to habitats within the study area and among alternative future scenarios. It is likely that some aspects of the risk assessment will be supported by high quality data and other aspects will be subject to limited data availability and high uncertainty. Exposure criteria are specific to a habitat-stressor pairing, and will be applied to the exposure portion of the risk modeling equation.
This selection chooses the equation that will be used when calculating risk to a given habitat. This risk raster takes into account each of the criteria that apply to the habitat and stressor, as well as the user-specified risk equation. Through our print and digital media platforms, continuing education activities, and events, we strive to deliver relevant, cutting-edge information designed to support the highest level of oral health care.
For example, the user could differtiate between areas of high and low recruitment for a particular habitat or species within the study area. If the number of overlapping stressors provided is too low, results will likely show more medium and high risk areas than are present.
By default, any criteria in the Sensitivity or Resilience categories will be assigned to Consequence (C) within the risk equations, and any criteria within the Exposure category will be assigned to Exposure (E) within the risk equation. However, the user has the option to weight the importance of each criterion in determining overall risk.
The default criteria provided are derived from peer-reviewed literature and are recommended as a good set of contributers to risk in a system, but users do have the option to add or remove criteria if desired.
You can use this intensity criteria to explore how changes in the intensity of one stressor might affect risk to habitats. Reducing risk through management is likely to be more effective in situations where high risk is driven by high exposure, not high consequence. Due to its nebulous nature, it is important to understand the risks associated with utilizing cloud computing. The cloud’s economies of scale and flexibility are both a friend and a foe from a security point of view.4 The chief information officer (CIO) of the company engaged an information systems (IS) auditor to conduct a review and assess the risks of offering a SaaS solution and adopting IaaS cloud computing for this arrangement. There are also IT domain-specific risk frameworks, practices and process models such as ISO 27001 and IT Infrastructure Library (ITIL).
Starting with the set of generic risk scenarios helps ensure that the IS auditor does not overlook risks and attains a more comprehensive view of IT risk. This leads to a model that is extensible and reusable and that can scale up to IT risks affecting the entire company. Figures 3–105 represent a selection of the audit program for the higher-risk areas in figure 2.
However, implementing too many controls may not be the best risk-mitigation approach because the benefit from implementing controls should outweigh the cost.
This case study represents a one-time attempt at risk assessment of the cloud computing arrangement.
Risk assessment has a long history in the field of ecotoxicology, and is now emerging as a valuable method in ecosystem-based fisheries management (Astles et al. For each grid cell, if the habitat overlaps with a stressor, then spatial overlap = 1 and the model calculates exposure, consequence and risk using scores for the other criteria (below).
Maximum Criteria Score (required) The maximum criteria score is the user-reported highest value assigned to any criteria rating within the assessment. A raster containing the final risk calculation for the given habitat and stressor combination. Dimensions of Dental Hygiene is committed to the highest standards of professionalism, accuracy and integrity in our mission of education supporting oral health care professionals and those allied with the dental industry.
The second step combines the exposure and response values to produce a risk value for each stressor-habitat combination. This figure can be used to determine which habitats are at highest risk from human activities, and if this risk is mostly due to high cumulative exposure (exogenous factors that can be mitigated by management) or high cumulative consequence (endogenous factors that are less responsive to human intervention). The user has the option of scoring data quality to put greater weight on the criteria for which confidence is higher in the calculation of risk (eq.
The model will use a subregions shapefile to generate an HTML table of averaged exposure, consequence, and risk values within each subregion by habitat and stressor. If the user chooses to use a different scale for ratings, however, this should be the highest value that could be potentially assigned to a criteria. It will also create a figure showing cumulative ecosystem risk for all subregions habitats in the study.
Stressors that have high exposure scores and high consequence scores pose the greatest risk to habitats.
As a result, the model may over- or under-estimate the cumulative risk depending on the set of stressors occurring in the study region.


Further, Risk IT offers an extensive mapping between the generic risk scenarios and the COBIT control objectives that are customizable for each situation.
Other risk-mitigation measures such as transferring, avoiding or accepting the risk are worth considering as well. The risk assessment helped uncover some of the key risks, prioritize those risks and formulate a plan of action.
Risk assessment is an integrative process, which requires a substantial amount of data on many attributes of human and ecological systems.
As discussed in the introduction, several recent papers examine risk to marine fisheries, stocks, habitats and ecosystems (Halpern et al. For example, in a nearshore grid cell that contains some coral reef, mangrove and soft bottom habitat, the ecosys_risk value reflects the risk to all three habitats in the cell. Conversely, if the number of overlapping stressors is too high, it will be difficult for areas to break the threshold to show up as medium or high risk.
This layer is informative for users who want to know how cumulative risk for a given habitat varies across a study region (e.g.
An ecological method for qualitative risk assessment and its use in the management of fisheries in New South Wales, Australia.
Initial sensitivity testing suggests that overall, the euclidean and multiplicative approaches will agree on the same highest and lowest risk species and habitats; however, there may be differences in the rank order of species at intermediate risk, depending on the values for E and C. This exercise will help the CIO in determining what Company A needs to protect, prioritizing the risks and determining a response. The Cloud Controls Matrix released by CSA is designed to provide security principles to guide cloud vendors and assist prospective cloud clients in assessing overall security risks of a CSP. Figure 2 illustrates the mapping between the high-level risk scenarios and the corresponding COBIT control objectives created by the IS auditor for the cloud computing arrangement. Given the evolving nature of risks in cloud computing, no longer can one-time risk assessments suffice.
The highest value for this input is the total number of stressors in the study area; however, it is unlikely that all stressors will ever realistically overlap in a single grid cell. Risk assessment is used to determine the likelihood that a hazard will cause undesired consequences (Burgman 2005).
Medium risk pixels use the same guidelines, but are defined by risk that falls between 33% and 66%. The euclidean approach may provide more conservative, higher overall estimates than the multiplicative approach. The NIST guidelines on security and privacy in public cloud computing (NIST Special Publication [SP] 800-144), which are currently in draft form, contain the guidelines required to address public cloud security and privacy. As newer risks emerge, risk assessments need to evolve and the mitigation approach needs to innovate. In the final step, the user has the option of assessing risk at a subregional scale, which is larger than the resolution of the grid cells and smaller than the size of the study area.
Habitats that lose a high percentage of their structure when exposed to a given stressor are highly sensitive, while habitats that lose little structure are less sensitive and more resistant. If the risk equation chosen was Euclidean, the distance from the stressor point to the origin represents the average risk for that habitat, stressor pair within the selected AOI. Cells are classified as MED if they have individual stressor or cumulative risk scores between 33%-66% of the total possible cumulative risk score. The Risk IT:  Based on COBIT® framework from ISACA fills the gap between generic risk management frameworks and domain-specific frameworks based on the premise that IT risk is not purely a technical issue. A risk assessment needs to occur before an enterprise enters into a cloud computing arrangement—to help avoid surprises and minimize the costs of implementing and maintaining controls. As a default, the model provides a set of typical considerations for evaluating risk of stressors to habitats. Each directory should be independent of the others so as to avoid incorrect repetition in the outputs, and should contain ONLY layers that are desired within this assessment.
Outputs from the model are useful for understanding the relative risk of human activities and climate change to habitats within a study region and among alternative future scenarios. Cells are classified as LOW risk if they have individual or cumulative risk scores of 0-33% of the total possible risk score for a single stressor or multiple stressors, respectively.
Resilience criteria will likewise be applied to the consequence portion of the risk equation, but are specific to an overall habitat. In the HRA model, we define risk as the likelihood that human activities will reduce the quality of nearshore habitats to the point where their ability to deliver ecosystem services is impaired. The HRA model in Marine InVEST builds on these approaches and allows users to evaluate the risk posed by a variety of human activities to key coastal habitats in a transparent, repeatable and flexible way. Each grid cell for each type of habitat is classified as HIGH, MED or LOW risk based on risk posed by any individual stressor or the risk posed by the cumulative effects of multiple stressors.




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