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Key message Black spruce (Picea mariana (Mill.) B.S.P.) has historically self‑replaced following wildfire, but recent evi‑ dence suggests that this is changing. One factor could be negative impacts of intensifying fire activity on black spruce seed rain. We investigated this by measuring black spruce seed rain and seedling establishment. Our results suggest that increases in fire activity could reduce seed rain meaning reductions in black spruce establishment. Context Black spruce is an important conifer in boreal North America that develops a semi‑serotinous, aerial seed‑ bank and releases a pulse of seeds after fire. Variation in postfire seed rain has important consequences for black spruce regeneration and stand composition. Aims We explore the possible effects of changes in fire regime on the abundance and viability of black spruce seeds following a very large wildfire season in the Northwest Territories, Canada (NWT ). Methods We measured postfire seed rain over 2 years at 25 black spruce ‑ dominated sites and evaluated drivers of stand characteristics and environmental conditions on total black spruce seed rain and viability. Results We found a positive relationship between black spruce basal area and total seed rain. However, at high basal areas, this increasing rate of seed rain was not maintained. Viable seed rain was greater in stands that were older, closer to unburned edges, and where canopy combustion was less severe. Finally, we demonstrated positive rela‑ tionships between seed rain and seedling establishment, confirming our measures of seed rain were key drivers of postfire forest regeneration. Conclusion These results indicate that projected increases in fire activity will reduce levels of black spruce recruit ‑ ment following fire. Keywords Seed rain, Non‑linear relationships, Fire return interval, Combustion severity, Fire size, Postfire regeneration Handling editor: Paulo Fernandes *Correspondence: Jennifer L. Baltzer email@example.com Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Reid et al. Annals of Forest Science (2023) 80:4 Page 2 of 16 age and basal area (Greene and Johnson 1999; Viglas 1 Introduction et al. 2013), we expect that increases in fire frequency Wildfire is the primary large-scale disturbance in the should result in a higher proportion of younger stands boreal forest where it is a major determinant of forest burning; this would tend to reduce postfire seed avail - age structure, species composition, and carbon stocks ability and recruitment in the long run. Third, as fire and fluxes (Bond-Lamberty et al. 2007; Beck et al. 2011). size increases, the distances from burned to unburned As a result of a changing climate and associated warm- areas on the perimeter of or within the burn may also ing and drying at high northern latitudes (Johannessen increase. Although seed fall rates from unburned black et al. 2016), the frequency of extreme fire weather is pro - spruce are thought to be relatively low, and mean seed jected to increase (Wang et al. 2017), altering the historic dispersal distance is also low (Payandeh and Haavisto fire regime outside the norms to which biota are adapted 1982; McCaughey et al. 1986), the contribution from (Flannigan et al. 2009; Seidl et al. 2017). Consequently, these sources may not be negligible. For example, John- an increase in the frequency of large, severe crown fires stone et al. (2009) found significant decreases in seed is projected through the twenty-first century (Wotton rain with distance from unburned edges for black et al. 2017), leading to an increase in the mean annual spruce in interior Alaska. Therefore, an increase in fire area burned (Wang et al. 2022). Many of these predicted size may reduce postfire seed availability and recruit - changes are already apparent; for example, annual area ment rates. burned in Canada surpassed the 31-year average for 6 Environmental constraints may also mediate patterns years of the 11-year period 2010–2020 (Canadian Forest of seed rain following fire via site productivity. For spe - Service 2018). Understanding the impacts of large, severe cies with an aerial seedbank, such as black spruce, the fires on the population dynamics of tree species is essen - size of the seedbank is positively correlated with site tial to predict the structure and composition of boreal productivity (Greene and Johnson 1999; Turner et al. forests and the ecosystem services that they provide. 2007). Conditions that lead to decreased black spruce Black spruce (Picea mariana (Mill.) B.S.P.) is one of the productivity can also reduce postfire recruitment most widespread and abundant tree species in boreal for- rates (Harper et al. 2005). For example, productivity ests of North America. This species is adapted to boreal and hence seed availability are expected to be highest fire regimes through its semi-serotinous cones which on mesic sites (i.e. sites with moderate soil moisture), maintain an aerial seedbank in the tree crowns. Fire is where moisture and/or nutrient limitations are less the main trigger for seed release, which is relatively low acute than on very dry or very wet sites (Bridge and in unburned trees (Zasada et al. 1992; Arseneault 2001). Johnson 2000). The majority of black spruce seed dispersal occurs in the In 2014, wildfires burned 2.85 million hectares of for - first 2 years following fire (Charron and Greene 2002; est in the NWT (Walker et al. 2018b); this was the largest Greene et al. 2013). This initial seed rain results in a large annual area burned on record for the territory (Canadian recruitment pulse (Greene et al. 1999), which supports Interagency Forest Fire Centre 2014). These wildfires black spruce self-replacement after fire (as reviewed in provided an opportunity to assess the implications of a Johnstone et al. 2010). The supply of viable seeds is an changing fire regime for postfire black spruce regenera - important factor limiting postfire seedling establishment tion under conditions that, however extreme relative to and subsequent patterns of stand development (Greene the recent historical record, may be indicative of what et al. 1999; Johnstone et al. 2004; Brown and Johnstone may be expected later in this century (Wang et al. 2022). 2012; Day et al. 2022). Based on the current state of knowledge, we hypothe- Following Whelan (1995), by “fire regime”, we mean sized that higher levels of total black spruce seed rain and the quantified characteristics of the fires that occur in viability would occur at burned locations that (i) have a region, including frequency, size, intensity, severity, relatively low levels of canopy combustion; (ii) have expe- cause, season of burning, and type (i.e. ground, surface, rienced longer fire-free intervals (i.e. older stand ages); or crown). Of these, increases in the severity, frequency, (iii) are closer to unburned edges; (iv) have higher black or size of fires could reduce both the rate and viability of spruce productivity; (v) have greater prefire basal area of postfire black spruce seed rain, thereby reducing seed - black spruce; and (vi) have moderate soil moisture con- ling recruitment through several processes. First, high ditions (mesic). To test these hypotheses, we established fire intensity or severity can increase canopy combus - a network of 250 seed traps deployed in 25 plots within tion and reduce recruitment by heat-induced damage three large 2014 fire scars in the Taiga Plains ecozone in to embryos within seeds or by partially or completely the NWT (Fig. 1). This work will help support predic - combusting the seedbank (Arseneault 2001; Johnstone tions of future responses and regeneration trajectories of et al. 2009; Splawinski et al. 2019). Second, because the boreal tree species to ongoing changes in fire activity. production of viable seeds is positively related to tree R eid et al. Annals of Forest Science (2023) 80:4 Page 3 of 16 Fig. 1 Locations of black spruce‑ dominated sampled plots that burned in 2014 and where seed traps were deployed, and seedling counts performed. Plots are located in three burn complexes (yellow shading) along the road corridor between Behchokǫ̀ and Hay River, Northwest Territories, Canada and are within the Taiga Plains ecozone (green shading in the inset) discontinuous permafrost (Heginbottom et al. 1995). 2 Material and methods Mean annual air temperature (1981–2010) for the Yel- 2.1 Study region lowknife and Hay River weather stations was −4.3 °C and This study took place in the Taiga Plains ecozone (Eco - −2.5 °C, respectively (Environment Canada 2017). The system Classification Group 2007) in the NWT, Canada mean January and July air temperatures were −25.6 and (Fig. 1). The Taiga Plains is 45% forested, 32% wetlands/ 17.0 °C for Yellowknife and −21.8 °C and 16.1 °C for Hay waterbodies, and 23% barren lands and grasslands River. Mean annual precipitation (1981–2010) was 289 (Environment and Natural Resources 2015). Forests mm for Yellowknife and 336 mm for Hay River (Environ- are composed of closed to open canopies of mixed and ment Canada 2017). pure stands of black spruce, jack pine (Pinus banksiana Lamb.), white spruce (Picea glauca (Moench) Voss), trembling aspen (Populus tremuloides Michx.), and 2.2 Estimation of seed rain paper birch (Betula papyrifera Marshall and Betula neo- Seed rain was measured at 25 black spruce-dominated alaskana Sarg.). The Taiga Plains ecozone is mainly flat plots within three burn scars (Fig. 1). We selected with extensive peatland coverage, level to undulating burned plots from a larger set of plots with road access uplands, and is underlain by glacial till (Ecosystem Clas- to represent areas that were black spruce-dominated sification Group 2007). This region is within the zone of Reid et al. Annals of Forest Science (2023) 80:4 Page 4 of 16 before fire and captured gradients of fire severity and 2.3 Plot‑level attributes site drainage (Walker et al. 2018a). Each plot was a 2 We measured or calculated the following variables at × 30 m belt transect. At each plot, 10 traps were posi- each plot: soil moisture class, distance to the nearest tioned at 3 m intervals along the transect (Figure 5 in unburned edge, time after previous fire in years (stand Appendix) and secured with a large nail in each cor- age), tree productivity index, canopy combustion, pre- 2 −1 ner (250 traps in total). Traps were rectangular gar- fire standing black spruce basal area (m ha ), density den flats (52 cm × 22.5 cm) with drainage holes, lined of prefire black spruce stems, and postfire black spruce with synthetic turf to trap the seeds. Turf was approxi- seedling counts. Plot soil moisture class was assessed in mately 6 mm high and was used to prevent seeds from the field following Johnstone et al. (2008). Our plots fell being blown out of traps by wind (Zasada et al. 1979); within three moisture categories: wet (mesic-subhygric, this design follows Johnstone et al. (2009). Traps were n = 8), with considerable surface moisture associated deployed in June 2015, and the order of deployment with depressions or concave toe slopes; mesic (n = 7), and collection was consistent for each sampling period with moderate surface moisture on flat terrain or shal - to standardize sampling lengths. Seed traps were left low depressions, including toe slopes; and dry (mesic- out from June 2015 to August 2016 and emptied three subxeric, n = 10), with less surface moisture on flat to times: first summer after fire (late June to late August gently sloping terrain. To estimate distance from the cen- 2015), second winter after fire (late August 2015 to tre of each plot to the nearest unburned edge, we found mid-May 2016), and second summer after fire (mid- the nearest edge by helicopter and took GPS coordinates May 2016 to late August 2016). The timing of these while directly overhead. collections was meant to ensure that seeds were not All prefire trees that reached breast height (1.3 m) were left in the field too long which could compromise via- identified, and diameter at breast height (DBH) meas - bility. Upon collection, the contents of each seed trap ured within the 2 m × 30 m belt transect. Trees that were were stored on ice in the field, frozen at a base camp alive prefire consistently retained their bark, and there in Fort Providence to prevent germination, and then was no combustion of the bole allowing for distinction shipped by air in coolers to Wilfrid Laurier University. between trees that were standing dead prefire and those In the lab, seeds from each trap were separated from that were alive prefire. Basal area of each tree in the tran - organic debris, classified by tree species, and the total sect was calculated as BA = π (DBH/2) , and plot basal number of seeds by species per trap was recorded, pro- area was estimated by summing stem basal area over all viding an estimate of total seed rain per trap. Sorted black spruce stems within plots. We used standing black seeds were stored at −2 °C until germination trials. spruce basal area rather than total black spruce basal area There was no evidence of seed predation in the field or (standing + downed) as a predictor variable in the mod- of fungal infection in the field or the lab. els because standing basal area was more strongly related The number of viable black spruce seeds was meas- to total seed rain than was total black spruce basal area ured per trap, following Leadem et al. (1997). Seeds (data not shown), supporting previous findings (John - were surface disinfected by immersing in 3% H O for stone et al. 2009). From this, we could also determine 2 2 −1 5 min, rinsed three times with de-ionized water, and prefire stem density (stems ha ) for the stand and for then stratified at 4 °C for 3 weeks. Seeds were placed each species individually. on moist filter paper in parafilm-sealed Petri dishes Canopy combustion for each tree measured within to germinate in a greenhouse for 21 days. Greenhouse this transect was categorized in the field on a four-point photoperiod and temperature were 16/8 h day/night scale: 0 — alive (no combustion); 1 — low combustion and 23/19 °C day/night, respectively. Seeds from each (only needles combusted); 2 — moderate combustion trap and period were germinated in separate petri (many small branches remaining); and 3 — high combus- dishes. Dishes were checked daily to ensure sufficient tion (only central trunk and branch stubs remaining). We moisture, and water was added as necessary. Germi- used the modal value of tree-level canopy combustion for nants were counted after 21 days. No seeds were lost black spruce trees within plots as our plot-level measure to fungal infection or rot over the course of the ger- of canopy combustion. mination trials. Viability of a subsample of ungermi- Stand age at time of burning in 2014 (i.e. time after pre- nated seeds (up to 10 seeds per sample) was assessed vious fire) was determined at each plot using tree ring by sectioning and staining with tetrazolium chloride counts. Five trees representative of the size and species of following (Leadem 1984) (see Appendix for additional trees found before the fire in each plot were determined details). We detected no viable ungerminated seeds, by taking either a cross-sectional sample (tree disk) or an indicating that our germination assay was a good esti- increment core as close to the base as possible. Tree disk mate of seed viability. and core samples were sanded with a progressively finer R eid et al. Annals of Forest Science (2023) 80:4 Page 5 of 16 grit until all rings were visible. The samples were then plyr, and ggplot2 R packages. All traps were established scanned and rings counted using Cybis CooRecorder within black spruce-dominated plots, and most trapped v.7.8 (Larsson 2006) or WinDendro 2009 (Regent Instru- seeds were black spruce (see Section 3), so this was the ments, Quebec City, Quebec, Canada). Tree ages within a only species modelled. Traps that were directly below plot were inspected for clustering, since it was expected the cone ball of a fallen black spruce tree had abnormally that tree age would cluster around the date of postfire high seed counts and were excluded from analyses. Traps recruitment. When most trees (> 50%) fell within 10–20 that were lost or destroyed in one or more of the three years of a central date, we assumed this to indicate the collection periods were also excluded. Our total sampling most recent fire, and the age of the oldest tree in the effort was 247 traps within 25 plots across the three sam - cluster was used to represent stand age (see Walker et al. pling periods. For analysis, we summed trap-level seed 2018a); 21 of 25 plots showed this clustered age distribu- counts in each trap across the three sampling periods, tion. In the absence of such clusters, we used the age of yielding total seed counts per trap over 60 consecutive the oldest stem to represent stand age. weeks postfire. Data are available in Baltzer et al. (2020) We used an index derived from tree size and age as a and Reid et al. (2022). proxy for average tree productivity within a plot. Tree- We tested our hypotheses of factors affecting total level productivity was estimated as the deviance from number of seeds and viability by fitting two general- a linear regression of basal diameter versus tree age for ized linear or generalized linear mixed-effects mod- our sample of black spruce trees aged using basal ring els using lme4 R package (Bates et al. 2015). First, we counts described above. Trees with a positive or negative modelled the total number of seeds per trap as overdis- deviation were interpreted as indicating relatively above persed count data (model 1) using a generalized linear or below average growth, respectively, for their age. The mixed effects model with a negative binomial distribu- mean of tree-level deviances for each plot was calculated tion, logarithmic link, and a random intercept for plot to produce a plot-level productivity index. to account for nonindependence of traps within plots. We included an offset term of log(trap area × 60), 2.4 E stimation of total seedling recruitment where trap area is 0.117 m and 60 is the total number Total seedling recruitment was sampled in five 1 m veg- of weeks of the three collection periods. With this off- etation sampling quadrats spaced at 6 m intervals along set, the model predicts mean seed-fall rates in units of −2 −1 one transect in each plot (Figure 5 in Appendix). Seed-seeds m week . We used package lme4 (Bates et al. ling counts took place in June 2016, 1 year after our seed 2015) to fit this model. Model diagnostics testing for traps were established (Table 1), approximately 2 years outliers, zero inflation, and overdispersion was per- postfire. The seedling counts reflected cumulative black formed using package DHARMa (Hartig 2020); these spruce regeneration from immediately postfire, up to the tests showed that model assumptions were not vio- date of seed trap deployment, and continuing past the lated. Marginal R (fixed effects only) and conditional end of the second seed trapping period. R (fixed and random effects) were calculated using package MuMIn (Bartoń 2019). Second, we modelled 2.5 Statistical analysis seed viability as the probability of germination per trap All statistical analyses were performed using R ver- (model 2) using a binomial generalized linear mixed sion 3.6.2 (R Core Development Team 2017). Data were effects model with logit link and a random intercept organized, and graphs were created using tidyverse, for plot. The response variable was the proportion of viable seeds per trap over the three sampling periods. Only data from traps with a nonzero total seed counts were included (n = 238). We fitted model 2 using R Table 1 Summaries of continuous predictor variables measured package lme4 (Bates et al. 2015), with diagnostics and in the 25 study plots 1 year after the 2014 fires. All plots were R values derived as per model 1. For both models, established in black spruce‑ dominated forests in the Taiga Plains predictors included distance to nearest unburned edge, region of the Northwest Territories, Canada. See Table 4 for plot‑ standing black spruce basal area, stand age, plot pro- level values. SE: standard error ductivity index, plot moisture class, and canopy com- Mean ± SE Range bustion. Continuous predictors were standardized to a mean of zero and standard deviation of one, so that Distance to unburned edge (m) 153.80 ± 11.38 17–710 estimated coefficients and effect sizes were compara- Stand age (years) 103.07 ± 2.63 71–232 −4 ble across predictors. Moisture class was represented Tree productivity index 1.0 × 10 ± 0.002 −0.05–0.06 as a three-level factor using treatment contrasts, with Standing black spruce basal area (m 8.94 ± 0.41 0–23.53 −1 ha ) the mesic class as the reference level. We characterized Reid et al. Annals of Forest Science (2023) 80:4 Page 6 of 16 canopy combustion as a two-level factor using treat- adjustment. We calculated raw and adjusted R for the ment contrasts, with the moderate combustion class as selected models. the reference level. Predictors were not strongly pair- wise correlated (r < 0.50). To account for possible non-3 Results linearities in the total and viable seed rain response, we The 247 seed traps collected a total of 3814 seeds over compared alternative full-saturated models including the 60 weeks. Most seeds (95%) were black spruce. Other linear or spline adjustments (with up to two degrees of tree species captured were trembling aspen (3.62%), jack freedom) for each continuous predictor. We selected pine (0.87%), and paper birch (0.16%). The mean number the adjustment with the lowest Akaike Information of black spruce seeds collected was 14.6 seeds per trap Criterion (AIC) score as being the best supported (range: 0–57, Table 4 in Appendix). Given the trap area of model corrected for parsimony (Crawley 2013).0.117 m and 60-week collection period, the mean black −2 −1 To test that our seed trap data were informative with spruce seed rain was 2.2 seeds m week . Of the 247 respect to black spruce, we modelled black spruce traps, 238 (74.8%) had nonzero total black spruce seed seedling counts measured 2 years postfire. The seed - counts. lings were counted in five 1 m quadrats along one 30 Overall, we found there were different significant pre - m transect at each of the sites where seed traps were dictors for total and viable seed rain. Total black spruce 2 −1 established (see Day et al. 2022 for full details of seed- seed rain (scaled to m week ; model 1) was significantly ling counts). It was not possible to pair seed traps with associated with standing black spruce basal area (Table 2; vegetation quadrats within sites, so we calculated plot- Fig. 2). Model selection supported a hump-shaped rela- level means of black spruce seedlings per quadrat and tionship between seed rain and basal area (spline adjust- of total and viable black spruce seeds per trap, stand- ment of two degrees of freedom) rather than a linear ardized to counts per square meter. Equivalently, the relationship (Table 5 in Appendix). Total seed rain 2 −1 three counts were standardized to sample mean densi- increased with basal areas up to ~12 m ha , after which ties. We then regressed seedling density against total this increase did not continue in plots with larger basal seed density (model 3a) and viable seed density (model areas. There was also a significative positive association 3b). We used linear models with spline adjustments of of total seed rain with the productivity index. Plots with up to three degrees of freedom to model possible non- moderate combustion appeared to have greater total seed linear responses. We selected the adjustment with the rain compared to high combustion, but this factor was lowest AIC score to determine the appropriate spline not significant. There were no significant effects for time −2 −1 Table 2 Negative binomial mixed‑ effects model of total black spruce seed rain (seeds m week ; model 1) and binomial mixed‑ effects model of black spruce seed viability (germination probability; model 2) with estimated coefficients, standard errors (SE), p‑ values and variance explained by marginal and conditional R . Significant predictors are presented in bold. Mesic plots were the reference level of the contrast against which both wet and dry plots were compared. Moderate combustion was the reference level of the contrast of moderate vs. high combustion Model 1: total seed rain Model 2: seed viability Predictors Coefficient est. SE P Coefficient est. SE p (Intercept) −0.39 0.34 0.259 −1.71 0.17 <0.001 Stand age 0.02 0.12 0.885 0.21 0.09 0.018 Combustion (high) −0.26 0.23 0.251 −0.32 0.17 0.060 Productivity index 0.21 0.11 0.046 −0.00 0.10 0.978 Basal area (1st degree) 2.25 0.52 < 0.001 0.03 0.09 0.767 Basal area (2nd degree) −0.48 0.46 0.301 Moisture (wet) 0.23 0.23 0.307 0.66 0.19 0.001 Moisture (dry) 0.30 0.26 0.250 −0.04 0.19 0.817 Distance to edge (1st degree) −0.10 0.10 0.354 −1.02 0.48 0.033 Distance to edge (2nd degree) 1.03 0.42 0.014 Random effects σ 0.26 3.29 2 2 Marginal R /conditional R 0.42/0.65 0.23/0.29 R eid et al. Annals of Forest Science (2023) 80:4 Page 7 of 16 Fig. 2 Predicted total black spruce seed rain as a function of prefire black spruce basal area (A) and productivity index (B), at two canopy combustion levels (model 1, Table 2). Shaded areas represent 95% confidence intervals. Values of other continuous covariates were held constant at their mean or reference levels after fire, site moisture class, or distance to edge, on total not with standing black spruce basal area (model 2; 2 2 seed rain. Model 1 R (marginal R ; fixed effects only) Table 2, Fig. 3). Model selection supported a hump- 2 2 was 0.42, and the R (conditional R ; fixed and random shaped relationship between seed viability and distance terms) was 0.65. to edge (spline adjustment of two degrees of freedom) Our germination trials showed that 498/3637 black rather than a linear relationship (Table 5 in Appen- spruce seeds were viable (13.7%). The number of viable dix). Thus, seed viability was greater in plots closer to seeds per trap ranged from 0 to 15 (mean ± stand- an unburned edge (~100–200 m; Fig. 3B). Soil mois- ard deviation: 2.02 ± 2.29, Table 4 in Appendix), and ture was also a significant predictor with seed viabil - the proportion of viable seeds per trap ranged from 0 ity being highest on wet compared to mesic or dry to 1.0 (mean ± standard deviation: 0.17 ± 0.21). Ger- sites. There was a marginally significant negative effect mination probability increased with stand age, but of canopy combustion on seed viability with higher Reid et al. Annals of Forest Science (2023) 80:4 Page 8 of 16 Fig. 3 Predicted probability of black spruce seed viability as a function of prefire stand age (a) and distance to unburned edge (b) at two levels of canopy combustion levels and three levels of moisture regime (model 2; Table 2). Values of other continuous covariates were held constant. Shaded areas represent 95% confidence intervals canopy combustion trending toward lower seed viabil- seedling densities increased with total or viable seed rain 2 −2 −2 ity compared to moderate combustion. Model R for up to ~200 seeds m or ~20 seeds m , respectively, the seed viability model (marginal R ; fixed effects only) after which further increases in both seed rain variables 2 2 was 0.23, and the R (conditional R ; fixed and random led to no further significant increases in seedling estab - terms) was 0.29. lishment (Table 3; Fig. 4). The analysis of plot-level seedling regeneration showed that density of black spruce seedlings was positively 4 Discussion related to both total and viable seed rain (model 3; Our models of black spruce seed rain and seed viability Table 3, Fig. 4). However, the variation explained by the in boreal forests of the Northwest Territories (NWT), model including total seed rain as a predictor was higher Canada, confirmed expected positive relationships than the model including viable seed rain (Table 3). Both between standing black spruce basal area and postfire models displayed a concave relationship (spline adjust- total seed rain. However, these relationships were non- ment with 2 degrees of freedom, Table 6 in Appendix); linear: this increase in postfire seed availability is not R eid et al. Annals of Forest Science (2023) 80:4 Page 9 of 16 Table 3 Linear models of plot‑level black spruce seedlings 2016). In serotinous conifers, greater prefire basal area 2 2 per m (model 3) against total (A) and viable (B) seeds per m translates into larger aerial seedbanks (Greene and John- showing estimated coefficients, standard errors (SE), p‑ values, son 1999), thus supporting ample seed rain following fire. and variance explained by marginal and conditional R squared. This is an example of a positive neighbourhood effect that Significant predictors are in bold promotes stand self-replacement and regeneration after Predictors Estimates SE p disturbance (Frelich and Reich 1999), which may be par- ticularly important for seeds with low dispersal distances such as black spruce. Our findings support this mecha - (Intercept) −0.07 2.17 0.975 nism; however, our data also suggest that when basal area Total seeds (1st degree) 19.22 5.76 0.003 gets too high, reproductive outputs can be negatively Total seeds (2nd degree) −6.09 3.41 0.089 impacted. The lack of continued increase in seed rain 2 2 R /R adjusted 0.43/0.38 2 −1 at basal areas greater than ~12 m ha could be due to negative effects of competition on reproductive outputs (Intercept) 2.16 2.25 0.349 (e.g. Rossi et al. 2012). To evaluate this, we investigated Viable seeds (1st degree) 11.62 5.55 0.049 relationships between our site productivity index, density Viable seeds (2nd degree) −6.30 4.71 0.196 of black spruce stems, and basal area (Figure 6 in Appen- 2 2 R /R adjusted 0.26/0.18 dix). While stem density was significantly negatively correlated with the productivity index, a concave rela- tionship existed between basal area and both stem den- maintained at high basal areas. Contrary to our expec- sity and site productivity. This meant that the plots with tations, high canopy combustion was not a significant highest productivity had both moderate stem densities predictor of total seed rain and only had a marginal and basal areas, which corresponded with the plots with negative effect on postfire seed viability. As expected, the greatest reproductive outputs (Fig. 2). It is notewor- seed viability rates were greater in older sites and closer thy that our range of sampled sites included many with to unburned edges and, surprisingly, were higher in wet high basal areas compared to other studies (range: cur- 2 −1 than in dry or mesic sites. Finally, seedling densities were rent study, 1.4–37.1 m ha ; Johnstone et al. 2009, 0.1– 2 −1 2 −1 related to both total and viable seed rain, with the high- 28 m ha ; Zasada et al. 1979, 1.8–6.9 m ha ), which est levels of recruitment corresponding with moderate may explain why a concave response was not detected in levels of total and viable seed counts, suggesting that previous studies. Thus, our study has added knowledge of beyond a certain level of seed rain, other factors become basal area relationships in seed rain that have not been limiting to establishment. captured previously. The concave relationship between site black spruce Although seeds may be directly destroyed by high basal area and total seed rain has not previously been severity fire in the tree canopy (Arseneault 2001; Splaw - reported to our knowledge. Basal area has been shown to inski et al. 2019) leading to reduced total or viable seed be a principal mechanism supporting black spruce self- rain, we did not find evidence of reduced total seed rain replacement after fire across North America (Greene in the present study. However, our models indicate that and Johnson 1999; Johnstone et al. 2009; Splawinski et al. severe canopy combustion marginally reduced seed −2 Fig. 4 Predicted black spruce seedlings (m ) as a function of total seed rain (A) and viable seed rain (B) for 25 plots. Shaded areas represent 95% confidence intervals. Model fits are presented in Table 3 Reid et al. Annals of Forest Science (2023) 80:4 Page 10 of 16 viability. This finding supports previous studies from of heating experienced by cones, thereby helping to other regions in North America that greater combus- maintain seed viability. tion results in lower seed viability either at the individual Distance to unburned edge was a significant positive level (Johnstone et al. 2009) or in response to within- predictor of seed viability, but not of seed rain. These crown variability in combustion (Splawinski et al. 2019). findings align with previous studies of seed rain in inte - Other studies have also found that severe canopy com- rior Alaska (Johnstone et al. 2009). We found that seed bustion may reduce viable seed rain with no apparent viability declined up to distances of ~100–200 m from impact on total seed rain (e.g. Johnstone et al. 2009), an edge, after which it remained constant (Fig. 3B); this suggesting that loss of seed viability due to heating asso- is consistent with the previously measured dispersal ciate with greater canopy combustion may be a more distances of approximately 100–120 m (Payandeh and common mechanism of reduced reproductive potential Haavisto 1982; McCaughey et al. 1986). This implies than complete combustion of seeds or cones. that contributions of viable seed from relatively distant The positive relationship between stand age and seed unburned sources can affect regeneration of this species. viability suggests that shortened fire return intervals Large fires are common in the NWT and are expected may negatively impact postfire regeneration of black to become even more common under climate warming spruce in the NWT. Our study provides an underly- (Amiro et al. 2004; Burton et al. 2008). Our findings sug - ing mechanism for previous findings of reduced black gest a moderate negative impact of large fire complexes spruce seedling density in areas experiencing more fre- on postfire regeneration in black spruce. quent stand replacing fires in the NWT (Whitman et al. It is notable the models of total seed rain and of seed 2019, Day et al. 2022) and Yukon (Brown and Johnstone viability identified different significant covariates. Seed 2012). With the increased fire activity predicted for the rain and viability are only the first of multiple limit - boreal biome (de Groot et al. 2013; Wang et al. 2022), ing factors in postfire regeneration; actual germination the average age at which stands burn must necessar- of viable seed on suitable substrate, seedling estab- ily decrease, which will have wide-ranging impacts in lishment, and survival to maturity is also important these forests. As stands age, the accumulation of black (Greene et al. 2007; Brown et al. 2015). The concave spruce basal area slows and can even decrease as une- relation of seedlings to seed rain implies that seed sup- ven age- and size-class structures develop (Miquela- ply is not limiting on all sites. Our results address the jauregui et al. 2016). This decoupling between age and initial stages of this complex chain of processes and basal area may explain the differential responses we provide novel information regarding postfire regen - observed for total and viable seed rain to these predic- eration in the extensive black spruce forests of the tors. Specifically, our results suggest that basal area is NWT. The implications are that regeneration depends an important determinant of total seed rain, but that on fairly local properties of stand structure (i.e. both the age of the stand is more relevant for determining stand age and basal area), on stand-level moisture/ how much of this seed will be viable, a critical compo- drainage, and also on the level of canopy combustion, nent of regeneration. which interacts with stand structure in boreal black The greater viability rate of seeds on wet plots was spruce forests (Miquelajauregui et al. 2016). Vegeta- unexpected. Wet sites with thick organic soils are tion dynamics models that do not consider these local stressful locations from a growth perspective, mean- phenomena may be inadequate to forecast future forest ing trees may increase allocation to reproductive out- conditions in these regions. put. Indeed, greater reproductive efforts in trees are Our data and models are of ecological relevance inso- favoured in response to other resource-related mecha- far as they inform on drivers of black spruce regen- nisms, such as shade (Paz and Martínez-Ramos 2003; eration postfire. However, we may also have missed Quero et al. 2007) and intraspecific resource competi - temporal variation in seed rain and seed viability due tion (Lebrija-Trejos et al. 2016). Indirectly, it is possi- to constraints in our experimental design. The period ble that differences in fire behaviour and cone heating of postfire seed dispersal from black spruce is brief; a amongst plot moisture classes not captured in our can- large majority of seed rain occurs in the first two grow - opy combustion estimates explain this finding. Other ing seasons following fire (Charron and Greene 2002), things being equal, elevated soil moisture is associ- resulting in a large recruitment pulse (Greene et al. ated with lower rates of fuel consumption (Forestry 1999). In addition, seeds released in the first months Canada Fire Danger Group 1992) and hence with lower after fire may come preferentially from cones at the fire intensity. Lower fire intensity would reduce scorch periphery of tree crowns. These seeds might have lower height (Van Wagner 1973), and presumably the amount viability than later-falling seeds from more protected, R eid et al. Annals of Forest Science (2023) 80:4 Page 11 of 16 interior cones (Splawinski et al. 2019). Earlier seed predictive modelling of the future state of this vast and trap deployment and more regular seed trap empty- globally important biome. ing would indicate the timing of release of the ‘most’ and ‘least’ viable seed and reduce seed loss or viabil- ity reduction by shortening the length of time seeds Appendix are left in traps. Data from earlier seed trap deploy- Seed viability determination ments might also reveal whether there is an absolute To determine viable seed rain, germination trials were difference in number of seeds released from coneballs conducted using the sorted seeds from the seed traps. experiencing high vs. low combustion. To capture all The protocol for the germination trials follows Leadem postfire seed rain, traps would need to be deployed et al. (1997). Specifically, all seeds were surface steri - immediately after late-burning fires are extinguished lized by immersing them in H O for 5 min and rinsed 2 2 by the onset of winter snow, which creates logistical three times with de-ionized water. All seeds were then challenges difficult to overcome in any study (e.g. John - stratified by soaking for 24 h in 20–25 °C de-ionized stone et al. 2009). By starting in June 2015, our study water, following which the seeds were placed in plas- may have missed upwards of 50% of the total seed tic bags or vials for 21 days at 2–5 °C. Seeds were then rain attributable to the 2014 fires (Greene et al. 2013). placed on moist filter paper on Petri dishes to germi - However, despite missing this early period of seed rain, nate in a greenhouse for 21 days where conditions were our data show significant relationships between black 23 °C for 16 h of light and 19 °C for 8 h of dark. Dishes spruce seedling establishment and both total and viable were set up in a randomized blocking design with 10 seed rain, suggesting that our estimates of seed rain are independent blocks and dishes distributed randomly meaningful from an ecological perspective. amongst blocks. Samples from each trap were tested separately unless there were more than 100 seeds in 5 Conclusion a sample in which case the sample was separated into Our findings both support and extend previous work subsamples. Dishes were checked daily to ensure suf- in the North American boreal forest showing that total ficient moisture. At the end of the 21-day germination and viable black spruce seed rain are driven by vari- period, the number of germinated seeds was counted. ous combinations of standing basal area, distance to Viability tests were then run on a subsample of seeds an unburned edge, stand age, and canopy combustion that did not germinate to determine whether they are severity (Zasada et al. 1979; Arseneault 2001; Johnstone viable to germinate (i.e. filled seeds) or not. Ten unger - et al. 2009). Importantly, we also show the relevance minated seeds from each site were tested for viability (n of these measures for natural seedling establishment. = 250 seeds). Protocol for the viability test follows Lea- Taken together, these results suggest that black spruce dem (1984). Seeds were soaked overnight in 20 °C water recruitment after fire may decline under projected to soften the tissues. A thin layer of the endosperm increases in fire activity in this region (Wotton et al. was sliced off, and the cut seeds were placed in Petri 2017), most notably fire frequency and to a lesser extent dishes and covered with 1% tetrazolium (TZ) solution fire size. Indeed, there is already evidence of changing (pH = 6.5–7). Seeds were then incubated for 2–8 h regeneration patterns in black spruce across much of and removed when staining is complete. To determine boreal North America (Baltzer et al. 2021). The non-lin - when staining is complete, an additional dish of seeds ear nature of several of the modelled relationships high- was stained, and seeds were cut periodically to assess lights the importance of capturing broad environmental, how far the staining has progressed and to ensure that stand structural, and climatic gradients to better reflect the staining did not get too dark. When staining was complex ecological relationships. Further exploration of complete, the TZ solution was drained, and the seeds non-linearities between key measures of forest recov- were rinsed 2–3 times with water. Seeds were then cut ery and environmental or stand characteristics, such in half to view the embryo. The protocol by Leadem as those identified here, will help to refine projections (1984) provides figures which were then used to assess of postfire forest recovery. As the fire regime continues the make-up of inside of the seed and assess viability. to change, there is an increased need for detailed and None of the seeds that were tested for viability was via- generalizable understanding of the responses of domi- ble, indicating that all seeds that were viable had germi- nant boreal forest species to such changes, to support nated during the preceding experiment. Reid et al. Annals of Forest Science (2023) 80:4 Page 12 of 16 ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ ‑ Table 4 Summary of studied variables from each of the 25 study plots Plot Burn date Total seed Viable seed Distance to Stand age Moisture regime Canopy combustion Tree productivity Standing black Black a a (Julian days rain countrain count unburned edge (years) index spruce basal spruce 2 −1 in 2014) (m)area (m ha ) stem count SS3301B 189 165 35 17 171 Mesic High 0.0025 9.2 38 SS3303C 183 60 10 315 232 Dry High 0.0035 23.5 53 SS3306C 183 70 14 105 72 Wet Moderate 0.0080 1.4 32 SS3309A 183 183 27 378 90 Wet High −0.0346 7.1 134 SS3311B 183 105 14 56 163 Dry High 0.0019 23.5 105 ZF2004A 203 107 13 23 150 Dry Moderate 0.0139 2.9 53 ZF2006B 196 271 18 354 110 Dry High 0.0060 12.4 37 ZF2009C 193 75 8 34 85 Wet High −0.0505 4.5 139 ZF2010C 194 92 28 66 84 Wet High 0.0011 5.6 55 ZF2011C 196 72 4 44 83 Mesic High 0.0036 14.7 158 ZF2012B 197 145 16 575 73 Mesic High 0.0354 11.5 58 ZF2034B 211 268 42 39 87 Dry Moderate 0.0388 16.2 36 ZF2040A 212 40 10 710 167 Dry High −0.0179 3.8 97 ZF2043C 210 104 10 133 74 Wet High −0.0084 12.5 124 ZF2052C 212 89 11 135 93 Mesic High −0.0382 14.0 171 ZF2059B 211 26 0 55 74 Dry High −0.0033 0 35 ZF2064A 209 31 3 62 75 Dry High 0.0624 0 18 ZF4601B 203 325 43 51 72 Wet High 0.0125 11.3 85 ZF4615B 203 377 62 51 116 Dry Moderate −0.0023 6.9 43 ZF4618B 220 169 16 153 71 Wet High −0.0146 2.2 36 ZF4621B 213 155 31 55 72 Wet Moderate 0.0142 9.5 88 ZF4630B 205 110 22 60 102 Wet Moderate −0.0222 9.8 160 ZF4635C 205 322 38 31 110 Mesic Moderate 0.0103 13.0 39 ZF4648A 225 22 0 251 71 Mesic High −0.0530 1.2 88 ZF4651A 214 224 23 71 71 Mesic Moderate 0.0330 3.9 30 Sums over all traps over the entire sampling period R eid et al. Annals of Forest Science (2023) 80:4 Page 13 of 16 Table 5 Comparison of AIC values in negative binomial mixed‑ effects model of total black spruce seed rain (model 1) and binomial mixed‑ effects model of black spruce seed viability (model 2), using linear and nonlinear relationships (nl) of 2 degrees of freedom (df ) for the continuous predictors, i.e. Stand age, productivity index, basal area, and distance to edge. The lowest AIC for each model is highlighted in bold Fixed effects Model 1 Model 2 AIC AIC Stand age + combustion + productivity index + 1687 835 basal area + moisture + distance to edge nl(Stand age, df = 2) + combustion + productivity 1679 831 index + basal area + moisture + distance to edge Stand age + combustion + nl(productivity index, 1687 836 df = 2) + basal area + moisture + distance to edge Stand age + combustion + productivity index 1676 832 + nl(basal area, df = 2) + moisture + distance to edge Stand age + combustion + productivity index + 1689 829 basal area+ moisture + nl(distance to edge, df = 2) Table 6 Comparison of AIC values in linear models of plot‑level black spruce seedlings per m (model 3) against total (a) and viable (b) seeds per m showing estimated coefficients, standard errors (SE), using linear and nonlinear relationships (nl) of 2 degrees of freedom (df ) for the continuous predictors, i.e. total and viable seeds per m . The lowest AIC for each model is highlighted in bold. Model 3a Model 3b Fixed effects AIC Total seeds 149 nl(total seeds, df = 2) 138 Viable seeds 148 nl(viable seeds, df = 2) 144 Fig. 5 Schematic of plot design. Each plot was a pair of parallel 30 m belt transects, 2 m apart. Ten seed traps were positioned at 3 m intervals along the center line. Traps were rectangular garden flats (52 cm × 22.5 cm) with drainage holes and lined with synthetic grass turf to trap the seeds. Five of the traps were collocated with five 1 m quadrats where all seedlings were counted in June 2016 (squares marked S, adjacent to the right hand transect) Reid et al. Annals of Forest Science (2023) 80:4 Page 14 of 16 Fig. 6 Relationships between standing black spruce basal area and tree productivity index (a), density of black spruce stems and tree productivity index (b), and standing black spruce basal area and density of black spruce stems (c). Regression lines are shown as black lines and cubic polynomial lines as blue lines. Insets in the panels show Pearson correlation coefficients between plotted variables Fig. 7 Relationships between total seed rain (a), viable seed rain (b), and seedling recruitment (c) with latitude. Regression lines are shown as black lines and cubic polynomial lines as blue lines. Insets in the panels show Pearson correlation coefficients between plotted variable R eid et al. Annals of Forest Science (2023) 80:4 Page 15 of 16 Acknowledgements References We gratefully acknowledge the Wilfrid Laurier University — government Amiro B, Logan, Wotton M et al (2004) Fire weather index system components of the NWT Partnership Agreement for providing logistical support and of large fires in the Canadian boreal forest. Int J Wildland Fire 13:391–400 laboratory space. We also thank Q. Decent, C. Dieleman, G. Degré‑ Timmons, J. 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R package version 1.43.15 XJW; methodology, all authors contributed; formal analysis and investiga‑ Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed‑ effects tion, KAR, NJD, XJW, and RAS with support from JLB, JFJ, and SGC; writing — models using lme4. J Stat Softw 67:1–48 original draft preparation, KAR with support from JLB, NJD, JFJ, RAS, and SGC; Beck PSA, Goetz SJ, Mack MC et al (2011) The impacts and implications of an writing — review and editing, all authors contributed; funding acquisition, JLB, intensifying fire regime on Alaskan boreal forest composition and albedo: JFJ, SGC, MRT, and MCM; resources, JLB, MCM, and MRT; supervision, JLB and fire regime effects on boreal forests. Glob Chang Biol 17:2853–2866. ND. All authors read and approved the final manuscript.https:// doi. org/ 10. 1111/j. 1365‑ 2486. 2011. 02412.x Bond‑Lamberty B, Peckham SD, Ahl DE, Gower ST (2007) Fire as the domi‑ Funding nant driver of Central Canadian boreal forest carbon balance. 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Annals of Forest Science – Springer Journals
Published: Jan 23, 2023
Keywords: Seed rain; Non-linear relationships; Fire return interval; Combustion severity; Fire size; Postfire regeneration
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