Spatial variability in pollen and resource limitation for fruit production of two species in interior Alaska: Vaccinium uliginosum and V. vitis-idaea

Many recent studies assessing fruit productivity of plants in the boreal forest focus on interannual variability across a forested region, rather than on environmental variability within the forest. Frequency and severity of wildfires in the boreal forest affect soil moisture, canopy, and community structure at the landscape level, all of which may influence overall fruit production at a site directly (through resource availability) or indirectly (through impacts on pollinators). We evaluated how fruit production in two boreal shrubs, Vaccinium uliginosum (blueberry) and V. vitis-idaea (lingonberry), was explained by factors associated with resource availability (such as canopy cover and soil conditions) and pollen limitation (such as floral resources for pollinators and pollen deposition) across boreal forest sites of Interior Alaska. We classified our study sites into upland and lowland sites, which differed in elevation, soil moisture (lower in upland sites), and active layer (deeper in upland sites). We found that resource and pollen limitation differed between the two species and between uplands and lowlands. Lingonberry was more pollen limited than blueberry, and plants in lowland sites were more pollen limited relative to other sites while plants in upland sites were relatively more resource limited. Additionally, canopy cover had a significant negative effect in upland sites on a ramet’s investment in reproductive tissues and leaves versus structural growth, but little effect in lowland sites. These results point to importance of including pollinator abundance as well as resource availability in predictions for changes in berry abundance.


Introduction
At least 50 species of plants produce fleshy fruits (hereafter: "berries") in Alaska [1] In Interior Alaska, a region bordered by the Alaska Range to the south and the Brooks Range to the north, Vaccinium vitis-idaea L. (lingonberry, cowberry, partridgeberry, lowbush cranberry, hereafter: lingonberry) and V. uliginosum L. (lowbush blueberry, bog bilberry, bog blueberry, hereafter: blueberry) are two of the fruits most commonly consumed by both humans and animals [2]. Many species including bears (Ursus spp.), foxes (Vulpes vulpes), geese (e.g., Branta hutchinsii), and voles (e.g., Myodes rutilus) eat the berries [3][4][5][6][7]. Nearly three quarters of all berries collected in rural communities in Interior Alaska in 2015 were from these two species [8]. Berry production is a multi-year process dependent upon weather, pollinator activity, light availability, and soil conditions [9][10]. Recent studies assessing berry production in boreal plants have focused on interannual variability across a region [11][12] but berry production varies spatially within the region as well [13][14]. Due to the multi-year development period of Vaccinium flowers, interannual models of fruit production account for some of the effects of changing weather and climate; however, abiotic factors that affect spatial variability are often overlooked.
Interior Alaska is undergoing rapid climate change, altering not just temperature but also the frequency, severity, and extent of wildfire [15][16][17]. Understanding how Vaccinium berry production responds to heterogeneous environmental factors such as variation in resource availability (limiting growth and berry development) and variation in pollinator availability (limiting fertilization) within Alaska's boreal forest can provide a foundation for modelling berry crops for humans and animals (i.e. where on the landscape we would expect changes in berry production). Models assessing how changes in wildfire, soil moisture, and permafrost in Interior Alaska may affect plant community structure, already exist [18][19][20]. However, vegetative plant growth and fruit availability are not always correlated [21]. Fruit production in all plants is limited by four factors: 1) resources (e.g., light and soil moisture), 2) pollination, 3) external pulses such as herbivory, disease, or harsh weather, and 4) genetics [22][23][24]. This paper will focus on resource and pollen limitation.
Slope, aspect, elevation, and fire frequency drive plant community structure in the boreal zone [25][26][27]. North-facing slopes receive limited sunlight, have cold, poorly drained soils underlain with permafrost, and are primarily composed of Picea mariana (black spruce) stands with a moss understory [25,27]. South facing aspects tend to have warmer, well drained soils occupied by deciduous trees and P. glauca (white spruce) [25][26]. Slope, aspect, and elevation have not changed over the past century, but wildfires are getting larger and returning more quickly across North America's boreal forest regions [15,[28][29]. Fire shapes ecosystem dynamics including plant succession and soil condition. In most situations, low shrubs (including many of the berry species of Interior Alaska) are the dominant cover for 10-20 years after a fire, after which tall shrubs and deciduous trees begin to take over and the canopy closes, limiting the light available [30]. If the seed bank survived the wildfire, deciduous trees generally give way to spruce and the canopy opens again [25][26].
Black spruce forests on north facing and lowland sites are typically underlain by permafrost, which leads to cold, wet soils with low nutrient availability [27]. The presence of shallow permafrost cools the soil and inhibits drainage, so water collects from weather events through the growing season as well as from the thawing ground. Fires can remove much of the moss and soil layer that insulates the permafrost, drastically increasing the depth of the active layer (the layer that freezes and thaws annually) depth and thus moisture and temperature conditions of the forest stand [16]. We would therefore expect fire history to affect resource availability both by altering the canopy cover and by altering soil moisture and depth of thaw.
A species' ability to efficiently respond to changes in canopy conditions vegetatively may allow for greater investment in reproduction. While they are closely related, blueberries and lingonberries differ in their life history strategy. Lingonberries produce thick, evergreen leaves that last about three years (CPH Mulder, pers. obs.) and replace 39% of standing biomass each year, while blueberries produce deciduous leaves and have an annual turnover of 62% standing biomass [31]. Blueberries thus fall closer to the resource acquisitive end of the leaf economic spectrum [32][33] and are potentially able to respond to changes in habitat conditions more quickly than lingonberries, which are on the resource conservative end of the spectrum. We would therefore expect a stronger relationship between canopy cover and investment in reproduction in blueberries than in lingonberries. Similarly, because of their higher nutrient demands, blueberries may be more negatively impacted by low soil nutrients than lingonberries.
Environmental variation may affect berry production indirectly through effects on pollinator abundance and activity. Pollinator and floral diversity are low in the boreal forest so many flowers use multiple pollinator species and those pollinators visit many flower species [34]. In Interior Alaska, bumblebees (Bombus spp.), syrphid flies (Syrphidae), and solitary bees (e.g., Adrena spp.) carry the most blueberry and lingonberry pollen [35][36]. High flower density around Vaccinium plants may lure pollinators away from the Vaccinium flower, as suggested by the floral market hypothesis, but could also draw pollinators into the area that otherwise would not have visited [37]. Pollen availability explained the most variation in Finnish bilberry (V. myrtillus) fruit production models [38]. Given the overall low pollinator availability in black spruce forests [34,39], we expected plants in neighborhoods with high total floral resources to have greater pollen loads and lower pollen limitation than those in neighborhoods with low total floral resources.
Environmental conditions can also affect pollinator activity. In general, pollinators are expected to be more active in warmer sites; bees are strongly limited by temperature in interior Alaska [40]. However, high canopy cover may also be indicative of good growing conditions for deciduous species, and result in high floral resources, resulting in a complex relationship between canopy cover and pollinator activity.
We assessed the relative effects of light (as indicated by canopy cover), nutrient resources (as indicated by depth of the active layer and soil moisture), and pollen availability (as indicated by pollen load) on flower and berry production in the boreal forest around Interior Alaska. We hypothesized that multiple variables would directly affect berry production and expected interactions among predictors. Specifically: 3. Blueberry ramets' relative biomass allocation to flowers and fruits was expected to be more responsive to changes in canopy cover than lingonberries' due to the differences in the two plants' life history strategies. We also expected this greater responsiveness to result in greater variability within sites.
Ericaceous shrubs such as Labrador tea (Rhododendron groenlandicum L.), blueberry, and lingonberry are dominant species in the understory [1]. This study focused on black spruce stands that span 13 to 200 years since last fire (stand age) and vary in slope, aspect, and forest structure to encompass a variety of growing conditions for Vaccinium (S1 Table). We evaluated berry production at 17 sites within the Bonanza Creek LTER Regional Site Network (Fig 1) where previous surveys found both blueberry and lingonberry ramets [44] and which were accessible by foot or all-terrain vehicle during early summer.

Plant selection
From the center of each site we marked the nearest flowering Vaccinium ramet to a set of 12 randomly generated coordinates composed of compass degree (0-359°) and distance (0-20 m), with a search area up to 2 m. If we tagged a ramet too early in the season to distinguish fully between flower and leaf buds, and on the next visit it was clear the ramet was non-reproductive, we moved the tag to the nearest conspecific with distinguishable flower buds. Sites without flowering lingonberry or blueberry within our random points were thoroughly searched and any reproductive blueberry or lingonberry ramets were tagged. We monitored 186 blueberry ramets (mean 10.6 tagged reproductive ramets per site, range: 1-12 tagged ramets across sites) and 194 lingonberry ramets (mean: 11.3 tagged reproductive ramets per site, range: 2-12 ramets across the sites) in total. We counted flowers as they developed, and the number of berries produced when the berries at each site began to ripen. Depending on the site and the species, counts took place from mid-July to early August.

Hypothesized drivers of berry production
We used five variables, measured at the site level, to investigate spatial variability and resource limitation across the landscape: elevation, active layer depth, time since fire (stand age), soil moisture, and soil temperature. Active layer depth and time since fire are both positively related to soil nutrient availability while soil moisture is negatively related to nutrient availability in Interior Alaska [45]. Much of Interior Alaska is underlain by permafrost and is water-logged, creating areas of high soil moisture and low nutrient availability due to anoxia reducing microbial activity [46][47]. Soil temperature and active layer depth are both likely proxies for net mineralization, since cold soils inhibit microbial activity [46]. The presence of shallow permafrost cools the soil and inhibits drainage, so water collects both from weather events through the growing season as well as from the thawing ground [48]. We obtained elevation, time since fire, and active layer depth from the Bonanza Creek LTER data catalog [49]. The LTER team measured active layer depth at 20 points at each site via soil probe in the fall of 2015. We measured soil moisture (% vol; HH2, Delta-T Devices) and soil temperature (HANNA HI145) in July and August of 2018 at five points across each site, two measurements at each point (four corners and the center for a total of ten measures each visit), after 5 days without rain.
Due to weather events and lack of access to sites from poor road conditions after rains or damaged all-terrain vehicles, we could not obtain reliable soil measures in 2017. Since we were interested in relative soil moisture and temperature between sites, we averaged the 20 measurements per site for all analyses. To measure canopy cover over each study ramet, we averaged three readings of a concave spherical densiometer measured 2 cm above the ramet, each reading taken 120° apart while kneeling. The above variables make up what we will refer to as "environmental variables" (elevation, active layer depth, stand age, soil moisture, and soil temperature).
Flowers on the study plants were counted as soon as they were distinguishable, in late Pistils were mounted on microscope slides in basic fuschin gel [50] within a few days of collection. Following Spellman et al. 2015, a ramet was considered "well-pollinated" when the mean number of pollen tetrads on neighboring stigmas was >10. Blueberries produce about 45 ovules per flower and lingonberries about 32 in Interior Alaska [40] so 10 pollen tetrads (40 pollen grains) were expected to be enough for fertilization of most or all ovules. It is unknown how many ovules must be fertilized for the plant to create a fruit. We quantified fruit set as the ratio of berries to flowers on a ramet.

Allocation measurements
Ramets, with their leaves still attached, were dried in an oven for 48 hours before leaves were removed for surface area and mass measurements. Berries from each reproductive plant were placed in a coin envelope while in the field and left in a drying oven for two weeks to ensure complete desiccation. The biomass measurements were used to assess proportion of resources allocated to leaves, stems, and berries. For each ramet we investigated reproductive and vegetative allocation by calculating the ratios of leaf mass to stem mass, flower number to leaf mass, and berry mass to leaf mass.

Statistical Analyses: Structural Equation Models
We expected environmental variables to be highly correlated, so to categorize the physical environment of the sites we used a principal component analysis (PCA) to sort the sites based on correlations of mean values of the environmental variables: time since fire, elevation, soil moisture, soil temperature, and active layer depth. Missing values were replaced with means from all other sites. We standardized the site averages to a mean of zero and standard deviation of one. The PCA was performed with the built-in R function princomp() [51]. Values for both PCA axes were used as explanatory variables in the structural equation models.
We created a hypothetical structural equation model (SEM) [52] to assess direct and indirect effects of multiple variables on blueberry and lingonberry fruit production in 2017 ( Fig   2). In our a priori model, we collapsed the environmental variables into two indices represented by principal components 1 and 2 (PC1 and PC2), which explained 45% and 34% of variation ( Fig   3). PC1 was positively correlated with elevation and active layer depth and negatively correlated with soil moisture (Table 1), and reflections position on the landscape. Principle component 2 (PC2) was positively associated with stand age and soil temperature ( Table 1) and reflects site history. We used both PC1 and PC2 scores in the SEM model, calling them "geography" and" stand history" respectively.   3). Two sites (GSM4 and BFY6) were located near the center but both fell below PC1=0 so we grouped them with the lowland sites. We reran the models separately for each group. In all, we ran 6 models: an SEM for each species with ramets from all sites (2 models) and two for each species with only upland sites or only lowland sites (4 models). We did not force the regression  On PC1 elevation and active layer depth were positively correlated while soil moisture was negatively correlated. Sites were divided above and below PC1 =0 (grey and black dots) for analysis in the structural equation models, with PC1 < 0 constituting "lowland" sites and PC1 > 0 "upland" sites. Further details about the sites in S1 Table.

Statistical Analyses: Allocation Patterns and Comparisons Between Species
To evaluate whether canopy cover or stand history affected allocation to reproductive vs.
vegetation biomass, we ran regressions of each of the biomass ratios (leaf mass to stem mass, number of flowers to leaf mass, and berry mass to leaf mass) against canopy cover or stand history for each species (for all sites and for upland and lowland sites separately). To determine whether blueberries were more variable at the within-site level than lingonberries we calculated the coefficient of variation in flower production and berry production for each species at all 17 sites and used a Student's t-test to test for differences between the two species. All statistical analyses other than SEMs were performed using base packages in R version 3.3.2 [51].

Pollen loads and berry production
Blueberry produced more flowers per ramet than lingonberries, and were also more variable in flower production (blueberry mean ±SD: 8.2±13.7; lingonberry: 5.1±3.3) Across all sites, 72% of blueberry flowers and 40% of lingonberry flowers were classified as well pollinated (Fig 4a; mean pollen load was 26 and 12 tetrads, respectively). For both species, upland sites had higher percentages of well-pollinated ramets than lowland sites (blueberry: 88% vs. 61%; lingonberry: 55% vs. 24%). In blueberry 24% of flowers produced fruit and in lingonberry 31% (Fig 4b). The mean number of berries produced per ramet at each site ranged from 0.08 to 9.83 (total berries per site: 0-118) and 0 -1.92 (total: 0-23) for blueberries and lingonberries, respectively (Fig 4c). Upland sites produced more fruits than lowland sites for both species (Fig 4c).  5). The models using all sites explained 31% of the variation in blueberry fruit production but only 9% of lingonberry fruit production. When models were run separately for upland and lowland sites, fit statistics improved: CMIN/df was 2.735, CFI was 0.902 and RMSEA was 0.068 (90% CI: 0.050 -0.086); more paths were significant, and R 2 values improved (Fig 6).

Limitations for fruit production: flower numbers and pollen loads
As expected, flower number had a positive effect on berry number for both species, although the relationship was much stronger for blueberry than for lingonberry (Fig 5). In contrast, pollen load had a clear positive impact on fruit number only in lingonberry ( Fig 5).
However, when the sites were divided into upland and lowland, the positive relationship between berry production and pollen load was only seen in the lowland sites in both species (Fig 6). Total floral resources were only important in the lowland model for lingonberry (Fig 6d), making this the only model in which both components of the pathway from total floral resources to pollen and pollen to berries were significant.

Limitations for flower and fruit production: canopy cover
In the SEM that included all sites the only significant effect of canopy cover was on blueberry flower production (a negative relationship; Fig 5a). Upland sites in our study had higher canopy cover than lowland sites (45 ± 20% vs. 31±17 % for blueberry sites and 49±19 % vs. 35±18% for lingonberry sites; F (1,350 )=51.6, P<0.001 for all sites combined). When upland and lowland sites were evaluated separately, canopy cover had differing effects on flower production depending on the species and environment (Fig 6). In upland sites the relationship between canopy cover and flower number was negative in blueberries and positive in lingonberries while in lowland sites there was no relationship for either species. Canopy cover negatively influenced berry number at upland sites and positively influenced berry number in lowland sites for both blueberry and lingonberry (Fig 6).

Direct and indirect effects of stand history and geography
The only direct effects of stand history (PC2) were a positive relationship with flower production for the lowland lingonberry ramets and a positive relationship with berry production in lowland sites for both species (Figs 5, 6), indicating that plants in older (burned longer ago) lowland sites were more productive. However, stand history had indirect effects as well: it was strongly positively related to canopy cover in upland sites, and negatively related to total floral resources in five out of six models (all except upland sites for lingonberry; Fig 6). As a result, the total impact of stand history was positive for lowland sites in both species (where positive direct effects outweighed negative indirect effects), but negative for upland blueberry sites and neutral for upland lingonberry sites (where negative indirect effects outweighed positive direct ones; Table 3). Geography (PC1) had no clear direct impacts on berry production, but indirect positive effects via flower production in lowland blueberry and upland lingonberry sites, and additional indirect effects via positive relationships with canopy cover in upland sites (Fig 6). Opposing effects resulted in weak total relationships between geography and berry production for all four models (Table 3).

Most important drivers
When looking at all sites, flower production had the greatest impact on blueberry production and pollen load on lingonberry production ( Table 2). When ramets were split into upland and lowland sites, flower production was the most influential variable in all models, but other drivers differed (Table 3). For blueberry, canopy and stand history were second and third, but with opposite directions for upland sites (negative) and lowland sites (positive). For lingonberry, canopy cover and pollen loads were second or third, again with opposite directions for canopy cover (negative in upland sites, positive in lowland sites). Figure 5 in order of the absolute value of the total effect.  Table 3. Figure 6 in order of the absolute value of the total effect. "Geography" refers to PC2 scores and "Stand history" to PC1 scores. "TFR" is total floral resources. Dashes indicate this link was not assessed in the model.

Biomass allocation given canopy cover
When ramets from all sites were included, relationships between canopy cover and allocation patterns were weak (Table 4), with only allocation to leaves (as measured by leaf mass to stem mass) showing an R 2 > 0.10 (sites with higher canopy cover have lower allocation to leaves). In contrast, when we divided the ramets into the upland and lowland groups, there were multiple relationships for upland sites (Table 5a). In both blueberries and lingonberries in investment in leaves relative to stems decreased as canopy cover increased, while investment in berries relative to leaves decreased. Plants in lowland sites showed little change in allocation with canopy cover (Table 5b).

Table 4. The relationships between biomass ratios and canopy cover for all blueberry (Vaccinium uliginosum) and lingonberry (V. vitis-idaea) ramets.
Response variable ratios Blueberry Lingonberry

Differences between species
Canopy cover explained more than twice as much of the change in allocation to leaves in lingonberry as it did in blueberry (R 2 =0.24 vs. R 2 =0.10). However, flowering rates in blueberry decreased rapidly with canopy cover (Fig 7a) while lingonberry flowering rates were unresponsive to canopy cover (Fig 7b). The variation in production of flowers and berries differed considerably across all 17 sites (S2 Table). However, while variation in flower production was greater in blueberries than in lingonberries (coefficient of variation: 0.87 vs. 0.33; t =-5.79, p =<0.001), there was less evidence for a difference in variability in berry production (coefficient of variation: 1.57 vs. 0.79; t =-1.82, p =0.096).

Discussion
Our primary goal in this research was to assess pollen versus resource (light and nutrient) limitation on berry production of blueberry and lingonberry across the landscape in black spruce of Interior Alaska. We found that the most important drivers of berry production differed between habitats and species. In general, lower elevation, wetter sites with shallower active layers tended to be more pollen limited than the upland, drier sites, while canopy cover was a strong predictor of berry production and allocation in upland but not lowland sites. Also, lingonberry plants tended to be more pollen limited than blueberry plants. These results suggest that the expected changes in boreal forest fire regime and subsequent effects on vegetation composition and soils are likely to have different impacts on productivity of blueberry and lingonberry, and different impacts in upland versus lowland habitat.

Pollen limitation
Lingonberries were more pollen limited than blueberries, especially in lowland sites (Table 3). Lingonberry is partially self-incompatible [55] and may be more dependent on pollinators for fertilization than blueberries. Factors other than self-incompatibility may also play a role: a much higher proportion of blueberries than of lingonberries were "well-pollinated" (pollen loads large enough to potentially fertilize all ovules), suggesting that either blueberries are more attractive to pollinators than lingonberries, or that they are more likely to occur in areas with high pollinator abundance. Additionally, flower number was more important in blueberry than in lingonberry in driving berry number, but that is likely simply the result of the much greater variability in flower number.
Both species showed stronger evidence for pollen limitation in lowland sites than upland, and for lingonberry the total floral resources (number of flowers of all species in the immediate area) also played a positive role in lowland sites. Previous work by Spellman et al. [39] found canopy cover, total floral resources, and air temperature were all important in explaining V. vitisidaea pollination rates in black spruce sites but not mixed deciduous sites (analogous to our lowland and upland delineations). Overall, the results reinforce the idea that in cold, wet habitat the low abundance of pollinators limits fruit production.

Resource limitation
In upland sites canopy cover was a strong negative influence on both blueberry and lingonberry fruit production. High canopy cover may result in light limitation, though competition from tree and shrub species for nutrients and water may also play a role. The increased investment by both species in stems relative to leaves in upland sites as canopy cover increased is consistent with greater competition for light. The positive correlation between canopy and berry production in lowland sites for both species may be the result of variation in growing conditions at the small scale (around individual ramets), leading to high productivity in both Vaccinium and its neighboring species. This is supported by the higher blueberry fruit set (the ratio of berries to flowers) with higher canopy cover at low elevation sites. Overall, these results suggest that light limitation plays a larger role in upland sites, while nutrient limitation (the result of cold, wet soils) plays a larger role in lowland sites.
In upland blueberries, high canopy cover not only reduced berry number but also flower number. This is consistent with flower production of globe huckleberry (V. globulare) in Montana, where reduced flower numbers were attributed to resource limitation above 30% canopy closure [56]. Surprisingly, the relationship between canopy cover and flowers in upland lingonberries was positive, thereby somewhat mitigating the negative direct effects on berry number. However, the negative relationship between canopy cover and blueberry flowers was almost twice the strength of the positive relationship between upland canopy cover and lingonberry flowers.
Stand history had a significant, positive effect on canopy cover in upland sitesthe longer it had been since a fire, the more shrubs and trees had grown around the Vacciniumbut no relationship with canopy cover in lowland sites. This suggests that a major driver of variation in canopy cover is successional stage, while in lowland habitat other factors (such as local drainage conditions) drive variation. The different regeneration patterns were also found in the boreal black spruce forests of Quebec [57]. In that study, less productive sites (those with excess moisture) had slower rates of regeneration and a different community structure post fire than sites with drier conditions. These results suggest that increased fire frequency may have a positive effect on blueberry productivity in upland sites (through a reduction in canopy cover) but not in lowland sites (where older and high canopy-cover sites were most productive) nor in lingonberry (where stand history had a minimal impact).

Differences between species in responsiveness
We had predicted that blueberries would be more responsive to variation in the environment than lingonberries because of their more resource-acquisitive life history and shorter leaf lifespan. This was supported by the greater variability in flower number, blueberries being more limited by flowers production than lingonberries, and the much stronger relationship between proportion of ramets that were reproductive (produced at least one flower) and canopy cover (Fig 7). Blueberry responsiveness was also reflected in the greater ability of the SEMs to explain variation in berry production in blueberries. However, the importance of pollen limitation for lingonberry, especially in lowland sites, likely accounts for the much smaller difference between the two species in variability in berry production.

Study limitations
All measurements, except soil moisture and temperature, took place in a single growing Both blueberry and lingonberry are clonal, but lingonberry can form dense mats of genetically identical ramets that share resources [1]. The trade-offs between vegetative and reproductive growth may not be occurring within a single ramet but across many connected ramets in an area. Additionally, lingonberry leaves last for several years, so trade-offs in allocation to leaves vs. flowers or fruits under changing environmental conditions are likely to be difficult to detect when all leaves are included in the analysis (as in this study). Future work should focus on trade-offs between flower and leaf initiation (both of which take place a year before flowering and leaf-out; CPH Mulder, pers. obs.) or between fruit production and leaf production in the following year.
Patterns in berry production and resource allocation were stronger in the upland sites.
Lowland sites encompassed a greater range of site conditions, so it appears that environmental limitations were driven by something we missed in our study. Studies of Vaccinium species and boreal plant communities in Sweden have found soil pH and soil microbial activity play a role in community composition and Vaccinium biomass allocation [58][59]. Additionally, Interior Alaska contains a variety of wetland types with different combinations of water movement, soil type, and permafrost, all of which affect the plant communities above them [60][61].

Potential changes in berry production under an altered fire regime
The significance of canopy cover on berry production in the uplands leads to two potentially contrasting outcomes for future berry production in Interior Alaska. The change in forest fire dynamics caused by climate change is leading to an increase in fire size, severity, and frequency [15,[28][29]. The increase in size and frequency will lead to a lower median stand age, generating canopy cover in the range most conducive to berry production (<30%). Research in Russia and Montana has found berry production peaks 10-20 or 25-60 years after a wildfire, respectively [56,62]. Upland sites could see an increase in berry production under lower canopy cover. However, lowland sites may still be limited by pollinator abundance or other resources not associated with canopy. Additionally, fires are also changing in severity. More severe fires consume not just the plant communities above the soil but much of the soil organic layer itself [28], changing the immediate and long-term successional dynamics of the forest [63]. More severe and more frequent fires create a new stable state of succession that instead of transitioning from hardwoods to spruce stays hardwood until the next fire [64][65][66].
In summary, our results show that both resource limitation and pollen limitation play a role in limiting fruit production of blueberries and lingonberries, with light limitation being a primary factor in upland sites while pollen limitation is important in lowland sites. We recommend that models predicting productivity under a changing climate incorporate pollinator availability as well as changes in resources.