S1 Text Further details of the methods are given in text sections A-N. A. Annual net value of wood product production

We first obtained timber product (all produced roundwood including fuelwood, pulp, and sawlogs) output data at the county level as reported in 2012 (USDA-USFS 2012). The 2012 US Forest Service (USFS) timber product output report (TPO) allocates annual county-level production of timber products across several common species while the remaining production is aggregated at the species group level. Production allocated to the species level is indexed by k and production aggregated to the species group level is indexed by j. Reported roundwood production of k and j in county i is given by Qik and Qij, respectively, and is measured in cubic-feet.

Next, for species groups j, we allocated Qij across the species k that that are members of group j in county i (this subset of k species does not include any species with given species-level county production Qik). In this case Qij was allocated across potential species in group j in county i, 𝑘 ∈ 𝑖𝑗 according to species k’s proportional representation in group j in county i. Specifically, we calculated each species k proportion of the ij total net cubic-foot sawlog volume as given in the USFS Forest Inventory and Analysis (FIA) surveys of live trees from the years 2007 to 2012 (https://apps.fs.usda.gov/fia/datamart/datamart.html). There are a few caveats to this approach. First, if a no species from group j were present in FIA plots in county i then we assigned Qij to the species k in group j with the greatest volume summed across all FIA plots in the state where county i is located. Second, in the rare cases where no species in species group j were present in the state’s FIA plots, we excluded these cases. In the end we only had a set of Qik, total roundwood production of species k in county i. In other words, all product volume in county i had been allocated to the species level k.

To convert Qik into an annualized money metric we multiplied Qik by the annualized net return per cubic foot of species k in county i. The annualized net return we use assumes that the stand of k in i will be managed to maximize the profit from an infinite series of harvest rotations with this annualized net return per cubic foot of species k in county i given by 𝐴𝑁𝑅𝑇)*∗ . Finally, 𝑉𝑇)* = 𝑄)*𝐴𝑁𝑅𝑇)*∗ indicates the annualized net value of species k’s roundwood production in county i.

We took several steps to derive 𝐴𝑁𝑅𝑇)*∗ . First, we modeled tree growth for each species group j in each county i using a permutation of von Bertalanffy’s function for organic growth (von Bertalanffy 1938, Van Deusen and Heath 2016):

𝑓(𝑇)1) = 𝛾)1(1 − 𝑒789:;)< (1)

where 𝑓(𝑇)1) is the average cubic feet found in a T year-old tree j in county i, 𝛾)1 is the asymptotic limit of tree volume for species group j in county i, and 𝛽)1 is an ij-specific growth parameter. We set the parameters 𝛾)1 and 𝛽)1 for each ij equal to the values that minimize the sum of the squared deviations of (1) when using observed tree growth data from the FIA (we used all data in the FIA which began sampling forests in 1982; USDA-USFS 2018). We found

these 𝛾)1 and 𝛽)1 values, given by 𝛾>)1 and 𝛽?)1, using the Broyden–Fletcher–Goldfarb–Shanno quasi-Newton computational method. Finally, we used the average harvest age (𝑇?)1) for species group j in i’s state as recorded in the FIA for Tij.

Next we calculated the annualized net present value of harvesting a mature tree of j in county i in years 𝑇?)1, 2𝑇?)1, 3𝑇?)1, … (an infinite series of harvests) for each ij with (Conrad 2010),

𝐴𝑁𝑅𝑇𝑇𝑟𝑒𝑒 = FGHF

(I9:7J9)KL;?9:,MN9:,8O9:P

QRSO9:7G

(2)

where 𝑃𝑖𝑗 is the average stumpage price per cubic foot for species group j in county i over the

period 1997 to 2014 (multiple data sources; see Table SI 4), 𝐶) is the estimated afforestation cost per cubic foot of tree in county i (Nielsen et al. 2014), 𝑓L𝑇?)1, 𝛾>)1, 𝛽V)1P is the volume in harvested tree ij harvested at 𝛾>)1, 𝛽?)1, and 𝑇?)1, and the per annum discount rate 𝛿 is set to 5 percent.

Next, we calculated the annualized net present value of harvesting a cubic foot of ij over an infinite series of harvests with,

𝐴𝑁𝑅𝑇)1∗ =

XYZ;;[QQ\9:

(3)

where 𝐴𝑁𝑅𝑇)1∗ indicates the annualized net present value per cubic foot of infinitely harvested ij, and 𝑉)1 measures the ratio of observed tree volume to the number of trees observed in each ij (volume per tree) (USDA-USFS 2018).

Besides mean 𝐴𝑁𝑅𝑇)1∗ we also generated low and high 𝐴𝑁𝑅𝑇)1∗ . First, we calculated the 5th and 95th percentile 𝛾)1 and 𝛽)1 values for each ij using the delta method in the R package nlWaldTest. Second, using these estimates of 𝛾)1 and 𝛽)1, we calculated the 5th and 95th percentile 𝑓L𝑇?)1, 𝛾)1 , 𝛽)1P for each ij where we use the same 𝑇?)1 as we used for the central estimates. Third, we used the 5th and 95th percentile 𝑓L𝑇?)1, 𝛾)1 , 𝛽)1P for each ij with equations (2) and (3) to generate the low and high𝐴𝑁𝑅𝑇)1∗ . Finally, we set the low and high 𝐴𝑁𝑅𝑇)*∗ =𝐴𝑁𝑅𝑇)1∗ for the k that belongs to species group j in county i.

While we would prefer to be more precise than ‘low’ and ‘high’ to indicate bounded estimates, because𝑃)1, and Ci do not change when estimating low and high 𝐴𝑁𝑅𝑇)1∗ , we cannot call low and high 𝐴𝑁𝑅𝑇)1∗ values 5th and 95th percentile 𝐴𝑁𝑅𝑇)1∗ values despite having 5th and 95th percentile 𝑓L𝑇?)1, 𝛾)1 , 𝛽)1P. To calculate such values we would need to vary 𝑃)1, and Ci

appropriately according to their distributions. However, we do not have information on the underlying distributions on estimated 𝑃𝑖𝑗, and Ci. Our measure of average stumpage price is the price that landowners observed over the recent historical past, and serves as their price signal in determining the value of their stand. Error from both average price and harvest age can be attributed to measurement error because of spatial mismatches. For example, if price was observed at the county level, it would differ slightly from what a particular forestland owner would observe at the plot level.

As mentioned above we constructed county-level stumpage price 𝑃𝑖𝑗data using numerous sources including several state-level natural resource departments, university extension services, the USFS, and private reporting services (Table SI 4). For counties where j is grown but there is no observed price for j we interpolated the price. The spatial interpolation algorithm we used first looks for the missing price in neighboring counties that share a boundary. If multiple prices are found, then the volume weighted average price is used. This is repeated for 2nd and 3rd degree neighbors (i.e. two and three counties away). Finally, when county-neighbor price is unavailable, the state or regional weighted average is used (Mihiar 2018).

In many cases the composition of the species groups in the net returns dataset differed from the composition of the species groups in the TPO data. Because the TPO data accounts for the current availability of wood products we extrapolate the net returns data to match the TPO data. We employ a spatial algorithm similar to the above price interpolation algorithm. In this case the algorithm searches neighboring counties and regions for the net return to the species’ associated species group and major species group. For example, Pinus palustris belongs to the species group longleaf and slash pines and the major species group pines.

We also mentioned above that forest establishment costs were estimated by Nielsen et al. (2014) for each county in the contiguous United States. Their estimation is based on enrollment data from the USDA’s Conservation Reserve Program. Our model assumes that land starts as bare so that planting costs enter equation (2) both through the cost of initial planting and the discounted sum of all future re-plantings.

B. Annual net value of tree crop (fruits and nuts) production value

Annualized net revenues from fruit and nut production were generated for the following tree species: 1) Carya illinoinensis; 2) Citrus limon; 3) Citrus paradisi; 4) Citrus reticulata; 5) Citrus sinensis; 6) Citrus tangelo; 7) Corylus avellane; 8) Ficus carica; 9) Juglans regia; 10) Malus domestica; 11) Olea europaea; 12) Phoenix dactylifera; 13) Pistacia vera; 14) Prunus cerasus; 15) Prunus amygdalus; 16) Prunus armeniaca; 17) Prunus avium; 18) Prunus domestica; 19) Prunus persica; 20) Punica granatum; and 21) Pyrus communis. We collected spatial data on fruit and nut farm-gate price, fruit and nut yield, and fruit and nut production acreage from the years 2010, 2011, and 2012. We let the price for crop k generated by species k in state s in year t be given by pskt (in 2010 $; USDA-NASS 2015), the per acre yield of crop k generated by species k in state s in year t be given by Yskt (USDA-NASS 2015), and the acreage of species k in county i in year t be given by Aikt (USDA-NASS CDL 2015).

For each crop k generated by species k in state s, we generated four estimates of annualized per acre production cost: a lower bound (LB) estimate, a low (L) estimate, a high (H) estimate, and a higher bound (HB) estimate. We also report the mean of the L and H estimate. This is our central estimate. In general, we let the estimate of annualized per acre production cost of crop k in state s be given by csk (in 2010$). See Table S5 for low and high csk.

Estimating csk values required several analytical steps. First, we downloaded as many enterprise budget sheets for the twenty-one fruit and nut tree species as we could find. Second, for each budget sheet we used an annualization formula to turn the sheet’s assumed time series of costs across an orchard’s rotation into csk. By eliminating or tweaking some of the itemized costs on a budget sheet, each budget sheet generated a low and high ck. For example, we found an enterprise budget for sweet cherry production in California. We then used the cost annualization formula to convert costs across 25 years of a sweet cherry orchard rotation into two annualized cost estimates of sweet cherry production in California. In the high cost estimate all costs in the budget sheet were accounted for. In the low cost estimate we did not include the rental cost of land nor did we include the orchard’s cash overhead costs. In the classic Ricardian approach to valuing the net returns from land, land rents are not included in the net returns to land calculation (net returns to land minus land rent determines economic profit). Further, cash overhead costs are taxes and fees that are redistributed back to the public and do not represent a resource use cost to society. We repeated this process with every enterprise budget sheet we found, creating a low and high cost estimates from each one. If there was more than one budget sheet for species k in state s, we set s’ low csk, given by cskl, equal to the lowest low estimate of ck across sk’s budget sheets. If there was more than one budget sheet for species k in state s, we set s’ high csk, given by cskH, equal to the highest high estimate of csk across sk’s budget sheets. These are the low and high csk values presented in Table S5. Finally, we set s’ lower bound on csk, given by cskLB, equal to the lowest estimate of cskL across all states and we set s’ upper bound on csk, given by cskUB, equal to the highest estimate of cskH across all states. Therefore all states have the same cskLB and cskUB for each species k.

In several cases, we could not find a budget sheet for crop k in state s, even though the crop was produced in the state. If a state had no estimates of potential csk but we knew the state grew the crop, we assigned the state the low and high csk from the physically closest state. For example, we could not find a sweet cherry enterprise budget sheet for Utah. Therefore,

low and high csk for sweet cherries in Utah was set equal to the low and high csk for sweet cherries in California.

The annualized net return (in 2010 USD) to crop k generated by species k in county i in year t is given by,

𝑉𝐹𝑁𝑃)*_ = (𝑟*𝑝a*_𝑌a*_ − 𝑐a*)𝐴)*_ (1) where rk is a rotational deflator and csk could be any one of a state’s five estimates of annualized per acre production cost for species s (the 5th being the mean of cskL and cskH). Note that 𝐴)*_ is the only county-level variable in (1). The state-level variables used in (1), 𝑝a*_, 𝑌a*_, and 𝑐a*, are assigned according to the i’s home state. To understand the impact of rk, consider the example of a California county with many almond orchards. Some orchards will have just been planted, others will have been in their second year, and others in their third year, etc. If we assume the distribution of orchard ages in a county is uniform, almond orchards do not bear marketable fruit until their 4th year and almond groves have a 25-year rotation, only 84% of almond acres in the county bear fruit in any given year. Assuming all almond orchards in the county are replaced by a new orchard every 25 years, this is a steady-state value that holds year-in, year-out. Thus rk is equal to 0.84 for almonds.

Finally, we set 𝑉𝐹𝑁𝑃)*, the annual value of species k’s crop production, equal to the average of 𝑉𝐹𝑁𝑃)*,deGe, 𝑉𝐹𝑁𝑃)*,deGG, and 𝑉𝐹𝑁I)*,deGd, with five estimates of 𝑉𝐹𝑁𝑃)* given five estimates of annualized cost: cskLB, cskL , cskH, cskUB, and the mean of cskL and cskH. In the paper, our central estimates of 𝑉𝐹𝑁𝑃)* used the mean of cskL and cskH and our low and high estimates of 𝑉𝐹𝑁𝑃)* use cskL and cskH, respectively.

C. Annual net value of Christmas tree production

We let Rsk indicate the revenue raised by selling Christmas tree species k in state s in 2009 (2010 USD; USDA 2009) annd Ysk indicate the number of Christmas tree species k sold in state s in 2009 (USDA 2009). If both Rsk and Ysk for a k and s combination were given, we used the data to calculate the price per tree of species k in state s in 2009, given by psk,

psk = Rsk / Ysk (1)

We found the average price for a tree of Christmas tree species k across the nation in 2009 (2010 USD) by taking the average of all of k’s psk values. Every s and k combination that did not have a price was assigned k’s national average price. Finally, for every s and k combination with revenue data but no tree harvested data (i.e., Rsk data but no Ysk data) we estimated Ysk with,

Ysk = Rsk / psk (2)

To find the annualized cost of growing a Christmas tree of species k, we used the following procedure. First, we downloaded as many Christmas tree enterprise budget sheets as we could find. Second, for each budget sheet we used an annualization formula to turn the sheet’s assumed time series of costs across a tree farm’s rotation into two potential estimates of annualized per acre production cost. We let cskL indicate the low cost estimate of annualized per acre production cost for species k in state s. In the low cost estimate we did not include the rental cost of land nor did we include the tree farm’s cash overhead costs. In the classic Ricardian approach to valuing the net returns from land, land rents are not included in the net returns to land calculation. Further, cash overhead costs are taxes and fees that are redistributed back to the public and thus do not represent a resource use cost to society. We let cskH indicate the high cost estimate of annualized per acre production cost for species k in state s. In the high cost estimate we included the rental cost of land and the tree farm’s cash overhead costs.

Next we multiplied each estimate of cskL and cskH by the species k’ rotation time in s to get the low and high present value of producing an acre of species k in state s. We let these values be given by tcskL and tcskH, respectively. Finally, we divided tcskL and tcskH by the number of trees harvested from an acre of k in s to get the low and high present value of the cost of growing a single tree of type k in s, with these costs given by lcskL and lcskH.

We also assigned each state s that grew k a lower and upper bound on lcks. The lower bound lcsk, given by lcskLB, was set equal to the lowest lcskL found across all s. The upper bound lcsk, given by lcskUB, was set equal to the highest lcskH found across all s. Therefore, for each state s that grows k there were four estimates of lcsk; lower bound, low, high, and higher bound.

The annualized net return (in 2010 USD) for the production of Christmas trees of species k in state s is given by,

𝑉𝐶𝑇𝑃a* = (𝑝a* − 𝑙𝑐a*)𝑌a* (3)

where 𝑙𝑐a* could be one of the four values noted above. Therefore, for each k and state combination we have four estimates of 𝑉𝐶𝑇𝑃a*, one with lcskLB, one with lcskL, one with lcskH, and one with lcskUB higher bound lcks. In the analysis presented here we took the average of NRkjt values generated with the low and high lcks. In the paper, we report the average of the 𝑉𝐶𝑇𝑃a*’s that use lcskL and lcskH. Finally, 𝑉𝐶𝑇𝑃a* was allocated across s’ counties according to i’s share of s’ 2011 population.

D. Annual value of climate regulation via carbon storage in US forests

Forest carbon stocks by species in every US county were quantified using data from the national forest inventory conducted by the USFS, Forest Inventory and Analysis (FIA) program (Heath et al 2011) using aboveground carbon estimates (Pan et al 2011) and the ecosystem components of forest floor (inclusive of litter, fine woody debris, and humic soil horizons), down dead wood, belowground biomass, and soil organic matter (US EPA 2011, Woudenberg et al 2010). Estimates of aboveground standing live and dead tree carbon stocks were based on biomass estimates obtained from inventory tree data following Woodall et al 2010 using the Component Ratio Method (CRM) (Woodall et al 2010, Wilson 2013).

We let the total stock of carbon in species k in county i be given by Cik.

𝑉𝐶𝐹)* = 𝐶)* × 𝐴𝑆𝐶𝐶 (1) VCFik indicates the annualized value of the carbon stored in species k in county i where ASCC is the annualized value of the Social Cost of Carbon (2010 USD). See section 1.4.4 for more on ASCC. Note that Cik does not include the carbon stored in orchards or Christmas tree farms.

E. Annual value of climate regulation via carbon storage in orchards

As before, we let the acreage of crop tree species k in county i in year t be given by Aikt

(USDA-NASS CDL 2015) and let the annual rate of carbon sequestration in the biomass of crop tree species k across an orchard acre in county i be given by Cik, measured in metric tons of carbon per acre per year. See S6 Table for Cik values and data sources. If a data source reported a non-linear sequestration curve over time for crop tree species k we used a linearized rate. For example, Marvinney et al. (2014) estimate a non-linear biomass carbon accumulation curve for almond orchards over its 25-year rotation time. They estimate 4.64 Mg of C accumulates in the biomass of an acre of almond orchard by its 25th year. We used an annual sequestration rate of 4.64 / 25 = 0.19 Mg of C per acre per year.

We assumed that the area of crop tree species k in county i in year t is evenly allocated across its assumed rotation time. For example, the assumed rotation time of an almond orchard is 25 years. Therefore, at the of year t, 4% of almond area in county i had just finished its first year of life, 4% of area had just finished its second year of life, etc. This rotational assumption also means that 4% of the orchard area was denuded of its almond trees. If these trees were burned then net sequestration in the county in year t would b 0 (the sum of carbon gains in orchard area in rotation years 1 through 24 would have been negated by the loss in accumulated carbon in the 4% of area re-established in almond orchard). We let 𝐹)* indicate the portion of removed species k biomass in county i that was treated in such a way that its carbon is retained for an indefinite time or displaces the use of other fossil fuels. For example, in California, orchard waste biomass has been used to produce electricity at regional electricity generation plants (Marvinney et al. 2014).

Therefore, the amount of carbon sequestered by crop tree species k in county i in year t is given by,

𝐶𝑆𝐹𝑁)*_ = 𝐴)*_𝐶)*𝐹)* (1)

For simplicity we assumed that Cik and Fik are the same across all counties i. We used Fik of 0.1, 0.3, and 0.5. Further, we removed the time index from 𝐴)*_ by using the average area of k in i across the years 2011, 2012, and 2013. Because there is variation in Cik and Fik we report minimum, mean, and maximum values of 𝐶𝑆𝐹𝑁)* for each i,k combination.

Next, we calculated carbon storage values for each production system k in county i by assuming all orchard acres are halfway through their rotations,

𝐶𝐹𝑁)* = 𝐶𝑆𝐹𝑁)* × 0.5𝑅)* (2)

where 𝑅)* indicates production system k’s typical rotation time in county i. We report three values of 𝑉𝐶𝑂)* for each i,k combination given there are three estimates of 𝐶𝐹𝑁)* for each i,k combination (minimum, mean, and high).

Finally,

𝑉𝐶𝑂)* = 𝐶𝐹𝑁)* × 𝐴𝑆𝐶𝐶 (3) indicates the annualized monetary value of the carbon stored in the orchards of k in county i.

F. Annual value of climate regulation via carbon storage on Christmas tree farms

We let Ysk indicate the number of Christmas tree species k sold (harvested) in state s in 2009 (USDA 2009). We let Xsk indicate the number of trees typically harvested from an acre of mature Christmas tree species k in state s in any given year. Therefore,

Ask = Ysk / Xsk (1)

estimates the number of harvested acres of Christmas tree species k in state s in 2009.

According to enterprise budget sheets it typically takes a Christmas tree 7 to 10 years to reach maturity. If we assume every Christmas tree species takes 10 years to reach maturity, no matter where grown, and Christmas tree farmers use even-age, rotational methods then the number of acres in Christmas tree production in 2009 was 10 times the number of acres harvested (i.e., 10Ask). However, if we assume every Christmas tree species takes 7 years to reach maturity, no matter where grown, then the number of acres in Christmas tree production in 2009 was 7 times the number of acres harvested (i.e., 7Ask). In either the average or typical acre of Christmas tree production has trees that are approximately 5 years old.

We let Crg indicate the metric tons of carbon stored in an acre of a 5-year old stand of species group g in region r (Smith et al. 2006). Data on Crg for various combinations of r and g are found in SI Table 6. Using Crg we created Csk, the metric tons of carbon stored in an acre of a 5-year old stand of Christmas species k in state s. We did this by setting Csk equal to Crg when state s was in region r and species k was in species group g. However, the various combinations of r and g are not universal; there are some combinations of k and s that are not covered by any of the combinations of r and g. Therefore, any missing Csk was given the value of 𝐶* where 𝐶* is equal to the average of Csk across states with valid Csk values.

Therefore, the amount of carbon stored in all productive acres of Christmas tree species k in state s in 2009 is,

𝐶𝐶𝑇a* = 𝑟𝐴a*𝐶a* (2)

where r = 7 is a low estimate (all species take 7 years to reach maturity) and r = 10 in a high estimate (all species take 7 years to reach maturity). In either case we assume the average or typical acre of Christmas tree production has trees that are approximately 5 years old. Finally, 𝐶𝐶𝑇a* is allocated across s’ counties according to i’s share of s’ 2011 population. Next we calculate,

𝑉𝐶𝐶𝑇)* = 𝐶𝐶𝑇)* × 𝐴𝑆𝐶𝐶 (3) where 𝑉𝐶𝐶𝑇)* indicates the annualized value of the carbon stored in the farms of Christmas tree species k in county i, ASCC is the annualized value of the Social Cost of Carbon (2010 $). See section 1.4.4 for more on ASCC. Because we estimate a low and high 𝐶𝐶𝑇)* for each ik there are low and high estimates of 𝑉𝐶𝐶𝑇)* for each ik. In the paper we report the average of the low and high estimates of 𝑉𝐶𝐶𝑇)* for each ik.

G. Annualized Social Cost of Carbon To monetize the value of carbon storage, we multiplied the metric tons of carbon stored (by species by county) by the social cost of carbon (SCC), which is an estimate of the monetary value of damages created by the release of an additional metric ton of carbon dioxide into the atmosphere. Therefore, it also measures the benefit (avoided damage) of retaining one more ton of carbon dioxide in a tree. We used an estimate of the U.S. Government’s Interagency Working Group on SCC of $135.68 per metric ton of carbon in 2007 USD. We used the consumer price index (CPI) to convert this to $142.46 per metric ton in 2010 USD.

However, all of the other monetary estimates in this study are flow values, i.e., net revenue per year, the annual monetary value of the pollutants removed by trees, etc. Multiplying carbon stored by SCC would not give us a flow value, rather a stock value. Therefore, we converted the price for storage of a metric ton in perpetuity – SCC – into an annual rental value. We let the SCC measure the annualized SCC or ASCC. ASCC measures how much we would be willing to pay per year to secure the $142.46 generated by a ton of storage into perpetuity. To get ASCC, we multiplied the SCC by a per annum interest rate of 3%. This gave an ASCC of $4.27 per metric ton of carbon.

H. Annual value of air quality regulation via avoided health damages from tree-based removal of air pollutants

We valued trees’ abilities to remove PM2.5 and O3 from the atmosphere. The value of air pollution removal was given by the damage avoided by not having these pollutants remain in the atmosphere. The damages avoided include damages to human health, crop and timber yield, visibility, man-made materials, and recreational opportunities (Muller and Mendelsohn 2007, Muller 2013). In this paper we specifically valued the reduction in human mortality in 2011 across the US due to tree removal of PM2.5 and O3.

The benefits from pollution reductions due to trees were estimated using the AP3 integrated assessment model. AP3 is an updated version of AP2 (Muller, 2011; 2014; Jaramillo and Muller, 2016), which is based on the APEEP model (Muller, Mendelsohn, 2009; Muller, Mendelsohn, Nordhaus, 2011). The manner in which AP3 was used in the present analysis is somewhat different than in prior applications. AP3 links emissions to concentrations of five common air pollutants: nitrogen oxides (NOx), sulfur dioxide (SO2), ammonia (NH3), volatile organic compounds (VOCs), and fine particulate matter (PM2.5). Exposures are estimated using detailed, county-level population estimates provided by the U.S. Bureau of the Census. These inventories are decomposed into 19 age groups because baseline risks of adverse health effects associated with air pollution exposure vary by age. Mortality rate data (provided by the Centers for Disease Control and Prevention: CDC Wonder) are also reported at the county-by-age-group level.

Exposures were translated into adverse health effects using concentration-response functions from the epidemiological literature. In this study, AP3 was used to track elevated mortality risk from exposure to PM2.5 and tropospheric ozone (O3). The concentration-response function linking PM2.5 exposure to premature mortality is from Krewski et al., (2009) and for O3 exposure is from Zanobetti and Schwartz (2008). Both are widely used in the relevant literature and policy analyses (USEPA, 1999; 2010). AP3 values mortality risk using the value of a statistical life (VSL) method (Viscusi, Aldy, 2003). This study used USEPA’s VSL of $7,570,229 (2015 USD). EPA’s VSL is an average of reported values from a sample of studies. This VSL has been used widely in the related literature and policy analyses (USEPA, 1999; 2010).

AP3’s air quality model links emissions of NH3, NOx, SO2, primary emitted PM2.5, and VOCs to ambient concentrations of PM2.5. That is, the contribution of emissions of SO2 to concentrations of sulfate (SO4), and ammonium sulfate ((NH4)2SO4) is modeled. Similarly, the contribution of emissions of NOx to concentrations of ammonium nitrate (NH4NO3) is also modeled. The role of NH3 emissions in the formation of both ammonium nitrate and ammonium sulfate is also captured by AP3. VOCs contribution to organic PM2.5 was modeled according to the method described in Muller and Mendelsohn (2007). Finally, the contribution of NOx and VOC emissions to the formation of tropospheric O3 was estimated (for details see: Muller and Mendelsohn (2007).

In the present paper, we employed the 2011 National Emissions Inventory (NEI), (USEPA 2014) along with AP3 to estimate baseline annual average concentrations of PM2.5 and O3. We then computed exposures, mortality risk, and monetary damages associated with this baseline level of emissions. This calculation was made at the county level and embodies the contribution of all reported emissions in the NEI to the formation of ambient PM2.5 and O3. Taking PM2.5 as

an example, we then divided monetary damage predicted by AP3 in county i from pollutant p = PM2.5 in 2011, given by 𝐷)m, by the ambient concentration of PM2.5 also in county i in 2011, given by 𝐶)m,

𝑑)m =

o9pJ9p

(1)

where 𝑑)m is an estimate of the damage in 2011 (in 2015 USD) from a ug/m3 of p = PM2.5 in county i. An analogous calculation is made for p = O3.

We used 𝑑)m and estimates of the annual mass of pollution removed by trees to compute the monetary benefits of trees through pollution removal circa 2011. The annual grams of PM2.5 and O3 removed by trees of species s in county i circa 2011 is provided by Nowak et al. (2014). However, because 𝑑)m is measured in ug/m3 estimates of annual removals of pollutants by trees were converted to a concentration. First, for county i, we coupled land area (reported in m2) with mixing height data from US-EPA (updated 9/27/2016) (https://www3.epa.gov/scram001/mixingheightdata.htm) to estimate the volume (in m3) of air space in which the pollution removed by trees occurred. This calculation yielded the ug/m3 of pollution species p removed by trees of species s in county i, denoted 𝑅)am. Therefore,

𝑉𝑅𝑃)am = 𝑑)m × 𝑅)am (2)

is the monetary benefit of 2011 removal of pollutant p by trees of species s in county i (2014 USD). To convert the 2015 USD estimates to 2010 USD estimates, we used a CPI correction factor of 0.9203.

We augmented this simple calculation with a Monte Carlo analysis to characterize the statistical uncertainty associated with our estimates. The Monte Carlo procedure focuses on the concentration-response function and the VSL. Specifically, we constructed two normal distributions, with means and variances that correspond to the estimated distributions associated with USEPA’s VSL (95% CI from Benmap Users Manual, page 400, Normal Distribution) and the concentration-response 95% CI parameters reported by Krewski et al., (2009) for PM2.5 and from Zanobetti and Schwartz (2008) for O3. We made 1,000 draws from these distributions, calculating benefits of pollution removal by county for each draw – thus constructing empirical distributions of our benefit estimates.

I. Explaining annual air quality regulation values across the US

To better understand the spatial pattern of air pollution removal value across the US we created a county-level map of air removal value per square mile (Figure S2). We reduce the bias of larger counties containing more value due to size effects when we normalize air quality regulation values by county size. On this same map we have superimposed urban areas as defined by the US Census.

According to Figure S2, the northeastern US receives the most value per square mile in general, although there are pockets of high value in the Southeastern and Western US. Value in an area is largely determined by tree abundance and levels of human population (trees can only provide air pollution removal value if there are people that can benefit from it).

To determine which of these two factors, tree or people abundance, better explained air pollution removal value, we regressed a county’s air quality regulation value per square mile on the county’s standardized distance to nearest large urban area (50 largest urban areas in terms of area) and the county’s standardized carbon storage per square mile (as a proxy for tree biomass abundance).

As alternatives to this model, we replaced the distance to the nearest large urban area (measured centroid to centroid) with standardized average distance to nearest five large urban areas (column II in Table S8), standardized distance to the nearest urban area (regardless of size) column III in Table S8) and standardized average distance to nearest five urban areas (regardless of size) (column IV in Table S8).

We found that for every 1 standard deviation increase in distance to the nearest urban area(s), annual air quality value per square mile as of 2010 to 2012 fell between $5,692 to $11,649 (2010 USD). Further, for every 1 standard deviation increase in carbon storage per square mile, annual air quality value per square mile as of 2010 to 2012 increased $14,494 to $17,431 (2010 USD). These results suggest that abundance of tree biomass in an area does more to explain a county’s air quality regulation value per square mile than the proximity of an urban area, all else equal.

J. Phylogenetic dispersion of ecosystem services

Phylogenetic dispersion, or the extent to which ecosystem services of trees are spread randomly, evenly or are clustered in the tree of life, was evaluated using standardized effects sizes for mean phylogenetic distance (SES MPD). We also calculated the mean nearest taxon distance (SES MNTD), which examines whether close relatives share more or less similar service values. Both were calculated using the picante package in R, following Webb et al 2002, selecting the phylogeny as the species pool for the null model.

K. Threats from tree pests and pathogens

Threats posed by pests and pathogens were quantified as the predicted proportional basal area loss 𝑙1* between the period of 2013 and 2027 for tree species 𝑗 in each county 𝑘. Those values were based on spatially explicit threat models developed by the United States Forest Service that accounted for pathogen prevalence of 55 pathogenic agents and tree vulnerability among other factors (https://www.fs.fed.us/foresthealth/applied-sciences/mapping-reporting/gis-spatial-analysis/national-risk-maps.shtml). The original model predictions are encoded as raster files can be downloaded from (https://www.fs.fed.us/foresthealth/technology/docs/L48_Hazard_by_tree_host.zip).

Because the data were not provided by county but as a raster, county level estimates for each species were computed as the average of all pixels encompassed by the area of each county. The threat data were often not associated with a species’ Latin name 𝑘, but instead with a tag 𝑘′ that consisted of a common name or taxonomic levels other than species. Data associated with common names 𝑘′ were mapped to scientific names 𝑘 using an FIA lookup table < https://www.dropbox.com/s/rhdtvjcx5pz1dq8/sp_lookup.csv?dl=0>. Whenever multiple elements of 𝑘′ mapped onto 𝑘, the data associated with the highest taxonomic resolution name 𝑘′ were chosen. For example, if the threat data had a raster associated with the tag Quercus and one with the tag Quercus alba, the latter was used. To estimate threat for each tree species, 𝑙*, we took the average of the projected basal area loss across counties 𝑙)* weighted by the biomass of the species in the county, 𝑤)*;

𝑙* = ∑Y)tG 𝑤)*𝑙)*/∑Y)tG 𝑤)* (1)

Finally, threats from pests and pathogens at the county level were calculated as the average predicted basal area loss of all species in the county weighted by each species’ biomass,

𝑙) = ∑Y*tG 𝑤)*𝑙)*/∑Y)tG 𝑤)* (2)

L. Threats from climate change

The assessment of threat due to climate change was based on current and projected bioclimatic values for North America available from (https://adaptwest.databasin.org/pages/adaptwest-climatena). Both current climate data (mean from 1981 - 2010), denoted 𝑅∘ , and future climate projections for 2050, 𝑅w , were obtained as raster files at a 1km resolution. The bioclimatic variables for 2050 were based on an ensemble of 15 Coupled Model Intercomparison Project 5 (CMIP5) models, which corresponds to IPCC5, assuming a RCP8.5 emission scenario. To estimate the threat of climate change, we first computed the difference between current (𝑅)x∘ ) and projected values (𝑅)xw ) of the summer heat moisture index (a measure of aridity) for every pixel ℎ in the raster, resulting in the raster 𝑅)z. Next, a county-level climate change dataset 𝑚)*

z for the bioclimatic variable was obtained by taking the average of all pixels from 𝑅)z encompassed by each county 𝑘. Finally, the impact of the climate change component 𝑖 on each species 𝑗 was calculated by averaging the projected change in each county 𝑘 weighted by the biomass of that species in that county, 𝑤)* (see the Land use change section above for the equation for 𝑤)*),

𝑚)1

z = ∑Y*tG 𝑤1*𝑚)*z /∑Y*tG 𝑤1* . (1)

M. Threats from change in frequency of major fires

Forest fire threat was quantified using the projected change in the number of large fires/weeks per county, 𝑞*z, from the historical late 20th century climate forcing 𝑞*∘ to the mid-21st century forcing scenario 𝑞*w , as described in Barbero et al. (2015). First, the raster from the Barbero et al. (2015) was used to compute the fire threat for each county 𝑘, denoted 𝑛*z, by taking the mean of the pixels from 𝑞*z that fell within the county area. Given the lack of species-specific fire threat data, we again assumed that the changes in fire threat in each county 𝑘 affected each species 𝑗 in proportion to their biomass in that county 𝑤1* (see the Land use change section above for the equation for 𝑤)*). Finally, fire threat for each species 𝑗 was estimated by taking the average of the fire frequency change in the counties where the species is present weighted by the biomass 𝑤1* of species 𝑗 in county 𝑘,

𝑛1z = ∑Y*tG 𝑤1*𝑚*

z/∑Y*tG 𝑤1* . (1)

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