SEISMIC REGENERATION VEGETATION DATA ANALYSIS RESULTS & DISCUSSION

Transcription

1 SEISMIC REGENERATION VEGETATION DATA ANALYSIS RESULTS & DISCUSSION PREPARED FOR: FOREST MANAGEMENT DEPT. OF ENVIRONMENT & NATURAL RESOURCES BOX 4354, LOT 173 HAY RIVER, NWT PREPARED BY: EDI ENVIRONMENTAL DYNAMICS INC nd AVENUE WHITEHORSE, YT Y1A 3T EDI CONTACT MATT POWER, A.SC.T. EDI PROJECT NO.: EDI PROJECT NO.: 15-Y-0012 DATE: MARCH 2015

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3 AUTHORSHIP This report was prepared by EDI Environmental Dynamics Inc. in conjunction with Dave Polster of Polster Environmental Services Ltd. Staff who contributed to this project includes: Allison Patterson, M.Sc.... Primary Author and Data Analysis Brett Pagacz, B.Sc.... Technical Review Matt Power, A.Sc.T.... Project Management & Review Dave Polster, M.Sc., R.P.Bio..... Senior Review EDI Project No.: 15-Y-0012 EDI ENVIRONMENTAL DYNAMICS INC. i

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5 TABLE OF CONTENTS 1 INTRODUCTION METHODS Indicators of Recovery Depth to Permafrost Vegetation Cover Predictor Variables Disturbance type and age Vegetation class and forest status Conditions in control plot STATISTICAL ANALYSIS RESULTS DEPTH TO PERMAFROST LICHENS MOSSES GRAMINOIDS DECIDUOUS SHRUBS LATERAL COVER VEGETATION DISSIMILARITY INDEX VEGETATION SIMILARITY INDEX DISCUSSION REFERENCES APPENDIX A. APPENDIX B. APPENDIX C. EXAMPLE CANDIDATE MODEL SET USED IN AIC C ANALYSIS VEGETATION TABLES DETAILED PARAMETER ESTIMATES FOR MODELS DESCRIBED IN THIS REPORT LIST OF TABLES Table 1. Predictor variables evaluated with AICc model selection EDI Project No.: 15-Y-0012 EDI ENVIRONMENTAL DYNAMICS INC. iii

6 Table 2. Definition of statistics included in AIC tables Table 3. Table 4. Table 5. Table 6. Table 7. Table 8. Table 9. Table 10. Table 11. Table 12. Table 13. Distribution of sample sites based on each categorical predictor. Sample sizes for each categorical variable are separated by region... 8 AICc table for difference in depth to permafrost. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (ωi), and cumulative model weights (Σ ωi). The complete set of candidate models included 116 a priori models and 16 post hoc models. Models with interactions are shown in italics with an * between interacting terms AICc table for lichen cover. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (Σ AICc ω). The complete set of candidate models included 116 a priori models and 21 post hoc models. Models with interactions are shown in italics with an * between interacting terms AICc table for difference in moss cover. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (Σ AICc ω). The complete set of candidate models included 116 a priori models and 13 post hoc models. Models with interactions are shown in italics with an * between interacting terms AICc table for difference in graminoid cover. Only the top 10 models are shown and none of the models considered were competitive with the null model. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 116 a priori models AICc table for difference in deciduous shrub cover. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 116 a priori models and 29 post hoc models. Models with interactions are shown in italics with an * between interacting terms AICc table for difference in lateral cover in the 1 to 2 m height class. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 116 a priori models and 19 post hoc models. Models with interactions are shown in italics with an * between interacting terms Frequency of occurrence and percent cover values for the most common early successional species found in disturbed plots AICc table for vegetation dissimilarity. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 71 a priori models and 15 post hoc models. Models with interactions are shown in italics with an * between interacting terms. Vegetation dissimilarity was log transformed for analysis Frequency of occurrence and percent cover values for the most common species found in control plots relative to their occurrence in adjacent disturbance plots. Median difference in cover is the median value of cover in the disturbance plot cover in the control plot AICc table for vegetation similarity. Only the top 10 models and the null model are shown. Post hoc models including interaction terms are indicated with italics. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. iv

7 model weights (ΣAICc ω). The complete set of candidate models included 116 a priori models and 35 post hoc models. Models with interactions are shown in italics with an * between interacting terms...27 Frequency of occurrence and percent cover values for plant species found in disturbed plots that did not occur in the adjacent control plot.... B-6 Frequency of occurrence and median cover values for the most common species found in control plots.... B-8 LIST OF FIGURES Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Figure 9. Estimated relationship between difference in depth to permafrost (cm) and initial depth to permafrost in control plots (cm). Estimates shown are for a line width of 8 m. Solid line shows the estimated effect of depth to permafrost in the control plot and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Permafrost ~ Control + Width Estimated relationship between difference in depth to permafrost (cm) and line width. Estimates shown are for sites with 50 cm depth to permafrost in the control plots. Solid line shows the estimated effect of seismic line width on difference in depth to permafrost and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Permafrost ~ Control + Width Estimated relationship between difference in lichen cover (%) and initial lichen cover in the control plot for each region, assuming an average line width of 8 m. Lines show the estimated effect of lichen cover in the control plot and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Lichen ~ Control * Region + Width Estimated relationship between difference in moss cover (%) and initial moss cover (%) in the control plot for organic and mineral soils when line width is 8 m. Lines show the estimated effect of moss cover in the control plot and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Moss Cover ~ Control + Width + Soil Estimated relationship between difference in moss cover (%) and line width (m) for organic and mineral soils when cover in the control plot is constant at 50%. Lines show the estimated effect of seismic line width and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Moss Cover ~ Control + Width + Soil Estimated relationship between difference in moss cover (%) and disturbance age for organic and mineral soils when cover in the control plot is constant at 50%. Lines show the estimated effect of disturbance age and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Moss Cover ~ Control + Width + Disturbance Age Relationship between difference in graminoid cover (%) and difference in depth to permafrost (cm). Solid line shows the estimated effect of depth to permafrost on graminoid cover and the shaded area is the 95% confidence interval. Symbols are observed values. Estimates are based on the model Δ Graminoid Cover ~ Δ Permafrost Estimated relationship between difference in deciduous shrub cover (%) and deciduous shrub cover in the control plot (%) for forested and non-forested sites when line width is 8 m. Lines show the estimated average effect of initial cover on recovery and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Shrub Cover ~ Control * Forest + Width Estimated relationship between difference in deciduous shrub cover (%) and line width (m) for forested and non-forested sites when shrub cover in the control plot is 25%. Lines show the estimated average effect of seismic line width on recovery and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Shrub Cover ~ Control * Forest + Width EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. v

8 Figure 10. Figure 11. Figure 12. Figure 13. Estimated relationship between difference in lateral cover (%) and initial lateral cover (%) for forested and non-forested sites when line width is 8 m. Lines show the estimated average effect of initial cover on recovery and the shaded areas are 95% conidence intervals. Symbols are observed values. Estimates are based on the model Δ Lateral Cover ~ Control + Forest * Width Estimated relationship between difference in lateral cover (%) and line width (m) for forested and nonforested sites, when lateral cover in the control plot in 30%. Lines show the estimated effect of seismic line width on recovery of lateral cover and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Lateral Cover ~ Control + Forest * Width Estimated relationship between vegetation dissimilarity (%) and line width (m) for forested and nonforested sites in the Dehcho (top) and Sahtu (bottom) regions. Lines show the estimated effect of seismic line width and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Dissimilarity ~ Width + Forest * Region. Vegetation dissimilarity was log transformed for analysis, model estimates are shown on the original scale Estimated relationship between vegetation dissimilarity (%) and difference in depth to permafrost (cm). Lines show the estimated average effect of difference in permafrost and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Dissimilarity ~ Δ Permafrost. Vegetation dissimilarity was log transformed for analysis, model estimates are shown on the original scale Figure 14. Estimated relationship between vegetation similarity, line width, and disturbance type for a site with 50% cover of common species in the control plot. Lines show the estimated average effect of line width similarity and the shaded areas are 95% confidence intervals. Symbols are observed. Estimates are based on the model Similarity ~ Control + Width * Disturbance Type Figure 15. Estimated relationship between vegetation similarity, cover of common species in the control plot, and disturbance type for an 8 m wide line. Lines show the estimated average effect of cover in the control plot and the shaded areas are 95% confidence intervals. Symbols are observed. Estimates are based on the model Similarity ~ Width + Control * Disturbance Type Figure 16. Estimated relationship between vegetation similarity and disturbance age class for a forested site with 50% cover of common species in the control plot. Points for the estimated average for each age class and error bars are 95% confidence intervals. Estimates are based on the model Similarity ~ Control * Forest + Age class EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. vi

9 1 INTRODUCTION Seismic lines are linear disturbances on the landscape that result from clearing vegetation for exploration purposes. Within the Northwest Territories (NT), seismic exploration can lead to potential issues such as habitat fragmentation and functional habitat loss for some species (i.e., boreal caribou). Studies in Alberta have shown that the behaviour of woodland caribou is affected by disturbances, such as seismic lines (Dyer et al. 2002). Our understanding of recovery processes and the factors involved in vegetation regrowth on seismic lines is important for addressing cumulative impacts, reducing habitat fragmentation and promoting the regeneration of vegetation on linear disturbances. Due to complex ecological interactions involved in recovery processes, there is a lack of understanding of vegetation regeneration on seismic lines (van Rensen et al. 2015). In the absence of research to study the effects of historic seismic exploration in NT, Government of Northwest Territories (GNWT) managers cannot easily assess seismic line recovery as it relates to forestry, wildlife or resource based management goals. An study was conducted in 2013 to assess seismic line recovery in the Dehcho and Sahtu regions of NT (Polster 2014). The intent of the study was to collect and analyze ecological data to determine and classify recovery patterns of seismic lines. Within the study areas, data collection included site conditions, vegetation characteristics, forest mensuration and wildlife use. The data collected were used as a base for further analyses of attributes that were selected as indicators of recovery or predictor variables for seismic line recovery. To improve understanding of seismic line recovery and address the final goals of the project (refer to cover letter), EDI Environmental Dynamics Inc. (EDI) was contracted to analyze the data collected in The objective was to investigate patterns of ecological succession on seismic lines and how this relates to ecological characteristics, disturbance factors, and time since disturbance. The analytical component is regarded as a vital step in achieving overall project goals. The analysis provides a detailed comparison of paired plots (disturbance and control) and statistical analyses evaluating relationships between vegetation recovery and seismic line characteristics (line age and width, initial disturbance, and vegetation community). Results will offer insight into the ecology of long-term recovery from seismic disturbances in the study area and information that can contribute to future planning and best management practices. EDI Project No.: 15-Y-0012 EDI ENVIRONMENTAL DYNAMICS INC. 1

10 2 METHODS The vegetation data analysis focused on particular ecological attributes that were collected during the 2013 study. Attributes were selected based on project-specific objectives and feedback from the GNWT. Select attributes were categorized as either an indicator of recovery or predictor variable. For a complete description of all field methods used during the initial study refer to Ecology of Seismic Line Recovery Northwest Territories (Polster 2014). Sample sites located within well-sites and adjacent to the old camp runway were excluded from the analyses, due to differences in disturbance type from sites sampled on seismic lines. The analyses used an information theoretic approach (Akaike s Information Criterion; Burnham and Anderson 2002) that assessed the relationships between ecological attributes, disturbance characteristics, and time since disturbance. A range of indicators of recovery were selected for the analyses that were considered important for vegetation recovery and/or wildlife consideration. Results of the analyses will be used to (1) improve understanding of seismic line recovery, (2) provide information towards improving best management practices for seismic operations in the NT, and (3) make recommendations for future studies of seismic line recovery in the NT Indicators of Recovery Recovery of seismic lines was evaluated by comparing conditions between paired disturbed and control plots, where the difference between paired plots was analyzed for selected indicators of recovery. This assumes that the control plot represents pre-disturbance conditions for each site and seismic line clearing was the primary difference between pairs of sample plots Depth to Permafrost The difference in depth to permafrost was calculated using a simple formula; depth to permafrost in the disturbed plot minus depth to permafrost in the control plot. Positive values indicated that permafrost has thawed on the seismic line, while negative values indicated permafrost has increased on the line. Depth to permafrost data was not available at some plots; only sites where measurements were made at both the disturbance and control plots were included in the analysis of depth to permafrost Vegetation Cover In the 2013 study, percent cover was estimated for each plant species in a plot. For a quantitative assessment of vegetation recovery, it was necessary to generalize detailed plant cover data into a smaller number of variables. Plant species were lumped into four plant functional groups including lichens, mosses, graminoids, and deciduous shrubs. These functional groups were selected based on project-specific objectives and feedback from the GNWT. Plant functional groups were selected (1) that would provide indication of change to ecological conditions on seismic lines (i.e., ground disturbance, changes in hydrological conditions etc.) or (2) for the potential importance to wildlife (i.e., boreal caribou and lichen). These analyses determined differences in total plant cover of each functional group for paired disturbed and EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 2

11 control plots. Results could indicate areas where finer-scale analysis may be beneficial to focus on particular indicator species. The analyses also examined differences in lateral cover between paired disturbed and control plots. Horizontal cover was measured using a 25 cm wide cover board divided into three height classes (<1 m, 1 to 2 m, and 2 to 3 m). Horizontal cover was measured as the percentage of the board covered by vegetation in each height class. Within disturbed plots, cover was measured along the center of the seismic line. In control plots, cover measurements were made at the center of the plot. The analyses focused on lateral cover between 1 and 2 m high, which likely has the greatest influence on sightlines for large mammals (e.g., boreal caribou, wolves and moose). Vegetation dissimilarity was measured as the total percent cover of plant species in disturbance plots that were not present in the adjacent control plot. Pioneer plant species were only included in the index of vegetation dissimilarity if there was more than trace amounts (at least 1% cover) in the disturbed plot. Higher values for vegetation dissimilarity indicate greater presence of pioneering species along a seismic line. Cover was estimated for each plant species independently; therefore index values could be greater than 100%. Moss and lichen species were excluded because the recovery processes for these species are likely to differ from vascular plants. Initial examination of the data also showed that cover values for pioneering mosses and lichens were generally higher than for vascular plants; therefore, including them would mask patterns in vascular plants. Plants that were not identified to species (i.e. Carex spp.) were also excluded from these analyses. Vegetation similarity was examined by assessing how well the five most common plant species within each control plot had recovered in the paired disturbance plot. Percent cover of these species was summed for each control plot and the paired disturbance plot. The vegetation similarity index was calculated as the difference in percent cover of the five most common species between the disturbed and control plots. Index values close to 0 indicate that the common species have recovered to conditions present in the control plot; positive values indicate the common species were colonizing disturbed sites at higher densities than at control sites; negative values indicate the common species have not recovered on disturbed sites at the same density as adjacent control sites. Cover was estimated for each plant species independently; therefore index values could be greater than 100%. If there were multiple species with tied cover values in the control plots, the species that was most common in the disturbed site was used to calculate vegetation similarity. Similar to the Dissimilarity index, mosses and lichens were excluded from the analysis, as well as plants not identified to the species level Predictor Variables Potential predictor variables included geographic region, soil type, pre-disturbance condition, disturbance type and age, dominant vegetation or forest status, and change to permafrost. A description of each predictor variable is provided in Table 1. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 3

12 Table 1. Predictor variables evaluated with AICc model selection. Predictor Type Level Details Region Categorical Sahtu Dehcho Line width (m) Continuous Distance between edges of the cleared line Disturbance type Categorical Seismic line Sites that have most recently been cleared for seismic exploration Fire Sites that have experienced fire since seismic line clearing Disturbance age Continuous Years since the most recent site disturbance Age class Categorical 1 to 10 years 11 to 20 years 20 to 30 years >30 years Soil type Categorical Organic Organic soils in the disturbed plot Mineral Sand, silt, or loam soils in the disturbed plot Forest status Categorical Forested Sites with >= 10% canopy closure in control plot Non-forested Sites with < 10% canopy closure in control plot Vegetation class Categorical Treed Conifer Sites with >= 10% canopy closure and >=70% coniferous spp. in control plot Treed Mixed Sites with >= 10% canopy closure and <70% coniferous spp. in control plot Shrub Sites with <10% canopy closure and >=30% shrub cover in control plot Bryoid/Lichen Sites with <10% canopy closure, <30% shrub cover, and >=30% bryoids (mosses, liverworts and hornworts) and lichens in control plot Wetland Sites classified as wetlands in the Earth Observation for Sustainable Forests (EOSD) vegetation classes Control 1 Continuous Conditions in the control plot for each focal response variable Δ Permafrost 2 Continuous The difference in depth to permafrost between disturbed and control plots (disturbance control) 1 Not included in analysis of vegetation dissimilarity index. 2 Not included in AICc model comparisons because of limited sample size Disturbance type and age Seismic line ages were estimated in 2013 from estimating release dates using counts of tree rings (Polster 2014). A general protocol was compiled to guide the determination of line ages and is provided below in order of precedence Plot Data: Plot data (line age based from tree ring counts) on the tally card was compared with photos. Line age values on the tally card were used if data and photos indicated a correct line age assumption. If two possible release dates were given, and field comments or photos did not support one estimate over the other, then the most recent year was used for analysis. 2. Spatial Assessment: Spatial datasets (in shapefile format) representing seismic lines were acquired from the National Energy Board (NEB) and GNWT and used to assess positional accuracy of the plots vs. seismic lines. In some instances, the year of line establishment was EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 4

13 indicated with the dataset. This value was used to determine the line year. As a safe guard, field observations were cross referenced to ensure correctness. 3. Recent Disturbance: Fire can reset vegetation recovery on a seismic line; therefore, analysis considered the most recent type of, and time since, the most recent disturbance. Evidence of fire at sites was recorded in the field and spatially cross-referenced with the GNWT records of fire history to determine when fires occurred. Where the first 2 points (above) failed to confirm a line age, an estimate was applied using fire as an indicator of the most recent disturbance type. A spatial assessment was done that overlapped the plot with a fire layer (shapefile); this information was used in tandem with 2013 field observations and photos to determine if fire was the most recent disturbance. If fire was confirmed as the most recent disturbance, the fire year was used as the line year. 4. Line Age Estimation: Where line age data were not available from Polster (2014) and could not be determined through processing the above methods, an adjacent plot or seismic line was used to determine year of clearing. Disturbance age was analyzed as a continuous and a categorical (age class) variable. This approach allowed for recovery in each response to be relatively constant over time (continuous) or to change at a different rate for recent and older lines (categorical). Disturbance age was classified into ten year categories up to 30 years old (refer to Table 1). All lines older than 30 years were included in a single age class Vegetation class and forest status Influence of the surrounding vegetation type on indicators of recovery was evaluated with two predictor variables: vegetation class and forest status. Vegetation community classes were simplified from the initial study, as it was not feasible to analyze 15 vegetation communities. Five vegetation classes were developed; these classes were determined by the dominant vegetation and hydrology in the control plot (refer to Table 1). Definition of vegetation classes followed the Earth Observation for Sustainable Development of Forests (EOSD) land cover classes (Wulder and Nelson 2003); however, site level classifications relied on vegetation cover values recorded in the field. Forest status differentiated between forested and non-forested sites Conditions in control plot Conditions in adjacent undisturbed habitat both influence line recovery and how lines are perceived by humans and/or wildlife. Observed values in the control plot were included in analysis of each recovery indicator except vegetation dissimilarity. 2.2 Statistical analysis Seismic line recovery is a complex ecological process that is influenced by a multitude of interacting factors, including: the type and intensity of initial and subsequent disturbances, time since disturbance, underlying ecological conditions, and the surrounding vegetation community. It is unlikely that a single statistical model will adequately explain this system. Traditional hypothesis testing assumes one of the models under consideration is the true model and it requires that models being compared are nested (i.e. include a subset EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 5

14 of the same predictor variables). Akaike s Information Criterion (AIC) provides an advantage over hypothesis testing for understanding ecological problems because it allows for comparisons between nonnested models and it assumes that multiple models could contribute to understanding the underlying process (Burnham and Anderson 2004). AIC values are used to assess each statistical model in a set of possible models to identify the simplest model(s) that preserves the most information from the original data. Preferred model(s) are the model(s) with the lowest AIC values; AIC values decrease with improved model fit and they are penalized for increasing model complexity. A candidate model set is evaluated based on the difference in AIC values (ΔAIC), if the lowest AIC value is much smaller than all other models this indicates there is strong evidence that this model is a better explanation of the data than all other models considered. If there are multiple models with similar low AIC values, this suggests there are competing explanations for the observed data. Models can also be evaluated in terms of the AIC weights (ωi) which indicate the level of support or probability of any model being the most parsimonious model in the candidate set. P-values are not evaluated because these are not part of the theoretical basis that underlies AIC model selection (Burnham and Anderson 2002, 2004). Parameter estimates and confidence intervals are evaluated to understand the modelled relationships between response and predictor variables. Akaike s Information Criterion, with a small sample size correction (AICc), was used to assess which combinations of predictor variables had the greatest influence on recovery patterns. A preliminary model set was developed to examine possible combinations of predictor variables. Only combinations of up to three predictors were included in each model; this step was taken to reduce the limit the total number of models under consideration and limit the analysis to models that could be readily interpreted. Because these variables were closely related, disturbance age and age class were never included in the same model and forest status and vegetation class were never included in the same model. Lower AICc values indicate that a model has more support than other models under consideration; models within 4 AICc units (ΔAICc) of the lowest scoring model were considered competitive. If models including multiple predictor variables were competitive we conducted a post hoc analysis to look for two-way interactions between these predictor variables. Among competing models (ΔAICc < 4), predictor variables that appeared most, and had parameter estimates with confidence intervals that did not overlap 0, were considered to have the greatest influence on indicators of recovery. Candidate model sets also included the null model and/or models with all possible predictors (global models). The null model is the model with no predictors. If this model were selected it would indicate that none of the variables considered were related to the response. If the null model had significant model weight and an AIC score close to the top ranked models it would indicate that none of the models under consideration were particularly informative. Conversely, no support for the null model (i.e. ΔAICc > 4 and AIC weight <0.01) and indicates that the supported models provide a better approximation of reality. The global model includes all predictor variables under consideration. The global model is used to evaluate overall model fit. Because the candidate models were limited to three predictor variables, support for a global model could also indicate that a more complex model be more informative. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 6

15 We used linear mixed effects (LME) models to test the effects of predictor variables on indicators of seismic line recovery. LME models included seismic line ID as a random effect to account for a possible lack of independence among sites sampled along the same seismic line and disturbance plots that shared the same control plots. Residuals from the global models and any competitive models were examined graphically to confirm the assumptions of normality and homoscedasticity. Results include an AIC table for the top 10 models and the null model as a reference point, Table 2 provides a definition of statistics included in AIC tables. All parameter estimates are presented with 95% confidence intervals. Change in the permafrost layer has the potential to influence vegetation recovery along seismic lines; therefore, we also examined the relationship between indicators of vegetation recovery and difference in depth to permafrost using LMEs. Models including difference in depth to permafrost were not evaluated using AICc, because it was data was not available from all plots. AICc comparisons are only valid among models that use the same response variables. Significance of this relationship was evaluated using p-values. All statistical analysis was done in R, version (R Core Team 2014). Linear mixed effects models were performed using the nlme (Pinheiro et al. 2014) package and AICc comparisons used the package AICmodavg (Mazerolle 2015). Table 2. Definition of statistics included in AIC tables. Statistic Description Definition K Number of model parameters The number of parameters included in each model. The value of K increases by 1 for each continuous variable and each level of a categorical variable. AICc penalizes models for complexity, to avoid over-fitting the data. AICc Akaike s Information Criterion corrected for small sample sizes A measure of the relative quality of a statistical model given a set of data and a candidate set of models for that data. Preferred model(s) have the lowest AIC values; AIC values decrease with improved model fit and they are penalized for increasing model complexity. This statistic has been corrected to account for small sample sizes or a large number of predictors relative to the sample size. Δ AICc Change in AICc The difference in AICc values between each model in the candidate set and the model with the lowest AICc. This value is used ω i Akaike model weights The probability, or weight of evidence, that a model is best supported model among a candidate set of models. Σ ω i Cumulative model weight The sum of model weights for all models with a lower AICc value than the current model. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 7

16 3 RESULTS The overall sample size included 68 paired disturbance and control plots from 50 different seismic lines. The majority of sample sites was located in the Sahtu region (65%) and had not experienced fires subsequent to line clearing (Table 3). Width of seismic lines sampled ranged from 3.5 to 12 m. Time since last disturbance ranged from 2 to 61 years. In the Dehcho region most sample sites were in the oldest disturbance age class of >30 years. Sample sites in the Sahtu region were evenly distributed among all age classes. Sixty percent of sample sites were forested and only 40% of sites were non-forested. Conifer forests (41%) and shrubs (24%) were the most heavily sampled vegetation classes. The results are presented for the parameters selected as indicators of recovery for seismic lines associated with the study. Predictor variables considered in the analysis are included in the results where it was relevant to the indicator of recovery. In reference to the objectives of the study and methods outlined in Section 2, the results focus on the difference between disturbed plots on the line and control plots in adjacent undisturbed vegetation. Table 3. Distribution of sample sites based on each categorical predictor. Sample sizes for each categorical variable are separated by region. Region Predictor Category Dehcho Sahtu Total # of Sites Region Disturbance Type Fire No Fire Age Class 1 to 10 years to 20 years to 30 years >30 years Soil Type Organic Mineral Forest status Forested Non-forested Vegetation Class Treed Conifer Treed Mixed Shrub Bryoid/Lichen Wetland EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 8

17 3.1 Depth to permafrost Difference in depth to permafrost was calculated for 57 sites and ranged from 32 cm to 111 cm, with an average depth of 57 cm. Depth to permafrost in the disturbed plots ranged from 29 cm to 163 cm, with an average depth of 79.9 cm. Permafrost was deeper in the disturbed plot than the control plot at 77.2% of sample sites, the average difference in depth between paired plots was 20.7 cm. There were three control plots in the Dehcho region which were excluded from the analysis of depth to permafrost, because they had extremely deep permafrost layers (>150 cm) that were well outside the range of data from other plots. The difference in depth to permafrost between disturbance and control plots was strongly related to permafrost depth in the control and line width (Table 4); both of these terms appeared in all but one of the competitive models and accounted for 76% of model weights. Sites with deeper permafrost at the control plot had less difference in depth to permafrost at disturbed plots, than sites with shallower permafrost in the control (Figure 1); for every 1 cm increase in depth to permafrost at the control plot, the average difference in depth to permafrost between the line and control plot decreased by 0.60 cm (CI = to cm). Wider seismic lines had greater difference in depth to permafrost between disturbed plots and control plots than narrower seismic lines. Difference in depth to permafrost increased with line width by an average of 4.94 cm (CI = 0.59 to 9.3 cm) per 1 m increase in width (Figure 2). At an average width of 8 m, depth to permafrost is expected to increase in the disturbed plot if depth to permafrost in the control plot is less than 80 cm. Depth to permafrost is not expected to be different in the disturbed plot, if permafrost is lower than 80 cm in the control plot (Figure 1). If permafrost depth is 50 cm in the control plot, then average depth to permafrost was expected to increase for lines greater than 4.7 m wide (Figure 2). Models including region, forest status, disturbance age, disturbance type, and soil type were also competitive; however, estimates of these parameters did not differ from 0 indicating they were not strongly related to changes in permafrost depth. Table 4. AICc table for difference in depth to permafrost. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (ωi), and cumulative model weights (Σ ωi). The complete set of candidate models included 116 a priori models and 16 post hoc models. Models with interactions are shown in italics with an * between interacting terms. Models K AICc Δ AICc ω i Σ ω i Δ Permafrost ~ Control + Width Δ Permafrost ~ Control * Forest + Width Δ Permafrost ~ Control + Width + Forest Δ Permafrost ~ Control + Region + Width Δ Permafrost ~ Control + Width + Soil Δ Permafrost ~ Control + Width + Age Δ Permafrost ~ Control * Width Δ Permafrost ~ Control + Width + Disturbance type Δ Permafrost ~ Control * Region + Width Δ Permafrost ~ Control + Width * Soil EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 9

18 Δ Permafrost ~ Null Figure 1. Estimated relationship between difference in depth to permafrost (cm) and initial depth to permafrost in control plots (cm). Estimates shown are for a line width of 8 m. Solid line shows the estimated effect of depth to permafrost in the control plot and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Permafrost ~ Control + Width. Figure 2. Estimated relationship between difference in depth to permafrost (cm) and line width. Estimates shown are for sites with 50 cm depth to permafrost in the control plots. Solid line shows the estimated effect of seismic line width on difference in depth to permafrost and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Permafrost ~ Control + Width. 3.2 Lichens Data for lichens were present in 80.9% of the control plots and only 42.6% of the disturbed plots. Average lichen cover was 24.0% in the control plots and 8.0% in the disturbed plots. The average difference in lichen EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 10

19 cover between pairs of disturbed and control plots was -16.0%. Lichen cover was higher in 7.35% of disturbed plots, and the maximum difference in lichen cover was 6%. There was strong evidence that lichen cover in disturbed plots was related to lichen cover in the control plots (Table 5); lichen cover in the control plot was included in all of the supported models; the top two models included an interaction between lichen cover in the control plot and region (Table 5). In the Sahtu region, there was a relationship between lichen cover in disturbed plots and control plots; the difference in lichen cover in the disturbed plot was negatively related to lichen cover in the control plot. Sites with high lichen cover in the control plot had lower lichen cover on the line, while sites with low lichen cover had similar lichen cover between control and disturbed plots. For every 1% increase in lichen cover in the control plot, the average difference in lichen cover between disturbance and control plots at Sahtu sites was 0.63% lower (CI = to -0.52%; Figure 3). In the Dehcho region, there was no relationship between lichen cover in disturbed plots and control plots (estimated average change -0.28%; CI = to 0.02%; Figure 3). There was a weak trend for lichen cover to be lower on wider seismic lines (average decline %; CI = to 0.34 %). For both regions, an average 8 m wide seismic line with less than 10% lichen cover in the control plot is expected to have similar lichen cover between control and disturbed plots (Figure 3). A site with 25% lichen cover in the control plot is expected to have 10.5% less cover in the disturbed plot than the control plot in the Dehcho region (CI = -3.3% to -17.7%) and 17.7% less lichen cover in the disturbed plot than the control plot in the Sahtu region (CI = to -22.7%). Although disturbance type, disturbance age, soil texture, and forest status were included in some of competitive models, parameter estimates for these terms always overlapped 0, indicating that these factors did not have a strong relationship with difference in lichen cover between disturbed and control plots. There was no evidence for a relationship between difference in depth to permafrost and difference in lichen cover (p = 0.17). Table 5. AICc table for lichen cover. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (Σ AICc ω). The complete set of candidate models included 116 a priori models and 21 post hoc models. Models with interactions are shown in italics with an * between interacting terms. Models K AICc Δ AICc AICc ω Σ AICc ω Δ Lichen cover ~ Control * Region + Width Δ Lichen cover ~ Control * Region Δ Lichen cover ~ Control * Width Δ Lichen cover ~ Control + Width Δ Lichen cover ~ Control Δ Lichen cover ~ Control * Width + Soil Δ Lichen cover ~ Control * Width + Region Δ Lichen cover ~ Control * Soil + Width Δ Lichen cover ~ Control + Region + Width Δ Lichen cover ~ Control + Width + Soil Δ Lichen cover ~ Null EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 11

20 Figure 3. Estimated relationship between difference in lichen cover (%) and initial lichen cover in the control plot for each region, assuming an average line width of 8 m. Lines show the estimated effect of lichen cover in the control plot and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Lichen ~ Control * Region + Width. 3.3 Mosses Data for mosses were available for 89.7% of the control plots and 77.9% of the disturbed plots. Average moss cover was 40.15% in the control plots and 32.51% in the disturbed plots. The difference in moss cover between pairs of plots ranged from -80% to 70%. The average difference in moss cover was -7.63%. There was strong evidence that moss cover in disturbed plots was related to moss cover in control plots (Table 66). The difference in moss cover for disturbed plots to moss cover in control plots was greater for sites where cover was high in adjacent undisturbed vegetation (Figure 4). There was strong evidence that moss cover in disturbed plots was related to soil texture in control plots (Table 6). Moss cover was higher at sites with organic soils than sites with mineral soils by an average of 23.4 % (CI = 8.61 to 38.2%; Figure 4 and Figure 5). Statistics also provided some support for an effect from line width. After accounting for moss cover in the control plot and soil type, cover in disturbed plots increased with line width (average increase 4.49%, CI = 0.59 to 8.39%; Figure 5). There was also some support for an effect of disturbance age/age class. The difference in moss cover between the line and control plots became less pronounced with more time since disturbance. For every year since disturbance, average moss cover on the line increased by 0.55% (CI = 0.05 to 1.06%; Figure 6). Results for disturbance age class followed the same pattern as disturbance age, with the greatest difference in moss cover between the 0 to 10 year and >30 year age classes (average difference 29%; CI = 11.4 to 46.6%). Models including interactions between these parameters were competitive with the additive model; however, including interactions did not significantly change the estimates. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 12

21 Although forest status and region were included in some of competitive models, parameter estimates for these terms always overlapped 0, indicating that these factors did not have a strong relationship with lichen recovery. One of the global models received some support, suggesting that a more complex set of parameters could better explain the observed pattern in moss recovery. There was no evidence of a relationship between the difference in moss cover and depth to permafrost (p = 0.97), even after accounting for initial moss cover (p = 0.54), soil (p = 0.93) or line width (0.61). Table 6. AICc table for difference in moss cover. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (Σ AICc ω). The complete set of candidate models included 116 a priori models and 13 post hoc models. Models with interactions are shown in italics with an * between interacting terms. Models K AICc Δ AICc AICc ω Σ AICc ω Δ Moss Cover ~ Control * Soil + Width Δ Moss Cover ~ Control + Width + Soil Δ Moss Cover ~ Control + Width * Soil Δ Moss Cover ~ Control + Disturbance Age + Soil Δ Moss Cover ~ Control + Age Class + Soil Δ Moss Cover ~ Control + Disturbance Age * Soil Δ Moss Cover ~ Control * Soil + Age Class Δ Moss Cover ~ Control * Width + Soil Δ Moss Cover ~ Control * Soil + Disturbance Age Δ Moss Cover ~ Control * Disturbance Age + Soil Δ Moss Cover ~ Null EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 13

22 Figure 4. Estimated relationship between difference in moss cover (%) and initial moss cover (%) in the control plot for organic and mineral soils when line width is 8 m. Lines show the estimated effect of moss cover in the control plot and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Moss Cover ~ Control + Width + Soil. Figure 5. Estimated relationship between difference in moss cover (%) and line width (m) for organic and mineral soils when cover in the control plot is constant at 50%. Lines show the estimated effect of seismic line width and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Moss Cover ~ Control + Width + Soil. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 14

23 Figure 6. Estimated relationship between difference in moss cover (%) and disturbance age for organic and mineral soils when cover in the control plot is constant at 50%. Lines show the estimated effect of disturbance age and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Moss Cover ~ Control + Width + Disturbance Age. 3.4 Graminoids Data for graminoids were available for 67.6% of the control plots; 16 control plots had more than 5% cover of graminoid species. Graminoids were present in 83.8% of the disturbed plots and 63.3% of disturbance sites had greater than 5% cover of graminoids. The difference in graminoid cover in paired plots ranged from -11% to 55%. On average, graminoid cover was 8.0% higher in disturbed plots than in adjacent control plots. None of the candidate models was supported relative to the null model (Table 7). This indicates that the changes in graminoid cover were not strongly related to dominant vegetation, forest status, disturbance age or type, region, soil type, or line width. Based on the null model, graminoid cover was 8.38% higher in disturbed plots than adjacent control plots (95% CI = 4.98 to 11.8%). There was weak evidence of a positive relationship between depth to permafrost and the difference in graminoid cover (p = 0.08). Generally, for sites where permafrost was deeper, graminoid cover was higher in disturbed plots than control plots. On average, graminoid cover in disturbed plots was 0.12% higher (CI = to 0.24%) than control plots for every 1 cm increase in depth to permafrost in the disturbed plots (Figure 7). For sites on the line where depth to permafrost increases by 50 cm, graminoid cover was predicted to be 14.7% higher (CI = 9.87 to 19.5%). For sites with no difference in depth to permafrost, graminoid cover is predicted to increase by only 6.2% (CI = 1.97 to 10.5%). EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 15

24 Table 7. AICc table for difference in graminoid cover. Only the top 10 models are shown and none of the models considered were competitive with the null model. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 116 a priori models. Models K AICc Δ AICc AICc ω Σ AICc ω Δ Graminoid Cover ~ Disturbance Type Δ Graminoid Cover ~ Width + Disturbance Type Δ Graminoid Cover ~ Region + Disturbance Type Δ Graminoid Cover ~ Region Δ Graminoid Cover ~ Null Δ Graminoid Cover ~ Region + Disturbance Type + Age Δ Graminoid Cover ~ Disturbance Type + Soil Δ Graminoid Cover ~ Control+ Disturbance Type Δ Graminoid Cover ~ Region + Width + Disturbance Type Δ Graminoid Cover ~ Width Figure 7. Relationship between difference in graminoid cover (%) and difference in depth to permafrost (cm). Solid line shows the estimated effect of depth to permafrost on graminoid cover and the shaded area is the 95% confidence interval. Symbols are observed values. Estimates are based on the model Δ Graminoid Cover ~ Δ Permafrost. 3.5 Deciduous Shrubs Data were available for deciduous shrub species in 94.1% of controls and 92.6% of disturbed plots. Average cover of deciduous shrubs in the control plots was 27.1%, with a maximum cover of 88.0%. Average deciduous shrub cover in the disturbed plots was 32.0%, with a maximum cover of 96.0%. The difference in deciduous shrub cover between pairs of disturbed plots on the line and control plots ranged from -55.0% to 73.0%, with an average difference of 4.84%. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 16

25 There were multiple competitive models for deciduous shrub cover (Table 8). The top two models included an interaction between deciduous shrub cover in control plots and forest status, line width, and disturbance type. Akaike weights for these two models were two to five times higher than other supported models. At forested sites, the difference in deciduous shrub cover declined by an average of 0.77% (CI = to %) for every 1% increase in deciduous shrub cover in the control plot. This indicates that for forested sites, where cover of deciduous shrubs in control plots was high the cover of deciduous shrubs in disturbed plots was low. There was no relationship between deciduous shrub cover in the control plot and average cover of deciduous shrubs at non-forested sites (average difference -0.18; CI = to 0.13). Average cover of deciduous shrubs increased by 3.65% (CI = 0.77 to 6.53%) for every 1 m increase in line width (Figure 8). The difference in deciduous shrub cover in disturbed plots was lower for seismic lines that had no evidence of recent fire than for sites that had data for fire occurrence, by an average of 17.6% (CI = to -3.19%). At an average width of 8 m, seismic lines within forested sites containing less than 30% cover in the control plot are expected to have more deciduous shrub cover along the line than in the adjacent forest (Figure 8). Conversely, forested sites with more than 50% cover in the control plot are expected to have less deciduous shrub cover along the line than in the adjacent forest. For non-forested sites, recovery of deciduous shrubs was not related to deciduous shrub cover in the control plot (Figure 8). If deciduous shrub cover for the control plot in forested areas is 25%, average deciduous shrub cover is expected to be similar between the disturbed and control plot on narrower lines (< 7.0 m). For lines wider than 7.0 m, in forested areas where cover is 25%, average deciduous shrub cover is expected to be higher in the control plot than in the disturbed plot (Figure 9). For non-forested sites with 25% deciduous shrub cover, lines between 5.8 m and 9.3 m wide had similar average deciduous shrub cover on the line and in control plots; for lines narrower than this, average shrub cover decreased on the line and for wider lines average shrub cover increased on the line (Figure 9). After accounting for other parameters, deciduous shrub cover in the disturbed plot is expected to be higher at sites that showed evidence of recent fire activity. There was no evidence of a relationship between difference in deciduous shrub cover and depth to permafrost (p = 0.31), even after accounting for initial shrub cover (p = 0.31), forest (p = 0.28), line width (p = 0.69), or disturbance type (p = 0.30). Table 8. AICc table for difference in deciduous shrub cover. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 116 a priori models and 29 post hoc models. Models with interactions are shown in italics with an * between interacting terms. Models K AICc Δ AICc AICc ω Σ AICc ω Δ Shrub Cover ~ Control * Forest + Width Δ Shrub Cover ~ Control * Forest + Disturbance Type Δ Shrub Cover ~ Control + Width+ Age Class Δ Shrub Cover ~ Control + Width+ Disturbance Type Δ Shrub Cover ~ Control + Disturbance Type+ Age EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 17

26 Class Δ Shrub Cover ~ Control * Soil + Disturbance Type Δ Shrub Cover ~ Control * Disturbance Type + Age Class Δ Shrub Cover ~ Control * Forest+ Age Class Δ Shrub Cover ~ Control + Age Class Δ Shrub Cover ~ Control +Disturbance Type + Forest Δ Shrub Cover ~ Null Figure 8. Estimated relationship between difference in deciduous shrub cover (%) and deciduous shrub cover in the control plot (%) for forested and non-forested sites when line width is 8 m. Lines show the estimated average effect of initial cover on recovery and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Shrub Cover ~ Control * Forest + Width. Figure 9. Estimated relationship between difference in deciduous shrub cover (%) and line width (m) for forested and non-forested sites when shrub cover in the control plot is 25%. Lines show the estimated average effect of seismic line width on recovery and the shaded areas are 95% EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 18

27 confidence intervals. Symbols are observed values. Estimates are based on the model Δ Shrub Cover ~ Control * Forest + Width. 3.6 Lateral Cover Lateral cover in the 1 m to 2 m height class ranged from 0 100% in control and disturbance plots. Lateral cover was absent in 26.5% of the control sites and 10.3% of control plots had 100% lateral cover. More than half of the disturbed plots (54.4%) had no lateral cover and 10.3% had 100% lateral cover. There were 16 paired sites that had no lateral cover in either the control or disturbed plot. The majority of paired sites (51.5%) had less lateral cover in the disturbed plot; however, 23.5% had more lateral cover in the disturbed plot than the control plot. The model that was strongly supported above all other models included an additive effect of lateral cover in the control plot and an interaction between line width and forest status (forest vs. non-forest) (Table 9). There was a negative relationship between lateral cover in the control plot and recovery of lateral cover in the disturbed plot (estimate -0.82%; CI = to -0.59); therefore, paired sites that had high lateral cover in the control had low lateral cover in the disturbed plot. Among forested sites, the difference in lateral cover between disturbance and control plots was higher for wider lines than narrower lines. For every 1 m increase in line width at a forested site, the difference in lateral cover between the disturbed and control plots was estimated to increase by 14.4% (CI = 8.87 to 20.0%). Among non-forested sites there was no relationship between line width and the difference in lateral cover (estimate 1.63%; CI = to 7.04%). At an average width of 8 m, seismic lines within forested sites are predicted to have more lateral cover in disturbed plots o when cover in the adjacent forest is low (<20%) and less lateral cover on the line when cover in the adjacent forest is high (>46%; Figure 10). At an average width of 8 m, seismic lines within nonforested sites are expected to have similar average lateral cover in both types of plots if cover in the control plot is less than 23%. If lateral cover in the control plot is greater than 23%, then lateral cover on the line is expected to be lower than the control plot (Figure 10). Lateral cover in forested sites increased with line width. For a forested site with initial cover of 30%, lateral cover is expected to be lower than the adjacent forest on narrow lines (< 7.0 m) and higher than the adjacent forest for line wide lines (> 8.5 m; Figure 10). Lateral cover in non-forested sites did not change significantly with line width (Figure 11). There was no evidence of a relationship between lateral cover and depth to permafrost (p = 0.51), even after accounting for initial cover (p = 0.62), forest status (p = 0.97), or line width (p = 0.76). EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 19

28 Table 9. AICc table for difference in lateral cover in the 1 to 2 m height class. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 116 a priori models and 19 post hoc models. Models with interactions are shown in italics with an * between interacting terms. Models K AICc Δ AICc AICc ω Σ AICc ω Δ Lateral Cover ~ Control + Width * Forest Δ Lateral Cover ~ Control * Width + Soil Δ Lateral Cover ~ Control * Width + Forest Δ Lateral Cover ~ Control * Disturbance Type + Width Δ Lateral Cover ~ Control + Width * Soil Δ Lateral Cover ~ Control * Width + Disturbance Type Δ Lateral Cover ~ Control * Width Δ Lateral Cover ~ Control + Width + Forest Δ Lateral Cover ~ Control + Width + Disturbance Type Δ Lateral Cover ~ Control + Width + Soil Δ Lateral Cover ~ Null Figure 10. Estimated relationship between difference in lateral cover (%) and initial lateral cover (%) for forested and non-forested sites when line width is 8 m. Lines show the estimated average effect of initial cover on recovery and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Lateral Cover ~ Control + Forest * Width. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 20

29 Figure 11. Estimated relationship between difference in lateral cover (%) and line width (m) for forested and non-forested sites, when lateral cover in the control plot in 30%. Lines show the estimated effect of seismic line width on recovery of lateral cover and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Δ Lateral Cover ~ Control + Forest * Width. 3.7 Vegetation Dissimilarity Index Vegetation dissimilarity was variable (ranging from 0% to 93%). Sites with higher percent values were interpreted as having larger differences in vegetation species composition between disturbed plots on the line and control plots in adjacent undisturbed vegetation. In general, half of the disturbed sites had 17% or less cover of early successional species. For the purposes of this study, early successional species are those that were present in disturbed plots and not in adjacent control plots. Early successional species are usually characterized by high growth rates, high degree of dispersal, shade intolerance and ability to colonize disturbed sites (Pidwirny and Jones 2014). Successional species that were most common in disturbed plots without being present in the paired control included beaked willow (Salix bebbiana), tamarack (Larix laricina), leatherleaf (Chamaedaphne calyculata), fireweed (Epilobium angustifolium), and bluejoint reedgrass (Calamagrostis canadensis) and are further described in Table 10. In reference to succession, forbs (54 species) and graminoids (19 species) had the greatest diversity across all plots. Dwarf birch (Betula glandulosa) had the highest median cover relative to frequency of occurrence (i.e., number of plots x median cover). A complete summary of species found in disturbed plots that were not present in control plots is presented in APPENDIX B. There was strong evidence that vegetation dissimilarity was related to line width. In general, vegetation dissimilarity was greater on wider lines. On average, vegetation dissimilarity increased by a factor of 0.27 (CI = 0.10 to 0.47) for every 1 m increase in line width (Figure 12). There was also strong evidence that vegetation dissimilarity was related to forest status (forest vs. non-forest). There was some indication that the effect of forest status was different between regions (Table 11). In the Dehcho region, vegetation EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 21

30 dissimilarity was lower at non-forested sites than forested sites by an average factor of 0.72 (CI = to ; Figure 12 top graph). Forested sites in the Sahtu region had similar vegetation dissimilarity as forested sites in the Dehcho region (estimated difference -0.21; CI = to 0.44); however, there was no difference in vegetation dissimilarity between forested and non-forested sites in the Sahtu region (estimated difference -0.19; CI = to 0.46; Figure 12 bottom graph). There was evidence that vegetation dissimilarity increased at sites with greater difference in depth to permafrost (p = 0.02). On average, vegetation dissimilarity increased by a factor of 0.01 (CI = to 0.02) for every 1 cm increase in depth to permafrost on the line (Figure 13). There was weak evidence that forested sites had higher vegetation dissimilarity than non-forested sites after accounting for difference in permafrost (p = 0.08), by a factor of 0.62 (CI = to 1.78). The model that included line width and vegetation class received some support (Δ AICc = 3.81); in this model vegetation dissimilarity was higher for the mixed tree sites than the bryoid/lichen and shrub sites. Models that included soil and disturbance type were also competitive, but parameter estimates always overlapped 0 indicating these factors was not strongly related to vegetation dissimilarity. Table 10. Frequency of occurrence and percent cover values for the most common early successional species found in disturbed plots. Number of Cover (%) Functional group Species plots Average Median Max Min Tree Larix laricina Betula papyrifera Populus balsamifera Evergreen Shrub Chamaedaphne calyculata Rhododendron groenlandicum Vaccinium vitis-idaea Deciduous Shrub Salix bebbiana Dasiphora fruticosa Betula glandulosa Forb Epilobium angustifolium Parnassia palustris Equisetum arvense Graminoid Calamagrostis canadensis Carex aquatilis Carex capillaris Other Lycopodium annotinum Polytrichum juniperinum Polytrichum commune EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 22

31 Table 11. AICc table for vegetation dissimilarity. Only the top 10 models and the null model are shown. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 71 a priori models and 15 post hoc models. Models with interactions are shown in italics with an * between interacting terms. Vegetation dissimilarity was log transformed for analysis. Models K AICc Δ AICc AICc ω Σ AICc ω Dissimilarity ~ Width + Forest * Region Dissimilarity ~ Width + Forest Dissimilarity ~ Width + Forest + Soil Dissimilarity ~ Region + Width + Forest Dissimilarity ~ Width + Disturbance Type + Forest Dissimilarity ~ Width + Soil Dissimilarity ~ Width * Forest Dissimilarity ~ Width * Soil + Forest Dissimilarity ~ Width Dissimilarity ~ Width * Region + Forest Dissimilarity ~ Null EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 23

32 Figure 12. Estimated relationship between vegetation dissimilarity (%) and line width (m) for forested and non-forested sites in the Dehcho (top) and Sahtu (bottom) regions. Lines show the estimated effect of seismic line width and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Dissimilarity ~ Width + Forest * Region. Vegetation dissimilarity was log transformed for analysis, model estimates are shown on the original scale. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 24

33 Figure 13. Estimated relationship between vegetation dissimilarity (%) and difference in depth to permafrost (cm). Lines show the estimated average effect of difference in permafrost and the shaded areas are 95% confidence intervals. Symbols are observed values. Estimates are based on the model Dissimilarity ~ Δ Permafrost. Vegetation dissimilarity was log transformed for analysis, model estimates are shown on the original scale. 3.8 Vegetation Similarity Index For most plots, vegetation similarity ranged from to 15.0%, however, there was one extreme negative value (< -100%) and two extreme positive values (>50%). Approximately one third (30.8%) of the paired plots had vegetation similarity index values within 10%, indicating that the five most common plant species in the control plot had similar cover in the paired disturbance plot. The majority of plots (60.3%) had vegetation similarity index values less than -10%, indicating that these common species had not re-colonized disturbed plots. Only 7.0% of disturbance plots had significantly higher densities of common species than control plots (index > 10%). The most common species in all plots, as determined by percent cover and frequency of occurrence, were black spruce (Picea mariana), Labrador tea (Rhododendron groenlandicum), and dwarf birch (Betula glandulosa) (refer to Table 12). Black spruce and Labrador tea had negative median difference values, indicating that these species tended to be less prevalent in disturbed areas. In contrast, the next most common species included dwarf birch, beaked sedge (Carex utriculata), and water sedge (Carex aquatilus); these species did not have negative difference values. This indicates that these species were more prevalent in disturbed plots than control plots. Cover of common species in the control plot, disturbance type, line width, and age class were each included in multiple competitive models (Table 13). After accounting for cover of common species in control, sites without fire had higher vegetation similarity on wider lines (average 6.05%; CI = 2.60 to 9.50%), but sites with fire had no relationship with line width (average -6.27%; CI = to 1.15%; Figure 14). After accounting for line width, vegetation similarity decreased with cover of common species in the control plot on seismic lines without fire (average -0.88%; CI = to -0.62%), but sites where fire occurred after line EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 25

34 clearing had no relationship with cover in the control plot (average 0.06%; CI = to 0.62%; Figure 15). In summary, line width and cover in the control plot did not have a strong effect on vegetation similarity in plots that were exposed to fire after line clearing. In contrast, line width and cover in the control plot had some effect on vegetation similarity where seismic lines were not exposed to fire after clearing. Lines with high cover of common species and sites on narrower lines had lower vegetation similarity (Figure 14 and Figure 15). Vegetation similarity was lowest in the 1 to 10 year age class, increased by 22.9% (CI = 6.99 to 38.7%) in the 11 to 20 year class, and was consistently higher in the two oldest age classes (Figure 16). This indicates that disturbances 1 to 10 years old were more likely to have less cover of common species on the line than in the control plot than older disturbances. In summary, older disturbances had higher vegetation similarity than newer disturbances. There was no evidence of a relationship between vegetation similarity and difference in depth to permafrost (p = 0.44), even after accounting for cover in the control plot (p = 0.61), line width (p = 0.12), disturbance type (p = 0.84), and age class (p = 0.11). Table 12. Frequency of occurrence and percent cover values for the most common species found in control plots relative to their occurrence in adjacent disturbance plots. Median difference in cover is the median value of cover in the disturbance plot cover in the control plot. Functional Number of plots Median cover (%) group Species Control Disturbance Control Disturbance Difference Tree Picea mariana Larix laricina Betula papyrifera Evergreen Shrub Rhododendron groenlandicum Rhododendron tomentosum Vaccinium vitis-idaea Deciduous Shrub Betula glandulosa Alnus viridis Salix bebbiana Forb Rubus chamaemorus Comarum palustre Equisetum arvense Graminoid Carex aquatilis Eriophorum vaginatum Calamagrostis canadensis Other Polytrichum juniperinum Polytrichum commune EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 26

35 Table 13. AICc table for vegetation similarity. Only the top 10 models and the null model are shown. Post hoc models including interaction terms are indicated with italics. Table includes the number of parameters (K), AICc score (AICc), difference in AICc scores (Δ AICc), model weights (AICc ω), and cumulative model weights (ΣAICc ω). The complete set of candidate models included 116 a priori models and 35 post hoc models. Models with interactions are shown in italics with an * between interacting terms. Models K AICc Δ AICc AICc ω Σ AICc ω Similarity ~ Control + Width * Disturbance Type Similarity ~ Width + Control * Disturbance Type Similarity ~ Control * Forest + Age Class Similarity ~ Control * Disturbance Type + Age Class Similarity ~ Control * Disturbance Type + Disturbance Age Similarity ~ Control * Forest + Width Similarity ~ Control + Width + Age class Similarity ~ Control + Width + Disturbance Age Similarity ~ Control + Width Similarity ~ Control + Age Class Similarity ~ Null Figure 14. Estimated relationship between vegetation similarity, line width, and disturbance type for a site with 50% cover of common species in the control plot. Lines show the estimated average effect of line width similarity and the shaded areas are 95% confidence intervals. Symbols are observed. Estimates are based on the model Similarity ~ Control + Width * Disturbance Type. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 27

36 Figure 15. Estimated relationship between vegetation similarity, cover of common species in the control plot, and disturbance type for an 8 m wide line. Lines show the estimated average effect of cover in the control plot and the shaded areas are 95% confidence intervals. Symbols are observed. Estimates are based on the model Similarity ~ Width + Control * Disturbance Type. Figure 16. Estimated relationship between vegetation similarity and disturbance age class for a forested site with 50% cover of common species in the control plot. Points for the estimated average for each age class and error bars are 95% confidence intervals. Estimates are based on the model Similarity ~ Control * Forest + Age class. EDI Project No.: 15-Y-0012 EDI ENIVORNMENTAL DYNAMICS INC. 28