The Evaluation of Small- and Medium-Stream Carbon Pools in the Riparian Forests in Latvia

Abstract

Riparian forests are considered to be productive transitional zones between aquatic and terrestrial environments. Despite the complex systems of riparian forests, little is known about their potential for carbon storage, particularly under temperate climates. This study aimed to calculate the major carbon pools (woody biomass) of riparian forests surrounding small and medium streams in the hemiboreal zone in Latvia. The estimated woody carbon pool of the studied riparian forests was comparable to that in commercially managed forest stands within the region. The total woody biomass carbon pool was 141.6 ± 62.5 Mg C ha⁻¹, the majority of which was formed by stem biomass. Similar to other studies, there was high spatial variability, while significant differences were observed between dominant tree species. The results suggest that the carbon storage of the studied riparian forests was not as high as it was expected to be; however, the results did not include soil carbon pool assessment. Grey alder stands, which are considered undesirable near streams due to erosion and nutrient leakage, formed a significantly lower carbon pool, supporting their management for the multipurpose goal of riparian forests.

1. Introduction

Riparian forests are the interface between aquatic and terrestrial ecosystems, connecting and regulating both environments [1,2,3,4]. The functioning and hydromorphology of rivers are influenced by riparian and aquatic vegetation [5,6], as plant roots affect mechanical and hydraulic soil properties, which stabilize banks and soil moisture regimes [7]. Living and dead trees protect riverbanks, reinforce floodplains, stabilize landforms and influence the direction and resistance of flow [7]. Despite the spatio-temporal variations, the importance of riparian forests far exceeds their proportion of land cover [8], as they provide diverse services [9,10,11] and might have a long-term potential for carbon storage [12].

Riparian forests are considered to be carbon sinks, and thus they contribute to sequestration [12,13]. In riparian forests, carbon is stored in four major reservoirs of standing biomass, lying deadwood, sediment, and in-stream biomass [8], which are dynamically altered by floods and underlying processes [8,14]. Sedimentation, the availability of nutrients and water determine species composition, distribution and density of riparian forests [13,15], mostly facilitating biomass accumulation [8,16,17]. However, floodplain width, the gradient of inundation, valley geometry, channel complexity and flow regime can have stronger effects on carbon storage than on stand diversity [8], particularly under a changing climate [12,18,19].

The highest uncertainty regarding the carbon storage of riparian forests arises from the lack of knowledge across diverse climates [12]. Carbon stocks in riparian forests are known to differ greatly across regions, as well as in local areas [8]. Higher storage is being observed under cool climates, and lower under semi-arid climates [17], due to decay rates, water availability and primary productivity [17]. Within the boreal zone, and within arid, temperate and tropical regions, carbon storage appears to be highly variable across thermal and moisture regimes [20]. This highlights the necessity for local estimates of carbon storage for the accurate assessment of carbon budget [21]. However, studies on carbon stocks of riparian forests are scarce and most of them have been conducted in South and North America [15,18,22] with limited information about Europe [12,13,16,23,24,25,26].

Due to the explicit functional importance of riparian forests [2,3], their management meets various degrees of restrictions [27,28,29]. In Fennoscandia, the retention and management of buffer strips rely on forest certification [27]. In the Baltic countries, where the frequency of small and medium streams is high [28,29], the management of riparian forests is restricted by national legislation [27], resulting in up to 500 m wide buffer strips [27,28]. In these strips, harvesting is generally prohibited, with the exception of grey alder (Alnus incana) stands, which might be clear-cut in small areas (1 ha) with a retention of hardwoods to prevent erosion and nutrient leakage [30]. Nevertheless, there are uncertainties regarding the carbon stock under the restrictedly managed riparian forests in the Baltics.

The aim of this study was to assess the carbon storage of riparian forests surrounding small and medium streams in the hemiboreal region in Latvia. We hypothesized that the carbon storage of riparian forests would exceed other forests of similar composition and age.

2. Materials and Methods

2.1. Site Selection and Sampling

To assess carbon storage in riparian forests in the hemiboreal zone in Latvia (55°40′–58°05′ N and 20°58′–28°14′ E), where relatively flat terrain, a humid climate and geological structure have formed a dense network of streams [31], 15 small (length < 40 km) and medium (length 40–70 km) rivers were selected (Figure 1). Additional selection criteria were that rivers had to flow through forested area with a minimum width of 300 m and length of 4 km. The selected rivers were of potamal and ritral types and their catchment areas and mean water slopes ranged from 6 to 526 km² and from 0.1 to 1.8 m/km, respectively (

Table 1. The description of the rivers surrounded by the studied riparian forest stands and most common forest types within them. The forest types are provided according to eastern Baltic phytocoenological classification.

River	Length, km	Catchment Area, km2	Mean Stream Volume, m3/s	Mean Slope, m/km	River Sediment	River Type	Most Common Type of Surrounding Forest Stands
Abuls	52	435	3.2	1.3	boulders, pebble, gravel, sand, mud	medium ritral	Hylocomiosa
Jaunupe	5	6	0.1	0.1	No data	small potamal	Hylocomiosa, Aegopodiosa Myrtillosoi-polytrichosa,
Koja	25	88	1.0	0.7	No data	small potamal	Myrtillosa, Hylocomiosa, Myrtilloso-sphagnosa, Filipendulosa
Korģe	14	113	0.9	1.1	boulder, pebble, gravel, sand	medium ritral	Oxalidosa, Aegopodiosa
Līgatne	31	89	1.1	1.8	boulder, pebble, gravel, sand	small ritral	Oxalidosa
Rīva	53	213	2.4	0.7	gravel, sand, mud	medium potamal	Cladinoso-callunosa, Myrtillosa Hylocomniosa
Seda	62	526	4.4	0.2	gravel, sand, mud	medium potamal	Myrtillosa, Hylocomniosa, Myrtillosa mel
Sidrabe	49	147	0.6	0.6	No data	medium potamal	Hylocomniosa, Oxalidosa, Aegopodiosa
Svētaine	18	43	0.2	0.4	sand, mud	small potamal	Hylocomniosa, Myrtilloso-sphagnosa
Svētupe	58	479	4.2	0.5	gravel, sand, mud	medium potamal	Oxalidosa, Aegopodiosa, Oxalidosa turf. mel.
Tērvete	68	440	2.6	0.7	gravel, sand, mud	medium potamal	Hylocomniosa, Myrtilloso-polytrichosa
Vilce	48	313	1.5	0.6	sand, mud	medium potamal	Hylocomniosa, Oxalidosa, Aegopodiosa
Vildoga	10	25	0.3	1.0	No data	small ritral	Oxalidosa
Vitrupe	49	193	1.7	0.5	pebble, sand, mud, clay	medium potamal	Oxalidosa, Aegopodiosa, Vaccinioso-sphagnosa, Dryopteriosa
Zaņa	48	256	2.2	0.8	pebble, gravel, sand, mud	medium potamal	Oxalidosa

). The streams were geologically new, implying the ongoing penetration of bedrock and active deposition of the eroded material [31]. The rivers flew through post-glacial sediments including moraine, hence their sediment ranged from mud to boulders (Table 1). During the past 100 years in Latvia, many streams have been managed (e.g., straightened), substantially altering the surrounding landscape and forests.

Within each forested area, three 80 m transects were established perpendicular to flow. The selected forested areas consisted of oligotrophic to eutrophic mixed and monospecies stands. The preferred distance between the transects within the forest area was 1 km; however, their position could be moved to different areas of the riparian forests without visual signs of recent management (in the past 20 years). On each transect, two 20 × 20 m sample plots were established at a 10 (adjacent) and 60 (distant) m distance from the stream bank (ninety sample plots were established in total). All of the sample plots were located in the 100 m buffer strips of a stream.

Most of the studied streams were surrounded by riparian forests of diverse stand types (Table 1). Nevertheless, the sampled stands, were dominated by grey alder (Alnus incana), Norway spruce (Picea abies) and Scots pine (Pinus Sylvestris), according to the relative basal area per sample plot. Grey alder formed pure stands or had a slight admixture of spruce, silver birch (Betula pendula) and wych elm (Ulmus glabra). The spruce and pine stands were either pure or mutually mixed. The understory of the studied stands was mostly formed by spruce, with admixture of elm, birch, spruce, small-leaved lime (Tillia cordata) and bird cherry (Padus avium).

Within each sample plot, diameter at breast height (DBH) of all living trees ≥ 6.1 cm (due to convenience of measurements) was measured and their affiliations with the canopy layer were noted. No height threshold was used. Heights of 10–15 living canopy trees, representing the distribution of DBH of stands, were measured with an accuracy of 0.2 m. For the standing dead trees, DBH of ≥6.1 cm, height, species and decay stage were recorded. Within the plot, length (≥1.0 m) and diameter of lying deadwood (diameter at thicker end ≥ 6.1 cm) were measured and decay stage at both ends was noted, and species were identified if possible. The decay stage of standing and lying deadwood was determined according to three-class classification (recently dead, slightly decayed, and moderately to almost completely decomposed) by the “knife” method [32].

2.2. Data Analysis

For each tree in the sampling plots, height was estimated based on DBH according to Näslund’s and Gaffrey’s approach [33]. The volume of stems of living and standing dead trees with tops was calculated according to a local equation [34], based on the measured DBH and the estimated tree heights.

Stand age was obtained from the database of State Forest Service of Latvia in 2021. The total above- and belowground tree biomass (woody organs without foliage) was calculated based on DBH and tree height according to local equations by Liepiņš et al. [35] for commercially important species. For other species, calculations were appropriated according to systematic affiliation. Considering the lack of instrumental measurements of carbon content in riparian forests within the region [8], the value of 50% was used for estimation [36,37].

The volume of lying deadwood was calculated by Huber’s formula (1), and it was converted to biomass using decay class-specific density and carbon content for the main tree species [32]. The deadwood carbon storage was estimated applying decay stage-specific density and carbon content, according to Köster et al. [32]:

$$V=\frac{L \pi d_m{}^2}{4}$$

(1)

where V is the volume, L is the length of the lying deadwood and d_m is the mid-diameter of deadwood.

The carbon storage (pool) of living tree biomass and deadwood depending on stand volume, dominant tree species and distance from stream and size and type of river were assessed with a linear mixed effect model. Such a model was used to account for hierarchical structure, and thus the dependencies of the data. The model in general form was:

$$y=\mathit{\mu }+M+S+D+L+T+\epsilon$$

(2)

where y is the carbon pools, M is the stand volume (as numeric covariate), S is the dominant species, D is the distance from stream bank, L is the stream size and T is the stream type. The river was used as the random effect. The significance of the fixed effects was determined using type II Wald χ² tests. The levels of significant fixed effects were compared using Tukey post hoc test. Model assumptions were checked by diagnostic plots. The models were estimated in the program R v.4.1.1. [38], using package “lme4” [39].

3. Results and Discussion

The studied stands were structurally and compositionally diverse as both canopy and understory trees were present. According to inventory data, the studied stands represented a wide spectrum of age, ranging from 19 to 239 years, with a mean age of 82 years. The DBH of canopy trees within the stands greatly varied, ranging from 11.3 to 55.0 cm, with a mean (±standard deviation) of 31.9 ± 10.8 cm. The mean DBH of understory trees ranged from 7.4 to 26.3 cm, with a mean value of 13.7 ± 4.0 cm. Nevertheless, the mean DBH of all measured trees showed a lower variability (19.1 ± 2.7 cm). The height of the measured canopy trees ranged from 10.2 to 37.0, with the mean value of 24.3 ± 5.7 m (

Table 2. Standing structural parameters of the studied riparian forest stands of small-to-medium rivers in Latvia according to distance from the stream bank. The mean values (±standard deviation) are shown. DBH—diameter at breast height.

Parameters	Adjacent (10–30 m)	Distant (60–80 m)
DBH of canopy trees, cm	31.0 ± 6.4	32.9 ± 7.0
DBH of understory, cm	13.3 ± 2.2	14.1 ± 2.6
Height of canopy trees, m	23.9 ± 3.9	24.7 ± 3.4
Height of understory, m	12.6 ± 2.2	12.8 ± 2.7
Basal area, m2 ha−1	36.2 ± 9.1	36.6 ±6.8
Standing volume, m3 ha−1	385.9 ± 146.2	406.0 ± 114.1
Stand density, trees ha−1	968 ± 190	902 ± 216

). These values fall within the range of stands of a similar type and age in non-riparian forests, where, according to the data from the National Forest Inventory for 2014–2018, the mean DBH and tree height were 31.0 ± 9.1 cm and 25.2 ± 5.3 m, respectively.

The density of the studied stands ranged from 325 to 2600 trees ha⁻¹, (the mean of 934 ± 367 trees ha⁻¹), resulting in a mean stand basal and standing volume of living trees of 36.4 ± 13.4 m² ha⁻¹ and 396.1 ± 205.6 m³ ha⁻¹, respectively. The volume of deadwood was highly variable among stands. The mean volume of deadwood was 43.3 ± 48.9 m³ ha⁻¹, ranging from 0.0 to 256.8 m³ ha⁻¹. The mean volume of standing and lying deadwood was similar (18.5 ± 27.2 and 24.1 ± 26.2 m³ ha⁻¹, respectively). The standing deadwood was formed by recently dead, weakly and moderately to almost completely decomposed trees (8.3 ± 3.9, 3.1 ± 2.7 and 2.5 ± 6.3 m³ ha⁻¹, respectively). The volume of lying deadwood was in similar decay stages (3.5 ± 1.5, 3.7 ± 1.8 and 2.2 ± 1.6 m³ ha⁻¹ for recently dead, weakly decomposed and moderately to almost completely decomposed, respectively). The amount of deadwood, however, was lower compared to unmanaged aspen and birch stands of a similar age in Latvia [40,41].

The studied riparian stands were estimated to contain 141.6 ± 62.5 Mg C ha⁻¹ (ranging from 35.0 to 360.0 Mg C ha⁻¹) in total, with the majority being living tree biomass (134.6 ± 61.3 Mg C ha⁻¹), from which 94% was stem biomass (

Table 3. The estimated woody carbon pools (mean ± standard deviation, Mg C ha⁻¹) of the studied small and medium river riparian forest stands in Latvia according to distance from the bank of stream.

Carbon Pool	Adjacent Zone	Distant Zone
Aboveground tree biomass	102.8 ± 39.7	100.0 ± 30.8
Belowground tree biomass	32.7 ± 17.0	36.9 ± 32.0
Living tree biomass	134.5 ± 44.2	134.7 ± 31.7
Total deadwood	6.8 ± 3.1	7.0 ± 4.6
Total carbon	141.4 ± 45.7	141.7 ± 33.3

), highlighting their relevance [8]. The estimated belowground tree carbon pool was 34.8 ± 27.6 Mg C ha⁻¹, ranging from 2.8 to 185.0 Mg C ha⁻¹. The deadwood accounted for the smallest carbon pool with the estimated mean value of 7.0 ± 1.4 Mg C ha⁻¹. Stands growing in riparian areas are exposed to hydrological conditions, which can change the lateral and vertical structure, affecting biomass carbon pools [8,16,42]. However, the estimates of carbon pools (Table 3) were comparable to commercially managed forest stands within the region [12,16,42,43,44], despite differing climatic conditions [6]. This still suggests that studied riparian forests could provide a long-term potential for carbon storage [12]. The high structural diversity of woody carbon pools (both living and deadwood) and shifting gradient of disturbances increases the complexity of carbon dynamics [6,7,8], raising uncertainties about long-term carbon storage. On the other hand, the structural and functional diversity of riparian forests are a precondition for increased biodiversity, providing distribution corridors for specialist species [5]. Similar to volume, the carbon pool of deadwood was comparable with non-riparian deciduous stands within the region [40,41]. This hints that studied riparian stands of small and medium streams are average in terms of carbon storage, despite the high expectations [15,45], even though management has been restricted, facilitating biomass accumulation [27,29,30]. Nevertheless, local data are crucial for accurate national estimates of carbon budget [21]. However, soil carbon pool was not included in the estimates, thus affecting the results of the total carbon pool. Furthermore, all of the riparian forests should be evaluated based on the complex services they provide, including biodiversity conservation and water protection, which are dependent on both living and deadwood biomass [2,3,16,42].

As expected, the total standing volume appeared as a good and highly significant predictor (covariate) of the largest carbon pool (

Table 4. The effect (χ²) of distance, dominant tree species, size and type of river and total wood volume (fixed effects), and the variance of river (as random effect) on carbon pools of the studied small and medium river riparian forest stands in Latvia. The asterisks denote statistical significance (p-values) of the fixed effects: p < 0.01, and *** p < 0.001.

Carbon Pool	Total (Woody)	Living Tree Biomass	Aboveground Biomass	Belowground Biomass	Deadwood
Fixed effect, χ2
Distance	1.1	1.3	3.3	0.6	0.06
Dominant species	37.5 ***	60.6***	25.2 ***	9.1	2.2
River size	0.9	1.5	0.1	0.4	0.1
River type	2.1	0.9	0.5	0.2	1.3
Total wood volume	1289.9 ***	2278.5 ***	1501.5 ***	96.6 ***	0.1
Random effect, variance
River (object)	21.9	1.7	400.0	728.8	9.6
Residual	146.1	82.5	70.1	82.6	63.4

), as stems account for most of tree biomass [35], and a constant carbon content was presumed. Although, the productivity of biomass accumulation and carbon storage of riparian forests is increased by sedimentation, particularly closer to a stream [8], the distance from the bank of the stream lacked a significant effect (Table 4), implying comparable conditions (Table 3). This might also be an artefact of the sampling design, as the distance of sampling plots (Figure S1) from streams might not be sufficient for the estimation of the effect of hydrogeomorphological processes, such as sedimentation. Nevertheless, the non-significant effect of distance was also supported by the lack of a significant effect on the river size and type; however, the variability among rivers, as indicated by random effect variance, was particularly high for above- and belowground biomass carbon pools (Table 4). The differences in random variances of river for living biomass and its component pools (above- and belowground biomass) (Table 4) suggested a differing trade-off in carbon allocation, likely due to geomorphological differences [42].

Among other studied factors, dominant tree species had a significant effect on total, living and aboveground biomass carbon pool in the studied forest stands (p < 0.001) (Table 4), similarly to other studies [12]. The highest carbon storage of aboveground biomass (without foliage) was estimated for silver birch, while the lowest was estimated for Norway spruce and Scots pine stands (

Table 5. The estimated marginal means (±standard error) of total woody, living and aboveground biomass carbon pools according to dominant tree species of the studied small and medium rivers of riparian forest stands in Latvia. The similar letters (as indices) indicate the lack of pairwise significant differences according to the Tukey’s post hoc test.

Dominant Species	Total (Woody)	Living Tree Biomass	Aboveground Tree Biomass
European aspen	134 ± 12.7 ab	127 ± 9.6 abc	102 ± 11.1 ab
Silver birch	155 ± 4.4 a	148 ± 3.4 ab	114 ± 6.9 a
Grey alder	136 ± 3.1 b	131 ± 2.4 c	104 ± 6.4 ab
Norway spruce	145 ± 2.5 ab	136 ± 2.0 ac	101 ± 6.3 b
Small-leaved lime	168 ± 9.0 a	160 ± 6.8 b	116 ± 8.8 ab
Black alder	163 ± 9.0 ab	157 ± 6.8 ab	119 ± 8.9 ab
Scots pine	137 ± 3.2 b	128 ± 2.4 c	99 ± 6.5 b
Willow	138 ± 12.8 ab	133 ± 9.7 abc	106 ± 11.0 ab

) Regarding the total woody and living tree biomass, the highest carbon storage was estimated for small-leaved lime, while the lowest was estimated for Scots pine and grey alder stands (Table 5). Stands of grey alder are considered undesirable due to erosion and nutrient leakage; however, the lowest carbon pools support their management in riparian forests in Latvia [30]. The contrasting carbon stock reflects the tree species capacity of biomass accumulation [16,42], which can significantly differ for conifer and deciduous tree species [12,42], primarily due to differences in wood density [35]. Alternatively, this might be related to species’ specific trade-offs in carbon allocation to above- and belowground parts of trees in response to hydrogeomorphological processes [6]. Although higher carbon storage is expected in wet stands [44] this apparently was not the case in the studied stands, as suggested by similar estimated values for black alder stands, which predominantly occupy overly moist sites.

4. Conclusions

The estimated carbon storage of the woody biomass of riparian forests surrounding small and medium rivers in Latvia was highly variable; however, contradicting the hypothesis of this study, it was comparable to that in commercially managed non-riparian forests within the region. This suggest that the studied riparian forests had an average level of carbon storage when compared to commercial stands. However, the carbon storage was affected by dominant tree species, with the highest values estimated for the deciduous stands. Considering the lack of management in the studied stands over the last 20 years, we speculate that commercial forest management might facilitate carbon storage, although it can cause disturbances. Grey alder stands, which can be harvested to minimize erosion and nutrient leakage, had a below-average carbon pool, supporting this management for the achievement of multiple goals. However, forest management to improve carbon storage should not cause excessive disturbances, thus minimizing the negative effects on other ecosystem services.