Next Article in Journal
The Relationship between Sap Flow Density and  Environmental Factors in the Yangtze River Delta  Region of China
Next Article in Special Issue
Climate Impacts on Soil Carbon Processes along an Elevation Gradient in the Tropical Luquillo Experimental Forest
Previous Article in Journal
Elevated Atmospheric CO2 and Warming Stimulates Growth and Nitrogen Fixation in a Common Forest Floor Cyanobacterium under Axenic Conditions
Previous Article in Special Issue
Erratum: Spatial Upscaling of Soil Respiration under a Complex Canopy Structure in an Old-Growth Deciduous Forest, Central Japan; Forests 2017, 8, 36
Article Menu
Issue 3 (March) cover image

Export Article

Forests 2017, 8(3), 75; doi:10.3390/f8030075

Article
Partitioning Forest-Floor Respiration into Source Based Emissions in a Boreal Forested Bog: Responses to Experimental Drought
Tariq Muhammad Munir 1,*, Bhupesh Khadka 1, Bin Xu 1 and Maria Strack 2
1
Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada
*
Correspondence: Tel.: +1-403-971-5693
Academic Editors: Robert Jandl and Mirco Rodeghiero
Received: 1 February 2017 / Accepted: 7 March 2017 / Published: 10 March 2017

Abstract

:
Northern peatlands store globally significant amounts of soil carbon that could be released to the atmosphere under drier conditions induced by climate change. We measured forest floor respiration (RFF) at hummocks and hollows in a treed boreal bog in Alberta, Canada and partitioned the flux into aboveground forest floor autotrophic, belowground forest floor autotrophic, belowground tree respiration, and heterotrophic respiration using a series of clipping and trenching experiments. These fluxes were compared to those measured at sites within the same bog where water-table (WT) was drawn down for 2 and 12 years. Experimental WT drawdown significantly increased RFF with greater increases at hummocks than hollows. Greater RFF was largely driven by increased autotrophic respiration driven by increased growth of trees and shrubs in response to drier conditions; heterotrophic respiration accounted for a declining proportion of RFF with time since drainage. Heterotrophic respiration was increased at hollows, suggesting that soil carbon may be lost from these sites in response to climate change induced drying. Overall, although WT drawdown increased RFF, the substantial contribution of autotrophic respiration to RFF suggests that peat carbon stocks are unlikely to be rapidly destabilized by drying conditions.
Keywords:
forest floor respiration; root respiration; autotrophic respiration; heterotrophic respiration; disturbance; water table; drought; climate change; modeling; soil temperature

1. Introduction

Peatlands contain one of the largest terrestrial carbon (C) stocks, estimated at ~600 Gt C [1], with northern peatland C storage accounting for ~390–440 Gt [1,2]. The large C stock has been accumulated as a result of only a marginal difference, over millennia, between photosynthetic C uptake and loss of C as ecosystem respiration, methane (CH4) emissions, and water-borne outflows [3]. The stored C is present in the form of highly mineralizable organic C [4,5] protected in water-saturated, anoxic conditions and is highly sensitive to warmer and drier climate [5,6,7]. Therefore, any increase in carbon dioxide (CO2) emissions in response to the expected changes in climate has the potential to provide a positive feedback to global warming [4,7,8,9]. In general, many northern peatlands are expected to be drier under future climates [10,11], and while the response of peatland ecosystem respiration to water-table drawdown has been extensively studied [12,13,14,15,16,17], controlled field experimentation for partitioning ecosystem respiration into its source-based major components remains largely unexplored [18]. Research is needed to investigate source-based respiration fluxes in relation to potential changes in environmental conditions to improve our understanding of changes in ecosystem C storage or emissions to the atmosphere under climate change scenarios [19,20].
Ecosystem respiration includes the emission of CO2 to the atmosphere from above and belowground parts of vegetation (autotrophic respiration; RA), and from microbial decomposition of the soil organic matter including litter (heterotrophic respiration; RH). The aboveground parts mainly include plant leaves and stems while the belowground parts comprise living roots with their associated mycorrhizal fungi and microbial populations [21,22]. Many northern peatlands are treed [23,24]. When respiration is measured at the ground layer of a treed peatland, this forest floor respiration (RFF) includes respiration associated with tree roots, while respiration of the aboveground tree biomass is excluded. This research partitioned the bulk RFF into source-based above and belowground respiration flux components as: (1) forest floor aboveground autotrophic respiration (RFF_A_ag) and, (2) belowground shrub + herb (roots) autotrophic respiration (RA_SH_bg) and belowground tree (roots) autotrophic respiration (RA_T_bg). Separating the RA_SH_bg from RA_T_bg is important as specific vascular plant functional types may respire at different rates [25,26] due to the difference in their respective above and belowground productivities [27], and therefore modify the response of RFF to changes in water-table (WT) level and soil temperature at 5 cm depth (T5) [4,28].
Peatland respiration flux components are highly responsive to environmental changes such as WT level [26,29,30,31,32], T5 [15,33,34], and plant functional (shrubs + herbs, trees) type [35,36,37,38]. Therefore, increased atmospheric or soil temperatures and subsequent WT lowering [39,40] may increase soil respiration rates [22] and ultimately alter the peatland C sink or source strength ([37] resulting from increasing atmospheric CO2 [41]. However, increases in RFF alone do not necessarily indicate a loss of soil C if only autotrophic respiration increases, illustrating the importance of determining the source of respiration and the relative response of each component to changing environmental conditions [42].
Ericaceous shrubs (e.g., Rhododendron groenlandicum) are important contributors to ecosystem productivity in many northern bogs [43,44], also making important contributions to ecosystem respiration. The contribution of shrub autotrophic respiration to RFF in a shrub-dominated bog in Patuanak, Saskatchewan, Canada was estimated to be ~75% [45]. A median value of root: shoot ratios estimated from 14 sites in boreal forest was found to be 0.39 [46], suggesting that root respiration is likely to make a significant contribution to measured autotrophic respiration. Overall, root/rhizospheric respiration has been found to account for between 10% and 90% of RFF depending upon vegetation type and season of year [28,47]. As similar controls apply to the above and belowground productivities of shrubs [48,49], therefore, the response of above and belowground respiration to environmental change should be similar [46].
Experimental partitioning of soil autotrophic and heterotrophic respiration components has been attempted using different methods with varying results. For example, methods include application of stable isotopes [50,51], root biomass regression with RFF to determine belowground root respiration [52], and comparison of soils with and without root exclusion to determine tree root respiration [17,28]; however, evaluating the responses of various respiration sources (RFF_A_ag, RA_SH_bg, RA_T_bg, and RH) to environmental change has not been completed. Moreover, responses may vary between microforms (hummocks and hollows) due to initial differences in WT level, soil properties, and vegetation community [16,17,32]. Therefore, this study focused on partitioning RFF emissions along a microtopographic gradients in order to evaluate responses of each respiration component to short (2 years) and longer term (12 years) water-table drawdown.
Our specific objectives were to:
  • Partition RFF between the aboveground ground-layer autotrophic respiration (RFF_A_ag), belowground autotrophic respiration of shrubs + herbs (RA_SH_bg) and trees (RA_T_bg), and heterotrophic respiration (RH) across water-table (WT) treatments (control, experimental, drained),
  • Evaluate differences in source contributions to RFF along a microtopographic (hummock, hollow) gradient in a boreal forested bog and how these contributions changed in response to WT treatments, and
  • Assess the respiration components’ responses to the WT and soil temperature at 5 cm depth (T5) over one growing season.
We hypothesized that experimental WT lowering would lead to increases in all respiration components, with greatest increases at hollows. We also hypothesized that increases in RH would be greatest at hollows, while hummocks would have greater increases in autotrophic respiration (both above and belowground) and that all components of RFF would have significant positive correlations with depth to WT and T5.

2. Materials and Methods

2.1. Sites Description and Experimental Design

During the growing season (1 May to 31 October) of 2012, this research was conducted in a forested bog within the southern boreal forest and near the town of Wandering River, Alberta, Canada. Based on 30-year (1981–2010) averages, the mean growing season (May to October) temperature and precipitation for this region are 11.7 °C and 382 mm, respectively [53]. The mean growing season air temperature and precipitation measured during the 2012 study using a meteorological station installed at the study sites were 13.2 °C and 282 mm, respectively.
Within the dry ombrotrophic forested bog, three research sites, CONTROL (55°21′ N, 112°31′ W), EXPERIMENTAL (55°21′ N, 112°31′ W), and DRAINED (55°16′ N, 112°28′ W), were chosen or created (Figure 1). The control was an undisturbed site with a mean WT level of −38 cm whereas the experimental site was created adjacent to the control by ditching around, lowering the mean WT level to 78 cm below surface. One year prior to this study, WT level (± Standard Deviation (SD)) at the control (−56 ± 22 cm) and experimental (−57 ± 20 cm) sites were not different (negative values denote belowground WT; ANOVA, F1, 5 = 0.55, p = 0.492). The drained site (part of the same bog and located 9 km to southwest) was drained inadvertently 12 years prior to the study as a result of a peat harvesting preparation on an adjacent section, and had a mean WT level of ~−120 cm in 2012 (Figure 2).
Based on plant species indicators, the studied bog was classified as a forested low shrub bog [55] with two distinct microtopographic features: hummock and hollow. One year prior to this study, the control and experimental site microforms had equal coverage of mosses with sparse shrubs, whereas the drained hummocks had the highest coverage of shrubs and the drained hollows had the greatest coverage of lichens [44]. At all sites, black spruce (Picea mariana (Mill.) B.S.P.) was the most abundant type of tree constituting >99% of the tree stand, with 25,766 stems·ha−1 consisting of 37% taller trees (>137 cm height) up to 769 cm high [44]. The black spruce stand had an average canopy height of 168 cm, projection coverage of 42%, and basal area of 73.5 m2·ha−1 [17]. Trees were generally evenly distributed across the study plots (i.e., not clustered).
Mean (±SD) pH and electrical conductivity (µS·cm−1) of pore water in the control (4.1 ± 0.1 and 16.6 ± 0.7, respectively) and experimental (4.4 ± 0.3 and 15.2 ± 2.5, respectively) sites were similar (ANOVA, pH: F1, 5 = 2.6, p = 0.166; EC: F1, 5 = 0.84, p = 0.401) prior to any manipulation. All the study sites had an average peat depth exceeding 4 m, and were underlain by a sandy clay substrate. Because all the study sites were part of the same bog and their initial primary characteristics of vegetation composition, WT levels, and chemistry were similar, their secondary attributes (e.g., respiration rates) were also assumed to be similar prior to WT manipulation.
From the available microtopography, we chose eight hummock and eight hollow microforms at each of the control and drained sites, and four of each microform type at the experimental site (due to its smaller area). In early May 2012, each of the chosen microform plots was fitted with a 60 cm × 60 cm collar having a groove at the top for placing the CO2 flux chamber. The collar was carefully inserted into the peat surface to a depth of ~5 cm to keep the disturbance minimal [56]. Outside each collar, a Polyvinyl Chloride (PVC) well (length = 200 cm, diameter = 3.5 cm) with perforation and a nylon cloth covering on the lower 150 cm was inserted into a bore-hole drilled to a depth of 150 cm. WT level was manually measured at all the water wells every time CO2 flux was measured during the growing season of 2012. The WT levels were also monitored during the study period at 20-min intervals using automatic, temperature compensating pressure transducers (Levelogger Junior 3001, Solinst, Georgetown, Ontario, Canada) installed in two randomly selected water wells at each site: one at a hummock and the other at a hollow plot.

2.2. CO2 Flux Measurements

All CO2 flux measurements were made during the day time of the growing season (May to October, 2012) using the same equipment. We used a closed chamber with dimensions 60 cm × 60 cm × 30 cm (width × length × height), made of opaque acrylic and fitted with a low-speed battery-operated fan to circulate air within the chamber headspace during and between CO2 concentration measurements. The chamber had no pressure equilibrium port installed. A portable infrared gas analyzer (EGM-4, PP Systems, Amesbury, MA, USA) with a built-in CO2 sampling pump operating at a flow rate of 350 mL·min−1 and compensating for temperature fluctuations within the chamber headspace was used to measure the instantaneous CO2 concentration inside the chamber headspace. The chamber headspace temperature was measured using a thermocouple thermometer (VWR International, Edmonton, AB, Canada). The CO2 concentration and temperature measurements were made every 15 s during a short chamber deployment period [57,58] of 1.75 min. Immediately after the CO2 concentration measurements at a plot, soil temperature at a depth of 5 cm (T5) was measured using a thermocouple thermometer, and the WT level relative to moss surface was manually measured from a permanently installed water well adjacent to the plot. The CO2 flux was calculated from the linear change in CO2 concentration in chamber headspace over time [17], as a function of air temperature, pressure, and volume within the chamber headspace, following the ideal gas law.

2.2.1. Forest Floor Respiration (RFF)

Prior to any manipulation at a plot, a CO2 efflux measurement represented forest floor respiration (RFF). During a 5-day long RFF measurement campaign in May 2012, we measured the fluxes on four to five occasions in each plot. The measured RFF is divided into major source-based respiration flux components as:
RFF = RFF_A_ag + RA_SH_bg + RA_T_bg + RH,
where RFF accounts for forest floor aboveground autotrophic respiration (RFF_A_ag), belowground shrub + herb and tree autotrophic (rhizospheric) respiration (RA_SH_bg + RA_T_bg, respectively), and soil heterotrophic respiration (RH). At the end of the RFF measurement campaign, we clipped all plots using sharp scissors at the base of capitulum at 1 cm below the moss surface [27,59]. All the plots had their surface carefully cleared of any plant litter. The clipped shrubs + herbs were placed in labelled paper bags, taken to the Ecohydrology Lab, University of Calgary, AB, oven dried at 60 °C for 48 hours and weighed to calculate mean biomass (g·m−2) at each plot at each site.

2.2.2. Partitioning Forest Floor Respiration

Following the RFF measurement campaign, a 5-day long campaign for measuring the post-clipping CO2 emissions from every plot was performed. During the campaign, we measured the emissions on four to five occasions in each plot. At plot level, the CO2 emissions (g·CO2·m−2·day−1) at clipped plots subtracted from RFF (g·CO2·m−2·day−1) represented RFF_A_ag (g·CO2·m−2·day−1).
To estimate RA_T_bg at hummock or hollow microform at each site, we used a trenching method [17,52]. One half of the instrumented hummock or hollow plots (60 cm × 60 cm) were chosen at each site for the trenching procedure, whereas the other half of the plots were left untrenched. The trenched hummock or hollow plot RFF was not significantly different from those of the untrenched plots at control (ANOVA, Hummock: F1, 25 = 0.667, p = 0.422; Hollow: F1, 23 = 0.316, p = 0.580), experimental (ANOVA, Hummock: F1, 7 = 0.000, p = 0.990; Hollow: F1, 7 = 0.605, p = 0.466), or drained (ANOVA, Hummock: F1, 23 = 0.041, p = 0.841; Hollow: F1, 23 = 0.070, p = 0.790) site. Therefore, in June 2012, we incised around the trenched plots to a depth of 30 cm and installed a thick polythene sheet to prevent root ingrowth while keeping the disturbance minimal. All the trenched and intact, untrenched plots were measured for CO2 emission between July and September, 2012 to quantify the difference in the respiration rate for estimation of RA_T_bg. The trenching method at this site had already been used at this bog [17].
We did not measure RA_SH_bg and calculated it by using regression equations (y = a + bx) generated by regressing the aboveground biomass of shrubs + herbs (x) with RFF_A_ag (y) following a previous study [52]. We did not sample/measure shrub + herb root biomass (BSH_bg) to avoid disturbance to plots used for ongoing research and instead calculated BSH_bg as: BSH_ag × 0.39 [46], as previous research found the median root:shoot ratio of shrubs + herbs for 14 data points in boreal forest to be 0.39. Similar factors control net production both above and belowground, and the two rates were found to be related with each other [47]. The generated regression equations were used to determine the (RA_SH_bg) by substituting BSH_ag with BSH_bg in the equation. The RH was calculated from Equation (1) once all other components were estimated.

2.3. Seasonal Modeling

Only the measured respiration components (RFF, RFF_A_ag, RA_T_bg) during the growing season (May to October) of 2012 were modeled using a multiple linear regression model with T5 and WT level as:
RFF = a × T5 + b × WT level + c,
where a, b, and c are regression coefficients (Table 1). Seasonal RFF, RFF_A_ag, and RA_T_bg were estimated for each 20-min period between 1 May to 31 October 2012, averaged daily, and summed separately for the growing season using T5 (Onset®, HOBO®, Bourne, MA, USA) and WT level (Levelogger Junior, Solinst Canada Ltd., Georgetown, ON, Canada) measurements made on site. As the environmental variable logs were missing for the first 21 days of May 2012, they were filled by assuming that the first measured value was representative of the whole missing period. The field measured values of RFF, RFF_A_ag, and RA_T_bg were plotted against the model predicted values obtained using SPSS 24.0.0.1. Validation of the models showed excellent agreement between the measured and the modeled values (Appendix: Figure A1).
The seasonal respiration rates (RFF, RFF_A_ag, RA_T_bg) at hummock and hollow microforms were up-scaled by multiplying mean estimated growing season respiration by their corresponding coverage of 56% and 44% at the control, 55% and 45% at the experimental, and 52% and 48% at the drained site, respectively [60]. The seasonal value of RA_SH_bg was calculated separately for each microform/site combination by determining it as a proportion of corresponding instantaneous RFF value and then estimating it as this proportion of the modeled seasonal RFF. Seasonal RH was determined by difference according to Equation (1).

2.4. Statistical Analyses

To test the effects of WT level and microform on RFF, RFF_A_ag, RA_SH_bg, and RA_T_bg, we conducted a repeated, linear mixed-effects model analysis (LMEM; IBM SPSS Statistics 24.0.0.1, IBM corporation, Armonk, New York, USA) with WT level (control, experimental, drained) and microform (hummock, hollow) as predictor variables and RFF, RFF_A_ag, RA_SH_bg, or RA_T_bg as response variables, including the random effect of plot and repeated effect of time (as the same plots were used sequentially for all the measurements). We also tested the effect of BSH_ag in combination with site and microform on RFF_A_ag with random effect of plot using a repeated, LMEM. In this case, non-significant terms were removed from the model one at a time, starting with the highest p-value and the model was re-run until only significant terms remained. In all the LMEMs used in this study, a combined symmetry covariance structure was used. The relationships of WT level and T5 with RFF, RFF_A_ag, RA_SH_bg, and RA_T_bg were also tested for their significance using linear regression model fitting where applicable. Differences between regression slopes were tested [61] where applicable.

3. Results

The microclimate of the study sites was monitored over the growing season (May to October) of 2012 and was warmer by 1.4 °C and drier by 79 mm than the 30-year (1981–2010) regional averages measured at Athabasca, Alberta, Canada. The WT levels at the experimental and drained sites were as much as 36 cm and 82 cm lower than at the control site (Figure 2). In general, as a result of 12 years of drainage, the mosses at the drained hummocks were replaced by shrubs and mosses at the drained hollows were replaced by lichens. Detailed site hydrological responses to the warmer and drier climate at these sites have been reported [17].

3.1. Controls on Respiration Flux Components

All the measured component fluxes (RFF, RFF_A_ag, RA_SH_bg, RA_T_bg) were well correlated to T5 and WT (Figure 3) and therefore the modeled values matched the measured values well (Appendix: Figure A1). Generally, the highest respiration occurred with warm temperatures and deep WT position.

3.2. Measured Respiration Components

3.2.1. Mean Forest Floor Respiration Rate (RFF)

There were significant effects of each of the WT (control, experimental, drained) and microform (hummock, hollow) types on RFF measured in May, 2012; however, their interaction term did not significantly affect RFF (Table 2). The drained site had significantly higher RFF value (±SE) of 21.0 ± 2.1 g·CO2·m−2·day−1 compared with those measured at the experimental (13.2 ± 2.5 g·CO2·m−2·day−1; p = 0.042) and control (9.3 ± 2.1 g·CO2·m−2·day−1; p = 0.006) sites that were not different (p = 0.455) from each other. Bonferroni pairwise comparisons revealed that the hummocks had overall significantly higher RFF (17.9 ± 1.8 g·CO2·m−2·day−1) than hollows (11.2 ± 1.8 g·CO2·m−2·day−1). Comparing microforms across sites (Figure 4) revealed that RFF at hummocks was significantly different among all sites with highest rates at drained, followed by experimental, and then control, while hollows were not different (p = 0.526) across sites.

3.2.2. Mean Forest Floor Aboveground Autotrophic Respiration Rate (RFF_A_ag) and Belowground Autotrophic Respiration of Shrubs + Herbs (RA_SH_bg)

There were significant effects of WT and microform treatments individually and interactively on both the RFF_A_ag and RA_SH_bg (Table 2). Similar to RFF, the RFF_A_ag and RA_SH_bg values were highest at the drained site (5.2 ± 0.6 g·CO2·m−2·day−1; 2.8 ± 0.6 g·CO2·m−2·day−1, respectively), followed by values at the experimental (3.1 ± 0.9 g·CO2·m−2·day−1, 1.5 ± 0.2 g·CO2·m−2·day−1, respectively) and control (2.4 ± 0.6 g·CO2·m−2·day−1; 1.4 ± 0.2 g·CO2·m−2·day−1, respectively) sites. The RFF_A_ag and RA_SH_bg fluxes were also overall higher at the hummocks (4.9 ± 0.6 g·CO2·m−2·day−1; 2.5 ± 0.1 g·CO2·m−2·day−1, respectively) than that at the hollows (2.1 ± 0.6 g·CO2·m−2·day−1; 1.3 ± 0.1 g·CO2·m−2·day−1, respectively). Comparing microforms across sites, drained hummocks had significantly higher RFF_A_ag emissions than all other plots (p = 0.005; Figure 4) while RA_SH_bg at hummocks was significantly different between all three sites. There were no significant differences at hollows across the WT treatment sites for either RFF_A_ag or RA_SH_bg.
We used BSH_ag to calculate BSH_bg (BSH_bg = BSH_ag × 0.39; Table 3) and determine RA_SH_bg (Figure 4 and Figure 5) using the regression equations we generated by regressing BSH_ag with RFF_A_ag (explained in detail in Methods section). The BSH_ag was not different among sites or microforms due to large variation between plots; however, the drained site had the highest, while experimental had the lowest BSH_ag of all sites, and drained hummocks had higher BSH_ag than those of control and experimental hummocks in that order (Table 4, Figure 4). The BSH_ag and microform type significantly explained RFF_A_ag emissions individually and interactively (Table 4). Also, the RFF_A_ag was significantly related to an interaction between BSH_ag, site and microform, where BSH_ag was significantly related to the RFF_A_ag at all sites and microforms except at the experimental hollows which had the lowest or inconsistent BSH_ag values (Table 4; Figure 5). The overall regression lines’ slopes differed significantly at the hummocks and hollows (z = 4.43; 3.12, respectively). Regarding hollows, the slope was steeper at the drained site compared to control.

3.3. Modeled Respiration Components

Overall, all modeled seasonal (May to October) respiration components (g·CO2·m−2.growing season−1) were in the order of drained > experimental > control site, with greater overall increases at hollows than at hummocks at all the sites (Table 5). RH accounted for approximately 48%, 43%, and 37% of RFF at control, experimental, and drained sites, respectively. The RFF and RA_T_bg fluxes at the drained site were significantly higher than those at the control site, while RFF_A_ag and RA_SH_bg were not different among sites, but were different between drained hummocks and drained hollows. The seasonal RA_T_bg and RH values were highest at the drained hollows.

4. Discussion

In agreement with the previous seasonal (2012) RFF estimates made at these sites [17], this research found the greatest growing season RFF values at the drained site (422 ± 22 g·CO2·m−2), smaller values at the experimental site (354 ± 16 g·CO2·m−2), and the smallest values at the control site (255 ± 10 g·CO2·m−2; Table 2); however, in general, the values at hollows across sites were slightly lower in the present study, as these RFF values were modeled using measurements made over a shorter duration and from different plots (located in an area adjacent to the previous study plots [17]). The increased losses of CO2 at the short- and long-term drained sites that we observed compare well with those reported by others from experimentally drained boreal peatlands [9,32,44,62,63]. Declining WT level promotes desiccation of aquatic vegetation and soil that progresses over time.
The RFF was partitioned into major respiration components (RFF_A_ag, RA_SH_bg, RA_T_bg) which were then modeled for seasonal estimates with WT level and T5 as covariates. Model validations across sites/microforms showed excellent agreements within RSE of 0.24–2.41 g·CO2·m−2 growing season−1 (Table 1, Figure A1). Many investigations on peatland or forest respiration components have shown that warm and dry conditions enhance autotrophic [9,30,64,65] and heterotrophic [25,26,39] respiration emissions with greater impact over longer time scales [17,32,66]. Warmer air and soil temperatures stimulate microbial activity, resulting in increased respiration fluxes; however, T5 response of RH depends on substrate type and availability of nutrients and moisture [67,68]. Water-table lowering in peatlands prompts increased respiration emissions that are enhanced with increase in peat surface temperature, as we noticed that RFF values at our sites were well correlated to both WT level and T5 (Table 2 and Table 3; Figure 3). In contrast, a few studies suggest that the vascular vegetation (shrubs + herbs and trees) are less sensitive to WT lowering, as they can increase their rooting depth with deeper WT [15,30]. Peatland microforms have been observed to respond to changes in WT level and T5 with different magnitudes and in different directions [16,60,69] mainly due to differences in vegetation coverage and composition. Counter to our hypothesis, we observed a significant increase in RFF in response to WT lowering at hummocks, while RFF at hollows was not significantly affected. Hummocks were drier and had significantly higher coverage of shrubs + herbs compared to hollows that were dominated by Sphagnum mosses at the research sites prior to WT manipulation. Therefore, differences in RFF response to WT drawdown may be driven by changes in either autotrophic or heterotrophic respiration, or both.
At the control site, autotrophic respiration components (RFF_A_ag, RA_SH_bg, RA_T_bg) were similar at hummocks and hollows. As for RFF, drainage increased autotrophic respiration at hummocks, but only resulted in increased RA_T_bg at hollows. The greater RFF_A_ag and RA_SH_bg at hummocks (dominated by shrubs + herbs) was found to be related to BSH_ag and BSH_bg (Table 4 and Table 5), respectively, indicating that drainage-induced increase in shrubs + herbs biomass had a strong control on above and belowground autotrophic respiration components at hummock microforms, which is similar to the findings of several studies [44,70,71]. Minimal change in BSH_ag and BSH_bg at hollows (Table 3) resulted in the non-significant change in RFF_A_ag and RA_SH_bg. As tree roots likely extend across all microforms, particularly as the depth of aerated peat is thickened by WT drawdown, the overall increase in tree productivity in response to drainage [17] resulted in increased RA_T_bg across both hummocks and hollows. Although RFF_A_ag did not increase significantly at hollows following drainage, the slope of the RFF_A_ag versus BSH_ag became steeper, indicating increasing autotrophic respiration rates per unit increase in biomass. This may reflect the shift from moss dominated vegetation at the control site to more herbs and shrubs at the drained site, as vascular plants tend to have higher respiration rates than bryophytes [72,73].
Although RH increased at some microforms (i.e. drained hollows) in response to WT drawdown, there was no significant difference across the WT treatments sites (Table 5). We hypothesized that RFF increase would be greatest at hollows, as the WT drawdown would shift this microform position from largely anoxic to oxic conditions, resulting in large increases in RH that would drive shifts in RFF. We did observe substantial increases in RH at hollows following WT lowering; however, these were masked by autotrophic respiration. Previous studies have also reported that RFF_A_ag can account for the majority of peatland respiration [45]. As WT drawdown enhanced plant productivity at the study site [17], RH accounted for a declining proportion of growing season RFF from 48% at control to 36% at the drained site. Hummock RH did not change substantially in response to WT drawdown. As WT was initially deep below hummocks at the control site, due to the continental climate at the study site, the surface peat was well-aerated initially and RH was likely rarely limited by saturated conditions. Drainage may actually result in desiccation of the surface peat that could result in conditions too dry for optimal rates of RH [74]. The limited change in RH between the WT treatments is therefore partially driven by the differential microform response where RH is enhanced at hollows, with little change, or slight reduction at hummocks.
Overall, the substantial contribution of autotrophic respiration to RFF suggests that the large increase in RFF observed in response to WT drawdown will result in only slow loss of the C accumulated in peat, with these losses likely greatest at hollows [17]. However, comparison of RH is partially complicated by the fact that it was calculated by difference once all the other partitioned flux components were estimated, and thus also contains error associated with the estimation of individual components. Isolated partitioning of the source-based respiration components remains to be developed, although the few manipulative field experiments that have investigated how climate change factors interact with one another to alter soil respiration [41,75] were not able to separate soil respiration into its components without significantly disrupting the soil [28]. Separating peat soil C into various major components is an important challenge for improving our understanding of peatland C cycling response to climate change [76].

5. Conclusions

Experimental water-table (WT) drawdown in a treed boreal bog increased forest floor respiration (RFF). While all measured and estimated respiration components also increased following WT lowering, these were generally only significantly increased at hummock microforms. Increases in RFF at hummocks were largely driven by increases in autotrophic respiration as shrub biomass increased. Drainage increased heterotrophic respiration at hollows, with less response at hummocks, suggesting that carbon is more likely to be released from stored peat at hollows. Overall, shifts in RFF were largely driven by autotrophic respiration, indicating that rapid destabilization of peat carbon stocks under drying conditions are unlikely. Partitioning RFF into its subcomponents accurately and without substantial disturbance to the soil is difficult and the development of partitioning methods are needed to better understand the fate of peat carbon stocks under various disturbances.

Acknowledgments

This research was funded by Alberta Innovated Technology Futures to MS, and University of Calgary to TM. Supplementary awards “Queen Elizabeth II, John D. Petrie Award and Karl C. Iverson Award” to TM helped complete the research project. Tak Fung provided valuable input on statistical analyses. We thank Mendel Perkins for help with site set up and collection of data, and two anonymous reviewers for their helpful comments on an earlier draft of this manuscript.

Author Contributions

M.S. and T.M. conceived and designed the experiments; T.M., B.K. and B.X. performed the experiments; T.M. and M.S. analyzed the data; T.M. and M.S. wrote the paper.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Figure A1. Goodness of fit between measured (year 2012) and model estimated values of (a) forest floor respiration (RFF), (b) aboveground autotrophic respiration of forest floor (RFF_A_ag), and (c) belowground autotrophic (rhizospheric) respiration of tree roots, across sites and microforms. Lines represent 1:1 fit.
Figure A1. Goodness of fit between measured (year 2012) and model estimated values of (a) forest floor respiration (RFF), (b) aboveground autotrophic respiration of forest floor (RFF_A_ag), and (c) belowground autotrophic (rhizospheric) respiration of tree roots, across sites and microforms. Lines represent 1:1 fit.
Forests 08 00075 g006

References

  1. Page, S.E.; Rieley, J.O.; Banks, C.J. Global and regional importance of the tropical peatland carbon pool. Glob. Chang. Biol. 2011, 17, 798–818. [Google Scholar] [CrossRef]
  2. Loisel, J.; van Bellen, S.; Pelletier, L.; Talbot, J.; Hugelius, G.; Karran, D.; Yu, Z.; Nichols, J.; Holmquist, J. Insights and issues with estimating northern peatland carbon stocks and fluxes since the last glacial maximum. Earth-Sci. Rev. 2016. [Google Scholar] [CrossRef]
  3. Strack, M.; Cagampan, J.; Fard, G.H.; Keith, A.; Nugent, K.; Rankin, T.; Robinson, C.; Strachan, I.; Waddington, J.; Xu, B. Controls on plot-scale growing season CO2 and CH4 fluxes in restored peatlands: Do they differ from unrestored and natural sites? Mires Peat 2016, 17. [Google Scholar] [CrossRef]
  4. Raich, J.W.; Tufekcioglu, A. Vegetation and soil respiration: Correlations and controls. Biogeochemistry 2000, 48, 71–90. [Google Scholar] [CrossRef]
  5. Tarnocai, C. The effect of climate change on carbon in Canadian peatlands. Glob. Planet. Chang. 2006, 53, 222–232. [Google Scholar] [CrossRef]
  6. Peng, S.; Piao, S.; Wang, T.; Sun, J.; Shen, Z. Temperature sensitivity of soil respiration in different ecosystems in China. Soil Biol. Biochem. 2009, 41, 1008–1014. [Google Scholar] [CrossRef]
  7. Hiraishi, T.; Krug, T.; Tanabe, K.; Srivastava, N.; Baasansuren, J.; Fukuda, M.; Troxler, T. IPCC (2014) 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands; Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2014. [Google Scholar]
  8. Ise, T.; Dunn, A.L.; Wofsy, S.C.; Moorcroft, P.R. High sensitivity of peat decomposition to climate change through water-table feedback. Nat. Geosci. 2008, 1, 763–766. [Google Scholar] [CrossRef]
  9. Cai, T.; Flanagan, L.B.; Syed, K.H. Warmer and drier conditions stimulate respiration more than photosynthesis in a boreal peatland ecosystem: Analysis of automatic chambers and eddy covariance measurements. Plant Cell Environ. 2010, 33, 394–407. [Google Scholar] [CrossRef] [PubMed]
  10. Roulet, N.; Moore, T.I.M.; Bubier, J.; Lafleur, P. Northern fens: Methane flux and climatic change. Tellus B 1992, 44, 100–105. [Google Scholar] [CrossRef]
  11. Erwin, K.L. Wetlands and global climate change: The role of wetland restoration in a changing world. Wetl. Ecol. Manag. 2008, 17, 71. [Google Scholar] [CrossRef]
  12. Clymo, R.S.; Turunen, J.; Tolonen, K. Carbon accumulation in peatland. Oikos 1998, 81, 368–388. [Google Scholar] [CrossRef]
  13. Nykänen, H.; Alm, J.; Silvola, J.; Tolonen, K.; Martikainen, P.J. Methane fluxes on boreal peatlands of different fertility and the effect of long-term experimental lowering of the water table on flux rates. Glob. Biogeochem. Cycles 1998, 12, 53–69. [Google Scholar] [CrossRef]
  14. Bubier, J.L.; Bhatia, G.; Moore, T.R.; Roulet, N.T.; Lafleur, P.M. Spatial and temporal variability in growing-season net ecosystem carbon dioxide exchange at a large peatland in Ontario, Canada. Ecosystems 2003, 6, 353–367. [Google Scholar]
  15. Lafleur, P.M.; Moore, T.R.; Roulet, N.T.; Frolking, S. Ecosystem respiration in a cool temperate bog depends on peat temperature but not water table. Ecosystems 2005, 8, 619–629. [Google Scholar] [CrossRef]
  16. Strack, M.; Waddington, J.M.; Rochefort, L.; Tuittila, E.S. Response of vegetation and net ecosystem carbon dioxide exchange at different peatland microforms following water table drawdown. J. Geophys. Res. Biogeosci. 2006, 111. [Google Scholar] [CrossRef]
  17. Munir, T.M.; Perkins, M.; Kaing, E.; Strack, M. Carbon dioxide flux and net primary production of a boreal treed bog: Responses to warming and water-table-lowering simulations of climate change. Biogeosciences 2015, 12, 1091–1111. [Google Scholar] [CrossRef]
  18. Lee, K.-H.; Jose, S. Soil respiration, fine root production, and microbial biomass in cottonwood and loblolly pine plantations along a nitrogen fertilization gradient. For. Ecol. Manag. 2003, 185, 263–273. [Google Scholar] [CrossRef]
  19. Bond-Lamberty, B.; Bronson, D.; Bladyka, E.; Gower, S.T. A comparison of trenched plot techniques for partitioning soil respiration. Soil Biol. Biochem. 2011, 43, 2108–2114. [Google Scholar] [CrossRef]
  20. Shaw, C.; Bona, K.; Thompson, D.; Dimitrov, D.; Bhatti, J.; Hilger, A.; Webster, K.; Kurz, W. Canadian Model for Peatlands Version 1.0: Amodel Design Document; Northern Forestry Centre: Edmonton, AB, Canada, 2016. [Google Scholar]
  21. Ekblad, A.; Hogberg, P. Natural abundance of 13C in CO2 respired from forest soils reveals speed of link between tree photosynthesis and root respiration. Oecologia 2001, 127, 305–308. [Google Scholar] [CrossRef]
  22. Lalonde, R.G.; Prescott, C.E. Partitioning heterotrophic and rhizospheric soil respiration in a mature douglas-fir (pseudotsuga menziesii) forest. Can. J. For. Res. 2007, 37, 1287–1297. [Google Scholar] [CrossRef]
  23. Vitt, D.H.; Halsey, L.A.; Zoltai, S.C. The bog landforms of continental western Canada in relation to climate and permafrost patterns. Arct. Alp. Res. 1994, 26, 1–13. [Google Scholar] [CrossRef]
  24. Roulet, N.T.; Moore, T.R. The effect of forestry drainage practices on the emission of methane from northern peatlands. Can. J. For. Res. 1995, 25, 491–499. [Google Scholar] [CrossRef]
  25. Mäkiranta, P.; Minkkinen, K.; Hytönen, J.; Laine, J. Factors causing temporal and spatial variation in heterotrophic and rhizospheric components of soil respiration in afforested organic soil croplands in Finland. Soil Biol. Biochem. 2008, 40, 1592–1600. [Google Scholar] [CrossRef]
  26. Mäkiranta, P.; Laiho, R.; Fritze, H.; Hytönen, J.; Laine, J.; Minkkinen, K. Indirect regulation of heterotrophic peat soil respiration by water level via microbial community structure and temperature sensitivity. Soil Biol. Biochem. 2009, 41, 695–703. [Google Scholar] [CrossRef]
  27. Moore, T.R.; Bubier, J.L.; Frolking, S.E.; Lafleur, P.M.; Roulet, N.T. Plant biomass and production and CO2 exchange in an ombrotrophic bog. J. Ecol. 2002, 90, 25–36. [Google Scholar] [CrossRef]
  28. Hanson, P.J.; Edwards, N.T.; Garten, C.T.; Andrews, J.A. Separating root and soil microbial contributions to soil respiration: A review of methods and observations. Biogeochemistry 2000, 48, 115–146. [Google Scholar] [CrossRef]
  29. Murphy, M.; Laiho, R.; Moore, T.R. Effects of water table drawdown on root production and aboveground biomass in a boreal bog. Ecosystems 2009, 12, 1268–1282. [Google Scholar] [CrossRef]
  30. Murphy, M.T.; Moore, T.R. Linking root production to aboveground plant characteristics and water table in a temperate bog. Plant Soil 2010, 336, 219–231. [Google Scholar] [CrossRef]
  31. Davidson, E.A.; Janssens, I.A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 2006, 440, 165–173. [Google Scholar] [CrossRef] [PubMed]
  32. Chimner, R.A.; Pypker, T.G.; Hribljan, J.A.; Moore, P.A.; Waddington, J.M. Multi-decadal changes in water table levels alter peatland carbon cycling. Ecosystems 2016, 1–16. [Google Scholar] [CrossRef]
  33. Rustad, L.; Campbell, J.; Marion, G.; Norby, R.; Mitchell, M.; Hartley, A.; Cornelissen, J.; Gurevitch, J.; GCTE-NEWS. A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming. Oecologia 2001, 126, 543–562. [Google Scholar]
  34. Kunkel, V.; Wells, T.; Hancock, G. Soil temperature dynamics at the catchment scale. Geoderma 2016, 273, 32–44. [Google Scholar] [CrossRef]
  35. Alm, J.; Shurpali, N.J.; Minkkinen, K.; Aro, L.; Hytanen, J.; Lauriia, T.; Lohila, A.; Maijanen, M.; Martikainen, P.J.; Mäkiranta, P. Emission factors and their uncertainty for the exchange of CO2, CH4 and N2O in Finnish managed peatlands. Boreal Environ. Res. 2007, 12, 191–209. [Google Scholar]
  36. Ojanen, P.; Minkkinen, K.; Penttilä, T. The current greenhouse gas impact of forestry-drained boreal peatlands. For. Ecol. Manag. 2013, 289, 201–208. [Google Scholar] [CrossRef]
  37. Lemprière, T.C.; Kurz, W.A.; Hogg, E.H.; Schmoll, C.; Rampley, G.J.; Yemshanov, D.; McKenney, D.W.; Gilsenan, R.; Beatch, A.; Blain, D.; et al. Canadian boreal forests and climate change mitigation. Environ. Rev. 2013, 21, 293–321. [Google Scholar] [CrossRef]
  38. Jandl, R.; Bauhus, J.; Bolte, A.; Schindlbacher, A.; Schüler, S. Effect of climate-adapted forest management on carbon pools and greenhouse gas emissions. Curr. For. Rep. 2015, 1, 1–7. [Google Scholar] [CrossRef]
  39. Minkkinen, K.; Lame, J.; Shurpali, N.J.; Mäkiranta, P.; Alm, J.; Penttilä, T. Heterotrophic soil respiration in forestry-drained peatlands. Boreal Environ. Res. 2007, 12, 115–126. [Google Scholar]
  40. Strack, M.; Waddington, J.M.; Lucchese, M.C.; Cagampan, J.P. Moisture controls on CO2 exchange in a sphagnum-dominated peatland: Results from an extreme drought field experiment. Ecohydrology 2009, 2, 454–461. [Google Scholar] [CrossRef]
  41. Chen, X.; Post, W.M.; Norby, R.J.; Classen, A.T. Modeling soil respiration and variations in source components using a multi-factor global climate change experiment. Clim. Chang. 2011, 107, 459–480. [Google Scholar] [CrossRef]
  42. Jandl, R.; Rodeghiero, M.; Martinez, C.; Cotrufo, M.F.; Bampa, F.; van Wesemael, B.; Harrison, R.B.; Guerrini, I.A.; deB Richter, D.; Rustad, L. Current status, uncertainty and future needs in soil organic carbon monitoring. Sci. Total Environ. 2014, 468, 376–383. [Google Scholar] [CrossRef] [PubMed]
  43. Lafleur, P.M.; Roulet, N.T.; Bubier, J.L.; Frolking, S.; Moore, T.R. Interannual variability in the peatland-atmosphere carbon dioxide exchange at an ombrotrophic bog. Glob. Biogeochem. Cycles 2003, 17. [Google Scholar] [CrossRef]
  44. Munir, T.M.; Xu, B.; Perkins, M.; Strack, M. Responses of carbon dioxide flux and plant biomass to water table drawdown in a treed peatland in northern Alberta: A climate change perspective. Biogeosciences 2014, 11, 807–820. [Google Scholar] [CrossRef]
  45. Crow, S.E.; Wieder, R.K. Sources of CO2 emission from a northern peatland: Root respiration, exudation, and decomposition. Ecology 2005, 86, 1825–1834. [Google Scholar] [CrossRef]
  46. Mokany, K.; Raison, R.J.; Prokushkin, A.S. Critical analysis of root: Shoot ratios in terrestrial biomes. Glob. Chang. Biol. 2006, 12, 84–96. [Google Scholar] [CrossRef]
  47. Nadelhoffer, K.J.; Raich, J.W. Fine root production estimates and belowground carbon allocation in forest ecosystems. Ecology 1992, 73, 1139–1147. [Google Scholar] [CrossRef]
  48. Klepper, B. Root-shoot relationships. In Plant Roots: The Hidden Half; Dekker, M., Ed.; CRC Press: New York, NY, USA, 1991; pp. 265–286. [Google Scholar]
  49. Korrensalo, A.; Hajek, T.; Vesala, T.; Mehtatalo, L.; Tuittila, E.S. Variation in photosynthetic properties among bog plants. Botany 2016, 94, 1127–1139. [Google Scholar] [CrossRef]
  50. Schuur, E.A.G.; Trumbore, S.E. Partitioning sources of soil respiration in boreal black spruce forest using radiocarbon. Glob. Chang. Biol. 2006, 12, 165–176. [Google Scholar] [CrossRef]
  51. Biasi, C.; Pitkamaki, A.S.; Tavi, N.M.; Koponen, H.T.; Martikainen, P.J. An isotope approach based on 13C pulse-chase labelling vs. The root trenching method to separate heterotrophic and autotrophic respiration in cultivated peatlands. Boreal Environ. Res. 2012, 17, 184–193. [Google Scholar]
  52. Wang, X.G.; Zhu, B.; Wang, Y.Q.; Zheng, X.H. Field measures of the contribution of root respiration to soil respiration in an alder and cypress mixed plantation by two methods: Trenching method and root biomass regression method. Eur. J. For. Res. 2008, 127, 285–291. [Google Scholar] [CrossRef]
  53. Environment Canada. Canadian Climate Normals and Averages: 1971–2000. Natlonal Climate Data and Information Archives; Environment Canada: Fredericton, NB, Canada, 1 February 2010.
  54. DMTI Spatial. DMTI Canmap Postal Geograph (Retrieved from University of Calgary). DMTI Spatial [Producer(s)]: DMTI Spatial Mapping Academic Research Tools (SMART) Program; DMTI Spatial: Markham, ON, Canada, 2014. [Google Scholar]
  55. John, R. Flora of the Hudson Bay Lowland and Its Postglacial Origins; NRC Research Press: Ottawa, ON, Canada, 2003; p. 237. [Google Scholar]
  56. Heinemeyer, A.; di Bene, C.; Lloyd, A.R.; Tortorella, D.; Baxter, R.; Huntley, B.; Gelsomino, A.; Ineson, P. Soil respiration: Implications of the plant-soil continuum and respiration chamber collar-insertion depth on measurement and modelling of soil CO2. Eur. J. Soil Sci. 2011, 62, 82–94. [Google Scholar] [CrossRef]
  57. Lai, D.; Roulet, N.; Humphreys, E.; Moore, T.; Dalva, M. The effect of atmospheric turbulence and chamber deployment period on autochamber CO2 and CH4 flux measurements in an ombrotrophic peatland. Biogeosciences 2012, 9, 3305. [Google Scholar] [CrossRef]
  58. Koskinen, M.; Minkkinen, K.; Ojanen, P.; Kämäräinen, M.; Laurila, T.; Lohila, A. Measurements of CO2 exchange with an automated chamber system throughout the year: Challenges in measuring night-time respiration on porous peat soil. Biogeosciences 2014, 11, 347. [Google Scholar] [CrossRef]
  59. Loisel, J.; Gallego-Sala, A.V.; Yu, Z. Global-scale pattern of peatland sphagnum growth driven by photosynthetically active radiation and growing season length. Biogeosciences 2012, 9, 2737–2746. [Google Scholar] [CrossRef]
  60. Munir, T.M.; Strack, M. Methane flux influenced by experimental water table drawdown and soil warming in a dry boreal continental bog. Ecosystems 2014, 17, 1271–1285. [Google Scholar] [CrossRef]
  61. Clogg, C.C.; Petkova, E.; Haritou, A. Statistical methods for comparing regression coefficients between models. Am. J. Sociol. 1995, 100, 1261–1293. [Google Scholar] [CrossRef]
  62. von Arnold, K.; Hånell, B.; Stendahl, J.; Klemedtsson, L. Greenhouse gas fluxes from drained organic forestland in Sweden. Scand. J. For. Res. 2005, 20, 400–411. [Google Scholar] [CrossRef]
  63. Simola, H.; Pitkänen, A.; Turunen, J. Carbon loss in drained forestry peatlands in Finland, estimated by re-sampling peatlands surveyed in the 1980s. Eur. J. Soil Sci. 2012, 63, 798–807. [Google Scholar] [CrossRef]
  64. Aurela, M.; Riutta, T.; Laurila, T.; Tuovinen, J.-P.; Vesala, T.; Tuittila, E.-S.; Rinne, J.; Haapanala, S.; Laine, J. CO2 exchange of a sedge fen in southern Finland—The impact of a drought period. Tellus B 2007, 59, 826–837. [Google Scholar] [CrossRef]
  65. Straková, P.; Anttila, J.; Spetz, P.; Kitunen, V.; Tapanila, T.; Laiho, R. Litter quality and its response to water level drawdown in boreal peatlands at plant species and community level. Plant Soil 2010, 335, 501–520. [Google Scholar] [CrossRef]
  66. Strack, M.; Waddington, J.M.; Tuittila, E.S. Effect of water table drawdown on northern peatland methane dynamics: Implications for climate change. Glob. Biogeochem. Cycles 2004, 18. [Google Scholar] [CrossRef]
  67. Blodau, C.; Basiliko, N.; Moore, T.R. Carbon turnover in peatland mesocosms exposed to different water table levels. Biogeochemistry 2004, 67, 331–351. [Google Scholar] [CrossRef]
  68. Updegraff, K.; Bridgham, S.D.; Pastor, J.; Weishampel, P.; Harth, C. Response of CO2 and CH4 emissions from peatlands to warming and water table manipulation. Ecol. Appl. 2001, 11, 311–326. [Google Scholar]
  69. Waddington, J.M.; Roulet, N.T. Carbon balance of a boreal patterned peatland. Glob. Chang. Biol. 2000, 6, 87–97. [Google Scholar] [CrossRef]
  70. Laine, A.; Sottocornola, M.; Kiely, G.; Byrne, K.A.; Wilson, D.; Tuittila, E.-S. Estimating net ecosystem exchange in a patterned ecosystem: Example from blanket bog. Agric. For. Meteorol. 2006, 138, 231–243. [Google Scholar] [CrossRef]
  71. Laiho, R. Decomposition in peatlands: Reconciling seemingly contrasting results on the impacts of lowered water levels. Soil Biol. Biochem. 2006, 38, 2011–2024. [Google Scholar] [CrossRef]
  72. Riutta, T.; Laine, J.; Tuittila, E.-S. Sensitivity of CO2 exchange of fen ecosystem components to water level variation. Ecosystems 2007, 10, 718–733. [Google Scholar] [CrossRef]
  73. Ward, S.E.; Ostle, N.J.; Oakley, S.; Quirk, H.; Henrys, P.A.; Bardgett, R.D. Warming effects on greenhouse gas fluxes in peatlands are modulated by vegetation composition. Ecol. Lett. 2013, 16, 1285–1293. [Google Scholar] [CrossRef] [PubMed]
  74. Tuittila, E.-S.; Vasander, H.; Laine, J. Sensitivity of c sequestration in reintroduced sphagnum to water-level variation in a cutaway peatland. Restor. Ecol. 2004, 12, 483–493. [Google Scholar] [CrossRef]
  75. Wan, S.; Norby, R.J.; Ledford, J.; Weltzin, J.F. Responses of soil respiration to elevated CO2, air warming, and changing soil water availability in a model old-field grassland. Glob. Chang. Biol. 2007, 13, 2411–2424. [Google Scholar] [CrossRef]
  76. Fahey, T.J.; Tierney, G.L.; Fitzhugh, R.D.; Wilson, G.F.; Siccama, T.G. Soil respiration and soil carbon balance in a northern hardwood forest ecosystem. Can. J. For. Res. 2005, 35, 244–253. [Google Scholar] [CrossRef]
Figure 1. Geographical map of the Wandering River study sites located in a forested peatland complex within boreal forest in Alberta, Canada [54].
Figure 1. Geographical map of the Wandering River study sites located in a forested peatland complex within boreal forest in Alberta, Canada [54].
Forests 08 00075 g001
Figure 2. Daily mean site air temperature (°C), soil temperature (°C) at 5 cm depth, total precipitation (mm), representative hollow and hummock water-table (WT) level (cm) over the 2012 growing season (May to October). Note the two y-axes on the right side: daily mean WT level, and air and soil temperatures.
Figure 2. Daily mean site air temperature (°C), soil temperature (°C) at 5 cm depth, total precipitation (mm), representative hollow and hummock water-table (WT) level (cm) over the 2012 growing season (May to October). Note the two y-axes on the right side: daily mean WT level, and air and soil temperatures.
Forests 08 00075 g002
Figure 3. (a) Forest floor respiration flux (RFF; g·CO2·m−2·day−1), aboveground autotrophic respiration flux of forest floor (RFF_A_ag; g·CO2·m−2·day−1), belowground autotrophic respiration of shrubs + herbs (RA_SH_bg; g·CO2·m−2·day−1), and belowground autotrophic respiration of tree roots (RA_T_bg; g·CO2·m−2·day−1) versus soil temperature at −5 cm (T5) and (b) RFF, RFF_A_ag, RA_SH_bg, and RA_T_bg versus water-table (WT) level, at all sites/microforms. All the respiration components were statistically related to both the T5 and WT level.
Figure 3. (a) Forest floor respiration flux (RFF; g·CO2·m−2·day−1), aboveground autotrophic respiration flux of forest floor (RFF_A_ag; g·CO2·m−2·day−1), belowground autotrophic respiration of shrubs + herbs (RA_SH_bg; g·CO2·m−2·day−1), and belowground autotrophic respiration of tree roots (RA_T_bg; g·CO2·m−2·day−1) versus soil temperature at −5 cm (T5) and (b) RFF, RFF_A_ag, RA_SH_bg, and RA_T_bg versus water-table (WT) level, at all sites/microforms. All the respiration components were statistically related to both the T5 and WT level.
Forests 08 00075 g003
Figure 4. Mean (±SD) RFF, RFF_A_ag, RA_SH_bg, and RA_T_bg measured during the growing season (May to October) of 2012 at all sites/microforms. Mean RA_SH_bg was determined by using a biomass regression method (Table 3 and Table 4; Figure 5) [46,52]. Bars having no letters in common are significantly different (p < 0.05) while bars with same letters indicate no significant difference (p > 0.05); letters should be compared only within one flux component across all microforms.
Figure 4. Mean (±SD) RFF, RFF_A_ag, RA_SH_bg, and RA_T_bg measured during the growing season (May to October) of 2012 at all sites/microforms. Mean RA_SH_bg was determined by using a biomass regression method (Table 3 and Table 4; Figure 5) [46,52]. Bars having no letters in common are significantly different (p < 0.05) while bars with same letters indicate no significant difference (p > 0.05); letters should be compared only within one flux component across all microforms.
Forests 08 00075 g004
Figure 5. Aboveground autotrophic respiration of forest floor (RFF_A_ag; g·CO2·m−2·day−1) versus aboveground biomass of shrubs + herbs (BSH_ag; g·m−2) at (a) hummock and at (b) hollow microforms at each site. Regression lines were plotted for each microform type at each site when statistically significant at p < 0.05.
Figure 5. Aboveground autotrophic respiration of forest floor (RFF_A_ag; g·CO2·m−2·day−1) versus aboveground biomass of shrubs + herbs (BSH_ag; g·m−2) at (a) hummock and at (b) hollow microforms at each site. Regression lines were plotted for each microform type at each site when statistically significant at p < 0.05.
Forests 08 00075 g005
Table 1. Fitted model parameters, their values (±SE), residual standard error (RSE), p values, adjusted r2, and the number of values (n) included in the regression analyses for the forest floor respiration (RFF), forest floor aboveground autotrophic respiration (RFF_A_ag), and belowground autotrophic respiration of tree roots (RA_T_bg) models (Equation (2)) *.
Table 1. Fitted model parameters, their values (±SE), residual standard error (RSE), p values, adjusted r2, and the number of values (n) included in the regression analyses for the forest floor respiration (RFF), forest floor aboveground autotrophic respiration (RFF_A_ag), and belowground autotrophic respiration of tree roots (RA_T_bg) models (Equation (2)) *.
Site/MicroformFluxabcRSEpr2n
Dimensionlessg·CO2·m−2·day−1
Control
HummockRFF0.52 ± 0.14−0.47 ± 0.15−19.90 ± 5.11.09<0.0010.8024
RFF_A_ag0.36 ± 0.10−0.45 ± 0.13−22.51 ± 5.31.16<0.0010.6724
RA_T_bg0.43 ± 0.170.25 ± 0.177.88 ± 5.11.460.0440.1726
HollowRFF1.08 ± 0.21−0.29 ± 0.14−15.74 ± 5.12.45<0.0010.6124
RFF_A_ag0.85 ± 0.19−0.54 ± 0.18−24.59 ± 5.51.55<0.0010.7524
RA_T_bg0.86 ± 0.23−0.21 ± 0.1414.80 ± 5.72.340.0020.4322
Experimental
HummockRFF0.05 ± 0.10−0.24 ± 0.11−15.67 ± 11.80.240.0100.778
RFF_A_ag0.96 ± 0.50−0.83 ± 0.57−86.98 ± 55. 92.020.0490.588
RA_T_bg0.91 ± 0.52−0.72 ± 0.63−75.24 ± 61.51.240.0320.836
HollowRFF2.59 ± 0.800.70 ± 0.09−15.09 ± 13.21.590.0580.558
RFF_A_ag0.82 ± 1.60−0.27 ± 0.18−29.44 ± 10.02.560.0610.548
RA_T_bg0.94 ± 0.39−0.69 ± 0.87−61.08 ± 70.12.060.0210.797
Drained
HummockRFF−0.32 ± 0.16−1.40 ± 0.20−167.53 ± 30.93.27<0.0010.7624
RFF_A_ag0.93 ± 0.31−0.49 ± 0.18−70.88 ± 19.32.43<0.0010.7524
RA_T_bg1.69 ± 0.350.39 ± 0.6434.86 ± 89.92.41<0.0010.7924
HollowRFF0.79 ± 0.33−0.58 ± 0.17−61.70 ± 16.92.29<0.0010.7020
RFF_A_ag0.70 ± 0.23−0.32 ± 0.10−40.53 ± 10.62.15<0.0010.5624
RA_T_bg0.26 ± 0.19−1.08 ± 0.42−113.87 ± 46.41.28<0.0010.7725
* RFF, RFF_A_ag, and RA_T_bg models were developed for each microform type (n = 3) at the control, experimental, and drained sites for the growing season of 2012. a and b are soil temperatures at 5 cm depth and WT level (below-ground) coefficients, respectively, and c is a regression constant. All modeled parameters are significant at α = 0.05.
Table 2. Statistical results of repeated, linear mixed effects models. The models tested the fixed effects of site and microform on respiration of forest floor (RFF), aboveground autotrophic respiration of forest floor (RFF_A_ag), and belowground autotrophic respiration of shrubs + herbs and trees (RA_SH_bg and RA_T_bg, respectively), separately *.
Table 2. Statistical results of repeated, linear mixed effects models. The models tested the fixed effects of site and microform on respiration of forest floor (RFF), aboveground autotrophic respiration of forest floor (RFF_A_ag), and belowground autotrophic respiration of shrubs + herbs and trees (RA_SH_bg and RA_T_bg, respectively), separately *.
EffectFlux Component
RFFRFF_A_agRA_SH_bgRA_T_bg
FpFpFpFp
SiteF2, 7 = 8.10.015F2, 15 = 4.90.023F2, 12 = 25.3<0.001F2, 12 = 6.40.012
MicroformF1, 7 = 6.90.033F1, 15 = 10.30.005F2, 12 = 44.4<0.001F2, 27 = 2.50.123
Site × MicroformF2, 7 = 3.50.090F2, 15 = 4.10.037F2, 12 = 35.6<0.001F2, 12 = 0.90.437
* All models included a random effect of plot at sites to account for repeated measurements made at each site.
Table 3. Estimated aboveground biomass of shrubs + herbs (BSH_ag) and trees (BT_bg), and belowground biomass of shrubs + herbs and trees (BSH_bg), at control, experimental, and drained sites *. All values (±SD) are in g·m−2.
Table 3. Estimated aboveground biomass of shrubs + herbs (BSH_ag) and trees (BT_bg), and belowground biomass of shrubs + herbs and trees (BSH_bg), at control, experimental, and drained sites *. All values (±SD) are in g·m−2.
Vascular vegetationControlExperimentalDrained
SiteHummockHollowSiteHummockHollowSiteHummockHollow
Shrubs + herbs
BSH_ag90 ± 41110 ± 5465 ± 2762 ± 3470 ± 3252 ± 36152 ± 103245 ± 18052 ± 27
BSH_bg35 ± 1643 ± 2125 ± 1024 ± 127 ± 0.620 ± 159 ± 3595 ± 7020 ± 1
Tree
BT_ag2142 ± 376 1986 1964 ± 381
* all values are mean ± SD (n = 6 for each of the BSH_ag and BSH_bg, and n = 3 (except experimental site with n = 1) for BT_ag) [17]. Site biomass was determined by weighting the forest floor by the proportion of hummock and hollow microforms at each site (hummocks: control = 56%, experimental = 55%, drained = 52%), where applicable.
Table 4. Statistical results of a repeated, linear mixed effects model with fixed effects of site (control, experimental, drained), microform (hummock, hollow), and aboveground biomass of shrub + herb (BSH_ag; covariate), random effect of plot, and an outcome variable of aboveground autotrophic respiration of shrubs + herbs at the forest floor (RFF_A_ag) *.
Table 4. Statistical results of a repeated, linear mixed effects model with fixed effects of site (control, experimental, drained), microform (hummock, hollow), and aboveground biomass of shrub + herb (BSH_ag; covariate), random effect of plot, and an outcome variable of aboveground autotrophic respiration of shrubs + herbs at the forest floor (RFF_A_ag) *.
RespirationEffectFp
RFF_A_agBSH_agF1, 34 = 10.630.003
SiteF2, 34 = 4.090.026
MicroformF1, 34 = 6.000.020
BSH_ag × MicroformF1, 34 = 9.200.005
Site × MicroformF2, 34 = 9.570.001
BSH_ag × Site × MicroformF4, 34 = 3.930.010
* Random effect of plot was included in the model to account for the repeated measurements made at each site.
Table 5. Partitioning of the growing season (May to October, 2012) forest floor respiration (±SE; g·CO2-C·m−2) into major flux components *.
Table 5. Partitioning of the growing season (May to October, 2012) forest floor respiration (±SE; g·CO2-C·m−2) into major flux components *.
SiteMicroformRFFRFF_A_agRA_SH_bgRA_T_bgRH **
Control 255 ± 10.3 a38 ± 6.7 a37 ± 8.0 a58 ± 8.9 a122 ± 33.9 a
Hummock282 ± 4.6 a,b42 ± 2.1 a,b36 ± 6.2 a,b80 ± 3.9 a124 ± 16.8 a
Hollow221 ± 5.8 a33 ± 4.6 a,b39 ± 1.8 a,b30 ± 5.1 b119 ± 17.3 a
Experimental 354 ± 15.7 a,b45 ± 11.8 a40 ± 21.9 a117 ± 11.1 a,b152 ± 60.5 a
Hummock361 ± 2.2 a,b57 ± 6.5 a,b43 ± 6.8 a,b101 ± 17.4 a160 ± 22.9 a
Hollow346 ± 13.6 a,b31 ± 5.3 a,b36 ± 15.1 a,b137 ± 13.8 a142 ± 37.8 a
Drained 422 ± 21.9 b66 ± 23.4 a51 ± 22.7 a150 ± 9.0 b155 ± 77.0 a
Hummock429 ± 15.7 b108 ± 11.2 a69 ± 19.9 a147 ± 5.2 c105 ± 52.0 a
Hollow415 ± 6.2 b21 ± 12.2 b,c33 ± 2.8 b,c154 ± 3.8 c207 ± 25.0 a
* RFF, RFF_A_ag, and RA_T_bg denote forest floor respiration, above-ground autotrophic respiration of forest floor, and belowground autotrophic respiration of tree, respectively. RA_SH_bg represents belowground shrub + herb autotrophic respiration and was determined using biomass regression method (Table 2 and Table 3). The seasonal value of RA_SH_bg is calculated by determining it as a proportion of instantaneous RFF value and then estimating it as this proportion of the modeled seasonal RFF. ** RH was determined by difference using Equation (2). Values are significantly different if they have no letter in common; letters should be compared only within one flux component in a column between sites or microforms.
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top