InVEST +VERSION+ documentation

# Habitat Risk Assessment¶

## Summary¶

The condition of a habitat is a key determinant of the ecosystem 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. As human activities continue to intensify, so too does the need for quick, clear and repeatable ways of assessing the risks posed by human activities under various management plans. The InVEST habitat risk assessment (HRA) model allows users to assess the risk posed to coastal and marine habitats by human activities and the potential consequences of exposure for the delivery of ecosystem services and biodiversity. The InVEST HRA model is similar to the InVEST biodiversity model in that both models allow users to identify regions on a landscape or seascape where human impacts are highest. While the biodiversity model is intended to be used to assess how human activities impact biodiversity, the HRA model is better suited to screening the risk of current and future human activities to prioritize management strategies that best mitigate risk. We built and tested the HRA model in marine and coastal systems, and discuss it accordingly, but it easily can be applied to terrestrial systems, or mobile species.

Risk of human activities (e.g., salmon aquaculture, coastal development, etc.) to habitats (e.g., seagrasses, kelp forests, mangroves, reefs) is a function of the exposure of each habitat to each activity and the consequences for each habitat. Exposure to stressors can arise through direct overlap in space and time or through indirect effects (i.e. finfish farms in an enclosed bay may degrade water quality and thus impede eelgrass growth throughout the bay, even if the netpens are not situated directly over eelgrass beds). Consequence depends on the effects of activities on habitat area and density, and the ability of habitats to recover from these effects (i.e. through processes such as recruitment and regeneration). 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. Model outputs can help identify areas on the seascape where human activities may create trade-offs among ecosystem services by posing risk high enough to compromise habitat structure and function. The model can help to prioritize areas for conservation and inform the design and configuration of spatial plans for both marine and terrestrial systems.

## Introduction¶

Nearshore habitats such as kelp forests and eelgrass meadows provide valuable ecosystem services including the protection of shorelines from storms, nursery habitat for fisheries and carbon storage and sequestration. As these habitats become degraded by human activities, the ecosystem services they provide are threatened. The impacts of human activities in coastal areas, both on land and in the sea, are pervasive in coastal ecosystems. Recent global analyses have revealed that almost no area of the world’s oceans is untouched by human impacts (Halpern et al. 2008). Thus, an understanding of the location and intensity of human impacts on nearshore ecosystems is an essential component of informed and successful coastal and ocean management. The InVEST HRA model allows users to assess the threat of human activities to the health of these ecosystems.

### InVEST Biodiversity Model vs. InVEST Habitat Risk Assessment Model¶

A primary goal of conservation is the protection of biodiversity; biodiversity is intricately linked to the production of ecosystem services. While some consider biodiversity itself to be an ecosystem service, the InVEST biodiversity model treats it as an independent attribute of natural systems, with its own intrinsic value (InVEST does not monetize biodiversity). InVEST includes a biodiversity model because natural resource managers, corporations and conservation organizations are becoming increasingly interested in understanding how and where biodiversity and ecosystem services align in space and how management actions affect both. The biodiversity model uses habitat quality and rarity as a proxy for diversity.

When developing a similar model with marine systems in mind, differences in data availability (e.g., the lack of an analog to land-use/land-cover maps in marine systems) and differences in thinking (e.g., the prevalence of a risk-assessment framework in fisheries science) led us to the development of the habitat risk assessment model described in this chapter.

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. Indeed, while the two models are segregated into the marine and terrestrial toolboxes, they can be used across systems. However, the modeling approaches differ in several ways. First, the exposure-consequence framework of the HRA model allows model results to be interpreted in a manner that helps users explore which types of management strategies are likely to most effectively reduce risk (Figure 1). For example, ecosystems with high exposure and high consequence may be targeted for intense active management, while effective strategies for ecosystems with low exposure to human stressors but high consequence may include close monitoring but little active intervention unless exposure increases. Second, the transparent flexible structure and explicit visualization of data uncertainty in the HRA model facilitate its use in both data-rich and data-poor situations. Finally, the biodiversity model is better suited for terrestrial applications than marine applications because it requires a land use land cover map as an input. The HRA model can be used in both marine and terrestrial systems.

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. 2006, Hobday et al. 2011). Risk assessment is used to determine the likelihood that a hazard will cause undesired consequences (Burgman 2005). 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. 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. Researchers have made significant progress in evaluating human impacts on marine ecosystems in recent years. However many of these approaches lack generality because they are focused on the effects of a single sector (i.e. fisheries e.g. Astles et al. 2006, Hobday et al. 2011), or have limited transparency and flexibility because they are based on expert opinion (Halpern et al. 2008, Teck et al. 2010). 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.

## The Model¶

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. To determine exposure, users provide model inputs such as base maps of habitat distribution and human activities, the timing and intensity of the activity and the effectiveness of current management practices in safeguarding habitats. To determine consequence, users provide model inputs such as observed loss of habitat and the ability of habitats to recover. The model is flexible and can accommodate data-poor and data-rich situations. Data may come from a combination of peer-reviewed sources at the global scale and locally available fine-scale data sources. Model inputs and results can be updated as better information becomes available.

The HRA model produces information about risk at two scales and with several types of outputs. 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. 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. When run as part of a complete Marine InVEST analysis, the HRA model can be used to identify which human activities are likely to cause trade-offs in other ecosystem services. 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.

### How it Works¶

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. For example, exposure depends on the extent of geographic overlap between habitats and human activities, the duration of time that the activity and habitat overlap, the intensity of the stressor and the degree to which management strategies mitigate impact. Consequence depends on the degree of habitat loss as a result of exposure to a stressor, change in habitat structure and the ability of habitats to recover from these effects (i.e., through life history traits such as recruitment and regeneration rates). The modelling approach is flexible so if any of the default factors that influence exposure and consequence are irrelevant in a particular case, they can be excluded and/or replaced with alternative criteria (see Risk of Human Activities to Habitats). We begin by explaining the approach at the grid cell scale and later describe differences in the approach for the subregional outputs.

#### Risk of Human Activities to Habitats¶

The risk of human activities to habitats is modeled in four steps.

Step 1. The first step involves determining the likelihood of exposure of the habitat to the stressor and the consequence of this exposure. Exposure (E) and consequence (C) are both determined by assigning a rating (typically 1-3, with 0 = no score) to a set of criteria for each attribute. We have provided the user with a set of standard criteria used frequently in the scientific literature, but any criteria may be added or removed. Guidelines for scoring of the default criterion are summarized below, and abbreviated descriptions of scoring on a 1-3 basis are provided in the tables produced from HRA Preprocessor. Note that we treat “spatial overlap” which is one of the exposure criteria, differently from the other default criteria. For each grid cell in the study area, if a stressor and a habitat overlap in space, then spatial overlap = 1 and the model calculates E and C using the information about the other criteria and the equations below. If a stressor and a habitat do not overlap in a particular grid cell, we assume that spatial overlap = 0, E = 0, C = 0 and Risk = 0. Spatial overlap is determine by the model using the spatial layers for stressor and habitat provided by the user. The scores for all the other criteria are inputs to the model provided by the user. To ensure accuracy, we recommend that scores be determined using readily available data from peer-reviewed literature or published reports, however, you are free to use any data you believe to be the most accurate. For each score assigned, you may also indicate the quality of the data used to determine the score and the weighted importance of the criteria relative to other criteria. This allows you to assign greater weight to criteria where scoring confidence was higher, or to criteria which contribute more to risk in the system. Thus, the overall exposure $$E$$ and consequence $$C$$ scores are calculated as weighted averages of the exposure values $$e_i$$ and consequence values $$c_i$$ for each criterion i as

(1)$E = \frac{\sum^N_{i=1}\frac{e_i}{d_i \cdot w_i}}{\sum^N_{i=1}\frac{1}{d_i \cdot w_i}}$
(2)$C = \frac{\sum^N_{i=1}\frac{c_i}{d_i \cdot w_i}}{\sum^N_{i=1}\frac{1}{d_i \cdot w_i}}$

where $$d_i$$ represents the data quality rating for criterion i, $$w_i$$ represents the importance weighing for criterion i and N is the number of criteria evaluated for each habitat.

Step 2. The second step combines the exposure and response values to produce a risk value for each stressor-habitat combination. There are two options for risk calculation.

For Euclidean Risk calculation, risk to habitat i caused by stressor j is calculated as the Euclidean distance from the origin in the exposure-consequence space,

(3)$R_{ij} = \sqrt{(E-1)^2+(C-1)^2}$

For Multiplicative Risk calculation, risk to habitat i caused by stressor j is calculated as the product of the summed exposure and consequence scores.

(4)$R_{ij} = E \cdot C$

The user has the option of choosing which risk function to use. As discussed in the introduction, several recent papers examine risk to marine fisheries, stocks, habitats and ecosystems (Halpern et al. 2008, Teck et al. 2011, Hobday et al. 2011, Williams et al. 2011, Samhouri and Levin 2012). In each case, 2 axes of information are used to calculate total risk. In the InVEST HRA model, we refer to these axes as exposure and consequence. 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. The euclidean approach may provide more conservative, higher overall estimates than the multiplicative approach. If E and C values are widely different then the euclidean approach will produce relatively higher risk results. In contrast, the multiplicative approach will tend to produce relatively lower, less conservative values for risk and associate similarity in E and C with higher risk. If your system contains habitats for which there is a very high consequence of risk but low exposure (e.g., coral and shrimp trawling zones that currently avoid coral reefs) and you want to adopt the precautionary principle, then we advise you to choose the Euclidean approach. Either approach will produce useful results. We have included this explanation to provide you with insight into the assumptions of the different functions.

Step 3. In this step, the model quantifies the cumulative risk of all stressors on the habitats. Cumulative risk for habitat i is the sum of all risk scores for each habitat,

(5)$R_i = \sum^J_{j=1} R_{ij}$

Step 4. The model identifies areas of habitats that are risk ‘hotspots’. These are areas where the influence of human-derived stressors is so great that ecosystem structure and function may be severely compromised. In these areas, there may be trade-offs between human activities and a range of 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). 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. A classification of HIGH is assigned to grid cells with a cumulative risk of >66% of the maximum risk score for any individual stressor-habitat combination, or >66% of total possible cumulative risk. Total possible cumulative risk is based on the maximum number of stressors that can occupy a particular grid cell in the study area (see next paragraph). For example, maximum overlapping stressors = 3 if in the entire study region no more than 3 stressors (e.g., agriculture run-off, marine aquaculture and marine transportation) are likely to occur in a single grid cell. Cells are classified as MED if they have individual stressor or cumulative risk scores between 33%-66% of the total possible cumulative risk score. 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.

The maximum number of overlapping stressor is an input provided by the user. 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. This is because stressors are distributed differently in space (i.e., stressors like coastal development exist along the shore while shipping lanes exist offshore) and because some stressors can never exist in the exact same location (i.e., naval weapons testing areas and tourism). From applying this model in several locations, we have found that 3 or 4 is a common value for maximum number of overlapping stressors, but the user should either manually examine his/her maps for overlaps in stressors or use the overlap analysis model to calculate the highest number of overlapping stressors.

Step 5. 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. In a coastal and marine spatial planning process, subregions are often units of governance (i.e., coastal planning regions, states or provinces) within the boundaries of the planning area. Risk outputs at a subregional scale can be used to determine which activities are contributing the most to habitat risk in a particular region. This information can in turn be used to explore strategies that would reduce the exposure of a particular habitat to a particular activity, such as reducing the extent or changing the location of an activity. The model produces risk plots for each habitat that compare the consequence and exposure scores for all activities at a subregional scale. These plots help to user to understand if reducing exposure of particular activities through management actions is likely to reduce risk or if risk is driven by consequence, which is harder to perturb through management actions (see Figure 1 above). The model also produces tables listing E, C and Risk for each habitat-stressor combination at a subregional scale and calculates the percentage of cumulative risk by habitat that is due to a particular stressor in that region. Note that the subregional score for spatial overlap (a default exposure criteria) is based on the fraction of habitat area in a subregion that overlaps with a human activity (see below for more detail). The subregional score for all other E and C criteria are the average E and C score across all grid cells in the study area. Risk is estimated either using the Euclidean distance or multiplicative approach (see above).

#### Exposure and Consequence Criteria in More Detail¶

The model allows for any number of criteria to be used when evaluating the risk to habitat areas. As a default, the model provides a set of typical considerations for evaluating risk of stressors to habitats. With the exception of spatial overlap at a grid cell scale, these criteria are rated on a scale of 1-3, with 0 = no score. But we do not constrain the rating of these criteria to a 0-3 scale. If there is significant literature using an alternative scale, the model can accomodate any scale (i.e., 1-5, 1-10) as long as there is consistency across the rating scores within a single model run. It should be noted that using a score of 0 on ANY scale will indicate that the given criteria is not desired within that model run.

##### Exposure of Habitats to Stressors¶

The risk of a habitat being affected by a stressor depends in part on the exposure of the habitat to that stressor. Stressors may impact habitats directly and indirectly. 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. Other important considerations include the duration of spatial overlap, intensity of the stressor, and whether management strategies reduce or enhance exposure.

1. Spatial overlap . To assess spatial overlap in the study area, the model uses maps of the distribution of habitats and stressors. Habitat types can be biotic, such as eelgrass or kelp, or abiotic, such as hard or soft bottom. The user defines the detail of habitat classification. For example, habitats can be defined as biotic or abiotic, by taxa (e.g., coral, seagrass, mangrove), by species (e.g., red, black mangroves) or in whatever scheme the user desires. However, the user should keep in mind that in order for such detail to be useful and change the outcome of the model, these habitat classifications should correspond with differences between habitats in their response to the stressors.

The model also requires the user to input maps of the distribution of each stressor and information about its “zone of influence.” The zone of influence of each stressor is the distance over which the effects of the stressor spread beyond its actual footprint in the input stressor map. For some stressors, such as over-water structures that shade eelgrass beds, this distance will be small. For other stressors, such as finfish aquaculture pens where nutrients spread 300-500m, this distance may be large. The model uses the distance of influence of a stressor to create an intermediate output that is a map of the stressor footprint buffered by the zone of influence. The model uses the maps of habitat and buffered stressors to estimate spatial overlap between each habitat and each stressor at the grid cell and subregional scale.

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). If a habitat does not overlap with a stressor in a particular grid cell, then the model sets exposure, consequence and risk = 0 in that particular grid cell. At the subregional scale, the model calculates the fraction of area of each habitat that overlaps with each stressor. Next the model puts that fraction on a scale of 1- maximum risk score to match the scale for scoring the other criteria. For example, if spatial overlap = 50% of the habitat overlapped by a stressor, and our scale is 0-3, then 3*overlap + 1*(1-overlap) = 2. Lastly, the model averages the spatial overlap score with the average exposure score for the subregion. If there is no spatial overlap between the habitat and stressor at the subregional scale, then exposure = 0, consequence = 0 and risk = 0. If there are no exposure scores for that habitat-stressor combination, but spatial overlap does exist, the score will be entirely the spatial overlap.

2. Overlap time rating. Temporal overlap is the duration of time that the habitat and the stressor experience spatial overlap. Some stressors, such as permanent overwater structures, are present year-round; others are seasonal, such as certain fishing practices. Similarly, some habitats (e.g. mangroves) are present year round, while others are more ephemeral (e.g. some seagrasses).

We use the following categories to classify HIGH, MEDIUM and LOW temporal overlap:

High (3) Medium (2) Low (1) No score (0)
Temporal overlap Habitat and stressor co-occur for 8-12 months of the year Habitat and stressor co-occur for 4-8 months of the year Habitat and stressor co-occur for 0-4 months of the year N/A

Choose “No score” to exclude this criteria from your assessment.

3. Intensity rating. The exposure of a habitat to a stressor depends not only on whether the habitat and stressor overlap in space and time, but also on the intensity of the stressor. The intensity criterion is stressor-specific. For example, the intensity of nutrient-loading stress associated with netpen salmon aquaculture is related to the number of salmon in the farm and how much waste is released into the surrounding environment. Alternatively, the intensity of destructive shellfish harvesting is related to the number of harvesters and the harvest practices. You can use this intensity criteria to explore how changes in the intensity of one stressor might affect risk to habitats. For example, one could change the intensity score to represent changes in the stocking density of a salmon farm in a future scenario. One can also use this ranking to incorporate relative differences in the intensity of different stressors within the study region. For example, different types of marine transportation may have different levels of intensity. For example, cruise ships may be a more intense stressor than water taxis because they release more pollutants than the taxis do.

We use the following categories to classify HIGH, MEDIUM and LOW intensity:

High (3) Medium (2) Low (1) No score (0)
Intensity High intensity Medium intensity Low intensity N/A

Choose “No score” to exclude this criteria from your assessment.

4. Management strategy effectiveness rating. Management can limit the negative impacts of human activities on habitats. For example, policies that require salmon aquaculturists to let their farms lie fallow may reduce the amount of waste released and allow nearby seagrasses to recover. Similarly, regulations that require a minimum height for overwater structures reduce the shading impacts of overwater structures on submerged aquatic vegetation. Thus, effective management strategies will reduce the exposure of habitats to stressors. The effectiveness of management of each stressor is scored relative to other stressors in the region. So if there is a stressor that is very well managed such that it imparts much less stress on the system than other stressors, classify management effectiveness as “very effective.” In general, however, the management of most stressors is likely to be “not effective.” After all, you are including them as stressors because they are having some impact on habitats. You can then use this criterion to explore changes in management between scenarios, such as the effect of changing coastal development from high impact (which might receive a score of “not effective”) to low impact (which might receive a score of “somewhat effective).”

We use the following categories to classify HIGH, MEDIUM and LOW management effectiveness:

High (3) Medium (2) Low (1) No score (0)
Management effectiveness Not effective, poorly managed Somewhat effective Very effective N/A

Choose “No score” to exclude this criteria from your assessment.

##### Consequence of Exposure¶

The risk of a habitat being degraded by a stressor depends on the consequence of exposure. The consequence of exposure depends on the ability of a habitat to resist the stressor and recover following exposure, and can be assessed using four key attributes: change in area, change in structure, frequency of natural disturbance, and recovery attributes. We describe each in turn below.

1. Change in area rating. Change in area is measured as the percent change in areal extent of a habitat when exposed to a given stressor and thus reflects the sensitivity of the habitat to the stressor. 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.

We use the following categories to classify HIGH, MEDIUM and LOW change in area:

High (3) Medium (2) Low (1) No score (0)
Change in area High loss in area (50-100%) Medium loss in area (20-50%) Low loss in area (0-20%) N/A

Choose “No score” to exclude this criteria from your assessment.

2. Change in structure rating. For biotic habitats, the change in structure is the percentage change in structural density of the habitat when exposed to a given stressor. For example, change in structure would be the change in shoot density for seagrass systems, change in polyp density for corals, or change in stipe density for kelp systems. 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. For abiotic habitats, the change in structure is the amount of structural damage sustained by the habitat. Sensitive abiotic habitats will sustain complete or partial damage, while those that sustain little to no damage are more resistant. For example, gravel or muddy bottoms will sustain partial or complete damage from bottom trawling while hard bedrock bottoms will sustain little to no damage.

We use the following categories to classify HIGH, MEDIUM and LOW change in structure:

High (3) Medium (2) Low (1) No score (0)
Change in structure High loss in structure (for biotic habitats, 50-100% loss in density, for abiotic habitats, total structural damage) Medium loss in structure (for biotic habitats, 20-50% loss in density, for abiotic habitats, partial structural damage) Low loss in structure (for biotic habitats, 0-20% loss in density, for abiotic habitats, little to no structural damage) N/A

Choose “No score” to exclude this criteria from your assessment.

3. Frequency of natural disturbance rating. If a habitat is naturally frequently perturbed in a way similar to the anthropogenic stressor, it may be more resistant to additional anthropogenic stress. For example, habitats in areas that experience periodical delivery of nutrient subsidies (i.e. from upwelling or allocthonous inputs such as delivery of intertidal plant material to subtidal communities) are adapted to variable nutrient conditions and may be more resistant to nutrient loading from netpen salmon aquaculture. This criterion is scored separately for each habitat-stressor combination, such that being adapted to variable nutrient conditions increases resistance to nutrient loading from salmon aquaculture but not destructive fishing. However, high storm frequency may increase resistance to destructive fishing, because both stressors impact habitats in similar ways.

We use the following categories to classify HIGH, MEDIUM and LOW natural disturbance frequencies:

High (3) Medium (2) Low (1) No score (0)
Frequency of natural disturbance Annually or less often Several times per year Daily to weekly N/A

Choose “No score” to exclude this criteria from your assessment.

Note

The following consequence criteria are Recovery Attributes. These include life history traits such as regeneration rates and recruitment patterns influence the ability of habitats to recover from disturbance. For biotic habitats, we treat recovery as a function of natural mortality, recruitment, age of maturity, and connectivity.

1. Natural mortality rate rating (biotic habitats only). Habitats with high natural mortality rates are generally more productive and more capable of recovery.

We use the following categories to classify HIGH, MEDIUM and LOW natural mortality rates:

High (3) Medium (2) Low (1) No score (0)
Natural mortality rate Low mortality (e.g. 0-20%) Moderate mortality (e.g. 20-50%) High mortality (e.g.80% or higher) N/A

Choose “No score” to exclude this criteria from your assessment.

2. Recruitment rating (biotic habitats only). Frequent recruitment increases recovery potential by increasing the chance that incoming propagules can re-establish a population in a disturbed area.

We use the following categories to classify HIGH, MEDIUM and LOW natural recruitment rate:

High (3) Medium (2) Low (1) No score (0)
Natural recruitment rate Every 2+ yrs Every 1-2 yrs Annual or more often N/A

Choose “No score” to exclude this criteria from your assessment.

3. Age at maturity/recovery time. Biotic habitats that reach maturity earlier are likely to be able to recover more quickly from disturbance than those that take longer to reach maturity. Here we refer to maturity of the habitat as a whole (i.e., a mature kelp forest) rather than reproductive maturity of individuals. For abiotic habitats, shorter recovery times for habitats such as mudflats decrease the consequences of exposure to human activities. In contrast, habitats made of bedrock will only recover on geological time scales, greatly increasing the consequences of exposure.

We use the following categories to classify HIGH, MEDIUM and LOW age at maturity/recovery time:

High (3) Medium (2) Low (1) No score (0)
Age at maturity/recovery time More than 10 yrs 1-10yrs Less than 1 yr N/A

Choose “No score” to exclude this criteria from your assessment.

4. Connectivity rating (biotic habitats only). Larval dispersal and close spacing of habitat patches increases the recovery potential of a habitat by increasing the chance that incoming propagules can re-establish a population in a disturbed area.

We use the following categories to classify HIGH, MEDIUM and LOW connectivity:

High (3) Medium (2) Low (1) No score (0)
Connectivity Low dispersal (less than 10km) Medium dispersal (10-100km) High dispersal (>100km) N/A

Choose “No score” to exclude this criteria from your assessment.

#### Using Spatially Explicit Criteria¶

As an alternative to assigning a single rating to a criteria that is then applied to the whole study region, the model allows for spatially explicit criteria to be used as an input. Spatially explicit criteria ratings can be used for any of the exposure or consequence criteria. For example, the user could differentiate between areas of high and low recruitment for a particular habitat or species within the study area. As another example, the user may have information on spatial variation in a human activity, such as density of fish in aquaculture pens which could influence the intensity rating of this stressor. The spatially explicit criteria are vector layers, where each feature may contain a separate rating for that particular area. (See the HRA Preprocessor section for more information how how to prepare and use spatially explicit criteria within a complete model run.)

#### Guidelines for Scoring Data Quality¶

Risk assessment is an integrative process, which requires a substantial amount of data on many attributes of human and ecological systems. 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. 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. 2 and 3). 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. In addition, the information generated from this rating process can be used to guide research and monitoring effects to improve data quality and availability. We suggest the users first run the model with the same data quality score (e.g., 1) for all the criteria to determine if the overall patterns make sense based just on relationships between the stressors and habitats. Next, if users do not have verified information on the data quality of a given criteria, they should then re-run the model using a 2 or 3 to indicate lower and lowest possible data quality.

For each exposure and consequence score, users can indicate the quality of the data that were used to determine the score on a sliding scale where 1 indicates a limited knowledge of the data quality, and anything above that would be seen as increasingly trustworthy data.

Best data Adequate data Limited data
Substantial information is available to support the score and is based on data collected in the study region (or nearby) for the species in question. Information is based on data collected outside the study region, may be based on related species, may represent moderate or insignificant statistical relationships. No empirical literature exists to justify scoring for the species but a reasonable inference can be made by the user.

### Limitations and Assumptions¶

#### Limitations¶

1. Results are limited by data quality: The accuracy of the model results is limited by the availability and quality of input data. 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. In most cases, users will need to use data from other geographic locations for some of the stressor-habitat combinations because most of the data on the effects of some stressors have only been collected in a limited number of locations worldwide. To overcome these data limitations, we include a data quality score in the analysis. This score allows users to down-weight criteria for which data quality is low.
2. 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.
3. Results do not reflect the effects of past human activities. The HRA model does not explicitly account for the effects of historical human activities on the current risk. Exposure to human activities in the past may affect the consequence of human activities in the present and future. If users have historical data on the exposure of habitats to human activities (e.g. spatial and temporal extent), and information on how this affects current consequence scores, they may include this information in the analysis for more accurate results.
4. 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 (i.e. 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 assume equal importance. However, the user has the option to weight the importance of each criterion in determining overall risk.

#### Assumptions¶

1. Often information in the literature about the effect stressors on habitats comes from only a few locations. If using globally available data or data from other locations, users make the assumption that ecosystems around the world respond in similar ways to any given stressor (i.e. eelgrass in the Mediterranean responds to netpen aquaculture in the same way as eelgrass in British Columbia). To avoid making this assumption across the board, users should use local data whenever possible.
2. Cumulative risk is additive (vs. synergistic or antagonistic). The interaction of multiple stressors on marine ecosystems is poorly understood (see Crain et al. 2008 for more information). Interactions may be additive, synergistic or antagonistic. However, our ability to predict the type of interaction that will occur is limited. Due to the absence of reliable information on the conditions that determine additivity, synergism or antagonism, the model assumes additivity because it is the simplest approach. As a result, the model may over- or under-estimate the cumulative risk depending on the set of stressors occurring in the study region.

## Data Needs¶

The model uses an interface to input all required and optional data and a series of Comma Separated Value (CSV) files with which to score all criteria and their data quality. Here we outline the options presented to the user via the interface and the maps and data tables that will be used by the model. First we describe required inputs, followed by a description of optional inputs.

To run the model, three steps are required:

1. Run the HRA Preprocessor Tool
2. Fill out the Ratings CSVs
3. Run the Habitat Risk Assessment model

### HRA Preprocessor¶

Before running the HRA model, it is necessary to concatenate and rate all applicable criteria information. This can be accomplished by running the Preprocessor tool, then editing the resulting CVSs. If you have already run the model, or have the ‘habitat_stressor_ratings’ directory from a previous HRA Preprocessor run, you may skip this step and proceed to running the Habitat Risk Assessment tool.

To run the tool, run the HRA Preprocessor executable. This will launch a graphical user interface (GUI).

There are several pieces that should be used as inputs to this tool. At any time, you can click the blue question marks to the right of an input for additional guidance.

1. Workspace Location (required). Users are required to specify a workspace folder path. Running HRA Preprocessor creates a folder named ‘habitat_stressor_ratings’ within this workspace. This Folder will hold all relevant CSVs for criteria rating in the particular model run.

Name: Path to a workspace folder.  Avoid spaces.
Sample path: \InVEST\HabitatRiskAssess_3_0\

2. Calculate Risk To Habitat/Species Here you will select the habitats and/or species that will be inputs for this run of the model. Each of these inputs should point to a directory containing all of the named habitat or species shapefile layers that you wish to include in this model run. The file names are not required to contain an identifying number. 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. All layers must be projected in the same projection.:

Name: Path to a habitat or species folder.  Avoid spaces.
Sample path: \InVEST\HabitatRiskAssess_3_0\HabitatLayers

3. Directory for Stressor Layers Users should select a folder containing stressors to be overlapped with habitats and/or species. This directory should contain ONLY the stressors desired within this model run. All layers must be projected in the same projection.:

Name: Path to a habitat or species folder.  Avoid spaces.
Sample path: \InVEST\HabitatRiskAssess_3_0\StressorLayers

1. Criteria We have divided up criteria into 3 categories: Exposure, Sensitivity, and Resilience. Exposure criteria are specific to a habitat-stressor pairing, and will be applied to the exposure portion of the risk modeling equation. Sensitivity criteria are also applied to a specific habitat-stressor pairing, but will be applied to the consequence portion of the risk equation. Resilience criteria will likewise be applied to the consequence portion of the risk equation, but are specific to an overall habitat. Placing a criteria into one of these categories within the user interface will determine how user ratings are input into the HRA model. The default criteria provided are derived from peer-reviewed literature and are recommended as a good set of contributors to risk in a system, but users do have the option to add or remove criteria if desired. Only choose this option if the default criteria do not apply to the system being modeled, or do not correctly address all facets of the risk assessment.

1. Optional Determine whether spatial criteria are available and desired. These are vector layer files which would provide more explicit detail for a specific criteria in the assessment. If spatially explicit criteria is desired, this input should point to an outer directory for all spatial criteria. A rigid structure MUST be followed in order for the model to run. Within the outer spatial criteria folder, there MUST be the following 3 folders: Sensitivity, Exposure, and Resilience. Vector criteria may then be placed within the desired folder. Each feature in the shapefiles used MUST include a ‘Rating’ attribute which maps to a float or int value desired for use as the rating value of that spatial criteria area.
• Any criteria placed within the Resilience folder will apply only to a given habitat. They should be named with the form: habitatname_criterianame.shp. Criteria names may contain more than one word if separated by an underscore.
• Any criteria placed within the Exposure folder will apply to the overlap between a given habitat and a given stressor. They should be named with the form: habitatname_stressorname_criterianame.shp. Criteria names may contain more than one word if separated by an underscore.
• Any criteria placed within the Sensitivity folder will apply to the overlap between a given habitat and a given stressor. They should be named with the form: habitatname_stressorname_criterianame.shp. Criteria names may contain more than one word if separated by an underscore.
1. Run the tool. This will create a directory in your selected workspace called habitat_stressor_ratings. Keep in mind that if a folder of the name habitat_stressor_ratings already exists within the workspace, it will be deleted to make way for the new output folder. This directory can be renamed as necessary after completion, and will contain a series of files with the form: habitatname_ratings.csv, as well a file named stressor_buffers.csv. There will be one file for every habitat, and the one additional file for stressor buffers. HRA 3.0’s sample data includes a sample folder for use within the main HRA executable called habitat_stressor_ratings_sample, containing pre-filled criteria values relevant to the sample data for the west coast of Vancouver Island, Canada.

### Ratings CSVs¶

The CSVs contained within the habitat_stressor_ratings folder will provide all criteria information for the run of the Habitat Risk Assessment. There are two types of CSVs- habitat overlap CSVs and the stressor buffer CSV. Habitat CSVs will contain not only habitat-specific criteria information, but also all criteria that impact the overlap between that habitat and all applicable stressors. The stressor buffer CSV will be a single file containing the desired buffer for all stressors included in the assessment.

When preprocessor is run, the CSVs will contain no numerical ratings, only guidance on how each rating might be filled out. The user should use the best available data sources in order to obtain rating information. The column information to be filled out includes the following:

1. “Rating”- This is a measure of a criterion’s impact on a particular habitat or stressor, with regards to the overall ecosystem. Data may come from a combination of peer-reviewed sources at the global scale and locally available fine-scale data sources. Model inputs and results can be updated as better information becomes available. We provide guidance for well-known criteria on a scale of 0-3, but it should be noted that if information is available on a different scale, this can also be used. It is important to note, however, that all rating information across all CSVs should be on one consistent scale, regardless of what the upper bound is.
2. “DQ”- This column represents the data quality of the score provided in the ‘Rating’ column. Here the model gives the user a chance to downweight less-reliable data sources, or upweight particularly well-studied criteria. While we provide guidance for a scoring system of 1-3, the user should feel free to use any upper bound they feel practical, as long as the scale is consistent. The lower bound, however, should ALWAYS be 1, unless the user wishes to remove the entire criteria score.
3. “Weight”- Here the user is given the opportunity to upweight critiera which they feel are particularly important to the system, independent of the source data quality. While we provide guidance for a scoring system from 1-3, the user should feel free to use any upper bound they feel practical, as long as the scale is consistent. The lower bound, however, should ALWAYS be 1 unless the user wishes to remove the entire criteria score.
4. (Optional) “E/C”- This column indicates whether the given criteria are being applied to the exposure or the consequence portion of the chosen risk equation. These can be manually changed by the user on a single criterion basis, however, we would strongly recommend against it. If the user desires to change that criterion’s allocation, it would be better to change the allocation of the criterion within the Resilience, Exposure, Sensitivity categories using the HRA Preprocessor User Interface. 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.

Note

Required ratings data - We recommend users include information about all of the key components of risk (i.e., spatial overlap and other exposure criteria, consequence criteria and the components of consequence, resilience and sensitivity. Nevertheless, the model will produce estimates for risk with only the habitat and stressor spatial layers and no other exposure values (i.e., E = 0 = no score for all other exposure criteria). To produce these estimates, the model does require values for at least one consequence criteria, either sensitivity or resilience. Without this information, the model will return an error message. If the user inputs scores for only sensitivity or resilience, then the consequence score will be based on those data alone.

Note

Specifying No Interaction Between Habitat and Stressor - As of InVEST 3.3.0 the HRA model will allow users to indicate that a habitat - stressor pair should have no interaction. This essentially means that the model will consider the habitat and stessor have no spatial overlap. This enhancement is to deal with the issue of having fine resolution vector data where the values may share the same pixel space when converted to a raster grid format. To set a habitat - stressor pair to no overlap, simply fill in each criterias “Rating” column with an “NA” value for the given pair. ALL “Rating” values for that pair must be set to “NA” for the model to consider the pair to have no interaction / overlap. If an “NA” value is found, but not all are set, an error message will be presented.

Habitat CSVs should be filled out with habitat-specific criteria information as well as any criteria which apply to the overlap of the given habitat and stressors. The Stressor Buffer CSV should be filled out with the desired numerical buffer which can be used to expand a given stressor’s influence within the model run. This can be 0 if no buffering is desired for a given stressor, but may NOT be left blank.

Any criteria which use spatially explicit criteria (specified by the user during the HRA Preprocessor) will be noted in the CSV by the word ‘SHAPE’ in the rating column for that habitat, stressor, or combined criteria. The user should still fill in a Data Quality and Weight for these criteria, but should NOT remove the ‘SHAPE’ string unless they no longer desire to use a spatial criteria for that attribute.

### Habitat Risk Assessment¶

The main computation portion of the HRA model will be done by the Habitat Risk Assessment executable. First we describe required inputs. The required inputs are the minimum data needed to run this model.

1. Workspace Location (required). Users are required to specify a workspace folder path. It is recommended that the user create a new folder for each run of the model. For example, by creating a folder called “runBC” within the “HabitatRiskAssess_3_0” folder, the model will create “Intermediate” and “Output” folders within this “runBC” workspace. The “Intermediate” folder will compartmentalize data from intermediate processes. The model’s final outputs will be stored in the “Output” folder.

Name: Path to a workspace folder.  Avoid spaces.
Sample path: \InVEST\HabitatRiskAssess_3_0\runBC

2. Criteria Scores Folder (required). After running the HRA Preprocessor tool, a folder will be created which contains the collective criteria scores for all habitats and stressors. For this input, point to the outer folder containing all CSVs.

Name: Folder can be named anything, but avoid spaces.
Sample path: \InVEST\HabitatRiskAssess_3_0\runBC\habitat_stressor_ratings

3. Resolution of Analysis (required). The size in meters that is desired for the analysis of the shapefile layers at a grid cell scale. This will define the width and height of each unique risk grid cell. This must be a whole number. The user should base this size on the resolution of the habitat data and scale at which habitats are distributed in space. For example, small patches of seagrasses and kelp are often about 100-200 square meters, which is about the smallest resolution we recommend running the model. If the input habitat data are coarse, then a minimum of 500 meters is better. If you examine your risk outputs and find that the edges of patches of habitat have regular and distinct variation in risk, such that every high and medium risk cell on the edge of habitat patches are border by low risk cells, consider enlargening your resolution. We recommend running the model for the first time at a low resolution (500 m or 1 km) to verify that the model is running properly. Then use a higher resolution in subsequent runs.

4. Risk Equation (required). This selection chooses the equation that will be used when calculating risk to a given habitat. (See the Risk of Human Activities to Habitats section.) The user may choose either either a Euclidean risk model, or a Multiplicative risk model.

5. Decay Equation (required) This selection influences how the “zone of influence” (i.e., buffer distance) of a stressor will be applied to risk. The stressor buffer distance in the stressor buffer CSV can be degraded to provide a more accurate depiction of the influence of a stressor beyond its footprint. The decay equation decays the overall exposure rating (e.g., combined spatial overlap, temporal overlap, intensity, management effectiveness) before the value for E goes into the risk equation. For each pixel, the model uses the value of the decayed exposure score. The options for decay are as follows. “None” will apply the full exposure to the full range of the stressor footprint plus buffer, without any decay. “Linear” and “Exponential” will use the stated equation as a model for decay from the edges of the footprint to the extent of the buffer distance.

6. Maximum Criteria Score (required) The maximum criteria score is the user-reported highest value assigned to any criteria rating within the assessment. This will be used as the upper bounded value against which all rating scores will be compared. For example, in a model run where the ratings scores vary from 0-3, this would be a 3. 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. If the model run is using spatially explicit criteria, this value should be the maximum value assigned to either a criteria feature or to a CSV criteria rating.

7. Maximum Overlapping Stressors (required) The is the largest number of stressors that overlap withing the analysis zone. This will be used in order to make determinations of low, medium, and high risk for a given habitat. If the number of overlapping stressors provided is too low, results will likely show more medium and high risk areas than are present. 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. If unsure how many stressors overlap, we recommend running the overlap analysis tool without weighting.

8. Use Subregions Shapefile? (required). 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. 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. It will also create a figure showing cumulative ecosystem risk for all subregions habitats in the study. Each of the subregion shapefile features MUST contain a ‘Name’ attribute in order to be properly included in the subregion averaging. If subregion data is not available for the given study region, an AOI for the area could also be used in order to obtain averaged data per habitat-stressor pair. However, the AOI must also contain a ‘Name’ attribute.:

Name: File can be named anything, but avoid spaces.
File Type: Polygon shapefile (.shp)
Sample path: \InVEST\HabitatRiskAssess_3_0\runBC\subregions.shp


## Interpreting Results¶

### Model Outputs¶

Upon successful completion of the model, you will see new folders in your Workspace called “Intermediate” and “Output”. These two folders will hold all outputs, both temporary and final that are used in a complete run of the model. While most users will be interested only in the Output folder data, we will describe all outputs below.

#### Intermediate Folder¶

The Intermediate folder contains files that were used for final output calculations. All rasters within this file use the pixel size that the user specifies in the “Resolution of Analysis” text field of the Habitat Risk Assessment main executable.

• \Intermediate\Criteria_Rasters\spatial_criteria_name.tif

• If the user has included any spatially explicit criteria in the assessment, this folder will contain a rasterized version of that vector layer, with the ‘Rating’ attribute burned as the pixel value.
• \Intermediate\Habitat_Rasters\habitat_name.tif

• A rasterized version of all habitat or species vector files included in the assessment.
• \Intermediate\Stressor_Rasters\stressor_name.tif

• A rasterized version of all stressor vector files included in the assessment.
• \Intermediate\Stressor_Rasters\stressor_name_buff.tif

• This is a copy of the stressor_name.tif file in the same folder, but with each stressor’s individual buffering included. If a given stressor has a 0 buffer distance, this will be an exact copy of the rasterized vector file. For all other files, this will be buffered by the desired amount set forth in the “Stressor Buffer (m)” section of the Ratings CSVs, decayed from 1 to 0 using the equation chosen in the “Decay Equation” section of the Habitat Risk Assessment.
• \Intermediate\Overlap_Rasters\H[habitat_name]_S[stressor_name].tif

• A raster representing the overlap between each pair of the habitat or species rasters, and the buffered stressor rasters.
• \Intermediate\H[habitatname]_S[stressorname]_C_Risk_Raster.tif

• A raster representing the Consequence portion of the final risk calculations for the overlap of the given habitat and stressor.
• \Intermediate\H[habitatname]_S[stressorname]_E_Risk_Raster.tif

• A raster representing the Exposure portion of the final risk calculations for the overlap of the given habitat and stressor.
• Intermediate\H[habitatname]_S[stressorname]_Risk_Raster.tif

• A raster containing the final risk calculation for the given habitat and stressor combination. 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.

#### Output Folder¶

The following is a short description of each of the final outputs from the HRA model. Each of these output files is saved in the “Output” folder that is saved within the user-specified workspace directory:

##### GIS¶
• \Output\maps\recov_potent_H[habitat_name].tif
• This raster layer depicts the recovery potential of each cell for the given habitat. Recovery potential is typically based on natural mortality rate, recruitment rate, age at maturity/recovery time and connectivity, though these can be altered by the user if alternate criteria are desired. Recovery potential is useful to those who are interested in identifying areas where habitats are more resilient to human stressors, and therefore may be able to withstand increasing stress. Habitats with low recovery potential are particularly vulnerable to intensifying human activities.
• \Output\maps\ecosys_risk.tif
• This raster layer depicts the sum of all cumulative risk scores for all habitats in each grid cell. It is best interpreted as an integrative index of risk across all habitats in a grid cell. 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. The “ecosys_risk” value increases as the number of habitats in a cell exposed to stressors increases.
• \Output\maps\cum_risk_H[habitat_name].tif
• This raster layer depicts the cumulative risk for all the stressors in a grid cell on a habitat-by-habitat basis. For example, “cum_risk_eelgrass” depicts the risk from all stressors on habitat “eelgrass”. Cumulative risk is derived by summing the risk scores from each stressor (i.e. more stressors leads to higher cumulative 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. identify hotspots where eelgrass or kelp is at high risk from multiple stressors). Hotspots of high cumulative risk may be targeted for restoration or monitoring.
• \Output\maps\[habitat_name]_RISK.shp
• These shapefiles are habitat specific and are classified by amount of risk. Each feature in the shapefile has a ‘CLASSIFY’ attribute, which will be ‘LOW’/’MEDIUM’/’HIGH’, depending on the amount of risk each contains relative to the risk thresholds. The thresholds of low/med/high are determined in one of two ways. A particular habitat pixel is considered high risk if any of the habitat-stressor risk pixels which make it up are > 66% of the total potential risk of any habitat-stressor pixel, or if the habitat risk map itself is > 66% of the total user-defined max potential risk (as determined by the maximum overlapping stressors within a habitat). Medium risk pixels use the same guidelines, but are defined by risk that falls between 33% and 66%. Low risk is any pixels below 33%. There is one habitat risk shapefile for each vector file originally used within the assessment.
##### HTML and Plots¶

These outputs are optional, and their creation is dependent on user-provided subregion shapefiles.

• \Output\HTML_Plots\risk_plot_AOI[aoiname].html
• These figures show the cumulative risk for each habitat within a given subregion. There will be one subplot for every habitat or species. Within the habitat plot, there are points for every stressor. Each point is graphed by Exposure, Consequence values. 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. Stressors that have high exposure scores and high consequence scores pose the greatest 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.
• \Output\HTML_Plots\ecosystem_risk_plot.png
• This figure shows the cumulative risk for each habitat in the study region by subregion. 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).
• \Output\HTML_Plots\Sub_Region_Averaged_Results_[yr-mon-day-min-sec].html
• This HTML table is a concatenated set of data for all pairings of habitat and stressor within each provided subregion. For every pairing of habitat and stressor, the table provides average exposure, consequence, risk, and risk percentage (as a portion of total potential risk).

Additionally, we provide an online visualization tool that allows users to upload their workspace and share results with other users. The tool along with its documentation is located here http://vulpes.sefs.uw.edu/ttapp/hra-dash.php

##### Log File¶
• hra-log-yr-mon-day-min-sec.txt

• Each time the model is run a text file will appear in the workspace folder. The file will list the parameter values for that run and be named according to the date and time.
• Parameter log information can be used to identify detailed configurations of each of scenario simulation.

## References¶

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