DOI: 10.5061/dryad.bzkh1898j
title: Experimental evaluation of herbicide use on biodiversity, ecosystem services, and timber production tradeoffs in forest plantations

description: The value of non-commodity ecosystem services provided by forests is
 widely recognized, but intensive forest management practices are
 increasing, with uncertain consequences for a multitude of these services.
 Quantitative relationships among biodiversity conservation, timber
 production, and other ecosystem services remain poorly understood,
 especially during the early successional period of intensively managed
 forestlands.  We manipulated management intensity in regenerating
 forest plantations to test the prediction that treatments aimed at
 maximizing timber production decrease biodiversity conservation and
 non-timber services. We measured species richness of three taxonomic
 groups and thirteen proxies for provisioning, cultural, and regulating
 services within stands randomly-assigned to one of three herbicide
 application intensities or an untreated control.  Herbicides
 increased allocation of net primary production to crop trees, increasing
 projected timber volume and revenues at 40- and 60-year harvest ages.
 Commonly-used herbicide prescriptions reduced culturally-valued plants by
 71%, wild-ungulate forage by 41%, avian richness by 20%, and pollinator
 floral resources by 42%, the latter being associated with 38% fewer
 pollinator species. However, agriculturally-valued bumblebees, pollination
 of blueberries, avian-mediated arthropod control, ungulate observations,
 and regulation services tied to forest productivity appeared unaffected by
 increasing management intensity and timber production. Species richness
 and flora-provided services in young forest plantations exhibited strong
 tradeoffs with projected timber production, whereas post-treatment
 vegetation regeneration and site-level variation likely maintained a range
 of other services. Although vegetation recovery is important for
 supporting wildlife and some ecosystem services on industrial forestlands,
 it is unlikely that any single prescription can optimize both timber and
 non-timber benefits to society across managed forest landscapes. Instead,
 producing different services in discrete portions of the landscape may be
 necessary. Synthesis and applications. We tested the effects of intensive
 forest management via herbicides on ecosystem services and found that
 biodiversity responses and services from early-successional vegetation
 trade off with crop-tree production. A number of services appeared to be
 compatible with timber production, although no single prescription
 optimized the full range of services. Stand-level biodiversity
 conservation and a variety of services may be provided by treatment skips
 and less-intensive management on productive sites, although it is unlikely
 that all services can be optimized without landscape-level planning.
description: Experimental design: The experiment was conducted in the Oregon
 Coast Range, in stands harvested during 2009/2010. Four stands per block
 (8 blocks total) were selected based on the criteria of being in proximity
 to one another (no greater than 5 km) and harvested during the same time
 period. In 2010, each stand was randomly assigned to one of three
 herbicide treatment intensities (light, moderate, intensive) or an
 untreated control. The light treatment received an aerial applications of
 spring-herbaceous specific (in 2011) and fall woody-broadleaf specific
 treatments (in 2012). The moderate treatment was treated with aerial
 applications of a broad-spectrum, site preparation spray (in 2010),
 aerially-applied spring herbaceous specific spray (in 2011) and spot
 treatments of coppice-sprouting maple where present (in 2012; 3/8 stands
 received this treatment). The intensive treatment was sprayed with an
 aerial site preparation (in 2010), aerial spring herbaceous sprays (2011,
 2012, 2013), and fall woody-broadleaf sprays (2012, 2014).
 Vegetation (Native plant richness, floral resources,
 culturally-valued plants, wild ungulate forage): Within each stand, we
 measured plant species cover from 12, 1 m2 quadrats in a randomly located
 15 x 15 m permanent vegetation plot for six years, during the peak in
 vegetation production (i.e., July-August, 2011-2016). Due to the logistics
 of a companion wildlife exclusion study, we restricted vegetation
 measurements to 7 of the 8 blocks. We used information from Von Hagen et
 al. (1996) to classify plants as having cultural value for food,
 traditional medicine and as economically valuable non-timber forest
 products. We also used the USDA Plant Database to classify each species as
 native or non-native in the state of Oregon. For cultural plants, we
 summed the cover of all species within each group across the six years,
 2011-2016. For native plant species richness, we tallied the number of
 observed species from 2011-2016 and averaged years for an average-yearly
 richness estimate. Using information from Cook et al. (2016), Ulappa
 (2015), the USDA Plant Database and the US Forest Service Fire Effects
 Information System (FEIS), we assigned plant species to forage and
 non-forage groups. Further, we used Cook et al. (2016) and Ulappa (2015)
 to assign plants to selected and accepted forage groups to develop biomass
 regression equations for each specific forage group. In 2015, we randomly
 assigned 8 locations throughout each stand to harvest above-ground current
 annual growth of each species (not including woody biomass) up to 2 m from
 the ground (i.e., maximum browsing heights) and simultaneously recorded
 ocular cover estimates using the above methods. We dried plant samples at
 55°C and pooled forage group biomass samples for each quadrat and summed
 cover estimates among species for each group per quadrat, allowing cover
 values to exceed 100%. We defined forage groups as: selected broadleaf,
 neutral broadleaf, selected herbaceous (forbs and graminoids), neutral
 herbaceous, non-forage shrubs, non-forage herbaceous and non-forage
 conifers. We also directly collected and dried biomass per species from 4,
 50x50 cm quadrats, located within excluded and open plots. We used
 biomass-cover samples from 2015 to develop allometric equations predict
 forage biomass production for each year from 2011-2016. We fit models for
 each forage group, including the log-log relationship between biomass and
 cover estimates, with log-transformed average plant height as a covariate.
 We used Akaike Information Criterion (AIC) to compare full models with
 models only including cover as a predictor variable, generally opting for
 the full model. We then used the equations to predict biomass production
 at the plot-level from 2011-2016 using the equations below. For all
 models, we used the Consistent I estimator bias correction factor (Hayes
 & Shonkwiler 2007), calculated as biomass+exp(Mean Square
 Error/2). We then summed the biomass of all forage groups across years to
 obtain an estimate of net forage production. To estimate the frequency of
 animal-pollinated flowering plants, we recorded the presence or absence of
 flowers for each species in each quadrat. We then tallied the number of
 species-quadrat level observations from 2012 (when flower surveys began)
 until 2016. We used information from Niell and Puettmann (2013) to
 classify each flowering species as to whether they provide pollen sources
 for animals. Blueberry pollination: To test the effects
 of herbicide application on insect pollination efficiency, we conducted a
 pollination experiment using potted sentinel Northern Highbush Blueberries
 (<i>Vaccinium corymbosum</i>, variety 'Duke', Alpha
 Nurseries, Salem, Oregon, USA), an economically important crop plant in
 Oregon. Northern Highbush blueberries are self-fertile, but
 cross-pollination by pollinators increases seed set and berry mass, which
 is positively correlated with pollinator abundance (Isaacs & Kirk
 2010). Pollinators of blueberries in the study region include bumblebees
 and honeybees, but bumblebees are thought to be more efficient pollinators
 (Rao, Stephen 2009). Blueberry plants were planted in 1-gallon pots, and
 we added hydrogel to minimize desiccation. Before bringing the plants to
 the field, we kept them in a cooling chamber at 10oC for 10 days to
 synchronize flowering. We brought out a group of 4 blueberry plants that
 received no prior pollinator visits to each stand in early May and placed
 them at least 50 m from the stand edge. All plants per block were
 simultaneously in the field. Plants were protected from ungulate herbivory
 with a fence and watered every 5-6 days. Before placing the plants on the
 stands, we removed all open flowers, individually marked 5 branches per
 plant and counted the number of flowers on those branches. After 17-19
 days in the field, we brought the plants back to a mesh-covered exclosure
 to prevent further pollinator access. We counted all flowers that had
 opened during the field period (&gt;95% of the total) and removed
 flowers that had not yet opened. For all marked branches, we determined
 fruit set and total mass of ripe berries per branch.
 Wild ungulate observations: During four seasons (2012-2015), we
 assessed stand use by wild ungulates (black-tailed deer
 [<i>Odocoileus hemionus colombianus</i>] and Roosevelt elk
 [<i>Cervus canadensis roosevelti</i>]) using camera traps
 (Bushnell Trophy Camera, model 119436). Cameras were placed in the corner
 of each vegetation plot. We considered the number of photos taken of
 individuals within 10 m of the camera per day per sampling period
 (May-October from 2012 to 2015) for each species to be estimates of
 detection. We included the number of active camera days as an offset in
 the further analysis of ungulate detections, as the total observation time
 per stand ranged from between 397 to 736 days, due to occasional camera
 malfunction. To address detection issues regarding vegetation density, we
 deployed distance markers and only included photos taken of animals within
 10-m of each camera. We then determined that treatments did not differ in
 detection probability for pictures taken in 1-m bins from 1-10 meters,
 using Weibull and lognormal distributions. Pollinator
 richness: To quantify pollinator richness, we randomly assigned 8
 pollinator plots per stand in 7 research blocks. At every pollinator
 richness plot, an observer determined four 1 x 2 m subplots, situated 25 m
 away from the pollinator richness plot, one in every cardinal direction
 (N,E,S,W, n = 28*3*4 = 336 subplots). Every subplot was observed for 10
 minutes. All invertebrate pollinators touching reproductive plant organs
 were caught using netting or an aspirator and dispatched in ethanol for
 identification in the lab. After the 10-minute period, observers walked
 slowly back to the plot center, collecting all pollinators within the 1 m
 wide transect. For each block and round, two stands were surveyed in the
 morning and 2 stands in the afternoon of the first visit. This order was
 switched for the second visit. Observations only occurred during favorable
 weather conditions (&lt;50% cloud cover, no rain, no strong winds).
 Pollinators were air-dried and pinned for identification to the species
 level. Samples will be curated at the Oregon State Arthropod Collection
 (https://osac.oregonstate.edu). Bumblebee counts
 (<i>Bombus vosnesenskii</i>): We quantified the abundance of a
 native key pollinator species, the yellow-faced bumble bee <i>Bombus
 vosnesenskii</i>, on the experimental stands, as a proxy of
 regenerating forest stands to serve as source habitats for agriculturally
 relevant key pollinators. The yellow-faced bumble bee is critical for
 cranberry, blueberry and clover seed production in the region (Rao and
 Stephen 2009, 2010; Stephen et al. 2009; Broussard et al. 2011), and this
 species is widespread and abundant in early-successional forests (Rivers
 and Betts, in revision). Bumblebees were sampled during three sampling
 rounds in 2015 at three locations (corresponding to the avian point count
 stations) per stand. The rounds corresponded to late May to early June,
 mid to late July and early to mid-August. The locations were placed
 according to a stratified random design, in order to cover representative
 portions of the stands while avoiding stand edges and maximizing the
 distance between locations. At every location, we placed a total of four
 blue vane traps, at 25m distance away from the location center in each
 cardinal direction. Two opposing traps had propylene glycol in the bottom
 of the trap (1inch of fluid), while the two other traps were dry. The
 cardinal directions were chosen at random. Blue vane traps were collected
 and emptied after 24 hours. To preserve bumblebees, we washed them of
 propylene glycol using hot water, soap, and ethanol, and then air dried
 and pinned each bumblebee for identification. We then tallied the total
 number of <i>B. vosnesenski</i> collected in each stand.
 Samples will be curated at the Oregon State Arthropod Collection
 (https://osac.oregonstate.edu). Avian richness: We
 surveyed birds at three randomly placed point count plots during 6 years
 (2011-2016) using standard avian point count protocols (Ralph 1995). The
 three bird census points were located with at least 130 m separation
 between adjacent points, and at least 75 meters from the treated stand
 edge. Survey points were sampled four times during the breeding season (25
 May to 4 July) in each survey year. Survey order and observers were
 randomized throughout the season to avoid associated biases. Observers
 recorded every bird seen or heard with an associated first detection
 distance from the census point, including aerial insectivores but not
 fly-over individuals. Each point was sampled four times for a ten-minute
 interval during the breeding season (May 28–July 3rd) and were conducted
 between sunrise and 10 am. For all analyses, we aggregated data over all
 point count stations within a stand to obtain one response per harvest
 unit per visit. Using a 3-m radius subplots, we quantified broadleaf
 vegetation cover yearly around each point count location, a measure we
 then integrated into the detection sub-model of the avian statistical
 models. The spatially and temporally replicated sampling allowed us to use
 Bayesian N-mixture models that could account for differences in detection
 probabilities between stands (i.e. multispecies site occupancy and
 multispecies N-mixture abundance models). In brief, the occupancy process
 models included terms for block, treatment, year, and the treatment × year
 interaction.  The species-specific detection model included terms for
 year, percent cover of woody vegetation, and quadratic ordinal date (Table
 S1). We used the estimated latent occupancy and abundance state variables
 to compute species richness per stand and associated credibility
 intervals. Bayesian models were fit using JAGS 46 called from R version
 3.5.1 41 using the ‘jags’ function in package R2jags version 0.05-07 47,
 with three Markov chains of length 100,000 with a burn-in period of 20,000
 and 1/80 thinning. Avian-herbivore control: In the
 second year of stand regeneration following harvest (2012), we constructed
 two adjacent 15 m x 15 m exclosures: one fenced and one fenced and netted.
 Numerous terrestrial taxa prey on arthropods, yet our focus was on birds.
 Our netted exclosure excluded both birds and bats. However, bats in our
 study region have been shown to utilize old-growth forest and water as
 nesting and feeding sites at much higher rates than young-growth forest
 (up to 10.3 times, Thomas 1988, Clare et al. 2011). The Myotis bats that
 comprise the majority of the bat species in our study area are also more
 likely to feed on flies and aquatic insects (Waldien et al. 2000), and not
 the arthropods for which we found strong exclosure effects. Therefore,
 birds likely comprise the majority of vertebrate predators in this
 experiment. Each exclosure consisted of a 2.4 m high fence with a mesh
 size of 10 cm x 8 cm. The netted exclosure also included a net with mesh
 size 18 mm x 18 mm that was stretched over the top of the fence and draped
 over the sides, extended to the ground. Exclosures were randomly placed at
 least 50 m from stand edges, occurred on a wide range of slopes, covered
 all aspects, and avoided riparian areas, logging trails, and logging
 debris piles. Nets were removed in the winter to avoid snow loading and
 reinstalled in the spring. We sampled arthropods in each exclosure, once
 in July and once in August in 2012, 2013, 2014, and 2015 (3 to 6 years
 following harvest) using methods intended to sample a wide range of
 substrate and arthropod feeding guilds: sweep net, pitfall trap, and a
 restricted leaf-turning method. For the sweep net, an observer walked
 three evenly spaced parallel transect lines across the length of the
 exclosure, sweeping a net at ankle- to waist-height with sufficient force
 to dislodge arthropods while not damaging the vegetation. Three pitfall
 traps, using a 9 cm diameter plastic cup with rain cover, were evenly
 spaced across the middle of the exclosure and remained in place for 24
 hours. For the leaf-turning method, one representative specimen of the
 five most abundant tree and shrub species in the plots, one representative
 of bracken fern (<i>Pteridium aquilinum</i>), one
 representative of sword fern (<i>Polystichum munitum</i>), and
 all Douglas-fir (<i>Pseudotsuga menziesii</i>), were sampled.
 Each sample consisted of examining a fixed number of leaves per plant for
 deciduous species and five 30 cm branch segments for Douglas-fir. Sampled
 deciduous tree and shrub species included California hazelnut
 (<i>Corylus cornuta</i>), oceanspray (<i>Holodiscus
 discolor</i>), vine maple (<i>Acer circinatum</i>),
 big-leaf maple (<i>Acer macrophyllum</i>), cas¬cara
 (<i>Rhamnus purshiana</i>), red alder (<i>Alnus
 rubra</i>), <i>Rubus</i> spp.,
 <i>Vaccinium</i> spp., salal (<i>Gaultheria
 shallon</i>), and Oregon grape (<i>Mahonia nervosa</i>),
 depending on site. We detected no difference in the number of Douglas-fir
 trees in the fenced (Mean = 19.5, SE = 4.6) and netted (Mean = 20.7, SE =
 5.1) exclosures (paired t-test, t27 = -1.48, P = 0.15). Arthropods were
 identified to the family level in most cases and assigned to a feeding
 guild (predator, herbivore, detritivore, mixed). For arthropods that were
 difficult to identify, we assigned a higher taxonomic rank: Chilopoda
 (class), Acari and Collembola (sub-class), Isopoda, Microcorphyia, and
 Opiliones (orders); microwasp and midges (sub-orders). The length of each
 arthropod was measured prior to live release in the center of each plot.
 No vegetation was removed to sample arthropods. The composite count from
 the sweep net, pitfall trap, and leaf-turning methods from the two surveys
 (July and August) represents the relative arthropod abundance for that
 year. For the analyses, we included only herbivorous arthropods to
 quantify the potential effect birds have in controlling herbivorous
 arthropod abundance. To estimate an effect size for trophic strength, we
 calculated the log-response ratio (LRR) with pairwise comparisons of the
 treatment factors using the emmeans package in R (Russell 2018). Because
 our response variable was log-transformed, the pairwise comparison between
 the responses in the presence versus absence of birds results in LRR:
 ln[NB+] – ln[NB-] = ln[NB+ / NB-] = LRR where NB+ and NB- are the mean
 responses in the presence (fenced exclosure) and absence (netted
 exclosure) of birds, respectively. The log-response ratio is frequently
 used in trophic studies to estimate cascade strength (Hedges et al. 1999,
 Mooney et al. 2010). Timber projection: We conducted
 tree growth projections from a list of planted crop trees
 (<i>Pseudotsuga menziesii</i>) and naturally regenerating
 conifer and hardwood species, measured from 18-20 systematically located
 plots within each stand. All plots were measured at the end of the growing
 season in 2015 (i.e., October-December) and consisted of a 5-m radius
 plot, in which planted-crop trees were tagged and measured. We also
 established a nested 3-m radius plot in which naturally regenerating
 conifer and hardwood species were tagged and measured. Measurements
 included bole diameter at 15 cm from root collars, diameter at breast
 height (dbh, i.e., 1.37 m) and height to apical leader. To avoid
 overestimates of hardwood regeneration, stems from coppice-sprouting
 hardwood clumps were only included if the diameter for each stem was at
 least 80% the size of the largest stem within each sprouting clump. From
 the 3 m radius plots, we tallied the number of naturally regenerating
 conifer seedlings less than 20 cm in height and assigned them all a height
 of 15 cm. During the growing season of 2015 (i.e., July-September), we
 ocularly estimated the percent cover of competing vegetation, grouped by
 forb, graminoid, fern and broadleaf shrub and non-commercial broadleaf
 woody (i.e., excluding <i>Alnus rubra</i>, <i>Acer
 macrophyllum</i> and <i>Prunus emarginata</i>) life-form
 functional groups. Life-form cover estimates were fit into projection
 models as the average sum of competing life-form percentages per plot
 within each stand. We combined two growth models to project development of
 the 5-year-old tree lists for 100 years, thereby using the most
 appropriate equations to account for both the competing vegetation in the
 young plantations and hardwood growth in older stands. We projected tree
 lists from age 5 to 20 with Center for Intensive Planted-forest
 Silviculture (CIPS) annualized growth equations. These are a set of
 proprietary equations for diameter and height growth, and mortality of
 Douglas-fir and western hemlock plantations, and account for the negative
 effect of competing vegetation cover. Although these equations can be used
 to estimate growth of trees ranging from newly planted seedlings to mature
 stands, additional equations needed to be added to grow other species.
 Trees of the twenty-year-old projected tree lists were then further grown
 with SMC-ORGANON, a regional growth model constructed from plantation data
 in the Pacific Northwest (26). Because the CIPS equations cannot estimate
 growth of species other than Douglas-fir and western hemlock, other means
 were used to model growth for other tree species present on the plots.
 Although there were few other conifers sampled, grand fir (<i>Abies
 grandis</i>) and noble fir (<i>Abies procera</i>) were
 modeled as western hemlock (<i>Tsuga heterophylla</i>). No
 regional equations were available for bitter cherry (<i>Prunus
 emarginata</i>), so they were grown as bigleaf maple (<i>Acer
 macrophyllum</i>). Red alder (<i>Alnus rubra</i>)
 diameter growth was estimated with annualized ORGANON equations (26), with
 the red alder site index assumed to be 50% of the Douglas-fir site index.
 Bigleaf maple diameter growth was estimated with a linear interpolation of
 the ORGANON 5-year diameter growth equation (27) for maple. Estimated
 heights of both species were based on ORGANON height:diameter equations
 for alder (26) and maple (28), and ORGANON equations were also used to
 estimate crown base (alder: (26); maple: (29)) and mortality (alder: (26);
 maple: (27)). Assignment of diameter to hardwood stems surpassing 1.37 m
 was based on an equation constructed from data collected from this study.
 Douglas-fir 50-year site index (30) used within both models was based on
 landowner estimates. Western hemlock site index was based on a published
 conversion equation from the Douglas-fir site indices (31). We then
 calculated expected yield (board feet * ha-1) and expected yield gains for
 each herbicide treatment at three different rotation ages typical for the
 region (40 and 60 years). For this, we fitted a linear mixed effects
 models in R for each rotation age (9), with the expected yield per stand
 as the response, herbicide treatment as explanatory factor, and block
 random intercept. We chose the best fitting variance structure per model
 (Gaussian, VarExp, VarPower, varIdent(form=~1|Treatment)) using corrected
 Akaike Information Criteria (AICc) and residual plots, to account for
 residual heteroscedasticity. We fit final models using restricted maximum
 likelihood. Revenue projection: To compare the economic
 performance of the different herbicide treatments under a broad spectrum
 of realistic investment decisions (discount rates ranging from 4 – 10%),
 we performed a series of cash-flow analyses (plantations costs, herbicide
 costs, harvest costs, harvest income) and transformed those into land
 expectation values (LEV, also called soil expectation value). LEV is a
 standard discounted cash-flow technique used to compare timber management
 strategies for plantations with periodic harvest. LEV calculates the value
 of a forest plantation under perpetual production (a proxy for the
 willingness to pay for forest land). Unlike net present value, LEV values
 are not associated only with current timber rotations and can be compared
 between forest lands with different rotation ages.  Importantly, future
 cashflows are discounted at a rate (hereafter “discount rate”) which can
 be interpreted as a measure of opportunity cost of capital (i.e., the rate
 of return from the best alternative available investment project). We
 calculated land expectation values using estimated costs of planting ($650
 ha-1), logging ($32/m3), and hauling ($14/m3) that were based on
 communications with local professional foresters. Herbicide costs were
 based on the current costs of applying the specific products used (site
 prep ($200 ha-1); release 1 ($175 ha-1); release 2 ($150 ha-1)). Economic
 tabulations also included an annual 2% inflation rate and an annual 0.5%
 timber price increase. All calculations were performed for nominal
 discount rates of 6% representing the spectrum of applied interest rates
 in the region based on communications with local timber companies. We then
 assessed whether herbicides caused differences in expected LEV. Using the
 maximum LEV across rotation ages for each stand and for each discount rate
 (e.g. using the rotation age that maximized the LEV for the discount rate
 under consideration), we compared the expected LEV for each herbicide
 treatment under 4 and 6% nominal discount rate, rates typical of state and
 private-industrial private lands, respectively. We used the same
 statistical procedure as for the yield analysis.
 Standing carbon projections: Tree-level aboveground biomass was
 predicted for Douglas-fir using six-component biomass equations
 constructed from data collected in intensively managed stands (Mainwaring
 et al. in prep).  Tree-level aboveground biomass of other species was
 estimated using previously published equations (Ung et al. 2008).  Carbon
 content of aboveground tree tissues was calculated assuming a carbon
 content of 50% (Matthews 1993). Soils (nitrogen
 concentration, soil carbon stocks, litter decomposition): We collected
 organic and mineral soils from four subplots within all 225 m2 plots
 between June and November, 2015. Subplots were randomly located 2.35 m
 right or left of the center point of each side of the plots and 1.5 m from
 plot edges. These locations were selected to avoid interference with
 permanent vegetation plots. In each subplot, we clipped vegetation using a
 50 cm x 50 cm quadrat as a guide, and then removed the organic horizon
 (O-horizon) directly beneath by cutting around the perimeter of a 25 cm x
 25 cm frame with a knife. O-horizons were then carefully separated from
 mineral soils. We included harvest residuals in our samples, but we only
 collected branches that were  1 cm in diameter.  We collected mineral
 soils to a depth of 30 cm in all four subplots after organic layers were
 removed using a bulk density corer containing plastic sleeves with
 internal diameters of 4.8 cm. After removal, we split mineral soil samples
 into increments of 0-15 cm and 15-30 cm. For both O-horizons and mineral
 soils, we composited the four samples collected from each subplot.
 O-horizon and mineral soil samples were dried at 50ºC for 72 hours or
 until a constant mass was achieved. For mineral soils, we also dried
 subsamples at 105ºC for 48 hours to determine moisture content. After
 samples were dried, we noted that there was wide variability in the
 quantity of decay class IV and V buried wood among O-horizon samples. We
 elected to remove buried wood from the samples to reduce plot-level
 variation. We separated O-horizon samples into three components: 1) Oi
 material that was too large to pass through a 2 mm sieve; 2) a combination
 of Oe and Oa material, that did pass through a 2 mm sieve; and 3) buried
 wood. Buried wood was removed from both Oi and Oe/Oa fractions, although
 some fine buried wood could not be removed from the Oe/Oa fraction. Rocks
 were removed and discarded during the sieving process.  Once samples were
 separated into three fractions, we ground Oi material using an electric
 wood chipper to homogenize woody and herbaceous components. Oi subsamples
 were then ground to a fine powder using an IKA grinder. Subsamples of both
 Oe/Oa material and buried wood were pulverized using a Kleco ball mill. We
 analyzed ground samples of all three fractions for C and N concentrations
 using a ThermoFlash 1112 Elemental Analyzer. We sieved dried mineral soils
 into coarse (&gt;2 mm) and fine (&lt;2 mm) fractions, after which
 fine fractions were ground using a roller grinder and analyzed for C and N
 concentrations using a ThermoFlash 1112 Elemental Analyzer. We calculated
 C stocks of all five soil components (Oi, Oe/Oa, buried wood, 0-15 cm
 mineral soils, and 15-30 cm mineral soils) separately. For O-horizon
 components, we used the C concentrations from elemental analysis, dry
 masses, and known areas of our frames to calculate stocks on a Mg/ha
 basis. For mineral soils, we corrected fine fraction masses for moisture
 content to calculate bulk density and then used C concentrations and the
 moisture corrected bulk density values to determine C stocks for 0-15 cm
 and 15-30 cm layers. Total C stocks were calculated by summing organic and
 mineral stocks. We excluded buried wood C stocks from the total C stocks
 reported in the analysis. In spring of 2016, we collected needles from
 current year’s growth on three-year-old Douglas-fir branches from three
 locations in the Oregon Coast Range, all separate from our study sites.
 After collection, we dried needles at 50ºC for 72 hours.  Ten grams of
 needles were then placed in litterbags, which we constructed using 40 um
 polypropylene mesh bottoms, and 3.2 mm polypropylene mesh covers. To
 enable access by macroarthropods, we cut two 7 mm diameter holes in the
 top of each bag. We then attached five bags together using nylon
 contractor’s string. Needles from the three collection sites were not
 homogenized prior to filling litterbags, but with the exception of one
 plot, needles from a given collection site were consistently used in all
 plots in a block. Between late March and early April 2016, we placed one
 set of bags in randomly selected corners of all plots. We then collected
 bags after approximately 3, 6, 12, 18, and 26 months. Here, we only
 present data from the first year, as contamination from litterfall from
 surrounding vegetation likely influenced our measurements of mass
 remaining after the one-year mark. Upon collection, we dried litterbags at
 50ºC for 72 hours, removed any contaminants that had fallen into the bags
 (rocks and detritus that was visually distinct from Douglas-fir needles),
 and then weighed the needles. We calculated daily decomposition constants
 over the course of one year by regressing the natural log of the percent
 mass remaining against time and fixing the y-intercept to ln(100) at time
 0, using the following equation: ln(x_t/x_0 *100)=ln⁡(100)-k(t), where xt
 is the mass (g) of needles at time t (days), x0 is the original needle
 mass, and k is the decomposition constant (Matkins, 2009; Olsen, 1963). We
 calculated and present daily k constants rather than monthly or annual
 constants, as not all litterbags were in the field for the same number of
 days. However, all litterbags within individual blocks were placed and
 collected within one day of each other. We set intercepts to ln(100) given
 that we knew that 100% of the initial mass was remaining when bags were
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description: See the readme file for metadata descriptions.