DOI: 10.5061/dryad.j9kd51cpg
title: Plant nitrogen demand decouples net mineralization and nitrification in disturbed forests

description: Nitrification is a key process in the global nitrogen cycle, indicative of
 both soil nitrogen availability and potential for nitrogen losses that
 cause environmental degradation. Heterotrophic soil microbes and plants
 compete with nitrifiers for ammonium, thereby influencing the fraction of
 mineralized nitrogen converted to nitrate. Microbially available carbon
 constrains heterotrophic nitrogen demand and therefore regulates the
 coupling of net nitrogen mineralization and nitrification rates. Whether
 soil carbon availability remains a central control on the coupling of
 these processes in disturbed ecosystems with reduced plant nitrogen demand
 remains relatively unexplored. Using a series of partially disturbed
 forests that vary in aboveground biomass and soil carbon availability, we
 test the relative influence of plant and heterotrophic nitrogen demand on
 the relationship between net nitrogen mineralization and nitrification. We
 analyzed differences between harvested and unharvested stands, changes
 over time since harvest, and the effects of legacy overstory trees within
 harvested stands. Higher levels of canopy disturbance consistently
 strengthened the positive relationship between net nitrogen mineralization
 and nitrification rates. However, reduced plant biomass, rather than
 microbially available carbon, mediated the coupling of these processes in
 partially disturbed forest stands. Our findings emphasize the importance
 of assessing both the effects of plant and heterotrophic nitrogen demand
 on the coupling of nitrogen mineralization and nitrification rates
 following forest disturbances. These results have important implications
 for understanding coupled nitrogen cycling processes in ecosystems
 globally, which are increasingly experiencing disturbances that partially
 reduce aboveground biomass, such as drought and species invasions.
description: <em>Study area </em> We conducted
 our work at Yale-Myers Forest, a mixed hardwood, second-growth forest in
 the northeastern USA (41° 57' N, 72°07’ W). Yale-Myers Forest is an
 actively managed research and demonstration forest with annual timber
 harvests that can be used to study changes over time since harvest through
 a chronosequence approach. We selected 15 oak-hardwood stands with similar
 silvicultural treatments that were harvested from 1994 through 2016 as
 well as five unharvested controls that had comparable characteristics to
 the harvested stands and hence were all eligible for the same
 silvicultural treatment. Each of the 15 harvested stands were managed to
 promote the regeneration of shade-intolerant tree species, such as oaks
 (<em>Quercus</em> spp.), through the creation of large canopy
 openings interspersed with legacy overstory trees at a spacing of 35 – 80
 m. <em>Sampling design</em>
 To capture within-site variation in microbially available C and
 aboveground plant biomass, we stratified our sampling by 2 soil types, 9
 harvest years, 4 topographic classes, and 2 distances relative to legacy
 overstory trees. This sampling design yielded 144 plots arrayed across the
 15 harvested stands. The soil types included the Nipmuck-Brookfield
 complex, which consists of well-drained upland ablation tills derived from
 schist, granite, and gneiss, and the Paxton-Montauk and Woodbridge series,
 which are mesic, fertile basal tills of drumlins. For each of these soil
 types, we identified nine harvest years from 1994 to 2016. To stratify
 sampling by topography, we created four topographic classes with the most
 distinct features by running an unsupervised classification on slope,
 aspect, and elevation data using the “cluster” function in R with the
 CLARA K-medoids method. We then randomly located points ≥20 m from the
 harvest edge for each of these areas and established plots 2 m and 10 m
 from the nearest legacy overstory tree ≥20 cm diameter at breast height
 (DBH; 1.37 m) to stratify sampling by residual tree basal area. We used a
 similar design to locate plots within each of five unharvested stands (2
 soil types ´ 3 time-since-thinning classes ´ 4 terrain classes ´ 2 tree
 distances), which yielded 48 plots in the unharvested stands. 
 <em>Vegetation and soil sampling </em>
 We sampled vegetation and soils in May through June 2019. 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 and calculated total basal area (m<sup>2</sup>
 ha<sup>-1</sup>). We chose to use 5-m radius plots to capture
 vegetation conditions in the immediate vicinity of the soil sampling
 location where overstory trees were most likely to influence N
 mineralization and nitrification rates. Within 1 m of each plot center, we
 collected and pooled eight 2-cm diameter, 10-cm deep soil cores. In sites
 with a thick Oa horizon and low bulk density soil, we collected an
 additional 1-4 cores to ensure we had sufficient sample mass for the
 laboratory assays. We kept field collected soils in coolers and stored
 them at 4°C for less than 4 weeks prior to analysis.
 <em>Laboratory assays</em> We used
 laboratory assays to estimate microbially available C and net N
 mineralization and nitrification rates using field-moist soil samples
 adjusted to 65% water holding capacity. We first homogenized each sample
 and passed it through a 4-mm sieve. We estimated water holding capacity by
 saturating field-moist samples with water and allowing them to drain
 freely for 2 h. We measured gravimetric water content by oven-drying the
 samples to constant mass at 105°C. For microbially
 available C, we used 6 g equivalent dry mass of each sample to calculate
 cumulative C-CO<sub>2</sub> production rates over a 30-d
 incubation period. We integrated CO<sub>2</sub> efflux
 measurements sampled over 24-h periods with an Infra-Red Gas Analyzer
 (Li-COR model Li-7000, Li-Cor Biosciences, Lincoln, Nebraska, USA) at days
 1, 4, 11, 24, and 30. During the incubation period, samples were
 maintained at 65% water holding capacity and 20°C under a humid
 atmosphere. For net potential N mineralization and nitrification rates, we
 estimated NH<sub>4</sub><sup>+</sup> and
 NO<sub>3</sub><sup>-</sup> production over a 30-d
 incubation period by calculating the difference between initial and
 incubated samples. For the incubated samples, we stored 6 g dry weight
 equivalent of each sample at 20°C and 65% water holding capacity for 30-d
 under a humid atmosphere. For the initial and incubated
 NH<sub>4</sub><sup>+</sup> and
 NO<sub>3</sub><sup>-</sup> extractions, we shook
 each sample vigorously for 30 s with 25 mL 2M KCl, refrigerated the
 samples for 12 h, and filtered the samples with Whatman Grade 42 filters.
 We analyzed NH<sub>4</sub><sup>+</sup> and
 NO<sub>3</sub><sup>-</sup> concentrations using a
 flow analyzer (Astoria 2, Astoria‐Pacific, Clackamas, Oregon, USA). Net
 potential nitrification was calculated as the difference in
 NO<sub>3</sub><sup>-</sup> concentrations between
 the incubated and initial samples and net potential N mineralization as
 the difference between the initial and final sum of
 NH<sub>4</sub><sup>+</sup> and
 NO<sub>3</sub><sup>-</sup> concentrations over the
 30-d incubation period.
description: # Plant nitrogen demand decouples net mineralization and nitrification in
 disturbed forests
 [https://doi.org/10.5061/dryad.j9kd51cpg](https://doi.org/10.5061/dryad.j9kd51cpg) ## Description of the data and file structure This data was collected to assess the effects of changes in aboveground biomass and soil carbon availability following partial forest disturbances from timber harvesting on the coupling of net nitrogen mineralization and nitrification rates. 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 the coupling of net nitrogen mineralization and nitrification in 15 harvested stands managed to promote tree regeneration (*n* = 144 plots) and five unharvested controls (*n* = 48 plots) and over time since harvest in the harvested stands only (*n* = 144). Using the data from the harvested stands (n = 144), we also analyzed soil carbon availability and aboveground biomass as mediators of the coupling of net nitrogen and mineralization rates to understand the relative influence of plant versus heterotrophic microbial nitrogen demand on N cycling processes.  ### Files and variables #### File: Ward_et_al._data_Biogeochemistry_submission.csv **Description:**  ##### Variables * Plot_ID: Unique plot ID, which is a combination of soil type, harvest year, terrain class, and subplot for the 144 harvested and 48 unharvested plots.  * Type: Factor. Cassified as either a shelterwood (i.e. harvested) or reserve (i.e. unharvested). * 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 harvested plots, harvest year is the year each stand was cut. 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, which was used as a random effect in all linear mixed effects models.  * Years: Continuous. The number of years since harvest, based on the sampling year (2019).  * Net_N_Min: μg N g-1 day-1; continuous; Net potential nitrogen mineralization, which is net NH4+ and NO3- production over 30 days using laboratory incubations that controlled for temperature and moisture * Net_Nitr: μg N g-1 day-1; continuous; Net potential nitrification, which is net NO3- production over 30 days using laboratory incubations that controlled for temperature and moisture * Cmin: μg g dry wt soil-1 30 days-1; continuous; carbon mineralization over a 30 day incubation period, which is a measure of microbially available carbon * 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 ## Code/software The included R script: Ward_et_al._data_Biogeochem_script has the code for all analyses (R version 4.4.0). This R script analyzes how plant nitrogen demand influences net mineralization and nitrification in disturbed forests. Loaded packages are indicated in the beginning of the script.  ## Access information Other publicly accessible locations of the data: * A subset of this data (not including nitrification rates) is also available at: [https://doi.org/10.5061/dryad.4xgxd25jr](https://doi.org/10.5061/dryad.4xgxd25jr)