Recovery Rates of Black Spruce and Tamarack on Lowland Seismic Lines in Alberta, Canada

Abstract

The cumulative impact of decades of oil and gas exploration has left Alberta’s boreal forests densely fragmented by seismic lines, which are expected to naturally regenerate; however, recovery is often highly variable and generally poor in peatlands due to increased wetness and reduced microtopography. In this study, we evaluated seismic lines in lowland ecosites with some degree of successful natural regeneration to gain a better understanding of the natural recovery process in these areas. We compared stand characteristics between the seismic line (23 to 48 years post-disturbance) and the adjacent undisturbed forest. We found that soil properties were similar, but seedling (height < 1.3 m) density was significantly higher on the seismic line, with 252% more tamarack and 65% more black spruce than in the adjacent forest. Relative to the adjacent forest, there were significantly fewer trees (height > 1.3 m) on the seismic line, with an 84% and 50% reduction in black spruce and tamarack, respectively. By analyzing tree ring data from seismic lines, we found that the length of time before tree establishment was 10 years for black spruce and 8 years for tamarack. On average, it took 12 years for tree density to reach 2000 stems per hectare (sph). We modeled growth rates for black spruce and tamarack and found that they were growing faster than their adjacent forest counterparts, reaching 3 m after an average of 38 and 33 years, respectively. Stands on seismic lines were projected to a final stand age of 61 years using the Mixedwood Growth Model (MGM) to evaluate future stand characteristics.

1. Introduction

The legacy of oil and gas exploration in Alberta, beginning in the 1940s, has left a long-term impact on its landscapes, resulting in the proliferation of legacy seismic lines crisscrossing the boreal forest [1]. Historically, seismic lines were established as bulldozed clearings, ranging from 5 to 10 m wide, to provide access for oil and gas exploration equipment. This process involved the removal of vegetation and often resulted in an alteration of soil and groundwater [2]. In the mid-1990s, low-impact seismic (LIS) lines, which are less than 3 m wide and use lightweight equipment to minimize disturbance, became the standard practice [3,4]. While research has shown that natural forest regeneration occurs more rapidly on LIS lines compared to conventional seismic lines [5], the cumulative impact of seismic lines continues to significantly impact Alberta’s boreal forests [3,6]. According to data from 2021, there are over 800,000 km of seismic lines in Alberta, occurring in densities as high as 60 km per km² [7]. These features have fragmented critical woodland caribou habitat, altered wildlife movement, and degraded ecosystem services by removing forest cover [2,8]. Seismic lines, along with natural disturbances like fire, insect outbreaks, and disease, as well as human activities such as oil and gas development and forestry, collectively lead to substantial cumulative environmental impacts in the boreal region.

Despite their extensive presence and impact, there is no requirement for the reclamation of seismic lines under Alberta’s Environmental Protection and Enhancement Act, and natural regeneration is often assumed to address line recovery. Yet, seismic lines pose unique challenges to regeneration, with recovery influenced by many factors such as ecosite type, line width and orientation, climate, and subsequent re-disturbances [9,10,11,12]. In comparison to the adjacent forest, seismic lines generally have higher soil bulk densities [9,13,14], higher soil moisture and water tables [15,16], altered light conditions [17], and altered microclimates [10], all of which influence tree establishment and growth. Additionally, fast-growing graminoids and disturbance-tolerant species often colonize seismic lines, competing with tree seedlings for essential resources [2,18]. As a result, some lines experience rapid natural tree establishment, while others show little to no tree regeneration even decades after disturbance [11,19,20]. Of the estimated 250,000 km of seismic lines in caribou ranges across the province, only 40% are anticipated to have sufficient woody vegetation establishment without treatment intervention [21].

While seismic line recovery is highly variable, examples of recovery with high tree densities over 10,000 stems per hectare (sph) have been observed in upland ecosites [20,22] where the probability of regenerating is also high [12]. In lowland ecosites, tree regeneration is further impacted by terrain wetness [4,12] and flattened microtopography [12,23], resulting in a loss of suitable microsites for tree establishment. In peatlands with sparse tree cover, seismic lines have shown a transition to graminoids and herbaceous plants that thrive in wetter conditions [24], persisting even decades later [11,12]. Understanding tree growth on lowland sites is crucial because their regeneration is less predictable than upland sites. Fens generally recover more slowly than bogs following disturbance, as they often exhibit surface depression after industrial activity, which leads to prolonged flooding and challenging conditions for tree establishment, whereas bogs with more hummock-forming sphagnum species provide better microsites for potential tree growth [12]. Given that treed peatlands are the preferred habitat for caribou and serve as a refuge from wolves [25,26], slow seismic line recovery in these areas may increase predator access, disrupting caribou conservation efforts.

Recent seismic line restoration initiatives, including mounding and tree planting, aim to accelerate recovery [7], but baseline ecological knowledge of natural recovery processes remains sparse. The understanding of forest recovery on seismic lines, including the length of time before tree regeneration (also referred to as lag time), establishment rates, and growth rates, will help determine the time required for these features to be classified as ‘recovered’. Although there is no universally adopted metric for seismic line recovery, some Alberta government planning documents have equated recovery timelines to those used in forest harvest areas, often assuming a footprint is removed after 60 to 80 years, drawing on metrics from commercial forestry and caribou habitat guidelines [21,27]. Furthermore, despite the broad recognition of their significant footprint across the boreal, landscape-level models of land cover, biomass, and growth and yield currently fail to account for seismic lines, effectively excluding their contributions or losses to ecosystem services, including forest resources. Further understanding of vegetation recovery on lowland seismic lines would provide critical insights for future restoration activities and mitigation strategies.

While many studies have identified the variability of tree establishment on seismic lines [11,12], very few have rigorously evaluated tree establishment lag times [28], recruitment rates, and growth rates on seismic lines with successful natural regeneration [29]. Sutheimer et al. [28] modeled growth relationships for regenerating trees on a large number of seismic lines across a variety of forest types but only included the three tallest trees of the most abundant species from each site, thus representing the best-case scenario of natural recovery and not capturing recruitment over time.

The current study aims to better understand and predict natural tree recovery on seismic lines in lowland areas by intensively sampling a few seismic lines with natural recovery using methods similar to Jones et al. [29] with a particular focus on black spruce [Picea mariana (Mill.) B.S.P.] and tamarack [Larix lariciana (Du Roi) K. Koch]. While some debate exists around metrics for evaluating forest recovery on seismic lines [18,29,30], we based our recovery thresholds on previous studies and local guidelines, choosing a height threshold of 3 m [12,20,28,29] and density threshold of 2000 (sph) [20,29,31] to consider a seismic line ‘recovered’. The overall objectives for this paper are:

• Evaluate site and stand characteristics on seismic lines and in the adjacent mature forests, including non-metric multidimensional scaling (NMDS) analysis of edaphic site characteristics and comparisons of soil and vegetation characteristics between the seismic line and adjacent mature forest.
• Determine the postdisturbance lag time preceding the establishment of black spruce and tamarack on seismic lines.
• Model the establishment rate of trees on seismic lines and predict the time required to reach a tree density of 2000 sph.
• Model the growth rate of black spruce and tamarack on seismic lines and the adjacent forest, and predict the time required for these species to reach a height of 3 m on seismic lines.
• Model future growth of the seismic line and adjacent forest stands using the Mixedwood Growth Model (MGM) and evaluate the projected stand characteristics.

2. Materials and Methods

2.1. Site Selection

This study was conducted in the Boreal Forest Natural Region, in forest stands north of Lac la Biche, Alberta, within a 40 km radius of the town. The sites are within the Central Mixedwood Natural subregion [32] (Figure 1). The study sites were considered lowland sites, which are classified as either treed bogs (i), which are characterized by black spruce dominant stands occurring on poorly drained organic soils and having a poor nutrient regime, poor fens (j) comprising tamarack and black spruce dominant stands occupying poorly drained organic soils with intermediate nutrient regimes, treed rich fens (k), which are tamarack dominant stands occurring on very poorly drained organic soil areas, and having a rich nutrient regime, or labrador tea/horsetail (h) comprising black spruce and white birch stands occurring on poorly drained and medium to rich nutrient soils [32,33] (

Table 1. Site information including ecosite type, dominant tree species in both the forest and on the seismic line, seismic line age (based on historical imagery), and adjacent forest age (based on the oldest sampled tree).

Site ID	Ecosite	Dominant Species in Forest	Dominant Species on Seismic Line	Seismic Line Age	Forest Age
9	h—Labrador tea/horsetail	Black spruce	Tamarack	28 to 29	52
17	k—Treed rich fen	Tamarack	Tamarack	23	88
23	j—Treed poor fen	Black spruce	Tamarack	41	76
24	i—Treed bog	Black spruce	Black Spruce	41	67
30	j—Treed poor fen	Black spruce	Black Spruce	46 to 50	82

). Average regional climate (i.e., 2011–2020) is subhumid, with annual potential evapotranspiration of 548 mm and annual precipitation of 435 mm, with a mean annual temperature of 1.4°C [34].

Potential seismic lines for the study were identified and dated using the Pulse Seismic online database [35], then corroborated using historical imagery and shot tag data. Sites were selected using the following criteria: unharvested, late-successional h-, i-, j-, or k-ecosites dominated by black spruce and tamarack, with an east-west oriented seismic line located within 1 km of road access with no evidence of re-disturbance (see Table 1). A site visit was conducted to confirm that the selected sites had black spruce and tamarack trees established on the seismic line. Although no minimum density or height threshold was applied for site selection, seismic lines without any tree regeneration were excluded as the study focused on tree growth. Each site was a separate seismic line, and the lines were around 6 m wide. The dominant species and stand ages of the seismic line and adjacent forest are shown in Table 1. Plot measurements were conducted in July 2023 for sites 9 and 30, and in June 2024 for sites 17, 23, and 24.

Figure 1. (A) Location of the study area within the boreal forest in northwestern Alberta, Canada. Inset map (B) shows the location of the seismic lines (purple) and the location of the study sites (red). Figure was created using QGIS version 3.32.2 with spatial seismic line data assembled from the Human Footprint Inventory [36], boreal forest zone by Natural Resources Canada, and satellite imagery by Google.

Figure 1. (A) Location of the study area within the boreal forest in northwestern Alberta, Canada. Inset map (B) shows the location of the seismic lines (purple) and the location of the study sites (red). Figure was created using QGIS version 3.32.2 with spatial seismic line data assembled from the Human Footprint Inventory [36], boreal forest zone by Natural Resources Canada, and satellite imagery by Google.

Further details on soil characteristics of the seismic line and undisturbed forest at each site are shown in

Table A1. Soil characteristics, including total organic carbon (TOC), organic soil depth, organic soil pH, mineral soil pH, seismic lines (SL), and adjacent forests at each site. Values indicate the mean and standard deviation.

Site ID	Location	Organic Soil					Mineral Soil
		Depth (cm)	pH	EC (ds m−1)	BD (g cm−3)	TOC (%)	pH
9	SL	19 ± 3	7.1 ± 0.1	1.26 ± 0.15	0.16 ± 0.02	30.5 ± 1.5	6.4 ± 0.1
	Forest	23 ± 7	7.0 ± 0.2	0.95 ± 0.03	0.19 ± 0.03	24.9 ± 2.0	6.5 ± 0.1
17	SL	100+ *	6.3 ± 0.1	0.92 ± 0.05	0.10 ± 0.01	40.6 ± 0.7	5.7 ± 0.0
	Forest	100+ *	6.1 ± 0.1	0.79 ± 0.06	0.12 ± 0.02	39.6 ± 0.9	5.8 ± 0.0
23	SL	24 ± 7	5.4 ± 0.02	1.57 ± 0.12	0.08 ± 0.01	21.7 ± 9.0	5.9 ± 0.1
	Forest	33 ± 6	4.9 ± 0.4	1.14 ± 0.1	0.06 ± 0.01	15.1 ± NA	6.0 ± 0.01
24	SL	102 ± 8	3.2 ± 0.0	0.72 ± 0.04	0.07 ± 0.01	1.2 ± 1.2	4.3 ± 0.1
	Forest	100 ± 30	3.6 ± 0.3	0.81 ± 0.15	0.08 ± 0.01	3.9 ± 8.5	3.9 ±0.2
30	SL	21 ± 4	5.5 ± 0.0	0.78 ± 0.06	0.12 ± 0.01	27.4 ± 4.2	5.4 ± 0.0
	Forest	21 ± 16	5.7 ± 0.2	1.16 ± 0.12	0.09 ± 0.02	33.3 ± 1.8	5.7 ± 0.0

* Organic soil was only measured up to 100 cm.

. Organic soil depths in undisturbed forests ranged from 19 cm to >1 m (Table A1), shallower than the typical >2 m depths found in boreal lowland peatlands [37]. This likely reflects the selection of transitional wetlands, which seem to be more suitable for natural tree regeneration. Three sites with <40 cm of organic soil do not meet peatland criteria [37], further supporting our classification of these sites as lowland sites rather than true peatlands.

On the seismic lines, densities of mature black spruce and tamarack trees (>1.3 m) combined ranged from 833 to 4000 sph, with mean tree heights between 1.5 to 3.4 m for black spruce and 2.1 to 3.6 m for tamarack (

Table A2. Black spruce and tamarack mature tree (>1.3 m tall) densities (sph) and heights on the seismic line and adjacent forest at each site.

Site ID	Location	Density (sph)		Height (m)
		Black Spruce	Tamarack	Black Spruce	Tamarack
9	SL	500	667	2.2 ± 0.9	3.6 ± 1.9
	Forest	2667	333	8.5 ± 5.3	15.4 ± 3.8
17	SL	0	1667	NA	2.1 ± 0.7
	Forest	500	5500	7.4 ± 4.5	12.1 ± 4.7
23	SL	833	1667	1.8 ± 0.3	2.4 ± 1.2
	Forest	16,000	2000	2.8 ± 1.1	2.2 ± 0.5
24	SL	833	0	1.5 ± 0.1	NA
	Forest	2500	1000	5.3 ± 3.2	4.1 ± 1.9
30	SL	2667	1333	3.4 ± 1.8	2.8 ± 0.66
	Forest	9167	1667	4.8 ± 2.1	6.4 ± 3.9

). Black spruce and tamarack seedlings (<1.3 m) were more abundant, with densities ranging from 1333 to 62,333 sph (

Table A3. Black spruce and tamarack seedling (<1.3 m tall) densities (sph) and heights on the seismic line and adjacent forest at each site.

Site ID	Location	Density (sph)		Height (m)
		Black Spruce	Tamarack	Black Spruce	Tamarack
9	SL	3167	7000	0.5 ± 0.2	0.5 ± 0.2
	Forest	2167	0	0.8 ± 0.4	NA
17	SL	0	7333	NA	0.7 ± 0.3
	Forest	4167	167	0.2 ± 0.1	0.3 ± 0.0
23	SL	1667	13,333	0.8 ± 0.3	0.8 ± 0.2
	Forest	19,500	4500	0.8 ± 0.3	0.8 ± 0.2
24	SL	62,333	0	0.3 ± 0.2	NA
	Forest	11,000	500	0.5 ± 0.3	1.0 ± 0.1
30	SL	833	500	0.6 ± 0.3	0.9 ± 0.3
	Forest	4500	2833	0.7 ± 0.3	0.8 ± 0.3

). While all sites had black spruce and tamarack in the adjacent forest, one site had no tamarack regenerating on the seismic line, and one site had no black spruce regeneration on the seismic line (Table A2 and Table A3). Deciduous tree species were also present on some seismic lines (

Table A4. Tree and seedling densities (sph) by species on seismic lines (SL) and in the adjacent forest. Densities are estimates based on count data in 60 m² area.

Site ID	Location	Betula papyrifera	Larix laricinia	Populus balsamifera	Populous tremuloides	Picea mariana	Pinus contorta
9	SL	2833	7667	0	0	3667	0
	Forest	0	333	0	0	4833	0
17	SL	0	9000	0	0	0	0
	Forest	0	5667	0	0	4667	0
23	SL	0	15,000	2500	500	2500	0
	Forest	0	6500	0	0	35,500	0
24	SL	0	0	0	0	63,167	0
	Forest	0	1500	0	0	13,500	333
30	SL	0	1833	0	0	3500	0
	Forest	0	4500	0	0	13,667	0

); however, the focus of this study is on black spruce and tamarack. Shrub composition was dominated by bog Labrador tea (Rhododendron groenlandicum (Oeder) Kron and Judd) (

Table A5. Shrub density (sph) for the most common species and genera in vegetation transects on the seismic line (SL) and adjacent forest. Betula spp. includes Betula glandulosa and Betula pumila but not Betula papyrifera, as it is counted among trees and seedlings. Densities are estimates based on count data in a 60 m² area.

Site ID	Location	Alnus incana	Betula spp.	Rhododendron groenlandicum	Ribes spp.	Salix spp.	Symphoricarpos occidetalis	Vaccinium spp.
9	SL	1167	333	348,500	0	11,833	0	4000
	Forest	0	0	138,333	0	0	0	2333
17	SL	0	5833	0	0	5667	0	0
	Forest	0	0	39,833	0	1167	0	2667
23	SL	0	5667	28,667	667	62,000	0	0
	Forest	0	0	123,167	4667	833	0	0
24	SL	0	500	437,333	0	0	0	0
	Forest	0	0	831,833	0	0	0	0
30	SL	0	3333	19,0500	167	32,167	5667	0
	Forest	0	500	21,5500	0	8500	1500	0

).

2.2. Data Collection

Data collection was conducted during the summers of 2023 and 2024. Site setup followed a method adapted from Jones et al. [29] with sampling of the seismic line and the adjacent forest at each site. Although the adjacent forest is older with a different disturbance history than the seismic line, it provided a reference for the growth of similarly aged trees in a similar location. For vegetation sampling, six circular plots (1.78 m radius) were established diagonally across the width of the seismic line, spaced 1 m apart. The same layout of six circular plots was replicated in the adjacent undisturbed forest, located approximately 30 m away and running parallel to the seismic line. Within each plot, all trees and non-trailing shrubs were identified and counted, along with the height of all trees and the diameter at breast height (DBH) of trees ≥1.3 m.

Tree age data were collected by destructively sampling all trees within the circular plots, except when the first three undisturbed forest plots contained at least 50 trees, in which case, the remaining three plots were not sampled. For trees with a DBH <9 cm, the entire tree was felled at the base, and cookies were collected at 0 cm, 25 cm, and 130 cm. For trees with a DBH >9 cm, two increment cores were taken at 25 cm and 130 cm above the ground, spaced at least 90° apart around the tree.

Tree cookies and cores were dried at 60 °C for 7 days and then sanded with progressively finer grits to emphasize rings for scanning. Optical scans (1200 dpi) were taken of base cookies and cores taken at 25 cm above the ground and loaded in CooRecorder (version 9.6, Saltsjöbaden, Sweden) [38] to measure annual ring counts and ring widths, and CDendro (version 9.6, Saltsjöbaden, Sweden) [38] was then used to visually crossdate the samples before statistical verification in COFECHA [29,39]. Cookies collected at 25 cm and 130 cm above the ground, and cores collected at 130 cm above the ground, were counted under a stereo microscope. In instances where optical scans could not be accurately read in CooRecorder due to the size or quality of the sample, an age count was obtained using a stereo microscope (Leica, Mannheim, Germany).

Because some trees were sectioned into cookies at 0 cm and 25 cm, while larger trees were cored at 25 cm, we used the 0 cm and 25 cm cookies to estimate the base (0 cm) age of trees with only a core at 25 cm. To do this, we grouped tree cookies by species (tamarack and black spruce) and location (on seismic line and adjacent forest), creating four groups, and calculated the average number of years between the 0 cm and 25 cm sections. This average was then added to the ages of trees within the same group that were cored at 25 cm.

For soil sampling, a 30 m × 5 m transect was laid out along the seismic line. Three sampling transects were established along the seismic line: one in the center and two 1 m from the edge. Along the edge transects, three evenly spaced 50 cm × 50 cm plots were sampled, while along the center transect, five 50 cm × 50 cm plots were sampled, totaling 11 soil plots on the seismic line. In the undisturbed forest, two 30 m perpendicular transects were placed 5 m away from the seismic line, with four plots sampled at the end of each transect and one at the center, resulting in five soil plots in the undisturbed forest.

In each soil plot, a 10 cm × 10 cm sample of the organic layer was collected. Sampling continued to the full depth of the organic layer, or as deep as possible when the layer exceeded the sample bag’s capacity. The actual organic soil depth was recorded in all samples. Mineral soil cores were collected from 0 to 15 cm and 15 to 30 cm below the organic layer at each plot. In the center undisturbed forest soil plot, two additional cores were collected from 0 to 45 cm and 45 to 60 cm depths. All soil samples were dried at 100 °C for 24 h and for an additional 24 h at 70 °C if the sample was not completely dried after the initial 24 h. Once dried, organic and mineral soils were sieved using a 9.8 mm and 2 mm sieve, respectively. The weights of the sieved samples, along with removed rocks, pebbles, and roots, were recorded to calculate bulk density. Samples were sent for analysis of pH, electrical conductivity (EC), and total organic carbon (TOC, %).

2.3. Data Analysis

2.3.1. Site and Stand Characteristics

Data analyses and visualizations were conducted using the R statistical computing language and environment (version 4.1.2, Vienna, Austria) [40]. Linear mixed-effects (lme) models, implemented with the lme4 package [41], were used to analyze differences in site characteristics between seismic line transects and adjacent forest transects. The models included a unique site ID as a random effect (to account for the paired seismic line and forest plots within each site) and location (seismic line versus adjacent forest) modeled as a fixed effect. General linear mixed modeling was used to compare soil bulk density, organic soil depths, TOC, tree heights, and tree DBHs, and a generalized linear mixed model (GLMM) was used to compare stem densities of trees, shrubs, and seedlings. Wald chi-square tests via analysis of variance tests were used to assess the significance of fixed effects (car package) [42].

To evaluate whether the data met the model assumptions, we examined simulated residuals using the DHARMa package [43]. No transformations of data were required to meet model assumptions, except for tree height and DBH, which were square root transformed to maintain positive values within confidence intervals.

Table A6. General details and GLMM for each response variable analyzed using the lme4 package.

Response Category	Response Variable	Species	Fixed Effect(s)	Random Effect	Response Variable Transformation	Model Type
Site characteristics	Tree density (sph)	All, Black Spruce, Tamarack	Location	Site ID	-	Gzlm/poisson Log link
	Seedling density (sph)	All, Black Spruce, Tamarack	Location	Site ID	-	Gzlm/poisson Log link
	Total density (sph)	All, Black Spruce, Tamarack	Location	Site ID	-	Gzlm/poisson Log link
	Shrub density (sph)	All	Location	Site ID	-	Gzlm/poisson
	Tree height (m)	All	Location	Site ID	Square-root	glm/normal
	Tree DBH (cm)	All	Location	Site ID	Square-root	glm/normal
	Organic soil bulk density (g cm−3)	-	Location	Site ID	-	glm/normal
	Organic layer depth (cm)	-	Location	Site ID	-	glm/normal
	Organic soil pH	-	Location	Site ID	-	glm/normal
	TOC (%)	-	Location	Site ID	-	glm/normal
Post-disturbance Regeneration rates	Tree height (m)	Black spruce, Tamarack	Years since post-line regeneration lag end, Location	Site ID	-	Gzlm/gamma, Log link
	Stem density (sph)	Black spruce, tamarack	Years since line cut	Site ID	-	Gzlm/poisson Log link

lists the details of all models.

We used NMDS (non-metric multidimensional scaling) to identify possible groupings of sites based on the edaphic characteristics measured in the field. To determine the variables to include in the NMDS, we first created a matrix of Spearman correlations between all edaphic variables (organic soil pH, mineral soil pH, organic soil depth, organic soil bulk density, and TOC). For pairs with correlation values greater than r = 0.8, we chose only one to include in the NMDS. We used permutational multivariate analysis of variance (PERMANOVA) to test the significance of site groupings based on Euclidean distances between NMDS scores. We tested site groupings by ecosite, primary tree species of seismic-line regeneration, and primary tree species in adjacent forests. NMDS and PERMANOVA were performed using the Vegan package in R [44].

2.3.2. Post-Disturbance Establishment Lag Times

For tamarack and black spruce on each site, we estimated a baseline annual establishment rate based on average germinations per year in the undisturbed forest plots adjacent to the seismic line plots, using the timeframe between 1971 and 2023. We refer to these as ‘germinations’, though it is important to clarify that they represent only the surviving, established trees observed at the time of sampling, rather than actual germination counts. For sites where tamarack or black spruce were absent in the adjacent forest, the base germination rate was set as the average annual germination rate for that species from other sites. We defined the end of the post-seismic line regeneration lag as the first year when the annual germination rate on the seismic line surpassed the four-year rolling average of the adjacent forest’s germination rate. The germination peak was defined as the year with the highest number of germinations and the highest three-year rolling average of annual germinations for each species on each seismic line.

2.3.3. Establishment and Growth Rate Models

Prior to model building, we calculated the total lifetime growth rates of all trees (total centimeters growth per year of the tree’s life). We removed trees with growth rates that were more than three standard deviations from the mean (comparison groups were by species, location, and site ID), as they may have represented sampling error, unusual growth habits, or microclimates not indicative of the general population or site.

We calculated stem density over time following seismic line cuts using only tamarack and black spruce germination data from seismic line plots. Annual stem density values represent the cumulative number of germinations recorded each year since the line was cut. We modeled the change in stem density over time using a GLMM of cumulative sph, with years since line cut as a fixed effect and Site ID as a random effect (Table A6).

We estimated the growth curves of black spruce and tamarack on our study sites by creating and comparing general linear mixed models and GLMMs to describe the relationship between tree age and height, as well as the influences of other variables such as location, time between seismic line creation and tree establishment, and site identity. The most appropriate model for black spruce and tamarack growth was a GLMM where the tree’s total height at the time of sampling was modeled as a function of the tree’s chronological age in years. The location of the tree (seismic line versus adjacent forest) was included as a fixed effect with the Site ID included as a random effect to account for variability among sites (Table A6). Sigmoidal growth models were not appropriate for these datasets as the stand age was too young for trees on any sites to exhibit asymptotic growth. These growth rates, therefore, represent only the early-stage growth of the modeled trees, <40 years old for seismic lines, and <80 years old for the adjacent forest.

We used Wald Chi Squared tests to assess parameter significance. GLMMs were built using the lme4 package [41], and non-parametric bootstrapping was performed using the boot [45] and nptest [46] packages. Data manipulation was performed using tidyr [47] and dplyr [48], and data visualization was performed using ggplot2 [49].

To ensure model validity, we assessed model convergence and checked for singular fits. Model selection was based on Akaike Information Criterion (AIC), comparing alternative structures such as models with random slopes, nested random effects, and interaction terms.

We used nonparametric bootstrapping to estimate confidence intervals for correlation coefficients. The correlation coefficient was computed for 1000 bootstrap resamples drawn with replacement from the dataset. The bootstrap distribution was used to construct bias-corrected and accelerated (BCa) 95% confidence intervals, ensuring robustness to skewness.

Target recovery thresholds of 3 m height and 2000 sph were chosen for calculating estimates of recovery time from the establishment curves and growth curves. These threshold values were chosen to represent recovered seismic lines based on previous studies [12,20,28,29] and provincial reclamation guidelines for planted wellsites in forested lands [31].

2.3.4. Growth Projections Using MGM

Tree growth projections for the seismic line and adjacent forest were modeled using MGM version MGM21 S8.2.21.39 (MGM Development Team, 2021). MGM is an empirical individual tree-based stand growth model used in forest management planning to simulate the growth of boreal mixed forests, including inter- and intra-species competition and climate effects. MGM is used in several jurisdictions across the western boreal, including Alberta, British Columbia, Saskatchewan, and Manitoba [50] and has been validated against fire-origin, post-harvest permanent sample plots [51,52]. The black spruce model includes data from 4139 permanent sample plots spanning from Alaska to Manitoba [51,53]. MGM also includes specialized modules that allow for distinct spatially oriented stands with localized shading and edge effects, making it applicable for modeling tree growth on seismic lines as demonstrated by Jones et al. [29].

Individual tree lists were created for the seismic line stand and adjacent forest stand at each site by pooling data from the six circular plots (60 m² in total). Tree data inputs included species, height, DBH (for trees >1.3 m), age, and tree factor (the number of trees per hectare represented by each tree; 166.7 in this case). The Light/Adjacency Submodel (MSLight) of MGM was enabled to model tree growth based on shade cast from taller trees in the adjacent forest. To run this submodel, seismic lines were oriented E-W and assigned dimensions of 100 m × 6 m, and the adjacent forest was assigned dimensions of 100 m × 9 m to the NS of the seismic line. The MGM default growing period (16 April to 1 October) and diffuse radiation fraction (0.5) were used. MGM is a height-driven model that relies on site index (SI, height (m) at 50 years base age of 100 largest trees per hectare) curves to predict the growth of each tree [54]. Because of the small area sampled at each location (60 m²), the specific SI could not be calculated accurately. Additionally, the SIs for trees on seismic lines from Sutheimer et al. [28] are not applicable for use in MGM because they were only modeled up to 25 years due to the younger age of trees on seismic lines. However, SI is determined largely by soil characteristics and climate [55], so SIs were selected based on the region and ecosite from the Field Guide to Ecosites of Northern Alberta [33] for black spruce (h ecosite—9.5, i ecosite—9.8, j ecosite—10.4, k ecosite—7.2) and held constant for the seismic line and adjacent forest. Because MGM requires non-dominant species to be assigned an SI based on a similar dominant species, tamarack was assigned the SI of black spruce. To accommodate the application of climate-sensitive survival functions, the climate moisture index (CMI) for each site was calculated using historical normals from 1981–2010 generated by ClimateNA version 6.11 [34] as required by MGM. Stand development on the seismic line and adjacent forest was simulated using a single model for each site as well as a cumulative model where the individual tree inventories from all sites were combined. For the cumulative projections, the average CMI and SIs of the five sites were used.

The growth simulation end point of the model was set based on the 95% confidence intervals of the time for black spruce to reach 3 m in height, plus the average black spruce lag time. The model output variables of interest included conifer (black spruce and tamarack) total stand density (merchantable and non-merchantable), average conifer height, average conifer top height (the average height of the 100 largest DBH trees), and conifer volume. These variables were chosen because they are of interest from a caribou habitat restoration perspective and are indicators of overall stand conditions.

3. Results

3.1. Comparison of Site and Stand Characteristics

Overall, there was no significant difference between soil characteristics between seismic line transects and paired forest transects, including organic soil bulk density, organic soil depth, TOC, and pH (

Table 2. Mean and standard error of soil and vegetation characteristics in forest and seismic line locations, based on models from five paired sampled plots. Soil characteristics include bulk density, organic layer depth, pH, and total organic carbon (TOC). Vegetation characteristics include: densities of black spruce, tamarack, and all species combined for trees (>1.3 m tall), seedlings, and total (trees and seedlings); shrub (woody, non-trailing) density; tree height; and tree diameter at breast height (DBH). Letters denote significant differences within each row (α = 0.05); values without letters were not significantly different.

Response Variable	Forest	Seismic Line	p
Organic soil characteristics
Bulk density (g cm−3)	0.11 (0.01)	0.11 (0.01)	0.756
Depth (cm)	55.32 (8.02)	53.84 (5.44)	0.362
pH	5.5 (1.3)	5.5 (1.5)	0.742
TOC (%)	21.8 (2.89)	22.0 (1.64)	0.917
Tree (>1.3 m) density (sph)
Black spruce	6195 (2872) b	971 (454) a	<0.001
Tamarack	2143 (933) b	1072 (325) a	<0.001
Total (all species)	8406 (2780) b	2210 (528) a	<0.001
Tree (>1.3 m) characteristics (all species)
Height (m) *	6.82 (1.61) b	2.28 (2.79) a	<0.001
DBH (cm) *	6.8 (1.4) b	1.7 (0.2) a	<0.001
Seedling (<1.3 m) density (sph)
Black spruce	8305 (3192) a	13,663 (12,251) b	<0.001
Tamarack	1607 (892) a	5659 (2483) b	<0.001
Total (all species)	9912 (3883) a	20,227 (10,946) b	<0.001
Total tree and seedling density (sph)
Black spruce	14,500 (4294)	14,634 (12,259)	0.080
Tamarack	3750 (1291) a	6731 (2504) b	<0.001
Total tree (all species)	18,318 (6663) a	22,437 (11,474) b	<0.001
Shrub density (sph)	275,438 (143,039) b	229,862 (80,080) a	<0.001

* Values transformed with square root during analysis; non-transformed data displayed.

). Soil characteristic variation was greater between sites than between the seismic line and adjacent forest pairs (Table A1). In the adjacent forest soils, TOC ranged from 3.9 to 39.6%, organic depth from 21 to >100 cm, bulk density from 0.06 to 0.19 g cm⁻³, pH from 3.9 to 6.5, and EC from 0.81 to 1.16 dS m⁻¹. In contrast, soils on the seismic lines exhibited lower TOC (1.2 to 30.5%), slightly shallower organic layers (19 to >100 cm), a narrower bulk density range (0.07 to 0.16 g cm⁻³), slightly higher pH values (4.3 to 6.4), and a broader EC range (0.72 to 1.26 dS m⁻¹). Edaphic site characteristics included in the NMDS were organic soil pH, organic soil depth, and TOC (Figure A1). Organic soil bulk density and mineral soil pH were both excluded from NMDS due to their high collinearity with organic soil pH (Spearman’s r > 0.80). PERMANOVA results suggested that soil characteristic groupings correlated strongly with ecosite groupings, but the results were not significant, possibly due to the small sample size of only five sites (R² = 0.91, p = 0.2, df = 3). Groupings based on regenerating tree species (Black spruce, tamarack, or a mix of both) were predicted moderately well by NMDS scored soil characteristics (R² = 0.67, p = 0.27), and tamarack regeneration success (R² = 0.53, p = 0.20), but both groupings were statistically insignificant. Other tested groupings were black spruce regeneration success (R² = 0.04, p = 0.13) and dominant tree species in the adjacent forest (R² = 0.29, p = 0.4), which were all predicted poorly by NMDS score similarities, with statistically insignificant results.

As expected, the heights and diameters of trees (>1.3 m) in the adjacent forest were significantly taller than those on the seismic line, which reflects the difference in stand ages post-disturbance (Table A2). Compared to the adjacent forest, stem density of seedlings (<1.3 m) was significantly higher on seismic lines (p < 0.001), while stem densities of trees (>1.3 m tall) and shrubs were significantly lower on seismic lines (p < 0.001) (Table 2). Looking at total stems (trees and seedlings together), we found 22% more sph on the seismic line than in the forest (p < 0.001), with high tree and seedling densities dominated by black spruce (Table 2).

Examination of stem density differences between seismic lines and adjacent forest revealed strong interactions between species and location (seismic line versus forest). While both species exhibited decreased tree (>1.3 m tall) stem density on seismic lines compared to the forest, this effect was much stronger in black spruce (p < 0.001). Within the mature tree class, the seismic line had 84% less black spruce and 50% less tamarack compared to the adjacent forest. Conversely, while both species exhibited higher seedling density on seismic lines, the effect was much stronger in tamarack (p < 0.001). Within the seedling class, the seismic line had 252% more tamarack and 65% more black spruce compared to the adjacent forest (Table 2), although the seedlings on the seismic line were dominated by black spruce.

3.2. Post-Disturbance Regeneration

3.2.1. Regeneration Lag Times

Black spruce and tamarack generally exhibited delayed regeneration across most or all sites (Figure 2,

Table 3. Summary of regeneration lag analysis results. Sites without regeneration of a given species on the seismic line were excluded from that species’ average lag time calculations. When seismic line cut years were uncertain and a range of possible lag times was available, the midpoint of the range was used. Germinations per hectare per year are estimated based on stem densities measured in 60 m² plots.

Seismic Line ID	Seismic Line Year	Lag Time (Years)		Time to Germination Peak (Years)		Peak Germinations (sph Per Year)
		Black Spruce	Tamarack	Black Spruce	Tamarack	Black Spruce	Tamarack
9	1994 to 1995	9 to 10	9 to 10	19 to 20	17 to18	837	837
17	2001	>22	1	NA	7	0	1005
23	1983	>40	9	13	20	335	1842
24	1983	10	>40	28	NA	5358	0
30	1973 to 1977	8 to 12	9 to 13	17 to 21	12 to 16	502	502
Average (SD)		10 (0.29)	8 (4.5)	20 (6.2)	15 (5.6)	1406 (2223)	837 (680)

). At three of the five sites, black spruce showed clear regeneration pulses approximately 10 years after seismic line disturbance, with little variation (SD ± 0.29 years). However, at one site (Site 23), black spruce never reached the same annual germination rate as the adjacent forest, even 40 years after the seismic line was created. This site experienced low-level recruitment beginning four years post-disturbance, with a modest peak around year 13. At another site (Site 17), no black spruce regeneration was observed in the 22 years following disturbance.

Tamarack regenerated on four of the five study sites. Although lag times for tamaracks were generally shorter than for black spruce, they showed greater variability (7.6 ± 4.5 years). On average, tamarack germination peaked earlier (15 ± 5.6 years) than black spruce (20 ± 6.2 years) following seismic line creation.

For both species, the germination rates in the post-seismic line age pulses exceeded the average yearly germination rates in the surrounding forests for the duration of the age pulses (Figure 2). Black spruce demonstrated higher potential peak germination rates, with an estimated average of 1406 ± 2223 sph per year, while tamarack peak germination rates were slightly lower, at an average of 837 ± 680 sph per year.

On average, tamarack exhibited a shorter establishment lag than black spruce. Notably, on sites where both species showed post-disturbance recruitment pulses, these pulses occurred simultaneously (Figure 2, Lines 9 & 30). Tamarack showed shorter germination lags on sites where black spruce did not display strong post-disturbance recruitment (Figure 2, Lines 17 & 23). Conversely, longer germination lags for black spruce were observed on sites where tamarack did not regenerate at all following disturbance (Figure 2, Line 24).

3.2.2. Tree Establishment Rate

The most suitable model for explaining combined black spruce and tamarack establishment rate on seismic lines was a GLMM that yielded the following equation:

Total stem density = exp (4.495 + 0.262 × Line age (years))

According to the model, seismic lines in these site types are predicted to reach stem densities of 2000 sph for tamarack and black spruce combined within an average of 12 years (95% CI8 to 16 years) (Figure 3). This estimate includes the lag times calculated in Section 3.2.1. Stem density projections are based on germination years derived from base-height dendrochronology samples and represent the time required for all tamarack and black spruce individuals, including those currently present in the stand as mature trees, to reach the target density.

3.3. Black Spruce and Tamarack Growth Rates on Seismic Lines

The black spruce growth model described the majority of variation of black spruce growth rates, with a conditional R-squared (fixed + random effects) of 0.79 (Figure 4). Establishment on seismic line or adjacent forest had a significant effect on black spruce growth rates in their first 40 years (Wald χ², p < 0.001), and displayed a significant interaction with tree age (Wald χ², p < 0.001). Therefore, we describe black spruce growth using separate equations for the two locations.

Growth rates for black spruce on seismic line followed the equation:

Tree height_SL (m) = exp(2.502 + 0.08473 × Tree age (years))

Growth rates for black spruce in the adjacent forest followed the equation:

Tree height_F (m) = exp(3.168 + 0.04188 × Tree age (years))

Tamarack growth rate models were developed using data only from three of the five sites (Sites 9, 17, and 30). Site 24 was excluded because no tamarack regenerated on the seismic line, while Site 23 was omitted due to stunted growth among regenerating tamarack, which rarely exceeded 3 m in height. The final model explained most of the variation in tamarack growth, with a conditional R-squared (including fixed + random effects) of 0.87 (Figure 5). As with the black spruce models, while Location (seismic line or adjacent forest) was not statistically significant fixed effect on its own (Wald χ², p = 0.275), its interaction with tree age was significant (Wald χ², p = 0.001). As a result, Location was kept as a fixed effect in the models, warranting the development of two separate equations for tamarack growth rates.

Growth rates for tamarack on seismic line followed the equation:

Tree height_SL (m) = exp(3.049 + 0.08127 × Tree age (years))

Growth rates for tamarack in the adjacent forest followed the equation:

Tree height_F (m) = exp(3.595 + 0.04445 × Tree age (years))

Our models suggest that both species exhibit steeper growth curves and reach 3 m in height more quickly on average on seismic lines than in adjacent forests (Figure 4). However, this trend was statistically significant only for black spruce. On seismic lines, black spruce reached 3 m in approximately 38 years (95% CI: 29–51), while tamarack reached this same height in about 33 years (95% CI: 24–96). These estimates were calculated from the beginning of the post-disturbance recruitment pulse and do not include the initial regeneration lag period determined in Section 3.2.1.

3.4. MGM Stand Projections

Tree growth on the seismic line and in the adjacent forest was projected over a 25-year period, starting from the average seismic line stand age of 36 years (the mean across the five sites) and extending to the final stand age of 61 years. The end point was based on the estimated time for black spruce on seismic lines to reach a height of 3 m, which is 51 years (upper CI) as determined in Sectio 3.3, plus the average regeneration lag time of 10 years identified in Section 3.2. The following results represent the cumulative projection results where data from all sites were combined. Individual site projections are shown in Figure A2 and Figure A3.

By year 61, MGM projected an average top height (height of the 100 largest conifers) of 8.2 m on seismic lines, compared to 20.8 m in the adjacent forest (Figure 6A and Figure 7). The MGM predicted a decline in total conifer stem density (including merchantable and non-merchantable trees) over time, driven by inter- and intra-species competition. On seismic lines, conifer density was projected to decrease from 20,631 sph to 10,063 sph by year 61 (Figure 6B). In the adjacent forest, density declined from 15,612 sph to 6184 sph over the same period (Figure 6B). Despite decreasing density, conifer volume on the seismic line was predicted to increase over time as trees grow taller, rising from 1.8 m³ ha⁻¹ at year 36 to 12.8 m³ ha⁻¹ by age 61 (Figure 6C). Average conifer height on the seismic line, which includes only mature trees >1.3 m, initially declines for about a decade as seedlings (<1.3 m) grow taller than the 1.3 m height threshold and are included in the height calculation, thus lowering the average height. (Figure 6D). Once the majority of trees reach 1.3 m, the projected height increases over time, reaching 2.8 m on seismic lines, consistently lower than the 5.4 m projected for the adjacent forest (Figure 6D).

4. Discussion

4.1. Soil and Stand Characteristics

Tree growth on seismic lines in peatlands is often limited by high water tables and a lack of suitable microsites [11,12,15,57]. However, several studies have documented peatland seismic lines with high densities of naturally regenerating trees [7,10,57,58]. Our study focused on lowland sites where natural regeneration had occurred. All five study sites had adjacent forest canopies composed of black spruce and tamarack. The environmental conditions within these adjacent forests spanned a broad range; moisture regimes ranged from hygric to subhydric, nutrient regimes from very poor to rich, and organic soil properties varied considerably. In the adjacent forest soils, TOC ranged from 3.9 to 39.6%, organic depth from 21 cm to >1 m, bulk density from 0.06 to 0.19 g cm⁻³, pH from 3.9 to 6.5, and EC from 0.81 to 1.16 dS m⁻¹ (Table A1). For context, boreal peatlands typically have a pH of 3.6 to 4.2 in bogs and 5.4 to 7.5 in fens [59], organic soil bulk densities of 0.07 to 0.18 g m⁻³ in all peatlands [37], and organic carbon contents of 30% to 60% [60]. These five study sites thus represent a wide range of soil conditions across lowland ecosites where natural regeneration has occurred.

We found no significant difference in bulk density or organic soil depth between the seismic line and the adjacent forest. This contrasts the other studies that have reported compaction and reductions in organic matter on seismic lines in upland and lowland forests [9,13,14]. The minimal disturbance observed in our sites suggests that seismic line clearing likely occurred during winter, when frozen soils are less prone to compaction. The mean bulk density of 0.11 g cm⁻³ across our seismic lines falls within previously reported ranges for boreal peatlands of 0.04 to 0.15 g cm⁻³ [13,61,62].

In terms of tree densities, this study found age-class-dependent differences between seismic lines and adjacent forests. Mature tree density (particularly black spruce) was significantly lower on seismic lines, while seedling density (primarily tamarack) was significantly higher on seismic lines than in the adjacent forest. Overall, total stem density (trees and seedlings combined) was 22% greater on seismic lines, consistent with previous findings of increased seedling densities on seismic lines relative to surrounding forests [9,29,63]. One contributing factor to increased seedling density may be increased light availability on seismic lines [3,10,17]. For instance, Franklin et al. [10] reported a strong positive relationship between light intensity and regeneration density in black spruce-dominated sites. Filicetti & Nielsen [57] also found a 36% increase in regeneration density for every 1 m increase in line width and a 390% increase on E-W lines compared to N-S lines in rich fens.

Although this study did not directly measure microtopography or water table depth, which are both known to influence tree regeneration success [23,57,64], we did exclude sites where natural regeneration had failed, most likely due to water table depth and/or the loss of microtopography caused by seismic line creation. In peatland forests, microtopography is characterized by elevational hummock-hollow patterns, and a reduction in these microsites critical for seedling establishment can significantly hinder tree regeneration [11,19,64,65].

Although black spruce seedlings were more abundant than tamarack seedlings on seismic lines overall, the relatively greater increase in tamarack abundance compared to the adjacent forest, even where black spruce dominated, likely reflects tamarack’s lower shade tolerance [66]. The open conditions of seismic lines likely created a more favorable environment for tamarack establishment. While black spruce seedling densities were also high on seismic lines, their mature tree density was significantly lower than in adjacent forests. This is likely due, in part, to the younger stand age of the seismic lines, and we expect the mature tree density on the seismic line to increase as the seedlings grow into trees. However, competitive interactions may also be a factor, as tamarack outperformed black spruce in reaching heights above 1.3 m at the time of the study.

Shrub density, dominated by bog Labrador tea, was significantly lower on seismic lines than in the adjacent forest. Although ericaceous shrubs can inhibit tree growth through competitive pressures [67], they did not appear to limit tree recruitment on the seismic lines in this study. In fact, Fliesser [58] found a positive correlation between moderate shrub density (>15%) and black spruce seedling growth on seismic lines in fens, suggesting that shrubs may not always be a limiting factor for tree growth.

4.2. Regeneration Lag and Establishment Rates

Post-disturbance lag periods preceding tree establishment have been documented in several other seismic line studies [28,63]. In our study, the lag period before the onset of regeneration pulses varied by site and species, averaging 10 years for black spruce and 8 years for tamarack. The slightly shorter lag time for tamarack aligns with its higher short-term productivity and tendency to establish earlier than black spruce following disturbance, particularly under high light conditions [68]. Sutheimer et al. [28] reported similar regeneration lag times of 8 to 13 years for seismic lines in transitional and peatland forests, based on the germination dates of the three tallest trees per plot. Recruitment structures in our sample plots show that although small numbers of seedlings germinated and survived in the years immediately following disturbance, recruitment rates during these early periods were significantly lower than in adjacent undisturbed forests. Estimating lag time based solely on the germination dates of the oldest surviving trees, as was done by Sutheimer et al. [28], may underestimate the true lag period. The germination pulses identified in our study are a more conservative estimate of the time required for site conditions to support a pulse of seedling germination and survival, rather than the earliest establishment dates.

Black spruce maintains an aerial seed bank of semi-serotinous cones with pronounced seed crops every 2 to 6 years [69,70]. In the context of post-fire or post-harvest, the majority of recruitment typically occurs within the first 6 years [71]. However, on seismic lines, extended lag periods may result from delayed stabilization of abiotic conditions, such as increased surface wetness following vegetation removal [63]. Sutheimer et al. [28] further identified seismic line orientation and width as important factors influencing lag time, potentially due to their effects on soil temperature and light availability.

Although black spruce typically prefers mineral soil or a thin organic horizon for establishment [71], a thicker and moist organic substrate can also support germination [72]. On seismic lines in the Northwest Territories, Seccombe-Hett & Walker-Larsen [63] found black spruce seedlings more commonly established on moss and litter than mineral soil. In our study, organic soil depths were comparable between seismic lines and adjacent forest areas. However, it’s important to note that these measurements were taken several decades after seismic line creation. In the years immediately following disturbance, conditions on seismic lines may have been significantly less conducive to black spruce regeneration. The clearing of lines likely resulted in the loss of microsite heterogeneity, particularly the removal of hummocks, which are critical for seedling establishment. Over time, as vegetation recovered, the initial inhibitory effects of line clearing may have diminished, making current soil conditions appear more similar to those of adjacent forest stands. As vegetation establishes over time, it can modify site conditions by intercepting precipitation, transpiring water, and contributing to soil nutrient cycling through litter fall [73]. The observation that black spruce and tamarack often experienced germination pulses at similar times reflects a shared response to improved site or microsite conditions.

At some sites, species-specific regeneration patterns were observed. For example, at Site 24, only black spruce regenerated on the seismic line despite tamarack presence in the adjacent forest, while Site 17 had only tamarack regenerate on the seismic line despite adjacent black spruce. These differences likely reflect the change in site conditions post-disturbance, such as wetness and nutrient availability. Black spruce is physiologically adapted to persist in wet, nutrient-poor environments through features such as lenticel intumescences, adventitious roots, and aerenchyma tissue [74,75] and invests more in root development under low productivity conditions [76]. Tamarack, while also tolerant of short-term water-logged conditions, shows greater growth on well-drained sites [77] and tends to establish on elevated microsites in wet areas [78].

Similar to findings from upland sites by Jones et al. [29], tree recruitment on seismic lines continued over time. Peak germination generally occurred several years after the lag period ended. While our estimates do not account for seedling mortality prior to sampling, they provide insight into post-disturbance establishment patterns. Our model of tree establishment rates indicates that seismic line tree density reaches 2000 sph an average of 12 years post-disturbance, including the lag period. Taken together, these findings suggest that once germination pulses begin, high stem densities can be achieved relatively rapidly, even within as little as two years. However, at 12 years post-disturbance, most trees contributing to the 2000 sph metric would still be seedlings under 3 years old, originating from the recent germination pulse.

4.3. Tree Growth Rates

Comparing tree growth between the seismic line and adjacent forest provides a broad comparison controlling for local site conditions and climate; however, trees growing in the adjacent forest were growing beneath a canopy, while trees on the seismic line were growing in a clearing with more light. In peatlands, tree growth has been correlated with nutrient availability, soil aeration, ground water level, rates of water movement, plant community composition, and peat composition [79]. Our finding that black spruce was growing significantly faster on seismic lines relative to the adjacent forest, given that soil and nutrient conditions were similar, is likely related to the increased light availability on the seismic line. In forests, trees exhibit a growth response to gaps or openings in the canopy [71]. Black spruce is a shade-tolerant conifer usually reaching photosynthetic light saturation under 25% to 50% full sunlight [80]; however, black spruce seedlings grown under 100% light were significantly taller than those grown under 50% or 30% light after 4.5 months [81]. Shellian et al. [7] found available light was positively associated with black spruce seedling growth in poor fens, and Sutheimer et al. [28] reported that the north-south orientation of seismic lines correlated with shorter tree heights in peatlands, perhaps due to reduced light levels compared to lines oriented E–W. We found that tamarack growth rates were also faster on seismic lines relative to their adjacent forest counterparts, although the difference was not significant, likely because of the larger variability across tamarack growth rates on seismic lines. Fliesser et al. [58] also found that tamarack seedlings in fens had faster growth rates on seismic line sites than in the adjacent undisturbed forest. In upland ecosites, Jones et al. [29] found white spruce and balsam fir growing at similar rates to their adjacent forest counterparts.

We estimate that once a black spruce or tamarack seedling establishes on a seismic line in lowland ecosites, it will take approximately 33 or 38 years, respectively, to reach 3 m in height. Growth rates of tamarack were faster, although more variable, compared to black spruce growth rates, which aligns with tamarack being a faster-growing species able to capitalize on post-disturbance resource availability, while black spruce demonstrates a slower, more resource-conserving growth strategy more optimal for low-nutrient conditions [24]. Faster growth rates of tamarack relative to black spruce on seismic lines have also been observed by Shellian et al. [28] and Fliesser [58]. However, on seismic lines with poor soils, tamarack was found to grow slower than black spruce [57], which likely explains the stunted tamarack growth observed at one of our sites (Site 23) where tamarack rarely exceeded 3 m in height.

4.4. Recovery Time Estimates

To estimate the time required for naturally regenerating seismic lines in lowlands to reach 2000 sph at 3 m, we can add the average time to 2000 sph from Section 3.2.2 (12 years) to the time to 3 m height from Section 3.2 (33 years for tamarack, 38 years for black spruce). Assuming all seedlings survived to these ages, the minimum estimates are 45 to 50 years to reach 2000 sph at 3 m. Sampling in this study was conducted 16 to 32 years after the estimated time these sites would have reached 2000 sph, meaning that the trees used for this estimate had survived at least this long. However, further attrition of seedlings would likely occur before most of these trees reach 3 m in height, so the actual time for a stand to reach 2000 sph at 3 m is likely longer than these combined estimates. Sutheimer et al. [28] found that trees on seismic lines in peatlands would take at least 30 years to reach 3 m, including lag time, which is similar but slightly lower than our estimate, as it does not account for the density threshold. For seismic lines in upland ecosites, faster-growing species like white spruce and balsam fir were reported to take an average of 33 and 29 years, respectively, to reach 3 m tall [29].

4.5. Future Stand Projections

Seismic line stands were projected using MGM to an end stand age of 61 years, based on the combined average lag time (10 years) and growth time to 3 m tall (51 years, based on the upper 95% confidence interval) of black spruce. The predicted growth from the MGM is based on the site index, which is derived from the regional ecosite-specific site index curves (dominant height at age 50) for black spruce. For the cumulative model, the site index value was 9.5, indicating that the 100 largest diameter trees per hectare would have a mean height of 9.5 m at age 50. In our projection to 61 years, the average conifer top height was 8.2 m, with a mean conifer height of 2.8 m, slightly lower than the expected 3 m average at this time, as derived from the growth curves based on existing seismic line tree samples. These discrepancies may be due to the MGM’s inclusion of the inter- and intra-species competition on tree survival and growth.

Initial conifer density on the seismic line is above 20,000 sph, which is higher than in the adjacent forest, and decreases over time. The average conifer height, which includes only mature trees >1.3 m, decreases during the first 10 years of the projection as young seedlings grow taller and are included in the mature tree stand averages, lowering the overall height until around year 50, when the average height begins to increase again. It is important to note that MGM does not model natural ingress, meaning predicted densities always decline over time [82]. Additionally, MGM is validated against historical climate data, and therefore, it cannot account for potential impacts of climate change [83].

Despite high initial densities and an increase in volume over time on the seismic line, MGM predicts that tree volume remains significantly lower than the adjacent forest due to the shorter tree heights on the seismic line. This trend aligns with findings from Jones et al. [29] for seismic lines in upland ecosites.

4.6. Management Implications

Restoration of conventional seismic lines, particularly in treed peatlands, is a key priority for government and conservation organizations due to the importance of these habitats for declining woodland caribou populations [29]. Study sites fall within the Government of Alberta’s Caribou sub-regional planning for the Cold Lake area, which identifies restoration of legacy seismic lines as a key objective, as the practice is stated to support the ecological integrity of a working landscape [21]. The plan targets having 25% of seismic lines in the area being sufficiently stocked (either naturally or through treatment) by 2040. Findings from this study can inform restoration planning, particularly with respect to tree species performance and recovery timelines in lowland peatlands.

Our results highlight an 8 to 10-year lag period before natural regeneration begins, consistent with findings from other studies. This finding suggests that some seismic lines may appear unrecovered for extended periods before experiencing a germination pulse. Therefore, restoration interventions should prioritize seismic lines that are older in age and have remained unrecovered for at least 10 years, giving natural processes sufficient time to occur prior to applying active treatment.

Although tamarack seedlings were more abundant on seismic lines than in adjacent forests, black spruce seedlings occurred in high numbers across a broader range of sites. These results support planting both species in restoration treatments, an approach already employed in current seismic line restoration projects [7,58]. Tamarack, where site conditions are favourable, performs well in the increased light conditions typical of seismic lines. However, black spruce remains a more versatile option capable of establishing across a wider range of peatland conditions. The two species offer functional complementarity: tamarack establishes quickly and contributes to short-term productivity, while black spruce tends to dominate in the long term, promoting overall ecosystem stability [68].

Our study focused on sites where natural regeneration occurred without active treatment, indicating that some seismic lines have favorable conditions suitable for tree establishment and growth. While microtopography was not formally measured, we observed that both tamarack and black spruce seedlings commonly established on raised microsites or mounds. This observation aligns with findings from Shellian et al. [28], who demonstrated that mounding enhances seedling establishment in peatlands. Similarly, Fliesser [58] found that most naturally regenerated trees occurred within the top 5 cm of mounds. In wetter sites where microtopographic variation is limited, mounding may be an effective tool to support seedling survival. However, practitioners should be aware that mounding can also cause disturbances to peatland vegetation and arthropod communities [84] and increase carbon emissions following treatment [85].

4.7. Study Limitations

This study provides a general estimate of the recovery timeframe for black spruce and tamarack on seismic lines, specifically in lowland ecosites where natural regeneration was successful and trees were present. As such, it likely represents an optimistic scenario and does not reflect broader recovery rates or explain why some lines remain unrecovered. Due to the limited number of sites and the small sampling area per line, the findings should be interpreted with caution. Furthermore, because microtopography or hydrological data were not collected, the study cannot draw conclusions about the mechanisms behind recovery challenges.

Other important ecological indicators of recovery, such as understory vegetation, soil microbial communities, permafrost distribution, and wildlife habitat use, were also not assessed. To better understand forest recovery dynamics and causes of regeneration delays, future studies should expand in spatial scales and include a wider range of ecological variables across diverse boreal landscapes. Long-term monitoring studies with repeated measures would further enhance our understanding of the factors influencing lag periods in regeneration.

Considering the potential for climate change to affect peatland hydrology, seedling survival, and growth rates, future studies should incorporate climate-informed growth models or scenarios to assess the long-term resilience of restored areas. Tools such as future climate projections or species distribution models could provide critical insights into the performance of tree species and overall ecosystem stability on legacy disturbances like seismic lines.

The application of MGM for predicting tree growth on lowland seismic lines also comes with limitations. MGM relies on site index, yet data for non-productive peatland ecosites and seismic lines are limited. In this study, the same site index was applied to both the seismic line and the adjacent forest based on location and ecosite, which may reduce prediction accuracy. However, since site index is largely influenced by soil and climate [52], which are assumed to be similar between the seismic line and the adjacent forest, the impact may be modest. Another limitation is that MGM was developed for dominant species like black spruce, and it applies the same site index to secondary species such as tamarack, thus potentially misrepresenting their growth. Finally, MGM uses historical climate data, which does not account for the influence of ongoing or future climate change.

5. Conclusions

We sampled soil and vegetation on naturally regenerating seismic lines in lowland ecosites 23 to 48 years post-disturbance. While soil characteristics were similar between the seismic lines and the adjacent undisturbed forest, vegetation structures differed markedly. Seismic lines had significantly higher seedling densities, but lower densities of mature trees and shrubs. Tree-ring data indicated that a germination pulse typically occurred following a lag period of 8 to 10 years, with tamarack establishing slightly earlier, though more variably than black spruce. Tree recruitment continues over time, with stem densities exceeding 2000 sph on average by 12 years post-disturbance. Once established, black spruce and tamarack grew faster than their counterparts in the adjacent forest, reaching 3 m in height after an average of 33 and 38 years, respectively. Together, these results suggest a recovery timeline of approximately 45 to 50 years for seismic lines in lowland ecosystems to reach 2000 sph at a height of 3 m. MGM projections for seismic lines and adjacent forest stands show that although seismic lines may exhibit higher tree densities, tree height and volume remain consistently lower than the adjacent forest over time.

While this study contributes to understanding forest recovery on seismic lines, several knowledge gaps remain. Broader-scale studies that incorporate diverse ecosite types, as well as key ecological indicators such as hydrology, vegetation, soil, and wildlife interactions, are needed to fully understand the mechanisms behind regeneration variability. Improved growth modeling tools that account for future climate scenarios and incorporate more detailed site-specific data would also enhance predictive capabilities. Long-term monitoring and interdisciplinary approaches will be essential to support effective, evidence-based restoration and policy development in these sensitive boreal ecosystems.

Overall, our findings highlight the importance of adapting restoration strategies to local conditions and recovery timelines. Passive recovery may be viable in some instances, but should be monitored carefully before deciding on active interventions. The observed role of microtopography in seedling establishment reinforces the utility of techniques like mounding, though potential ecological trade-offs should be considered. In summary, this study enhanced our understanding of natural regeneration processes on seismic lines in lowland environments and offered practical recovery benchmarks for decision-makers based on species-specific lag times, establishment rates, and growth trajectories. Restoration planning can benefit from these insights by targeting sites where natural recovery has stalled and selecting species that align with site conditions and long-term ecosystem goals.