DOI: 10.5061/dryad.4xgxd25jr
title: Local controls modify the effects of timber harvesting on surface soil carbon and nitrogen dynamics

description: Managing for structural complexity to enhance forest ecosystem health and
 resiliency is increasingly incorporated in silvicultural treatments. High
 spatial variability in stands managed for structural complexity could
 obscure the effects of forest management on surface soils. Yet few studies
 have assessed how within-stand variation in forest structure and other
 local controls influence surface soil organic matter dynamics over time
 following timber harvests. We used a stratified random sampling design to
 capture variation in stand age, legacy structure, soil type, and
 topography in a second-growth, oak-hardwood forest in the northeastern
 U.S. We compared surface soil carbon and nitrogen content and availability
 in 15 harvested stands managed to promote tree regeneration (n = 144
 plots) and five unharvested controls (n = 48 plots). We also examined
 changes over time since harvest in just the harvested stands using a
 22-year chronosequence. Forest management strongly influenced surface soil
 carbon and nitrogen dynamics. The timber harvests had lower soil carbon
 and nitrogen, microbial biomass, and carbon mineralization but higher
 nitrogen mineralization. These differences were more pronounced in the
 drier, less fertile soil type than in more moist, fertile soils. Across
 the 22-year chronosequence, topography, soil type, and downed woody
 material density dictated the direction of changes in surface soil carbon
 and nitrogen over time. Soil carbon and nitrogen accrued over time at
 drier, higher elevation (~300 m) sites and under higher densities of fine
 woody material but declined at lower elevations (~180 m) and under lower
 fine woody material. Proximity to legacy trees was associated with higher
 soil carbon and nitrogen concentrations and availability. Our findings
 underscore the importance of silvicultural practices that retain
 structural legacies and downed woody material in shaping surface soil
 carbon and nitrogen dynamics over time. Our results also highlight how
 accounting for spatial variation in local controls on soil carbon and
 nitrogen, such as topography, can improve detection of changes from forest
 management practices that increase spatial heterogeneity within stands,
 such as irregular shelterwood and seed tree regeneration methods.
description: <em>Sampling design </em> We
 conducted our work at Yale-Myers Forest (41° 57' N, 72°07’ W), a
 3,213-ha mixed hardwood, second-growth forest in Connecticut, USA. We
 stratified our sampling by two soil types, nine timber harvest years, four
 topographic classes, and two distances relative to legacy overstory trees,
 which yielded 144 plots in 15 harvested stands managed through the
 regeneration treatments. We also established 48 plots in five unharvested
 stands following a similar sampling design. To stratify
 sampling by soil type, we first identified all U.S. soil series at
 Yale-Myers Forest. We limited our sampling to oak-hardwood stands, which
 are most commonly managed through shelterwood and seed tree regeneration
 methods. Three soil series collectively accounted for 86% of oak-hardwood
 stands at the Forest: Nipmuck-Brookfield complex (39%), Paxton-Montauk
 fine sandy loams (26%), and Woodbridge fine sandy loams (21%). We excluded
 other soil types since they were not adequately replicated. We aggregated
 the Paxton-Montauk and Woodbridge series since they share the same
 geological features and parent material and differ only in drainage class
 (well-drained versus moderately well-drained, respectively). 
 For each of these soil types, we identified areas of stands from
 nine harvest years that spanned the 1994 to 2016 chronosequence (2 soil
 types ´ 9 timber harvest years = 18 study areas). We preferentially
 selected stands that included areas with each of the two soil types to
 capture within-stand heterogeneity. Within these 18 areas, we used the
 “cluster” function in R (version 4.0.3) with the CLARA K-medoids method to
 run an unsupervised classification on slope, aspect, and elevation raster
 bands from publicly available Digital Elevation Model data to create four
 classes with the most distinct topographic features. We randomly located
 points at least 20 m from the harvest edge for each of the resulting 72
 polygons (2 soil types ´ 9 timber harvest years ´ 4 terrain classes).
 Finally, we established plots 2 m and 10 m from the closest legacy tree
 (≥20 cm diameter at breast height [DBH] at 1.37 m) to stratify sampling by
 legacy overstory structure (Fig. 1). This sampling design yielded 144
 plots in the harvested stands managed to promote tree regeneration (2 soil
 types ´ 9 timber harvest years ´ 4 terrain classes ´ 2 distances relative
 to legacy trees). For the unharvested stands, we chose
 five stands with the same soil series (Nipmuck-Brookfield complex and
 Paxton-Montauk/Woodbridge) that had mean elevation (236–272 m, mean = 251
 m) and slope (slope: 1.24–21.1°, mean = 10.7°) values within the range of
 the plots in the irregular shelterwood/seed tree stands (179–305 m, mean =
 246 m; 0.64–28.9°, mean = 9.9°). Across the five stands, we identified
 three areas with no recorded timber harvests since Yale University
 acquired the property in 1930, two areas that were crown thinned for
 firewood 40+ years ago (1973 and 1979), and two areas that were crown
 thinned for firewood 35-40 years ago (1980 and 1984). We used a similar
 stratified random sampling design as the regeneration treatments to locate
 points within each of the unharvested areas (2 soil types ´ 3
 time-since-thinning classes ´ 4 terrain classes ´ 2 tree distances = 48
 plots). <em>Vegetation and soil
 sampling</em> For vegetation sampling, we
 identified the species and measured DBH of all living trees and shrubs
 ≥5-cm DBH in a 5-m radius fixed area plot and ≥1-cm DBH in a nested 1-m
 radius plot (Fig. 1). We calculated coarse woody material (CWM) volume for
 the portion of each piece of downed wood &gt;10 cm in diameter that
 fell within the 1-m radius plot and tallied all fine woody material (FWM)
 pieces ≥2-cm diameter but less than 10 cm.   We
 collected and pooled eight 2-cm diameter, 10-cm deep soil cores in the 1-m
 radius plots. In plots with low soil bulk density, we collected an
 additional 1–4 cores to ensure we had sufficient sample for the lab
 assays. We recorded the exact depth of each core to calculate bulk
 density. We kept field collected soils in coolers and stored them at 4°C
 for less than 4 weeks prior to analysis. We measured volumetric water
 content (VWC) to a depth of 12 cm at four locations using a HS2 HydroSense
 Soil Moisture Sensor (Campbell Scientific) and measured the depth of the
 Oa horizon at three locations within each 1-m radius plot. We also
 measured soil temperature to a depth of 30 cm using an HI 145 T-Shaped
 Soil Thermometer (Hanna Instruments, Inc.) and recorded the date and time
 of day of the measurement for temperature corrections.
 <em>Laboratory assays</em> We
 weighed field-moist samples for use in calculating soil bulk density and
 homogenized and passed each sample through a 4-mm sieve. We mixed samples
 with water in a 1-to-1 volumetric ratio and measured the pH of the
 supernatant after 10 min using a benchtop meter (VWR sympHony Sb70p). We
 measured gravimetric water content (GWC) by oven-drying field-moist
 samples to constant mass at 105°C. We estimated water holding capacity
 (WHC) by saturating each soil sample and allowing it to drain freely for
 2-h. We estimated SOM content by calculating mass loss on ignition of
 soils heated at 550°C for 12-h in a muffle furnace. We used a ball mill to
 grind air-dried subsamples to a fine powder and analyzed %C and %N with an
 elemental analyzer (Costech ESC 4010, Costech Analytical Technologies
 Inc.Valencia, California, USA). We used a series of
 laboratory assays to assess microbially available C and N, which we
 expected be more responsive to management than C and N concentrations. We
 estimated active microbial biomass with a modified substrate-induced
 respiration (SIR) method that uses autolyzed yeast extract as a labile C
 substrate. We then measured rates of CO<sub>2</sub> efflux
 over a 4-h incubation period with an Infra-Red Gas Analyzer (Li-COR model
 Li-7000, Li-Cor Biosciences, Lincoln, Nebraska, USA). To estimate
 potential C and N mineralization rates, we incubated samples for 30-d at
 20°C under a humid atmosphere and maintained 65% WHC, which is within the
 optimal range for microbial activity (Langenheder & Prosser, 2008;
 Paul et al., 2001). For the C mineralization assay, we calculated
 cumulative C-CO<sub>2</sub> production rates over the 30-d
 incubation period by integrating CO<sub>2</sub> efflux
 measurements sampled over 24-h periods at days 1, 4, 11, 24, and 30. For
 the 30-d net potential N mineralization assay, we shook 6 g
 dry-weight-equivalent of each soil sample with 25 mL 2M KCl, refrigerated
 the samples for 12 h, filtered the samples with Whatman Grade 42 filters,
 and analyzed [NH<sub>4</sub><sup>+</sup>] and
 [NO<sub>3</sub><sup>-</sup>] using a flow analyzer
 (Astoria 2, Astoria‐Pacific, Clackamas, Oregon, USA). We repeated this
 procedure for both the initial measurements and day 30 measurements. Net
 potential N mineralization was calculated as the difference between the
 initial and final sum of
 [NH<sub>4</sub><sup>+</sup>] and
 [NO<sub>3</sub><sup>-</sup>] over the 30-d
 incubation period.
description: # Local controls modify the effects of timber harvesting on surface soil
 carbon and nitrogen dynamics
 [https://doi.org/10.5061/dryad.4xgxd25jr](https://doi.org/10.5061/dryad.4xgxd25jr) This data was collected to assess the effects of timber harvesting on surface soil carbon and nitrogen dynamics in a 22-year time since harvest chronosequence that consisted of stands treated through irregular shelterwood/seed tree silvicultural treatments to promote regeneration as well as unharvested controls. We used a stratified random sampling design to capture variation in stand age, legacy structure, soil type, and topography in a second-growth, oak-hardwood forest in the northeastern U.S. We used this data to compare surface soil carbon and nitrogen content and availability in 15 harvested stands managed to promote tree regeneration (*n* = 144 plots) and five unharvested controls (*n* = 48 plots). We also examined changes over time since harvest in just the harvested stands using a 22-year chronosequence. ## Description of the data and file structure The data in the .csv file include the following variables:  Plot_ID: Unique plot ID, which is a combination of soil type, harvest year, terrain class, and subplot for the 192 shelterwood and 48 reserve plots.  Type: Factor. Cassified as either a shelterwood or reserve. Year_sampled: Year that field data was collected (2019 for all data) Soil_Type: Factor (Abtill, LodgTill). To stratify the sampling across soil types, soils were broadly classified as either ablation till ("Abtill") or basal till ("Lodgtill") based on U.S. soil series. See methods for detailed descriptions of these soil types and how they were assigned. Abtill: Binary. 1 = Ablation till soil type; 0 = Basal till soil type Harvest_Year: Continuous. For the 144 shelterwoods, harvest year is the year each stand was cut using the irregular shelterwood/seed tree regeneration method. For the 48 reference sites, harvest year is the last documented crown thinning of the stand, which fell into three categories (35-40 years ago, 40-50 years ago, 50+ years ago). For the 50+ year old reference stands, there were no known harvests documented during the history of Yale ownership. See methods for additional details.  Terrain_Class: Factor (1, 2, 3, 4). Terrain classes were used to stratify sampling by slope, aspect, and elevation data. Each combination of harvest year/soil type has 4 terrain classes which have the most distinct topographic features within the stand. These terrain classes were assigned using unsupervised classification of slope, aspect, and elevation data. The numbers 1-4 have no meaning (i.e. order) other than to distinguish the four classes within each stand. Subplot: Factor (A, B). Each terrain class has two subplots: A and B. Subplots were used to stratify sampling by proximity to legacy overstory trees where subplot A was always located 2 m from the base of a legacy tree and subplot B was always located 10 m.  Pair: Identifier grouping each of the pairs of subplots (i.e. subplots A and B).  Tree: Binary. 1 = 2m from legacy overstory tree; 0 = 10 m from legacy overstory tree Stand_Name: The name of the forest stand  Years: Continuous. The number of years since harvest, based on the sampling year (2019).  VWC: %; continuous; Volumetric water content, or percent soil moisture on a volumetric basis FWD: pieces m-2; continuous; Tally of fine woody debris pieces &gt;2cm in width but less than 10 cm, expressed as density.  CWD: m3 ha-1; continuous; Volume of coarse woody material &gt;= 10cm in width. Area sampled 3.14m2 BD: g cm-3; continuous; Soil bulk density. Not corrected for roots/stones.  pH: continuous; soil pH in water GWC: %; continuous; gravimetric water content; percent soil moisture on a mass basis WHC: %; continuous; water holding capacity; percent soil moisture at 100% water holding capacity. NA for A11_2B because value was unrealistically low (10%) LOI: continous; %; loss on ignition, which is an estimate of percent soil organic matter Net_N_Min: μg N g-1 day-1; continuous; Net potential nitrogen mineralization; 30 day incubation time SIR: μg CO2 g dry wt soil-1 h-1; continuous; substrate induced respiration, which is a measure of active microbial biomass Cmin: μg g dry wt soil-1 30 days-1; continuous; carbon mineralization over a 30 day incubation period, which is a measure of labile carbon N: %; continuous; nitrogen concentration  C: %; continuous; carbon concentration  CN: carbon:nitrogen ration elevation: feet; continuous; derived from publicly available digital elevation model data: [http://www.cteco.uconn.edu/data/download/flight2016/index.htm](http://www.cteco.uconn.edu/data/download/flight2016/index.htm) aspect: degrees; continuous; derived from publicly available digital elevation model data: [http://www.cteco.uconn.edu/data/download/flight2016/index.htm](http://www.cteco.uconn.edu/data/download/flight2016/index.htm) slope: degrees; continuous; derived from publicly available digital elevation model data: [http://www.cteco.uconn.edu/data/download/flight2016/index.htm](http://www.cteco.uconn.edu/data/download/flight2016/index.htm) BA: m2 ha-1; continuous; Total basal area of all trees &gt;=5cm DBH in a 5-m radius plot and &gt;=1cm and &lt;5cm in a 1-m radius plot on a per hectare basis Soil_Temp_Corr: degrees celsius; continuous; soil temperature, corrected for time of day and day sampled (see methods) ## Sharing/Access information NA ## Code/Software The included R script has the code for all analyses (R version 4.4.0).  Loaded packages are indicated in the beginning of the script.