4 Discussion
Is the regularization imposed on our random effects for sites appropriate if the distribution of site effect is bi-modal because of north vs south facing aspects?
Aspect is an important predictor that was not explicitly accounted for in our model, and may have led to inflated uncertainty
GS and LD treatments were generally much harder to navigate in the field.
Cost of treatments
1-hr redwood fuels differed from other studies in that we implemented a cutoff
Discuss the decision to allow surface fuels to extend to greater than 2 meters
similar bulk densities for duff and litter: Stuart. Also, Finney combined the two.
Little is understood about tanoak sprout response under shade (Waring & O’Hara, 2008; Wilkinson et al., 1997), this study contributes to that knowledge describing a range of tanoak sprout development responses with varying overstory density.
4.1 Composition
4.1.1 Minor species
Our sampling included relatively few minor species and differences across treatments were small. The only statistically detectable difference occurred between the LD and HA treatment where the latter had less basal area of minor species. While this could be due to low numbers overall numbers of occurrences of minor species and high variability within treatments, this phenomena is corroborated by the low vegetation response found in the HA treatment for the separately gathered fuels data (Figure 3.7), as well as for tanoak sprout basal area where the only statistically supported evidence of treatment difference was between the GS and HA treatments. Potential differences in minor species abundance is likely only relevant for grand fir, due to its shade tolerance (Webb et al., 2012). Red alder and wester hemlock were observed earlier in this experiment, but were not detected with our current experimental design (R. T. Muma, 2019).
Douglas-fir is a minor species of special interest due to it’s commercial value and ability to compete with redwoods in the first 100 years of stand development (Wensel & Krumland, 1986). We didn’t detect statistically supported differences between treatments in terms of basal area (Figure 3.1) or stem counts (Figure 3.3). While our observed recruitment density of Douglas-fir seedlings may be sufficient for the eventual production of a viable cohort in a multi-aged system (Schütz and Röhnisch, 2003, as cited in Schütz & Pommerening, 2013), only the LD and GS treatments are likely to maintain overstory densities conducive to Douglas-fir seedling development (Miller & Emmingham, 2001; Schütz & Pommerening, 2013) given the overstory basal areas recorded at the initiation of the experiment (R. Muma et al., 2022). Rapidly developing redwood and tanoak sprouts will likely further comprise Douglas-fir seedlings competitive ability.
4.1.2 Redwood and tanoak
We found redwood growth in terms of average total basal area of sprouts 10 years after harvest treatment, responded strongly and positively to increasing levels of openness and the tanoak response was comparatively modest. This is expected based on traditional shade tolerance theory (Horn, 1971) and what is commonly understood of these two species (CITATIONS).
The only statistically justified difference for tanoak was between the GS and HA treatments. That we found slightly higher tanoak basal area in the HD treatment compared to the HA may be confounded by the fact that this treatment resulted in a greater number of tanaok stems less than 2.54 cm dbh (383 in HA vs 496 in HD, data not shown) and that this size class was assigned the perhaps inappropriate midpoint diameter of 1.27 cm. Thus, the basal area of these bushier tanaok in the HD treatment may be slightly artificially elevated. On the other hand, the small difference between the HA and HD treatments was mirrored in the vegetation fuel loading data which was collected separately, adding support to the idea that the HA treatment did in some way minimize tanoak sprout growth. It could be that the lower light conditions in the HA and HD treatments approach a crossover point where greater overstory densities would result in more growth for the shade tolerant tanoak compared to the less shade-tolerant redwood. It is important to remember that the HA and HD “high-density” treatments only targeted a residual overstory relative density of 20% (that is 20% of assumed total carrying capacity of the site), and it was selected as an upper limit given the objective to maintain conifer growth. It seems likely that at higher overstory densities we might see tanoak growth, in terms of total basal area, exceed that of redwood. As mentioned previously, our use of basal area as a response did not distinguish between number of stems and size of stems. There have been numerous metrics used to attempt to assess shade tolerance (Forrester et al., 2014), our use of basal area (which conflates growth and survival of sprouts) is justified given the assumption that these are expected to be correlated in forests that don’t undergo a long harsh winter (Lin et al., 2002). In general, for tanoak we expect to see a reduction of about one half moving from open overstory conditions to 20% relative density.
Average redwood sprout basal area 10 years after treatment was much more dramatically affected by overstory density and each step increase in overstory density (GS -> LD -> HA) saw average redwood sprout basal area reduced by around half. Aggregated retention resulted in about 1/3 greater basal area than in the dispersed retention but the p-values for all comparisons except between GS and high density treatments were very high. In all treatments, the uncertainty in the average redwood basal area is much greater than that of tanoak, given that we modeled the total basal area of all species in each vegetation plot, this suggests that variability in redwood basal area was much greater than that of tanoak across macro plots and/or sites. The greater relative uncertainty we observed in redwood compared to tanoak may have to do with redwoods greater sensitivity to light conditions. We selected sites with both north and south facing slopes and this may have led to relatively larger differences for redwoods growth response compared to tanoak. It could also be that this uncertainty is due to variable light levels within macro plots. Given the importance of aspect for growth response, reducing our uncertainty around redwood basal area may require better incorporating the effect of aspect into our model. An important consideration for redwood in these plots, is that after thinning, a fraction of existing sprouts will be retained and these will generally be the largest and most vigorous. An interesting question that is overlooked by our focus on total basal area is if any treatment produced larger individuals that would eventually be selected for retention.
What about the potential effects of soil type What about the potential effects of below-ground competition what would tanoak abundance be if they were not competing with redwood?
4.2 Sprout height
Marginalizing over plots was important and resulted in approximately 0.2 greater height increment on average, compared to predictions made for an “average” plot (one where all random effects were set to zero).
4.3 Fuel loading
4.3.1 Pre-PCT
Fuel loading found in our treatments were comparable to those found in other studies in redwood systems. Our treatment level averages for duff and litter ranged from 40 to 55 Mg ha-1 in plots that had post-harvest basal areas (in 2012) of 0 to 40 m2 ha-1. This was comparable to total duff and litter loading found in 120-year-old redwood stands (range: 29 to 55 Mg ha-1) as well as old-growth stands (average: 50 Mg ha-1) and our raw average duff and litter depth of 6.2 cm (data not shown) was similar to that found in a <30-year-old mixed Douglas-fir/redwood stand (Finney & Martin, 1993a; Glebocki, 2015; Stuart, 1985). We opted to combine duff and litter into a single metric following previous studies that found similar bulk densities for duff and litter and that separating the two resulted in little difference from and average bulk density, given the wide variability found in duff and litter depthsFinney & Martin (1993a).
Our average total fine woody fuel loading, including one-, ten-, and one hundred-hour-fuels (15 Mg ha-1) was within the range found in 120-year-old stands (9 to 20 Mg ha-1, Finney & Martin (1993b)), double that found in very young, mixed stands (Glebocki, 2015), and 4 Mg ha-1 higher than that found in old growth stands (Stuart, 1985).
In the following I’ll compare the average fuel loadings found in this study to those found in the “old growth” and “very young, mixed” stands referenced above.
Our one-hr fuel loading which, which averaged about 1 Mg ha-1 was similar to that found in the old-growth stand, but about half that found in the very young, mixed stand.
Our average 10-hr fuel loading was higher than in the very young, mixed stand (3.4 vs. 2 Mg ha-1) but similar to the old growth stand.
Our average 100-hr fuel loading of about 11 Mg ha-1 was higher than in both the old growth stands (5 Mg ha-1) and the very young, mixed stands (3 Mg ha-1).
Our average 1,000-hr fuel loading of 42 Mg ha-1 was somewhat lower than found in old growth stands (63 Mg ha-1) as well as in the very young, mixed stands (54 Mg ha-1). These estimates are accompanied by relatively high standard errors, but it would not be surprising that State Park forest, with little to no harvest activity would have a larger amount of large downed logs then an actively managed forest such as ours.
Our elevated average 100-hr fuel loading may be the result of residual fuels left over from the harvest treatment that initiated our experiment which would have been absent from the other sites referenced here. In the very young, mixed stand, 100-hr fuels jumped to 10 Mg ha-1 immediately following a thinning treatment. For our 10-hr fuels, it might be reasonable to suspect that the smaller trees in the very young, mixed stand did not supply as much 10-hr fuels because of the sizes of their branches, whereas branch shedding patterns in the 10-hr time lags class were more similar between our stands and the old growth ones. Our reduced 1-hr fuel loading compared to the very young, mixed forest could be due to differing stand structures and species compositions. That forest was composed of a large proportion very young Douglas-fir and would be expected to have different forest floor characteristics and this notion is supported by our finding of similar 1-hr fuel loading as in the old growth study. Another source of possible error is differences in sampling method for 1-hr fuels. We established a cutoff where redwood particles smaller than roughly 2 mm were considered as litter, rather than 1-hr fuels. If we had counted every redwood leaf-spray, regardless of size, we would have likely found higher 1-hr fuel loads.
That we found similar average fuel loading in our Pre-PCT stands compared to two studies in very different redwood forest structures is congruent with our findings of few statistically significant differences between treatments in terms of fuel loading. This result is common among other studies and is expected due to the highly variable nature of forest fuels (CITATIONS). Statistical difference at the p < 0.05 level were only found among treatments for 1-hr and Vegetation loading, and nearly found for Duff & Litter. In all cases these differences involved the HA treatment, but the nature of the comparisons varied with fuel types. For instance, for Duff & Litter was lowest in the HA treatment and highest in the HD treatment (p = 0.07). Although this difference was not statistically supported, it was paralleled by a similar trend in vegetation fuel loading as well as in tanoak basal area (Figure 3.1). In a study across Sierra forests, fine fuel loading (including 1-, 10-, and 100-hr fuels) were found to be correlated with overstory canopy cover and proportion of shade tolerant species and associated these conditions, which represent recent shifts in forest composition resulting from fire suppression, with an elevated risk of high severity fire (Collins et al., 2016). The differences we observed in duff & litter, and 1-hr fuel loads could be a result of canopy cover and understory species composition directly, but it is likely also a result of the microclimatic differences resulting from these different stand structures. Fine fuel loads are a function of both recruitment and decomposition and the latter is a function of moisture and temperature (CITATIONS). Forest floor conditions in the HA treatment may have been somewhat more moist than the more open treatments (due to sheltering), but warmer than the HD treatment (due to greater light infiltration). While the differences in duff & litter loading we observed are not likely to have a significant impact on fire behavior, small differences in forest floor moisture can have significant implications for prescribed burning operations because these are often conducted under marginal conditions (CITATIONS).
For both 1-hr fuel loading and Vegetation fuel loading, the HA treatment was most similar to the HD treatment. In these cases it is not surprising that a greater density of overstory trees would result in more twigs and less light available for growth of vegetation at the surface.
compare to live fuel loading in Stuart 1985
Vegetation fuel loading was lowest in the HA treatment, and the only statistical difference was between the GS and HA treatments. This lower vegetation fuel loading in the HA treatment is reflected in the tanoak basal area results for the HA treatment, which were collected separately, and it is likely that tanoak is largely responsible for the vegetative fuel loading differences. Although vegetation fuel loading was not recorded by species, redwood and tanoak sprouts comprised the vast majority of vegetation fuels.
Consistently low foliar moisture content levels have been recorded for tanaok (Kuljian & Varner, 2010) and the foliage has been compared to that of other sclerophyllous species, suggesting high flammability (Fryer, 2008; McDonald, 1981).
Our understanding of the conditions under which crown fires initiate and spread are not well understood for several reasons and largely because of the difficulty in observing and studying large fires (Finney et al., 2021; Xanthopoulos & Athanasiou, 2020). Less still is known about crown fire behavior in this unique and transitional fuel type (mixed conifer/tanoak sprouts).
- there is little information regarding live fuel flammability and crown ignition, although some sources suggest it is likely with low canopy base heights.
- because of the relatively low bulk density of canopy fuels, smaller differences in fuel load equate to larger differences in total fuel volume and resulting fire behavior, as well as implications for aerial fuel continuity
- sclerophyllous fuels found in chaparral ecosystems are often characterized by intense, stand replacing fires
- these factors warrant more study into the fire behavior of this unique fuel type, including its requirements for combustion, fuel moisture, and more precise characterization of it’s mass and bulk density.
It was somewhat surprising that the modeled vegetation fuel loading differences were not more clearly differentiated because our field crew’s experience was that the GS and LD treatments were usually much more difficult to work in due to the amount of understory growth present in these treatments. Our experience in the field was partially validated by our findings of somewhat elevated basal area of tanoak and redwood in those treatments.
Our method of calculating vegetation fuel loading (estimated percent cover times estimated height within an estimated cylinder) might have been overly imprecise. Additionally, given the importance of understory light to the processes of surface fuel decomposition as well as for understory growth, inclusion of the variable: aspect might have led to better explanatory power in our models. Our plots were established over a range of aspects, but it is plausible that differences in aspect outweighed differences between treatments. We had hoped that the inclusion of site and treatment as nested random effects would have captured site and block level differences in aspect. Additionally, aspect is largely a proxy for insolation, and this may have also varied with shade conditions (very large trees or road cuts) outside of the plot.
4.3.2 Post-PCT
Duff and litter are not reported for post-pct stands for two reasons. First, these are not expected to have changed significantly given the relatively short timespan before and after PCT. Second, our sampling protocol did not make it clear how to quantify the loading for leaves attached to recently cut branches, especially given that these “suspended litter” particles were in a state of active transition to the ground, as they dry, abscise, and sift through the coarser woody fuels as they make their way to the ground. This class may deserve more attention because of it’s potentially dynamic relationship with the timing of prescribed fires: as suspended particles settle and begin to decompose they’re bulk density changes and bulk density is an important determinant of fire behavior.
Crews were guided to thin to achieve the same understory conditions across all four treatments and this is indicated by similar post-PCT vegetation fuel loads (Figure 3.8). Fuel differences resulting from PCT were driven by fuels in the vegetation fuels class, i.e., growth and productivity. Vegetative growth following thinning and harvest is an important consideration for fire informed management. In our experiment, 10 years of growth (followed by PCT) led to some increase in average 100-hr fuel loading but only for the GS treatment. Ten-hr fuels increased in the GS and LD treatments apparently as a function reducing competition in these treatments (Figure 3.9). One-hr fuels increased most, in the GS and LD treatments, but also started out somewhat lower 1-hr fuel loading than the HA and HD treatments, which resulted in the post-pct fuel loading being similar, except for in the HA treatment, where unexpectedly low vegetative fuel loading resulted in little to no increase in 1-hr fuels after PCT, likely because much of the existing vegetation (saplings) needed to be retained to meet the prescription (Figure 3.9). Pre-commercial thinning resulted in little change in 1,000-hr fuels, as thinned sapling stems were mostly under three inches, although a potential increase observed in the HD treatment may have been a result of some previously retained canopy trees being cut (12 inches dbh was the prescribed upper limit for thinning). The model selection process resulted in slightly different models after pre-commercial thinning, compared to before thinning for a given fuel class. These changes may be somewhat arbitrary, as the model selection process was guided by balancing parsimony, AIC, and the production of well distributed residuals. In a few cases, the higher values associated with post-PCT fuel loading predictions tended towards models that accounted for greater (variation in) variability.
The simple method we used probably over-estimated vegetation fuel loading, evidenced by the fact that the large fuel loading differences observed in the vegetation class are not reflected in the increase in fine fuels. Although the contribution of slash foliage is not accounted for, it appears our method may have over predicted vegetation loading by around a factor of three if we assume that the majority of the difference in vegetation fuels should be captured by the sum of the changes in fine fuels and the decrease in 100-hr fuels after PCT is assumed to be the result of sampling error.
It can be seen from this analysis resulting surfaces fuel conditions are expected to be the result of a complex interaction of productivity and decomposition rate. Whether any of the differences we observed result in a significant difference in prescribed or wild fire will need to be tested by those disturbances. It is possible that the elevated 10-hr loads in the GS and to a lesser degree, the LD treatments may represent greater fuel continuity which could support prescribed fire operations. Likewise, the HA treatment’s lower fuel loading across classes could signify patchy fuel distribution. Surface fuel moisture and wind speed are two variables which we did not study but which have a strong influence on fire behavior and are also affected by overstory density and arrangement. Given the relatively modest difference we observed in fuel loads, it seems likely that those unmeasured factors would have a greater bearing on fire outcomes.