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Valuing Wildlife Crossings and Enhancements for Mitigation Credits 27 3.0 POTENTIAL VALUATION METRICS FOR WILDLIFE CONNECTIVITY MITIGATION CREDITS One criticism of mitigation banking is the general lack of standardization of credit quantification and metrics. For conservation bank credits, which are typically based on a simple metric such as acres of habitat, the methods to quantify the value of the credits often differ among species, and even between USFWS field offices. Pindilli and Casey (2015) reported that the standardization of metrics for mitigation credits would reduce the administrative burden, increase transparency, and better facilitate the creation of markets for mitigation credit trading. Therefore, it is essential that state DOTs think critically about the unit of measurement (i.e., metric) that could be used for wildlife connectivity mitigation credits and provide a clear evaluation that explains the approach used to formulate the quantity (number) and value (price) of the metric chosen. This study reveals many potential metrics for valuing wildlife connectivity, but none that have been consistently applied by researchers. As discussed in the literature review above in section 2.1.2, the metrics explored in this study that are potentially useful are summarized in Table 2, including their potential unit of measurement and notes regarding their applicability. These metrics could be used to either quantify the benefits to wildlife from a wildlife crossing or other connectivity enhancement or measure the adverse effects to wildlife from other transportation projects. Each metric is assigned into one of four categories, which are defined as: â¢ Condition-based connectivity metricsâmeasurements that value wildlife connectivity or assess transportation project impacts based on the physical, chemical, and biological attributes of a system, such as various highway or ecosystem characteristics â¢ Function-based connectivity metricsâmeasurements that value wildlife connectivity or assess transportation project impacts based on wildlife habitats and ecosystem processes, such as the acreage of suitable habitat or patterns of wildlife movement â¢ Model-based connectivity metricsâmeasurements that value wildlife connectivity or assess transportation project impacts based on computer models that combine elements of function- and condition-based metrics to estimate wildlife connectivity â¢ Avoided cost metricsâmeasurements that value wildlife connectivity or assess transportation project impacts based on the economic value of wildlife or human life and/or property Metrics to value wildlife connectivity mitigation credits would likely be ecosystem- and species-specific. As discussed in section 2, the literature review, online survey, and practitioner interviews revealed that the ecological implications of wildlife connectivity mitigation measures would differ based on the species and ecosystems adjacent to a highway. The factors to consider would include the life history of the focal species and their habitats, the availability of research on wildlife crossings for those species, and the amount of existing data on WVCs and focal species occurrences. For many potential focal species, information about their habitat use and movement ecology is sufficient to quantify one or more potential metrics to measure the conservation value of a wildlife crossing or other connectivity enhancement (e.g., the acres of accessible suitable habitat that would be maintained or restored or the genetic diversity of a population that would be increased or maintained). On a national level, however, identifying universal metrics or quantification methods for valuing wildlife connectivity mitigation is infeasible. Because site-specific field data are lacking in many locations and given tight project budgets and schedules, many state DOTs may be inclined to use condition- and function-based metrics that measure conservation values rather than crossing performance. Alternatively, state DOTs may use model-based
Valuing Wildlife Crossings and Enhancements for Mitigation Credits 28 Table 2. Potential metrics to value wildlife connectivity mitigation, their potential units of measurement, and notes regarding their applicability Category Metric to Value Wildlife Crossings Unit of Measurement for Mitigation Credits Notes on Applicability to State DOTs a Condition- based Area of highway footprint within the highway crossing zone used by focal species b, c Acres of impervious surface or highway project boundary Caltrans used this metric for SR-19 Laurel Curve in Santa Cruz County, California. It assumes that, within the zone where wildlife connectivity would be improved, increasing permeability of a larger highway footprint would equate to greater benefits to focal species, which may not necessarily be true. With respect to impacts, the converse is also assumed, where transportation projects with larger footprints would require more credits. Overall project costs b, c A percentage (%) of total transportation project costs CDOT used this method to calculate mitigation funds ($) required for contribution into a Canada lynx in-lieu fee program. Habitat area used by focal species b, c Acres of focal species' habitat Wildlife connectivity mitigation that affects larger areas of suitable habitat for focal species could generate more mitigation credits because greater numbers of individual animals could be affected. Number of lanes b, c Area of new highway lanes Assumes that increasing permeability across more lanes of a highway would equate to greater benefits to focal species. Credits could be applied to transportation projects that decrease highway permeability and increase WVC risk due to increased roadway capacity.
Valuing Wildlife Crossings and Enhancements for Mitigation Credits 29 Category Metric to Value Wildlife Crossings Unit of Measurement for Mitigation Credits Notes on Applicability to State DOTs a Condition- based Traffic volume b, c AADT or vehicles per hour Species-specific data about animal movement over highways with different AADT volumes would be necessary to quantify potential benefits or impacts to a given focal species. More credits could be generated for wildlife connectivity mitigation on high-volume highways or applied to transportation projects that increase traffic. Roadway barriers c Length of fence, jersey barriers, median structures, or other potential barrier to the movement of focal species Credits could be applied to transportation projects that decrease highway permeability through the creation of permanent structures like jersey barriers or retaining walls. Speed limit c Increased speed limit Credits could be applied to transportation projects that decrease highway permeability and increase WVC risk due to increased speed limits. Function- based Safe passage of focal species b, c Number of individual animals of focal species crossing highway Methods have been developed to accurately monitor the number of individuals crossing highways or using wildlife crossings via cameras, track pads, and radio telemetry. Studies could be designed to quantify the number of individual animal crossings in paired treatment and control areas pre- and post- construction, but such studies are difficult because many other factors also influence wildlife.
Valuing Wildlife Crossings and Enhancements for Mitigation Credits 30 Category Metric to Value Wildlife Crossings Unit of Measurement for Mitigation Credits Notes on Applicability to State DOTs a Function- based Genetic interchange of focal species b, c Change in genetic diversity in comparison to current generation Connectivity among populations reduces the negative effects of inbreeding and genetic drift, but it takes relatively little exchange between populations to maintain genetic diversity. DNA profiling of individuals using wildlife crossings or crossing highways is a technique that could be carried out in a relatively short period of two to three years, and methods exist to monitor many species via noninvasive genetic sampling (e.g., hair snares). DNA assignment testing could be used to quantify the change in connectivity via enhanced or reduced genetic structure of focal species populations. Conservation of rare, threatened and endangered species b, c Acres of suitable habitat connected or fragmented There are no laws that require compensatory mitigation for adverse effects on wildlife connectivity, except for cases where federally listed or state-listed threatened and endangered species are affected. Thus, wildlife connectivity mitigation credits would most likely be focused on mitigating for impacts to regulated species (i.e., threatened and endangered animals). Migration of focal species b, c Number of individuals of affected migratory species crossing highway Effective wildlife connectivity mitigation should allow animals to travel and migrate to meet their requirements for seasonal habitats. Metrics to evaluate success include passage rates and the number of animals that use a wildlife crossing or other connectivity enhancement. Dispersal of focal species Number of dispersing juveniles crossing highway Effective wildlife connectivity mitigation should provide for demographic movement, which occurs when there are dispersing individuals from one sub-population to another. Dispersing individuals could be identified by noninvasive genetic sampling (e.g., hair snares).
Valuing Wildlife Crossings and Enhancements for Mitigation Credits 31 Category Metric to Value Wildlife Crossings Unit of Measurement for Mitigation Credits Notes on Applicability to State DOTs a Function- based Population size(s) of focal species b, c Number of affected individual animals of focal species Although there is widespread agreement that effective wildlife connectivity mitigation has the potential to enhance population viability of species impacted by roads, few studies have empirically addressed this and there are many confounding factors. Demonstrating population- level effects would require substantial time and funding, especially for wide-ranging, elusive species such as large carnivores. Habitat quality for focal species b, c Quality of connected or fragmented habitat to focal species Wildlife connectivity mitigation that connects or fragments high-quality habitat for focal species could generate more mitigation credits than measures that affect low-quality habitat. Standard or accepted protocols would need to be used to access habitat quality. Model-based Landscape connectivity models b, c Validated model values Credits could be generated or applied based on predictions from wildlife connectivity models for multiple focal species using graph theory, least- cost paths, circuit theory, landscape permeability, and linkage designs. Confidence in the application of models to the valuation of credits would be higher if the model results have been validated to assess their predictive power on other road sections in similar landscapes. Species-specific models b, c Validated model values For some focal species, researchers have developed animal movement models and habitat-based population viability models, among others, that could inform credit quantification for wildlife connectivity mitigation. Metrics based on statistical models and other geospatial landscape connectivity tools have the advantage of being transparent and repeatable.
Valuing Wildlife Crossings and Enhancements for Mitigation Credits 32 Category Metric to Value Wildlife Crossings Unit of Measurement for Mitigation Credits Notes on Applicability to State DOTs a Traffic forecasting models and/or predictive models of land development b, c Predicted AADT or vehicles per hour Researchers have combined traffic demand forecasting models with wildlife connectivity models to predict the crossing locations where future mitigation would be necessary for grizzly bears. Avoided Cost Property damage, human injury, and/or death from WVCs b Number of WVCs over a specified time period (seasonal or annual) Crash and carcass data could be used to calculate the potential safety improvement and/or ecological gain to focal species. Existing state DOT monitoring approaches could be used to track performance standards. Highway maintenance costs due to reduced WVCs b Number of roadkill carcasses over a specified time period (seasonal or annual) Most state DOTs have data-collection protocols in place, and roadkill surveys could quantify the number of decreased WVCs to monitor the effectiveness of a wildlife connectivity mitigation project. Economic value of hunted or watchable wildlife affected by WVCs b, c Number of roadkill carcasses of focal species over a specified time period (seasonal or annual) The value of credits generated could be based on the economic value of the wildlife species affected, using the cost of the animals that are unlawfully taken (i.e., restitution costs for poaching), hunting license costs and associated hunter expenditures, expenditures by non- consumptive uses such as wildlife watching, and passive use values. a Notes on applicability are based on the authorsâ findings from the literature review and interviews with experienced practitioners, as detailed in section 2.0; references are not repeated. b Metric potentially used to quantify the benefits to wildlife from a wildlife crossing or other connectivity enhancement (i.e., credits). c Metric potentially used to measure the adverse effects to wildlife from other transportation projects (i.e., debits).
Valuing Wildlife Crossings and Enhancements for Mitigation Credits 33 metrics because they could incorporate existing ecological datasets and results from prior modeling efforts to predict ecological gain for multiple species. When evaluating potential metrics to value wildlife connectivity mitigation, it would be useful for state DOTs to assess their potential importance using an approach recommended by the National Research Council (2000) for evaluating ecological indicators. This would involve state DOTs evaluating the following nine criteria for each potential metric, as modified for wildlife connectivity mitigation: â¢ General importanceâIs the metric informative about major changes in wildlife connectivity and associated ecological processes that affect wide areas? â¢ Conceptual basisâIs the metric based on principles of wildlife connectivity that are well understood and generally accepted? â¢ ReliabilityâHas the metric been demonstrated from previous experience as being successful at quantifying changes in wildlife connectivity? â¢ Temporal and spatial scalesâIs the metric appropriate for the scale of the focal speciesâ movements and life history and/or the scale of the landscape for which wildlife connectivity is evaluated? â¢ Statistical propertiesâIs the metric sensitive enough to detect important changes in wildlife connectivity but not so sensitive that changes are masked by natural variability? â¢ Data requirementsâCan data for quantifying the metric be collected with a reasonable level of effort and existing technology? â¢ Skills requiredâCan the metric be accurately measured in a straightforward process by individuals without highly technical, specialized knowledge? â¢ Data qualityâCan the metric be measured with clear documentation of sampling and analytical methods describing exactly how it is calculated? â¢ RobustnessâCan the metric be used in the future given anticipated technological changes or scientific advance? â¢ Costs, benefits, and cost-effectivenessâIs the cost of collecting data for quantifying the metric reasonable, or is there a less expensive metric that would yield the same information? Kindlmann and Burel (2008) argue that âthere is an urgent need for comparing and generalizing studies of landscape connectivity,â and that new connectivity metrics are needed âthat incorporate both information on species-specific movement behavior and landscape structure, and that are relatively simple to calculate.â Until such metrics are developed and more widely adopted, the selection of metrics to value wildlife connectivity mitigation will remain a challenge for state DOTs. Despite challenges to valuing wildlife connectivity mitigation credits, state DOTs and their partners could develop accurate metrics to value wildlife connectivity mitigation credits under certain circumstances. State DOTs would need to evaluate the current state of knowledge about wildlife connectivity issues in their state, weigh the costs and benefits of wildlife connectivity mitigation, and determine if it makes economic and ecological sense to develop guidelines identifying suitable metrics and quantification methods to value wildlife connectivity mitigation measures. In addition, any metrics used for valuing wildlife connectivity should be based on sound science and follow a transparent and consistent approach (Pindilli and Casey 2015). The metrics most readily converted into mitigation credits are those based on accurate measurements of project impacts and mitigation site benefits (Kagan et al. 2014). It would be constructive to create an interagency working group(s) of biologists, engineers, and planners from state DOTs and other state and
Valuing Wildlife Crossings and Enhancements for Mitigation Credits 34 federal agencies to evaluate potential metrics, as discussed below in section 4.2. Further transparency could also be provided if the working groupâs recommended metrics and quantification methods were analyzed under a planning process that includes an opportunity for public review and commenting. This process would address one of the largest issues of concern for mitigation banking practitioners, according to Pindilli and Casey (2015), which is a lack of transparency in pricing or standards for estimating credit values.