Next Article in Journal
Digital Approach to Successful Business Plans in Forestry and Related Fields
Previous Article in Journal
Characterization of Vegetation Dynamics on Linear Features Using Airborne Laser Scanning and Ensemble Learning
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 3
10.3390/f14030512
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Nitrate and Ammonium Deposition in the Midwestern Fragmented Forest

by
Luis D. Rivera-Cubero
*,
Asia L. Dowtin
and
David E. Rothstein
Department of Forestry, Michigan State University, East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
Forests 2023, 14(3), 512; https://doi.org/10.3390/f14030512
Submission received: 20 January 2023 / Revised: 25 February 2023 / Accepted: 2 March 2023 / Published: 5 March 2023
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Review Reports Versions Notes

Abstract

:
Whereas the impacts of N deposition on forest ecosystems have been well studied in remote areas in predominantly forested landscapes, we know relatively less about the impacts of N deposition on forests in heavily human-modified landscapes. We studied the influence of adjacent land use, local point sources, and woodlot stand structure on subcanopy N transport and enrichment via throughfall in three woodlot fragments in southern Lower Michigan, USA. We found that one site had markedly higher TF N concentrations compared to the other two; however, our data indicate that elevated TF concentrations resulted from differences in tree species composition, rather than differences in surrounding land use. Specifically, we observed that the local abundance of basswood (Tilia americana) was positively associated and the local abundance of northern red oak (Quercus rubra) was negatively associated with TF N concentrations. One site had markedly greater TF N fluxes compared to the other two, which was driven by a lack of understory vegetation, possibly due to higher deer browsing at this site. Together, the results of this study demonstrated that TF N concentrations and fluxes were more strongly influenced by the internal characteristics of fragmented woodlots, such as forest structure and species composition, than by the surrounding land use.

1. Introduction

Anthropogenic nitrogen (N) emissions associated with motor vehicles, power plants, agriculture, and other human activities have resulted in elevated rates of N deposition to forest ecosystems on a global scale [1,2,3]. The effects of anthropogenic N deposition on forests can include changes in the composition of plant communities, disruptions in nutrient cycling, increased emission into the atmosphere of the greenhouse gas nitrous oxide (N2O), accumulation of N compounds in the soil, increased availability of N to primary producers, soil acidification, and increased susceptibility of plants to other stress factors [4,5,6,7,8]. Whereas the impacts of N deposition on forest ecosystems have been well studied in remote areas in predominantly forested landscapes, we know relatively less about the impacts of N deposition on forests in heavily human-modified landscapes. Unique considerations in these landscapes include forest fragmentation, resulting edge-interior gradients, and the importance of local sources of N emissions.
Forests of the eastern United States are highly fragmented due to roads, suburban development, and agriculture [9,10]. Forests of the Southern Great Lakes Ecoregion (southern Michigan, northern Indiana, and northern Ohio) are the most fragmented in the country, with most forest lands consisting of remnant patches surrounded by urban land, suburban development, or agriculture [9]. Fragmented forests have a greater proportion of “edge” habitat that is influenced by adjacent land uses and lower proportions of interior habitats that are less affected by surrounding land uses [11]. Another potential impact of fragmentation is that forest fragments may receive additional N deposition from local sources. In agricultural landscapes, adjacent croplands can heavily impact forest fragments through fertilizer drift. Honnay et al. [12] found higher levels of ammonium (NH4+) in the edge habitat compared to the interior habitat in five Belgian forest fragments and attributed this to an influx of agricultural fertilizers from adjacent croplands. Livestock is also a major source of volatile ammonia (NH3) in the atmosphere across the globe [13]. When NH3 is emitted into the atmosphere, it can be converted to NH4+ aerosols and washed out by rain or by dry deposition. Ammonia can then be deposited within a few kilometers of the source due to its short atmospheric lifetime [14]. Thus, forests near livestock farms can experience very high levels of N deposition, leading to negative effects on the ecosystem [15]. Automobiles can also be major sources of atmospheric NH3 since the introduction of catalytic converters, causing roadways and adjacent vegetative surfaces to serve as hotspots for N deposition in urban and suburban areas [16,17].
These numerous forms of land use in agricultural and urban areas lead to N deposition in forest fragments adjacent to or within proximity to the emissions sources. Once deposited in these fragmented forests, N in its various forms can be subsequently distributed below the canopy by several mechanisms, including throughfall (i.e., precipitation that interacts with foliar and woody surfaces in the canopy or passes through canopy gaps en route to the forest floor) [18,19,20]. Throughfall chemistry is often enriched relative to that of open precipitation and may vary spatially within a fragmented forest based on species composition and stand structure, among other factors [21,22]. This spatial variability in throughfall chemistry impacts nutrient delivery to forest soils, creating the potential for biogeochemical hotspots. Rainfall volume and the length of a dry period [23] have also been shown to have an impact on throughfall chemistry and the contribution of deposition and exchange processes of deposition under the forest.
While these and other studies have helped elucidate our understanding of subcanopy N flux via throughfall, they have predominantly focused on rural forests. As higher proportions of forested lands are fragmented to accommodate expansive development, there remains a need to understand how these processes behave in highly developed areas. To determine the relative impacts of adjacent land use, local point sources, and woodlot stand structure on subcanopy N transport and enrichment via throughfall, we based our study on three woodlots in southern Lower Michigan. Within each woodlot, we monitored N deposition in throughfall along the edge to interior gradients to test the following hypotheses:
  • N deposition to forest fragments, as evidenced by throughfall N composition, will vary by adjacent land use.
  • Nitrogen enrichment of throughfall is highest at the edge and decreases with distance from the edge.
  • Spatial variability in throughfall N enrichment is impacted by species composition.
  • Throughfall N concentrations, flux, and enrichment ratios will be higher for storm events with a longer antecedent dry period.
  • Higher rainfall intensity will result in higher throughfall N concentration, flux, and enrichment ratios.

2. Materials and Methods

2.1. Study Area and Experimental Sites

We conducted this study in East Lansing, Michigan, USA (42.7370° N, 84.4839° W). East Lansing has a population of 47,741 [24] and is within the larger Lansing capital metro area. The city is 226 m above sea level and has a cold and temperate climate (Köppen–Geiger climate classification Dfa). East Lansing is in the USDA Hardiness Zone 5. The mean annual rainfall for the region is 949 mm. September is the wettest month in the region (µprecipitation = 88.9 mm), while February is the driest month (µprecipitation = 48.3 mm). The average annual temperature is 9.4 °C in East Lansing, with July being the warmest month of the year (µtemperature = 21.3 °C) and January typically the coldest month (µtemperature = −6.2 °C).
We conducted this study in three woodlots on the East Lansing campus of Michigan State University (MSU), all of which are within a fragmented landscape with a mix of urban, residential, and agricultural land use. We selected the three woodlots (Biebersheimer, Hudson, and Lott North) from a pool of 25 MSU Campus Natural Areas to be as similar as possible in their structure and species composition, but to vary in terms of landscape context relative to local sources of N emissions. All three woodlots were mature, mesic forests dominated by sugar maple (Acer saccharum Marsh.), with northern red oak (Quercus rubra Lobatae.), American beech (Fagus grandifolia Ehrh), and basswood (Tilia americana Linden) as the next most dominant to varying degrees (Table 1). Whereas the three woodlots were similar in species composition and stand structure, they were selected to vary in their location relative to landscape features likely to serve as local sources of N deposition (Figure 1). Biebersheimer woodlot is in one corner of a “cloverleaf” interchange of two interstate highways and is hereon referred to as the highway site. Lott North has varied surrounding landscape features, including agricultural fields (corn–soybean rotation), other woodlots, and a section of interstate highway. It is hereon referred to as the mixed land use site. Hudson is surrounded by agricultural fields (corn–soybean rotation), plus MSU’s Dairy Cattle Facility is located 750 m due west of the edge of this woodlot. Hudson is hereon referenced as the agricultural site.

2.2. Sampling Design

At each experimental site, three transects were established perpendicular to the western forest edge, since the prevailing weather systems move from west to east. Along each transect, eight throughfall collectors were placed at distances of 0, 10, 20, 30, 40, 50, 75, and 100 m from the windward edge, for a total of 24 throughfall collectors within each site and 1 located outside the woodlot for open precipitation. Outside of each woodlot, a single collector was placed in an adjacent open field to measure and collect samples of open precipitation. Throughfall collectors consisted of 219 cm2 high-density polyethylene (HDPE) funnels that were each attached to 3.78L HDPE containers. Collectors were suspended on posts ca. 1 m above ground to prevent interference by wild animals and rain droplets from splashing back into the collectors. Inside each funnel, we placed a small piece of glass wool to prevent any debris from contaminating samples. The glass wool was replaced periodically throughout the study.

2.3. Sample Collection and Analysis

Sample collection was conducted on an event basis; we visited the sites and emptied collectors within 48 h after each storm event. For this study, we defined an event as a storm that produced a minimum of 1 cm of precipitation, followed by a dry period of 12 h or more. We conducted sampling between 23 May and 4 October 2019. To identify storm events during our study period, we utilized precipitation data from the MSU Enviroweather MSU Hort weather station (42.634° N, 84. 4870° S, https://enviroweather.msu.edu, accessed on 20 January 2023). This weather station is located near all our sites (0.6 km from the highway site, 0.8 km from the mixed land use site, and 3.2 km from the agricultural site). Actual sampling dates, precipitation amounts, and antecedent dry periods are provided in Table 2.
At each collection date, we made volumetric measurements for throughfall for each collector. We also collected a 50 mL subsample of throughfall, storing these samples in clean HDPE vials for later laboratory analysis. Collectively, throughfall volume and hydrochemical data were used to determine the total throughfall depth and solute flux, respectively. To analyze our samples, we conducted colorimetric analyses for NH4+ concentration according to [25] and for NO3− according to [26]. Throughfall fluxes (TF) of NO3− and NH4+, as well as total inorganic N, were determined using Equation (1):
TF Flux = Volume (L) × Concentration (mg L−1)/Funnel Area (m2)
For throughfall, we also calculated the flux-based enrichment ratio, a metric used to quantify the degree to which the chemical composition of precipitation (e.g., throughfall) is modified due to its interaction with foliar and woody components of the canopy [27,28,29]. Flux-based enrichment ratios for our study were calculated following [22,24,25,26,27,28,29]:
Et = (CtT)/(CpPg)
where Et is the flux-based enrichment ratio of throughfall (unitless), Ct is the solute concentration in throughfall (mg L−1), T is the depth equivalent of throughfall (cm), Cp is the solute concentration of open collector rainfall (mg L−1), and Pg is the depth equivalent of open collector rainfall (cm). The enrichment ratio determines the degree to which the forest canopy serves as a source or a sink for NH4+ and NO3−. While all enrichment ratio values are positive, Et observations > 1 indicate a net contribution of N from the canopy to throughfall, whereas Et values < 1 indicate net uptake of N by the canopy from the incident precipitation.

2.4. Tree Species Influence

To quantify the potential influence of local variability in tree species composition within woodlots on throughfall concentrations, fluxes, and enrichment ratios, we measured the size and species of trees surrounding each collector. We used circular plots (8 m), centered on each collector, and measured the distance and diameter at breast height (dbh; 1.37 m) of every overstory tree (≥10 cm dbh) within an 8 m radius of the collector and measured the distance and dbh for every understory tree (<10 cm dbh) within a 3 m radius of the collector. For the three edge collectors (0 m on transect) at each woodlot, we sampled half-circles on the interior side. To assess the local influence of different species on each collector, we adapted the simple competition index of [30] as an “influence index”, as indicated in Equation (3):
Di/Disti
where Di = dbh (cm) of tree i and Disti = distance (m) from the collector to tree i. This index assigns greater influence to trees that are larger and closer to the collector and a smaller influence to trees that are smaller and more distant. Sugar maple, American beech, northern red oak, and basswood were the only species that occurred frequently enough for analysis.

2.5. Statistical Analysis

We used Kruskal–Wallis tests to determine differences among the three woodlots by hypothesis 1 using GraphPad Prism 9.3.1. Statistical outliers were identified by the ROUT test (Q = 0.1%) and removed [31]. For hypotheses 2 and 3, we analyzed throughfall concentrations, fluxes, and enrichment ratios using a mixed effects model, with date treated as a random effect. The following terms were treated as fixed effects: Site, Distance, Site x Distance interaction, Maple Influence, Basswood Influence, Beech Influence, and Red Oak Influence. The mixed-effects model was conducted with RStudio version 1.2.5033. The following packages were used for running the linear models with random effects: lme4, lmerTest, and the ggpairs functions for the graphs with GGally and ggplot2. The significance level was set at (p < 0.05)
We also investigated the influence of abiotic and biotic factors on the throughfall concentration, fluxes, and enrichment ratio to evaluate hypotheses 4 and 5. Accordingly, for the model, the following terms were treated as fixed effects: site, antecedent dry period, and rainfall intensity, with date treated as a random effect. The antecedent dry period was defined as the amount of time (days) that had elapsed between the end of one storm event and the beginning of a new one. Rainfall intensity was calculated by dividing precipitation depth by storm duration, as outlined in Equation (4):
Rainfall intensity = Precipitation depth (cm)/duration (hrs)

3. Results

3.1. Patterns in Throughfall Concentration, Fluxes, and Enrichment Ratios among Sites

The mixed land use had the highest volumes of TF due to lower canopy interception; across the study period, TF volumes averaged 53.4% of the gross precipitation at the agricultural site, 60.7% at the highway site, and 87.5% at the mixed land use site. Across all sampling dates, the throughfall NO3− concentrations were highest at agricultural and highway and lowest at mixed land use (Figure 2). The agricultural throughfall NO3− concentrations were significantly higher than those at both mixed land use (p < 0.0001) and highway (p < 0.001; Figure 2A). The highway NO3− concentrations trended higher than those at mixed land use, though this difference was not statistically significant (p = 0.065; Figure 2A).
However, the NO3− fluxes at the mixed land use site were significantly higher than those at the highway site (p < 0.0001; Figure 2C). The nitrate enrichment ratios were highest at the agricultural site compared to both the highway (p < 0.001) and mixed land use (p < 0.001; Figure 2E) sites. There was not a significant difference in the NO3− enrichment ratios between the highway and mixed landscape (p = 0.498; Figure 2E). While the enrichment ratio ranges did vary considerably in the agricultural (0.40–6.75), highway (0.1–4.02), and mixed land use (0.01–4.00) sites, the median enrichment ratio for NO3− was less than 1.0 for all the sites, indicating that the tree canopies at all 3 sites tended to serve as a sink, rather than a source for incoming NO3−. The median enrichment ratios were much lower for highway (0.15) and mixed land use (0.08), compared to agricultural (0.68), implying that the forest canopy at agricultural was a notably weaker sink for NO3−.
The concentration of NH4+ in throughfall was higher at the agricultural site (Figure 2) when compared to the mixed land use and highway sites with (p = 0.0002). The fluxes of NH4+ in the throughfall followed were higher for the mixed landscape site when compared to the agricultural and highway sites (p < 0.0001; Figure 2D). In our observations, we found that there was no difference in the NH4+ fluxes for the agricultural and highway sites (p > 0.999; Figure 2D). The NH4+ enrichment ratios were higher in the agricultural site compared to the highway (p < 0.0001) and mixed landscape sites (p < 0.0001; Figure 2F), with median values of 0.16 at the agricultural, 0.06 at the highway, and 0.08 at the mixed landscape sites. There was no statistically significant difference in the NH4+ enrichment ratios between the highway and mixed landscape sites (p > 0.999; Figure 2F). The ammonium fluxes and concentrations were higher in the open collectors than those found within the interior of the woodlots, indicating a net removal of NH4+ by forest canopies at all three sites.
For the open precipitation collectors, the median for the NO3− concentrations was 0.05 mg L−1 for all three sites, whereas the median NH4+ concentration in the open precipitation was comparable at 0.08 mg L−1 at the agricultural, 0.20 mg L−1 at the highway, and 0.12 mg L−1 at the mixed landscape sites. For the open precipitation, there was no significant relationship for the NO3− concentrations in the agricultural (p = 0.64), highway (p = 0.8204), and mixed land use (p = 0.95) sites, and for the NO3− fluxes agricultural (p = 0.57), the highway (p = 0.35) and mixed land use (p = 0.92) sites. There was no significant difference in open precipitation NH4+ concentrations among the three sites, (p > 0.05, at all sites), nor were there significant differences in NH4+ fluxes among the three sites (p > 0.05).

3.2. Spatial Patterns in Throughfall N within Woodlots

Within the study woodlots, the throughfall concentration, flux, and enrichment ratios of NO3− were influenced both by the position relative to the edge and by the local tree species composition around each collection point. At the agricultural site, the distance from the woodlot edge had a significant effect (p = 0.002) on the NO3− concentrations in the throughfall, with higher concentrations near the edge and lower concentrations in the interior, but these patterns were not observed for the mixed land use and agricultural sites (Figure 3). We found that there was no significant effect of distance from the woodlot edge on the NO3− flux (p = 0.20) or NO3− enrichment ratio (p = 0.73).
Local tree species composition had a significant effect on the NO3− concentrations in the throughfall, with NO3− concentrations increasing with increasing basswood influence (p < 0.001) and decreasing with increasing red oak influence (p = 0.03; Figure 4). It should be noted that basswood was present in all three woodlots; however, we did not encounter any red oak at the agricultural woodlot (Table 1). No other tree species had a significant effect on the NO3− concentrations, nor were there any significant interactions among species. The throughfall fluxes of NO3− were influenced by basswood (p = 0.007) and slightly by red oak (p = 0.082), with higher fluxes associated with greater basswood influence and lower fluxes associated with higher red oak influence. There were no effects of tree species’ local abundance on the NO3− enrichment ratio.
For the study, we found that there was no significant effect of distance from the woodlot edge on the NH4+ concentrations (p = 0.22), NH4+ flux (p = 0.67), or NH4+ enrichment ratio (p = 0.55). For the NH4+ concentration analysis, the site (p < 0.001) and sugar maple abundance (p = 0.021, Figure 4) were the only significant effects. Sugar maple abundance was associated with higher concentrations of NH4+ in throughfall. Sugar maple was the most dominant and widely distributed species across all three of our woodlots, with a sugar maple tree occurring within 8 m of 66 of the 72 (92%) collectors deployed within the woodlots, with most collectors having multiple sugar maple trees within 8 m. In contrast, other tree species were much less abundant and generally occurred as scattered individuals. Overall, only 20 of 72 collectors (28%) had a basswood tree within 8 m, and only 15 of 72 (21%) had a red oak within 8 m. No other tree species had a significant effect on the NH4+ concentrations, flux, and enrichment ratio, nor were there any significant interactions among species. The enrichment ratio analysis for NH4+ was found to be significant for only one site (p < 0.001). There were no effects of tree species’ local abundance on the NH4+ enrichment ratio.

3.3. Temporal Variability in Throughfall N

We investigated drivers of the temporal variability of the throughfall chemistry by assessing the relative impacts of rainfall intensity and antecedent dry periods. A detailed description of the rainfall patterns during the study period is presented in Table 2. Rainfall intensity had no apparent effect on NO3− concentration (p = 0.69), NO3− flux (p = 0.72), or enrichment ratio (p = 0.963). Similarly, there were no effects of rainfall intensity on NH4+ concentration (p = 0.72), NH4+ flux (p = 0.77), and NH4+ enrichment ratio (p = 0.61). For the antecedent dry period, there was no significant relationship found with NO3− concentration (p = 0.62), NO3− flux (p = 0.77), or enrichment ratio (p = 0.31). Similarly, there was no significant relationship between the length of the antecedent dry period and NH4+ concentration (p = 0.56), NH4+ flux (p = 0.58), or NH4+ enrichment ratio (p = 0.59).

4. Discussion

We hypothesized that N deposition to forest fragments would vary with adjacent land use and landscape context. In apparent support of this, we found that the agricultural woodlot, our site most strongly associated with row-crop agriculture and livestock operations, had the highest TF concentrations of NO3− and NH4+. However, examination of the net fluxes and enrichment ratios of NO3− and NH4+ suggests that internal characteristics of the woodlots—species composition and forest structure—may be additional drivers of TF N dynamics that are more important than the surrounding landscape context. Throughfall concentrations of NO3− and NH4+ were consistently higher at agricultural compared to the highway and mixed land use site; however, these were associated with consistently higher enrichment ratios at the agricultural site and not higher N concentrations in incoming precipitation (Figure 2) as would be expected if adjacent agricultural sources were driving this pattern. This suggests that the higher TF NO3− and NH4+ concentrations at agricultural woodlots were due to an effect of the forest canopy rather than an effect of the surrounding landscape. Individual values of enrichment ratios at agricultural site spanned a broad range but were consistently higher than at the other woodlots. For example, enrichment ratios for NO3− ranged between 0.40 and 6.75 (median = 0.68) at the agricultural, 0.01 to 4.02 at highway (median = 0.15), and 0.01 to 4.00 at mixed land use (median = 0.08), indicating that the forest canopy at the agricultural consistently allowed a greater proportion of incoming NO3− to pass through in throughfall, and even contributed foliar NO3− to incoming precipitation to a greater degree than the other woodlots. Thus, differences in throughfall NO3− concentrations among the sites appear to be attributable to differences in rainwater interactions with the forest canopy. These interactions could include differences in foliar N uptake, foliar N leaching, dry deposition, and subsequent wash-off, or some combination of all three [32,33,34,35].
Our model indicated that basswood abundance was positively associated, and northern red oak negatively associated, with TF NO3− concentrations and fluxes. Basswood was abundant at the agricultural woodlot, and red oak was absent (Table 1), suggesting that the higher TF N concentrations at agricultural woodlot may result from differences in tree species composition and abundance. This finding is likely attributable to differences in leaf chemistry, morphology, and physiology among the three most dominant tree species across our woodlots: sugar maple, basswood, and northern red oak. Basswood has twice the total leaf N concentration and 30 times higher leaf nitrate reductase activity than red oak and sugar maple [36]. Similarly, [37] documented that oaks typically have low foliar nitrate reductase activity levels. Nitrate reductase is the enzyme that catalyzes the first step in the process of NO3− assimilation, therefore very high leaf nitrate reductase activity in basswood indicates the presence of N in the form of NO3− in basswood leaves [38], which would be susceptible to leaching through contact with incoming precipitation. In contrast, low leaf nitrate reductase activity in northern red oak and sugar maple, suggests low levels of foliar NO3− susceptible to leaching.
In addition to higher leaf N and NO3− concentrations, basswood, and sugar maple also tend to have thinner leaves, with less of a waxy cuticle. Oaks, tend to have smaller crowns and waxier leaf surfaces that help shed intercepted rainfall [26]. With oaks having thicker, waxier, less permeable leaves [37], they are less likely to leach internal nutrients to incoming precipitation and less likely to collect dry deposition in dry periods between precipitation events. Therefore, these patterns suggest that high N fluxes at agricultural woodlot can be attributed to species composition rather than its surrounding land use. These findings aid in our understanding of how species composition might influence solute fluxes in the canopy by way of throughfall.
Another noteworthy difference among our woodlots was that the mixed land use consistently had the lowest concentrations of NO3− and NH4 in TF yet had TF fluxes of NO3− and NH4+ that were equivalent to or higher than the other two woodlots (Figure 2). To have equivalent or higher fluxes with lower concentrations TF volumes must be higher. Indeed, this is what we observed with 87% of gross precipitation passing through the canopy as TF at the mixed land use, compared to 50%–60% at the agricultural and highway sites. There is unlikely to be any difference in the amount of incoming precipitation among the three sites because they are all so close together. Differences in overstory structure do not appear to explain the higher TF volumes at the mixed land use because basal area and tree density are all very similar among the woodlots (Table 1). In contrast, the understory structure and composition were markedly different at the mixed land use compared to the other two woodlots.
The mixed land use site had only 368 stems per hectare of understory trees compared to 988 and 1449 for highway and agricultural woodlots. In addition, understory trees were much smaller at mixed land use with a mean diameter of 3.4 cm compared to 4.15 cm and 5.12 cm at the agricultural and highway. Thus, it appears likely that a lack of interception by the understory leaf area at the mixed land use site explains the higher TF volumes and N flux at this site.
Comparing our data on solute concentrations, fluxes, and enrichment ratios to other published studies indicates that our observations generally fall within the range of those found in the literature. In an oak–hickory forest, NO3− concentrations ranged from (0.10–0.25 mg L−1) which overlaps well with the range of our observed values (0.14–0.26 mg L−1) [22]. In contrast, the oak–hickory forest in the southern United States found that TF NH4+ concentrations averaged (0.53.0 mg L−1) depending on the species studied [22]. Our study observed a range of TF NH4+ concentrations from 0.56 to 1 mg L−1. The highest NH4+ values were associated with two species (Quercus shumardii and Quercus stellata), whereas NH4+ concentrations associated with other oak and hickory species were comparable to our results [22].

4.1. Edge Effect

In contrast to other studies, we observed little sign of edge effects on throughfall N in our woodlots. We observed a significant effect of distance from the woodlot edge for NO3− concentrations, and fluxes at one of our three sites (Agricultural; Figure 3), and we found no significant effect of edge-to-interior distance on NH4+ in TF at any of our sites. For NO3− at the agricultural, the magnitude of the edge-to-interior effect was comparable to what has been observed in other studies. At the agricultural woodlot, we observed an approximately 50% decline in NO3− concentrations from the forest edge to the interior, and a 40% decline in TF fluxes of NO3−. In comparison, there was a 55% decline in fluxes of NO3− and NH4+ along a 60 m transect from the edge to the interior of a pine forest in southern Sweden [39]. Similarly, there was a 60% decline in TF fluxes of NO3− along a longer 800 m transect from the edge to the interior of 6 spruce forests and 1 mixed spruce–beech in Southern Bavaria [40]. It is unclear why we observed a distinct edge effect at the agricultural site and not at any of the other sites. Many factors can influence the penetration depth of TF deposition from the edge, including the ion under consideration [40,41,42]; meteorological conditions, stand density [43,44,45], such as wind speed and direction [46,47]; and edge structural features, such as the leaf area index and of these factors, one that is suggested by our data is the differences in vegetation structure at the forest edge among our woodlots. Our stand inventories for the three sites indicated that while there were no significant differences in the density of overstory trees along the woodlot boundaries (i.e., trees located between 0–3 m from the edge), the density of understory vegetation along the boundary was higher at the agricultural site compared to the two other sites. To confirm the difference in vegetation structure, we looked at the basal area (BA) and trees per hectare (TPH) of the three sites (Table 3).

4.2. Temporal Variability

When evaluating the throughfall solute concentration and flux, we investigated the relationship between the antecedent dry period and rainfall intensity for NO3− and NH4+ concentrations, flux, and enrichment ratios. We found that neither the antecedent dry period nor rainfall intensity significantly impacted the throughfall chemistry at our three sites. This was surprising because many studies have found that TF concentrations of solutes are higher following longer antecedent dry periods [21,48,49] and/or during lower-intensity precipitation events [21,50]. The antecedent dry periods from our study are short but are similar to those found in the literature [49], and yet are shorter when they are compared to other studies [48]. One possible reason why we did not observe an effect of antecedent dry period is that the relationship between antecedent dry period (and other storm characteristics) and solute concentration and flux are easier to assess for those solutes that pass through the canopy with minimal or no stomatal uptake or other canopy exchange processes beyond wash-off (e.g., SO42− and Na+) [51]; NO3− and NH4+ are not such solutes, as their presence in throughfall reflects a series of canopy exchange processes. Another possible explanation is that we sampled a single time at the end of each storm event. Previous studies that have observed relationships between rainfall intensity and throughfall chemistry utilized intra-storm sampling to characterize these temporal patterns [48,51]. Perhaps another reason why we did not observe temporal variability in our study is the fact that the quantity of rainfall varied between precipitation events, and we did not consider other meteorological factors (e.g., wind direction and direction, and duration). Past studies have found relationships between throughfall chemistry and the antecedent dry period, and the rainfall intensity utilized multiple intra-storm samplings of the throughfall [21,48,51,52,53].

5. Conclusions

This study evaluated N deposition in fragmented forests, local tree species effects, and spatial and temporal variability in throughfall N composition. The results of this study suggest that TF N concentrations and fluxes were more strongly influenced by the internal characteristics of the woodlots, such as forest structure and species composition, than by the surrounding land use. Throughfall NO3− concentration and fluxes were higher under basswood and lower under red oak, whereas TF NH4+ was positively related to the local abundance of sugar maple. These results expand our understanding of how different tree species can affect nutrient fluxes in forest ecosystems. Stand density also impacted the spatial variability in throughfall, with NO3− and NH4+ tending to be higher in stands characterized by lower-density or absent understory vegetation, the latter of which may be attributed to deer browsing. We did not consider stemflow, which is an important factor in subcanopy nutrient flux, and variability in stemflow chemistry can be impacted by species heterogeneity. Incorporating stemflow in studies helps to investigate variability in subcanopy nutrient flux. Although we did not find a significant relationship between throughfall N composition and either antecedent dry period or rainfall intensity, future work would benefit from continued investigation into meteorological factors that impact subcanopy N fluxes. Our findings highlight the fact that even in highly human-modified environments where atmospheric deposition of N can be relatively high, stand structures have an important role in determining the subcanopy distribution of N via throughfall.

Author Contributions

L.D.R.-C.: investigation, data collection, data analysis, writing original draft; A.L.D.: writing-review and editing, supervision, funding; D.E.R.: writing-review and editing, supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by awards from US Department of Agriculture National Institute for Food and Agriculture; including, National Needs Graduate and Postgraduate Fellowship Program (Award # 2017-38420-26823), and McIntire Stennis Capacity Grants (Award # MICL06031 and MICL06006), and Michigan Department of Natural Resources Partnership for Ecosystems Research and Management.

Data Availability Statement

Data are available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Galloway, J.N. The global nitrogen cycle: Changes and consequences. Environ. Pollut. 1998, 102, 15–24. [Google Scholar] [CrossRef]
  2. Galloway, J.N.; Cowling, E.B. Reactive nitrogen and the world: 200 years of change. AMBIO A J. Hum. Environ. 2002, 31, 64–71. [Google Scholar] [CrossRef] [PubMed]
  3. Galloway, J.N.; Dentener, F.J.; Capone, D.G.; Boyer, E.W.; Howarth, R.W.; Seitzinger, S.P.; Asner, G.P.; Cleveland, C.C.; Green, P.A.; Holland, E.A.; et al. Nitrogen cycles: Past, present, and future. Biogeochemistry 2004, 70, 153–226. [Google Scholar] [CrossRef]
  4. Aber, J.D.; Nadelhoffer, K.; Steudler, P.; Melillo, J.M. Nitrogen Saturation in Northern Forest Ecosystems. Bioscience 1989, 39, 378–386. [Google Scholar] [CrossRef]
  5. Aber, J.; McDowell, W.; Nadelhoffer, K.; Magill, A.; Berntson, G.; Kamakea, M.; McNulty, S.; Currie, W.; Rustad, L.; Fernandez, I. Nitrogen saturation in temperate forest ecosystems: Hypotheses revisited. BioScience 1998, 48, 921–934. [Google Scholar] [CrossRef] [Green Version]
  6. Bobbink, R.; Hornung, M.; Roelofs, J.G.M. The effects of air-borne nitrogen pollutants on species diversity in natural and semi-natural European vegetation. J. Ecol. 1998, 86, 717–738. [Google Scholar] [CrossRef]
  7. Fenn, M.E.; Poth, M.A.; Aber, J.D.; Baron, J.S.; Bormann, B.T.; Johnson, D.W.; Lemly, A.D.; McNulty, S.G.; Ryan, D.F.; Stottlemyer, R. Nitrogen excess in North American ecosystems: Predisposing factors, ecosystem responses, and man-agement strategies. Ecol. Appl. 1998, 8, 706–733. [Google Scholar] [CrossRef]
  8. Driscoll, I.; Hamilton, D.A.; Petropoulos, H.; Yeo, R.A.; Brooks, W.M.; Baumgartner, R.N.; Sutherland, R.J. The Aging Hippocampus: Cognitive, Biochemical and Structural Findings. Cereb. Cortex 2003, 13, 1344–1351. [Google Scholar] [CrossRef] [Green Version]
  9. Heilman, G.E.; Strittholt, J.R.; Slosser, N.C.; Dellasala, D.A. Forest fragmentation of the conterminous United States: Assessing forest intactness through road density and spatial characteristics: Forest fragmentation can be measured and monitored in a powerful new way by combining remote sensing, geographic information systems, and analytical software. BioScience 2002, 52, 411–422. [Google Scholar] [CrossRef] [Green Version]
  10. Riitters, K.H.; Coulston, J.W.; Wickham, J.D. Fragmentation of forest communities in the eastern United States. For. Ecol. Manag. 2012, 263, 85–93. [Google Scholar] [CrossRef]
  11. Vallet, J.; Beaujouan, V.; Pithon, J.; Rozé, F.; Daniel, H. The effects of urban or rural landscape context and distance from the edge on native woodland plant communities. Biodivers. Conserv. 2010, 19, 3375–3392. [Google Scholar] [CrossRef]
  12. Honnay, O.; Verheyen, K.; Hermy, M. Permeability of ancient forest edges for weedy plant species invasion. For. Ecol. Manag. 2002, 161, 109–122. [Google Scholar] [CrossRef]
  13. Bittman, S.; Robert, M. Ammonia emissions from agricultural operations: Livestock. Better Crops 2009, 93, 28–31. [Google Scholar]
  14. Theobald, M.; Bealey, W.; Tang, Y.; Vallejo, A.; Sutton, M. A simple model for screening the local impacts of atmospheric ammonia. Sci. Total Environ. 2009, 407, 6024–6033. [Google Scholar] [CrossRef]
  15. López-Aizpún, M.; Arango-Mora, C.; Santamaría, C.; Lasheras, E.; Santamaría, J.; Ciganda, V.; Cárdenas, L.; Elustondo, D. Atmospheric ammonia concentration modulates soil enzyme and microbial activity in an oak forest affecting soil microbial biomass. Soil Biol. Biochem. 2018, 116, 378–387. [Google Scholar] [CrossRef]
  16. Bettez, N.D.; Groffman, P.M. Nitrogen Deposition in and near an Urban Ecosystem. Environ. Sci. Technol. 2013, 47, 6047–6051. [Google Scholar] [CrossRef] [PubMed]
  17. Fenn, M.E.; Bytnerowicz, A.; Schilling, S.L.; Vallano, D.M.; Zavaleta, E.S.; Weiss, S.B.; Morozumi, C.; Geiser, L.H.; Hanks, K. On-road emissions of ammonia: An underappreciated source of atmospheric nitrogen deposition. Sci. Total Environ. 2018, 625, 909–919. [Google Scholar] [CrossRef] [Green Version]
  18. Dunkerley, D. Measuring interception loss and canopy storage in dryland vegetation: A brief review and evaluation of available research strategies. Hydrol. Process. 2000, 14, 669–678. [Google Scholar] [CrossRef]
  19. Xiao, Q.; McPherson, E.G.; Ustin, S.L.; Grismer, M.E.; Simpson, J.R. Winter rainfall interception by two mature open-grown trees in Davis, California. Hydrol. Process. 2000, 14, 763–784. [Google Scholar] [CrossRef]
  20. Zhang, Y.-F.; Wang, X.-P.; Hu, R.; Pan, Y.-X. Throughfall and its spatial variability beneath xerophytic shrub canopies within water-limited arid desert ecosystems. J. Hydrol. 2016, 539, 406–416. [Google Scholar] [CrossRef]
  21. Levia, D.F., Jr.; Frost, E.E. Variability of throughfall volume and solute inputs in wooded ecosystems. Prog. Phys. Geogr. 2006, 30, 605–632. [Google Scholar] [CrossRef]
  22. Limpert, K.; Siegert, C. Interspecific differences in canopy-derived water, carbon, and nitrogen in upland oak-hickory forest. Forests 2019, 10, 1121. [Google Scholar] [CrossRef] [Green Version]
  23. Lovett, G.M.; Lindberg, S.E. Dry Deposition and Canopy Exchange in a Mixed Oak Forest as Determined by Analysis of Throughfall. J. Appl. Ecol. 1984, 21, 1013. [Google Scholar] [CrossRef]
  24. U.S. Census Bureau quickfacts: East Lansing City, Michigan. Available online: https://www.census.gov/quickfacts/eastlansingcitymichigan (accessed on 13 January 2023).
  25. Sinsabaugh, R.L.; Reynolds, H.; Long, T.M. Rapid assay for amidohydrolase (urease) activity in environmental samples. Soil Biol. Biochem. 2000, 32, 2095–2097. [Google Scholar] [CrossRef]
  26. Doane, T.A.; Horwáth, W.R. Spectrophotometric Determination of Nitrate with a Single Reagent. Anal. Lett. 2003, 36, 2713–2722. [Google Scholar] [CrossRef]
  27. Levia, D.F., Jr.; Herwitz, S.R. Physical properties of water in relation to stemflow leachate dynamics: Implica-tions for nutrient cycling. Can. J. For. Res. 2000, 30, 662–666. [Google Scholar] [CrossRef]
  28. Levia, D.F., Jr.; Frost, E.E. A review and evaluation of stemflow literature in the hydrologic and biogeochemical cycles of forested and agricultural ecosystems. J. Hydrol. 2003, 274, 1–29. [Google Scholar] [CrossRef]
  29. Siegert, C.M.; Levia, D.F.; Leathers, D.J.; Van Stan, J.T.; Mitchell, M.J. Do storm synoptic patterns affect biogeochemical fluxes from temperate deciduous forest canopies? Biogeochemistry 2017, 132, 273–292. [Google Scholar] [CrossRef]
  30. Hegyi, F. A simulation model for managing jack-pine stand simulation. RoyalColl. Res. Notes 1974, 30, 74–90. [Google Scholar]
  31. Motulsky, H.J.; Brown, R.E. Detecting outliers when fitting data with nonlinear regression–a new method based on robust nonlinear regression and the false discovery rate. BMC Bioinform. 2006, 7, 123. [Google Scholar] [CrossRef] [Green Version]
  32. Boerner, R.E.J. Foliar Nutrient Dynamics and Nutrient Use Efficiency of Four Deciduous Tree Species in Relation to Site Fertility. J. Appl. Ecol. 1984, 21, 1029. [Google Scholar] [CrossRef]
  33. Potter, C.S.; Ragsdale, H.L.; Swank, W.T. Atmospheric deposition and foliar leaching in a regener-ating southern Appalachian forest canopy. J. Ecol. 1991, 79, 97–115. [Google Scholar] [CrossRef]
  34. Lovett, G.M.; Nolan, S.S.; Driscoll, C.T.; Fahey, T.J. Factors regulating throughfall flux in a New Hampshire forested landscape. Can. J. For. Res. 1996, 26, 2134–2144. [Google Scholar] [CrossRef]
  35. Lodhi, M.; Ruess, R. Variation in soil nitrifiers and leaf nitrate reductase activity of selected tree species in a forest community. Soil Biol. Biochem. 1988, 20, 939–943. [Google Scholar] [CrossRef]
  36. Tietema, A.; Verstraten, J. Nitrogen cycling in an acid forest ecosystem in the Netherlands under increased atmospheric nitrogen input. Biogeochemistry 1991, 15, 21–46. [Google Scholar] [CrossRef]
  37. Lanning, M.; Wang, L.; Benson, M.; Zhang, Q.; Novick, K.A. Canopy isotopic investigation reveals different water uptake dynamics of maples and oaks. Phytochemistry 2020, 175, 112389. [Google Scholar] [CrossRef]
  38. Hasselrot, B.; Grennfelt, P. Deposition of air pollutants in a wind-exposed forest edge. Water Air Soil Pollut. 1987, 34, 135–143. [Google Scholar] [CrossRef]
  39. Spangenberg, A.; Kölling, C. Nitrogen deposition and nitrate leaching at forest edges exposed to high am-monia emissions in Southern Bavaria. Water Air Soil Pollut. 2004, 152, 233–255. [Google Scholar] [CrossRef]
  40. Wuyts, K.; De Schrijver, A.; Staelens, J.; Gielis, M.; Geudens, G.; Verheyen, K. Patterns of throughfall deposition along a transect in forest edges of silver birch and Corsican pine. Can. J. For. Res. 2008, 38, 449–461. [Google Scholar] [CrossRef]
  41. Weathers, K.C.; Cadenasso, M.L.; Pickett, S.T. Forest edges as nutrient and pollutant concentrators: Potential synergisms between fragmentation, forest canopies, and the atmosphere. Conserv. Biol. 2001, 15, 1506–1514. [Google Scholar] [CrossRef]
  42. Devlaeminck, R.; Bossuyt, B.; Hermy, M. Inflow of seeds through the forest edge: Evidence from seed bank and vegetation patterns. Plant Ecol. 2005, 176, 1–17. [Google Scholar] [CrossRef]
  43. Draaijers, G.P.J.; Erisman, J.W. A canopy budget model to assess atmospheric deposition from throughfall measure-ments. Water Air Soil Pollut. 1995, 85, 2253–2258. [Google Scholar] [CrossRef]
  44. Van Stan, J.T., II; Levia, D.F., Jr.; Inamdar, S.P.; Lepori-Bui, M.; Mitchell, M.J. The effects of phenoseason and storm characteristics on throughfall solute washoff and leaching dynamics from a temperate deciduous forest canopy. Sci. Total Environ. 2012, 430, 48–58. [Google Scholar] [CrossRef]
  45. Draaijers, G.P.J.; Ivens, W.P.M.F.; Bleuten, W. Atmospheric deposition in forest edges measured by monitoring canopy throughfall. Water Air Soil Pollut. 1988, 42, 129–136. [Google Scholar] [CrossRef]
  46. De Schrijver, A.; Nachtergale, L.; Roskams, P.; De Keersmaeker, L.; Mussche, S.; Lust, N. Soil acidification along an ammonium deposition gradient in a Corsican Pine stand in northern Belgium. Environ. Pollut. 1998, 102, 427–431. [Google Scholar] [CrossRef]
  47. Zhang, H.; Wu, H.; Liao, A.; He, B.; Liu, J.; Wang, N.; Xia, Y.; Cao, Y.; Zhu, Z.; Fu, C. Effects of Trunk Distance and Rainfall on Throughfall and Associated Chemical Alterations within a Subtropical Deciduous Forest. Forests 2022, 13, 1707. [Google Scholar] [CrossRef]
  48. Zimmermann, A.; Germer, S.; Neill, C.; Krusche, A.V.; Elsenbeer, H. Spatio-temporal patterns of throughfall and solute deposition in an open tropical rain forest. J. Hydrol. 2008, 360, 87–102. [Google Scholar] [CrossRef]
  49. Siegert, C.M.; Drotar, N.A.; Alexander, H.D. Spatial and Temporal Variability of Throughfall among Oak and Co-Occurring Non-Oak Tree Species in an Upland Hardwood Forest. Geosciences 2019, 9, 405. [Google Scholar] [CrossRef] [Green Version]
  50. Levia, D.F.; Keim, R.F.; Carlyle-Moses, D.E.; Frost, E.E. Throughfall and Stemflow in Wooded Ecosystems. For. Hydrol. Biogeochem. Synth. Past Res. Future Directions. 2011, 216, 425–443. [Google Scholar] [CrossRef]
  51. Rodrigo, A.; Àvila, A.; Rodà, F. The chemistry of precipitation, throughfall and stemflow in two holm oak (Quercus ilex L.) forests under a contrasted pollution environment in NE Spain. Sci. Total Environ. 2003, 305, 195–205. [Google Scholar] [CrossRef] [PubMed]
  52. André, F.; Jonard, M.; Ponette, Q. Spatial and temporal patterns of throughfall chemistry within a temperate mixed oak–beech stand. Sci. Total Environ. 2008, 397, 215–228. [Google Scholar] [CrossRef] [PubMed]
  53. Zhang, H.-X.; Wu, H.-W.; Li, J.; He, B.; Liu, J.-F.; Wang, N.; Duan, W.-L.; Liao, A.-M. Spatial-temporal variability of throughfall in a subtropical deciduous forest from the hilly regions of eastern China. J. Mt. Sci. 2019, 16, 1788–1801. [Google Scholar] [CrossRef]
Forests 14 00512 g001 550
Figure 1. Study Area.
Figure 1. Study Area.
Forests 14 00512 g001
Forests 14 00512 g002 550
Figure 2. Box-whiskers plots showing differences among sites in the concentration (A,B), fluxes (C,D), and enrichment ratios (E,F) of NO3− and NH4+. Lines connect sites that were significantly different from each other, and the asterisks show the level of statistical significance. (p = 0.05, p < 0.01, *** p < 0.001, **** p < 0.0001).
Figure 2. Box-whiskers plots showing differences among sites in the concentration (A,B), fluxes (C,D), and enrichment ratios (E,F) of NO3− and NH4+. Lines connect sites that were significantly different from each other, and the asterisks show the level of statistical significance. (p = 0.05, p < 0.01, *** p < 0.001, **** p < 0.0001).
Forests 14 00512 g002
Forests 14 00512 g003 550
Figure 3. Average concentration, fluxes, and enrichment ratio of NO3− and NH4+ from distance from the edge during the sampling period (May–October 2019).
Figure 3. Average concentration, fluxes, and enrichment ratio of NO3− and NH4+ from distance from the edge during the sampling period (May–October 2019).
Forests 14 00512 g003
Forests 14 00512 g004 550
Figure 4. Visualization of significant species’ effects on throughfall N from the mixed-effects model. Asterisks (*) indicate outliers. Influence of basswood and red oak is shown with box-whisker plots showing TF NO3− concentration and fluxes for collectors that had a basswood tree within 8 m, red oak within 8 m, neither species within 8 m, nor both species within 8 m. The influence of sugar maple is visualized as a scatter plot of TF NH4+ concentrations against sugar maple influence.
Figure 4. Visualization of significant species’ effects on throughfall N from the mixed-effects model. Asterisks (*) indicate outliers. Influence of basswood and red oak is shown with box-whisker plots showing TF NO3− concentration and fluxes for collectors that had a basswood tree within 8 m, red oak within 8 m, neither species within 8 m, nor both species within 8 m. The influence of sugar maple is visualized as a scatter plot of TF NH4+ concentrations against sugar maple influence.
Forests 14 00512 g004
Table
Table 1. Overstory species composition and structure of the three woodlots. Data are for all trees ≥ 10 cm dbh. Relative density is the percentage contribution of each species to total trees per hectare, whereas relative dominance is the percentage contribution of each species to the total stand basal area.
Table 1. Overstory species composition and structure of the three woodlots. Data are for all trees ≥ 10 cm dbh. Relative density is the percentage contribution of each species to total trees per hectare, whereas relative dominance is the percentage contribution of each species to the total stand basal area.
SiteDominanceStand Density (Trees ha–1)Basal Area
(m2 ha−1)		Rel. Density
(%)		Rel. Dominance
(%)
Highway WoodlotAcer saccharum76.712.627.839.4
Quercus rubra62.29.822.630.8
Fagus grandifolia37.32.813.58.8
Tilia americana291.510.54.6
Prunus serotina20.71.37.54.0
Acer rubrum6.21.02.33.2
Populus deltoides6.21.02.33.0
Carya spp. 16.60.86.02.5
Liriodendron tulipifera6.20.72.32.1
Ulmus spp. 6.20.32.30.9
Juglans nigra2.10.10.80.4
Ostrya virginiana6.20.12.30.2
Total275.632.0
Mixed Landuse WoodlotAcer saccharum93.314.035.445.5
Tilia americana51.84.819.715.6
Quercus rubra20.73.17.910.2
Acer nigrum18.72.47.17.9
Prunus serotina12.42.24.77.0
Fagus grandifolia18.71.87.17.9
Carya spp. 24.91.59.45.0
Ulmus spp. 12.40.84.72.6
Ostrya virginiana10.40.13.90.4
Total263.230.9
Agricultural WoodlotAcer saccharum93.314.035.445.5
Fagus grandifolia51.84.819.715.6
Tilia americana20.73.17.910.2
Acer nigrum18.72.47.17.9
Prunus serotina12.42.24.77.0
Fagus grandifolia18.71.87.17.9
Carya spp. 24.91.59.45.0
Ulmus spp. 12.40.84.72.6
Ostrya virginiana10.40.13.90.4
Total22528.1
Table
Table 2. Total precipitation (Pg), rainfall intensity, and antecedent dry period for the study period.
Table 2. Total precipitation (Pg), rainfall intensity, and antecedent dry period for the study period.
DatePg (mm)Site SampledRainfall Intensity (mm h−1)Antecedent Dry Period (Days)
25 May–25 May 20192.0Agricultural
Highway
Mixed landscape		2.01
1 June–4 June 2019 23.1Agricultural
Highway		1.355
5 June–6 June 2019 14.7Agricultural
Highway
Mixed landscape		14.71
24 June–25 June 20198.1Agricultural
Highway
Mixed landscape		1.164
26 August–28 August 20199.7Agricultural
Highway		1.2112
29 August–30 August 2019 2.5Agricultural
Highway		0.831
11 September–12 September 201915.2Agricultural
Highway
Mixed landscape		1.92
27 September–1 October 2019 43.2 Highway
Mixed landscape		1.145
2 October–3 October 2019 28.2 Highway
Mixed landscape		1.481
Table
Table 3. Vegetation data from the tree plots per site that were located right along the woodlot’s boundary.
Table 3. Vegetation data from the tree plots per site that were located right along the woodlot’s boundary.
SiteBATPH
Agricultural4.23773
Highway1.81650.7
Mixed land use0.4353.7
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Rivera-Cubero, L.D.; Dowtin, A.L.; Rothstein, D.E. Nitrate and Ammonium Deposition in the Midwestern Fragmented Forest. Forests 2023, 14, 512. https://doi.org/10.3390/f14030512

AMA Style

Rivera-Cubero LD, Dowtin AL, Rothstein DE. Nitrate and Ammonium Deposition in the Midwestern Fragmented Forest. Forests. 2023; 14(3):512. https://doi.org/10.3390/f14030512

Chicago/Turabian Style

Rivera-Cubero, Luis D., Asia L. Dowtin, and David E. Rothstein. 2023. "Nitrate and Ammonium Deposition in the Midwestern Fragmented Forest" Forests 14, no. 3: 512. https://doi.org/10.3390/f14030512

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop