Environmental Benefit Metrics Overview
We created 11 statewide metrics that depict the potential for conservation easements to produce environmental benefits based on their spatial location. Our metrics emphasize the links between land protection and benefits to human wellbeing such as recreation or drinking water quality. We also include metrics such as risk of conversion or proximity to population as additional considerations relevant to decision makers. Below is a brief summary of the data and assumptions underlying each metric. For a complete description of how the metrics were constructed, see the expanded documentation.
All metrics are presented as indices that reflect the performance of a given parcel relative to other parcels in the state where the lowest observed value is mapped to zero and the highest to one. Scores represent relative values for each individual metric, therefore metrics are not comparable to one another (e.g. a score of 0.5 for lake recreation is not equivalent to a score of 0.5 for wild rice). Differences in the distribution of benefits in the state result in higher scores being more common in some metrics than others.
The metrics were designed to rank the relative value of potential conservation easements, therefore any parcel submitted for scoring should currently be undeveloped. We do not assume that parcels will provide public access, although metrics do reflect indirect benefits such as reducing runoff into a public lake that improves recreation.
Carbon stored in the soil can be emitted to the atmosphere when land is developed. We created the soil carbon metric by multiplying the bulk density and percent carbon maps published in Ramcharan (2017). Soil carbon storage benefits are provided throughout the state, but some regions have much higher concentrations of soil carbon than others. For example, north central Minnesota has some of the highest concentrations of soil carbon in the state, often more than 15 times greater than soil in southern Minnesota. High scoring parcels are in carbon-rich areas.
Pollinated crops benefit from having an abundant supply of pollinators nearby. This metric uses the output from the InVEST pollination model along with the cropland data layer. The InVEST pollination model uses data on land cover and the foraging habits of bees to produce a bee abundance index. The model output used in this metric is described in Koh (2016). We used the cropland data layers from 2014 to 2017 to identify where pollinated crops such as sunflowers and apples are produced, and buffered these fields by the foraging distance of bees. Consistent with the base score used in other metrics, we assigned a value of 0.2 to land in proximity to pollinated crops. The metric is the sum of the pollinator abundance index and the presence/absence of pollinated crops. High scoring parcels are those that have high relative pollinator abundance and are in close proximity to pollinator-dependent crops.
Our metric combines data on the location of important bird habitat with data on the behaviors of bird watchers. To define important bird habitat we relied on the Audubon Society’s Important Bird Areas layer. To estimate the intensity and location of bird watching, we used the Cornell Lab of Ornithology’s citizen science initiative, eBird. The eBird database allows bird watchers to report when and where they engaged in bird watching. We interpolated the data to create a statewide layer with high scores for bird watching hot spots and declining scores with low reported visits. To combine the habitat layer and the visitation layer we set the value for ‘presence’ in the Important Bird Areas data such that the average of all of the values in the map was equal to the average of all of the values in the eBird map, and then summed the two maps. High scores for bird watching are found on parcels that have both high reported visitation and are located in important bird habitat.
For this metric, we assume that acquisitions within the catchment of a wild rice site identified by the DNR have the potential to provide wild rice benefits, while parcel outside wild rice catchments do not. If a parcel is partially within a catchment, its score is equivalent to the proportion of the parcel’s total area that is within the catchment. We do not differentiate among wild rice sites, nor does the metric account for the impact of management on wild rice habitat or water quality.
Risk of Conversion
We calculated risk of conversion by modeling the probability that a location will convert from natural land to developed land. This is a preliminary metric based on new, ongoing research at the University of Minnesota. To determine which grid-cells have the highest risk of conversion, the metric combines coarse-scale projections of land-use change from the Intergovernmental Policy Platform on Biodiversity and Ecosystem Services and the Land-Use Harmonization project (LUH) with fine-scale data on conversion probability based on physical suitability, adjacency to existing land-use types and conversion constraints for each grid-cell. A high scoring parcel is likely to convert to urban development or agriculture.
The nearby population metric represents the proportion of the state’s population that can easily access the benefits of a proposed acquisition. We assumed nearby population to be the people residing within a radius of 50 miles from each parcel. This distance is based on the US National Tourism Resources Review Commission’s definition of a “day trip”. The population within 50 miles was calculated using the EPA’s 30 meter population map. Higher scoring parcels are those with higher nearby population.
The lake recreation metric prioritizes protection of land that influences the water quality of lakes important for public recreation. It applies to the catchments of lakes with a publicly accessible water access site. Parcels outside of these catchments receive a score of zero for lake recreation. Among lakes with public access, prioritization is based on three attributes; the sensitivity of the lake’s clarity to additional phosphorus runoff, the public amenities (e.g., dock, boat ramp, restrooms) of the lake, and lake visitation. Catchments with publicly accessible lakes receive a minimum score of 0.2. The rest of the score is equally divided between a physical measure of the lake’s sensitivity to phosphorus, and measures of the social benefit of the lake as measured by proxies for visitation. High scoring parcels are those that are within a catchment of a publicly-accessible lake highly sensitive to additional phosphorus, which has public amenities and high scores for lake visitation.
The trout angling metric applies to the catchments of legally designated trout streams, and prioritizes among them using social media based visitation data. If an acquisition is within 66 feet (the buffer size often used in Aquatic Management Area acquisitions), it receives a higher score. Catchments with a legally designated trout stream receive a minimum score of 0.2. The remainder of the score is the weighted sum of the proportion of the parcel within the buffer, and visitation, weighted at 0.6 and 0.4, respectively. High scoring parcels have a large proportion of their area in close proximity to a trout stream that has high scores for visitation.
Trails in the state provide a wide range of recreation activities, such as hiking and biking on non-motorized trails, ATV and snowmobile used on motorized trails, and boating on water trails. Conservation of parcels via easements or acquisitions can protect the aesthetic experience around trails by providing scenic beauty and noise attenuation for trail users. Our metric scores parcels based on their proximity to existing recreational trails, as designated by the Minnesota DNR. A parcel’s score is equivalent to the proportion of the parcel’s total area that is within a 500 foot buffer of a trail, where higher scores are given to parcels with a greater proportion of their area in proximity to trails.
Abundant pheasant populations support pheasant hunters and related industries. Our metric is based on pheasant production models first published in Jorgensen (2014) and then refined in Wszola (2017). In brief, the metric uses relationships between the amount of grass, agriculture, small grains, trees and wetlands in one or five kilometer buffers around a proposed parcel to estimate relative pheasant abundance. Higher scores are given to parcels with greater potential pheasant abundance.
Nitrate in groundwater poses a threat to human health and increases water treatment costs, especially for rural communities. Our metric assumes that parcels located within identified Drinking Water Supply Management Areas (DWSMA) as mapped by the Minnesota Department of Health are more likely to contribute to drinking water protection than parcels outside DWSMAs. Parcels within DWSMAs receive a minimum score of 0.2, the remainder of the score is based on the amount of agriculture within the DWSMA (a proxy for threats to groundwater), and sensitivity of the geology to surface contamination. High priority parcels are within the boundary of a DWSMAs, have a high proportion of agricultural land cover, and are located in regions with soil and geologic characteristics that make groundwater more vulnerable to contamination.