Influence of Climatic Conditions and Elevation on the Spatial Distribution

Inﬂuence of Climatic Conditions and Elevation on the Spatial Distribution and Abundance of Trypodendron ambrosia Beetles (Coleoptera: Curculionidae: Scolytinae) in Alaska

Robin M. Reich, John E. Lundquist, and Robert E. Acciavatti

The objective of this study was to model the influence of temperature and precipitation on the distribution and abundance of the ambrosia beetles in the genus Trypodendron. Although these beetles do not attack and kill healthy trees, their gallery holes and accompanying black and gray stain associated with symbiotic ambrosial fungi can cause significant economic losses to commercial logs and wood products. Beetles were collected along a 1,100-km latitudinal transect 4 times at 2-week intervals at 43 sites beginning early July using Lindgren-funnel traps baited with ethanol, alpha-pinene, and lineatin. Average annual temperature and precipitation were used to partition the state into 25 climatic zones. Large-scale patterns of beetle distribution were correlated with elevation and the temperature and precipitation zones. Results indicate that reasonably accurate predictions of beetle abundance can be generated using models based on trap data collected across several climate zones. Predictions derived from this latitudinal transect can be extrapolated to more remote areas using species–environment relationships based on temperature and precipitation combinations. Partitioning large geographic areas using climatic zones offers a logical approach for predicting insect activity in remote areas, and if implemented on a long-term basis would be able to provide estimates of yearly and seasonal trends in the infestation. Such information would allow forest managers to evaluate the impact on forest ecosystem services and improve future assessments that predict the influence of a changing climate on insect pest migrations and their intensification during outbreaks. Keywords: aerial survey, negative binomial regression, pest survey, spatial pest models

hanges in temperature, precipitation, and other climatic Within the forests of this state, insects will probably be among the conditions are often reflected as changes in insect commu- first responders to varying climate and could serve as early bioindi- Cnities (Berryman 1986). Danks (1992) lists several ways cators of climate change. Many believe an “unprecedented” increase that climatic conditions can change insect communities: alterations in forest disturbances in Alaska as a result of climate change has in ecological roles and interactions; changes in insect assemblages, already occurred (Berman et al. 1999), that these changes will likely northern migrations; changing timing of life history events; and continue, and that their effects will cascade across many ecosystems. increase or decrease in host defense chemicals. In Alaska, both tem- As one travels northward across Alaska from the south coast, perature and precipitation show geographic patterns that are influ- growing seasons get shorter, winters get more extreme, and springs enced and often determined by the local and regional geography, come later and are cooler. Along this transect, different insect species and both can change with time. The distribution and abundance of exhibit varying patterns of distribution and abundance. In a recent different insect species in Alaska vary greatly among locations with forest pest survey, various insect pests were sampled with traps in differing climatic conditions (Werner 2007). stands of spruce located at various locations from Seward in south Effects of a changing climate are expected to be greatest in the central Alaska to the geographic limit of spruce trees at the southern northern latitudes (Stocks 2004). Because of its unique geographic foot of the Brooks Range in the north (Lamb and Wurtz 2008). position at the northern edge of various forest types, Alaska has been Observations made during this survey noted that Trypodendron am- referred to as the “poster state for global warming” (Weise 2006). brosia beetles were the most abundant and most widespread of all

Manuscript received August 3, 2012; accepted June 25, 2013; published online August 8, 2013. Afﬁliations: Robin M. Reich ([email protected]), Colorado State University. John E. Lundquist ([email protected]), USDA Forest Service, Anchorage, AK. Robert E. Acciavatti ([email protected]), Carnegie Museum of Natural History. Acknowledgments: We wish to thank Rick Kelsey, Yu Wei, and Ashley Steele for reviewing and editing earlier versions of this manuscript. This study was funded in part by the Western Wildland Environmental Threat Assessment Center.

308 Forest Science • April 2014 the insect species trapped and that their abundance varied through- southern slopes of the Brooks Range (latitude 68oN) in the north, a out the summer. Numbers trapped varied among different geo- distance of around 1,100 km. Sites were located using a systematic graphic regions with greater numbers occurring in the southern unaligned sample design that combines features of both simple ran- latitudes. Three Trypodendron species were identified: Trypodendron dom and systematic sampling. With this design, the 1,100-km tran- lineatum (Oliver), T. betulae (Swaine), and T. retusum (LeConte). sect was divided into 13 sections of varying lengths, and within each Based on published records and specimen data in the USDA Forest section 3 sites were randomly located for a total of 43 sites; two Service reference collections at Anchorage and Juneau, the most sections had only two sites. The length of the sections varied on the common Trypodendron species in Alaska is T. lineatum, the striped basis of ease of accessibility, presence of spruce forest types, and ambrosia beetle (Sikes 2009). The preferred hosts of T. lineatum are ownership (federal or state lands). Plots were located that visually white spruce (Picea glauca (Mill.) B.S.P.), Sitka spruce (Picea sitch- compared to photo series AKHD 07 to AKHD 11 in Ottmar and ensis (Bong.) Carr.), Lutz spruce (Picea lutzii Little), and western Vihnanek (2002). Specifically, sites were placed in stands of mixed hemlock (Tsuga heterophylla (Raf.) Sarg.) (Bright and Stark 1973, hardwood (primarily paper birch, Betula papyrifera Marshall) and Bright 1976, Furniss et al. 2002, Holsten et al. 2009). conifer, where white spruce was codominant or slightly emerging as Trypodendron ambrosia beetles are a group of small (around 4 dominant and represented 10–40% of the overstory. The distance mm) beetles whose larvae feed on fungi cultured by adults within the between sample sites within a section ranged from 0.1 km to 19 km, wood they infest. Adult T. lineatum beetles emerge from overwin- with an average distance of 2.3 km. All sample sites were georefer- tering sites in the forest floor duff layer during the spring when enced using a global positioning system unit (Garmin global posi- temperatures reach 15.5° C to 18.3° C (Lindgren 1990). The tioning systems (GPS) map 76CSx). emerging adults are attracted to recently dead or dying trees where mating occurs. The female tunnels through the bark into the wood to lay its eggs. Symbiotic fungi, introduced to the galleries by the Geographic Information System (GIS) Layers boring insects as a source of food for their larvae, give the name Raster layers representing the average monthly temperatures “ambrosia” to this type of Scolytinae beetle. These fungi grow and (° C) and precipitation (mm) for the state of Alaska were obtained fruit within the galleries where they stain the wood black/gray. Six to from the USGS Alaska Science Center, High Altitude Climate 8 weeks after eggs are laid, young adults emerge from the original Transects1 with a 1,000-m spatial resolution for use in a GIS. These holes created by their parents, and these disperse to overwintering layers were used to identify five temperature zones and five precip- sites in the forest floor. Except for a few hours once each generation itation zones in combinations that defined 25 unique climatic zones during which time the dispersal flight takes place, these insects live (Figure 1; Reich et al. 2008). The climate zones were based on a entirely within the host tree. histogram equalization approach that produced a uniform distribu- In previous years, when logging and timber production were tion of temperatures and precipitations across the state of Alaska more prevalent, the wood stain caused by ambrosia beetles resulted (Acharya and Ray 2005; Table 1). Zonal statistics were used to in significant economic losses to commercial conifer logs in Alaska summarize the variability in temperatures and precipitation in each due to timber degrade (Boyce 1961). Although the economic sig- of the 25 climate zones. One of the unique features of this approach nificance of Trypodendron ambrosia beetles has subsided in recent in defining the temperature and precipitation zones was a strong years, their ecological importance as decomposers of stressed, dam- linear correlation between the integers (1, 2, 3, 4, 5) used to repre- aged, and dying conifers is possibly underappreciated. sent the temperature and precipitation zones and the average Because Trypodendron ambrosia beetles infest recently dead or monthly temperature (␳ˆ ϭ 0.99) and average monthly precipita- dying trees, they may be an important indicator of forest health. T tion (␳ˆ ϭ 0.88) characterizing the temperature and precipitation Understanding the influence of climate on the spatial and temporal P zones. Each sample location was assigned to a unique temperature patterns of abundance is an important prerequisite for the use of this and precipitation zone (i.e., poststratification) to characterize the species as an indicator species. How Trypodendron ambrosia beetle climatic conditions of the sample sites. populations respond to climate change is a research question of considerable importance. The first step, however, in projecting po- A digital elevation model of the state was obtained from the tential future response is to be able to understand and represent in National Elevation Dataset as a seamless raster surface at a 90-m models the influence of climate and other environmental factors on resolution (US Geological Survey [USGS]; Gesch et al. 2002) and the distribution and abundance of Trypodendron ambrosia beetle resampled to a 30-m resolution using a bilinear interpolation tech- populations. nique (Edenius et al. 2003). This produced a more continuous The short-term objective of this study was to model the seasonal surface reflecting gradual changes in elevation at a 30-m spatial activity of adult Trypodendron ambrosia beetles using elevation, resolution. temperature, and precipitation. The long-term objective was to In addition, a 30-m raster layer of the major vegetation types was develop enough understanding of the relationship between insect obtained from the Department of Natural Resources of Alaska. To phenology and abundance and climatic patterns to enable the even- characterize the spatial distribution of potential hosts in Alaska, the tual establishment of a long-term monitoring system to follow the vegetation layer was used to develop a binary surface indicating the influence of current climate conditions on forest insect pests in presence or absence of spruce forests, which included both black (P. Alaska. mariana) and white spruce (P. glauca). The binary surface was resampled to 1,000-m resolution using a majority rule. The resampling Methods was done in increments; starting at 30 m, the raster layers were Study Area resampled to 100 m, then to 300 m, and then finally to 1,000 m. The area in Alaska used for this study consisted of a north-south This information was used to characterize the spatial distribution of latitudinal transect from Seward (latitude 60oN) in the south to the potential host trees in Alaska.

Forest Science • April 2014 309 Figure 1. Temperature and precipitation zones of Alaska. Average monthly temperatures and precipitation associated with the climate zones are found in Table 1.

Table 1. Summary statistics for the average annual precipitation Insect Identification and temperature associated with the temperature (T) and precipi- All Trypodendron specimens were sorted from each trap and iden- tation (P) zones identified in Alaska. tified to species (Wood 1982). The number of specimens of each Zone Min Mean Max CV (%) Trypodendron species within each sample was determined by an actual count for those with less than 100 specimens per species and Precipitation (mm) by a volume estimate for larger samples. Representative sample P1–Very dry 4.6 14.8 20.6 27.8 vouchers were prepared for the species present in the study and P2–Dry 20.7 25.0 29.1 9.0 P3–Moist 29.2 33.0 37.5 7.1 placed at the Carnegie Museum of Natural History, Pittsburgh, P4–Damp 37.6 46.2 62.0 14.1 Pennsylvania, for future reference. P5–Wet 62.1 116.0 275.5 36.0 Temperature (o C) T1–Very cold -34.3 -12 -10.2 15.9 Modeling the Influence of Climate and Elevation on the T2–Cold -10.1 -8.6 -7.4 9.3 Distribution of Trypodendron Ambrosia Beetle T3–Mild -7.3 -6.2 -5.1 10.7 Count data such as the number of adult ambrosia beetles trapped T4–Warm -5.0 -3.6 -2.1 23.4 T5–Hot -2.0 1.0 9.0 243.8 at each sample site are often modeled using the Poisson distribution or the negative binomial distribution if the data are overdispersed. The negative binomial distribution arises as a mixture of a Poisson distribution when the Poisson rate is not constant but has a gamma distribution (Hardin and Hilbe 2007, Hilbe 2007). In the case of Insect Collection the latitudinal transect, this could be thought of as the number of At each of the 43 sample sites, adult Trypodendron ambrosia trapped ambrosia beetles at individual sites having a Poisson distri- beetle abundance was measured using a standard stimulus consisting bution with mean rates for individual sites being gamma distrib- of an eight-funnel black Lindgren trap baited with commercially uted. The number of ambrosia beetles trapped on the sample sites available (Ϯ)-lineatin in flexlures (Synergy Semiochemicals Corp., were highly skewed with an index of dispersion ID ϭ 1,305.3. Burnaby, BC), a pheromone that attracts both males and females of Mixed-effects models using a negative binomial distribution were all Trypodendron species except, T. betulae (Borden 2004).A1cm2 used to model the change in insect counts over the summer season as of solid insecticide (Vapona) was placed in each collection cup. a response to climatic conditions and elevation. Independent vari- Traps were placed aboveground at least 200 cm and hung at least ables considered in the model included the sample period, the tem- 200 cm from the tree bole. Traps were baited once in late May and perature zone (T ϭ 1, 2, 3,4), the precipitation zone (P ϭ 1, 2, 3, emptied every 2 weeks through mid-August for a total of five col- 4), elevation (E), and two-way interactions between these variables. lection periods (June 16, 2009; July 2, 2009; July 17, 2009; July 31, All but elevation was treated as categorical variables. Interactions 2009; Aug. 18, 2009). Only the last four sample periods were used between the sample period (S) and the other variables were evaluated in this study because all sample sites were not installed until the end in the models because of the a priori predictions that the number of of the first sample period. The integrity of each trap catch was trapped beetles decreased throughout the season. Site was treated as maintained throughout its processing. a random effect to account for the variability in site conditions (e.g.,

310 Forest Science • April 2014 Figure 2. Number of ambrosia beetles (T. lineatum) caught in Lindgren funnel traps along a latitudinal transect through interior and south central Alaska during the summer of 2009: (A) July 2, 2009; (B) July 17, 2009; (C) July 31, 2009; (D) Aug. 18, 2009. The histograms are the observed counts, while the solid lines represent the predicted counts using the fixed effects of the negative binomial regression model, and the dashed lines are the predicted counts of the fitted model (fixed plus random effects). tree species composition, tree size class, basal area, tree health, dis- Relationship between Latitude and Phenology of Trypodendron turbances, etc.) on which the counts were observed. Ambrosia Beetle The glmmADMB package (Fournier et al. 2012), built on the Patterns in the number of T. lineatum trapped varied across open-source AD Model Builder platform, is an R (R Core Team latitudes from southern to central Alaska (Figure 2). Within a sam- 2012) package used for fitting the negative binomial mixed model ple period, the number of T. lineatum was always lowest in more (GLMM) to the count data. A likelihood ratio test statistic (West et northern latitudes. During sample period 1 (June 16–July 1) and sample period 4 (Aug. 3–20), the number of T. lineatum was great- al. 2007) was used to test the importance of including site as a est in the most southern latitudes. During sample periods 2 (July random effects term in the model. A forward and backward stepwise 2–July 20) and 3 (July 21–Aug. 2) the numbers of T. lineatum were regression procedure was used to select the fixed effects to include in highly variable among sample sites. the final model (West et al. 2007). The FIT statistic, which is defined as the correlation between the observed and predicted values squared, was used to assess the goodness-of–fit of the final model. Relationship of Temperature and Precipitation with the Insect Distribution Spatial Models.——Maps displaying the Abundance for Trypodendron Ambrosia Beetle The latitudinal transect crossed 12 of the 25 climatic zones iden- predicted abundance of T. lineatum throughout the state were de- tified in the state (Figure 1). These zones varied from average annual veloped for each sample period by passing the appropriate GIS layers low temperature of Ϫ8.6° C and low precipitation of 25 mm to an through the final regression model using the coefficients associated average high temperature of 1° C and precipitation of 116 mm with the fixed effects in the model. (Table 1). The 43 sample sites only represented eight of the 12 climate zones found along the transect (Table 2); four temperature Results zones; and four precipitation zones (Figure 3). The total number of trapped adult T. lineatum within the four temperature zones and the Insect Collections four precipitation zones were linearly correlated to the integers Over 44,000 Trypodendron ambrosia beetles were collected dur- ␳ ϭ representing the temperature (ˆT 0.86) and precipitation ing the assessment period. Collections were distributed over all sites, ␳ ϭ (ˆP 0.96) zones (Figure 4). The greatest numbers of adults were but the largest populations were found at the southern latitudes. captured at sites within climate zones characterized as having the Four species were identified: T. lineatum, T. borealis, T. retusum, hottest temperatures (T5) and highest precipitation (P5), and the and T. rufitarsis. Abundance varied greatly among species and sam- lowest numbers in the coldest (T2) and driest (P2) regions (Table 2). ple period. The most abundant was T. lineatum, which accounted This trend was observed in each of the sample periods, with the ␳ ϭ for 99.5% of all beetles captured, compared to T. borealis (0.30%), strongest linear correlations occurring in early July (ˆT 0.84, ␳ ϭ T. retusum (0.07%), and T. rufitarsis (0.09%). Because these latter ˆP 0.93). The weakest linear correlation was observed for precip- ␳ ϭ three species together only accounted for 0.50% of the total, they itation in mid-July (ˆP 0.33). Temperature and precipitation also were not included in the subsequent analyses. influenced when the greatest number of T. lineatum was caught

Forest Science • April 2014 311 Table 2. Average total count of adult T. lineatum by temperature and precipitation zone found along an 1,100 km latitudinal transect in Alaska.

Precipitation zone1

Temperature zone1 P1 Very dry P2 Dry P3 Moist P4 Damp P5 Wet Average by temperature zone T1–Very cold T2–Cold 79.7 79.7 T3–Mild 435.4 989.0 490.8 T4–Warm 260.0 26.0 1,175.7 511.3 T5–Hot 1,521.2 1,897.4 1,652.9 Average by precipitation zone 317.7 507.5 1,456.4 1,897.4 1,007.4 1 See Table 1 for deﬁnitions of the temperature and precipitation zones.

Figure 3. Elevation of the sample sites along the Alaskan latitudinal transect that runs south (point 1) to north (point 43). The vertical lines represent the boundaries between the different climatic regions (temperature (T) and precipitation (P)) found along the latitudinal transect. Figure 4. Relationship between the average number of ambrosia beetles (T. lineatum) caught in Lindgren funnel traps and the mean annual temperature (؇ C) and precipitation (mm) associated with the (Figure 5). In the warmer and wetter climate zones, the greatest temperature and precipitation zones sampled along the latitudinal number of T. lineatum was trapped between late June and early July, transect through interior and south central Alaska, 2009. while in the colder and drier climate zones it was delayed 2 weeks until mid-July. Influence of Environmental Variables on Abundance of Trypodendron Ambrosia Beetle Negative Binomial Regression Models of Abundance of The influence of temperature, precipitation, and elevation on the Trypodendron Ambrosia Beetle number of trapped T. lineatum can be inferred by inspecting the The abundance of ambrosia beetle differed significantly with the sign, magnitude, and trends in the estimated regression coefficients climatic conditions and topography along latitudinal transect over associated with the variables included in the negative binomial the summer season (Table 3). For most variables, the parameter model (Table 3). The coefficient for elevation was negative and estimates were significant at the 5% level. The number of adult significant (P value Ͻ 0.0001), with a coefficient of Ϫ0.0029. In ambrosia beetles trapped was influenced by the main effects for the percentage terms, the change in the number of adult T. lineatum ␤ precipitation zones, elevation and the sample period, as well the trapped equals (e -1)*100, where e is the base of the natural expo- interactions P ϫ E and T ϫ S. nential function and ␤ is the parameter estimate. With The mixed-effects model provided a good fit to the data and the ␤ ϭϪ0.0029 * 100 m ϭϪ0.29 this implies that counts of adult T. fitted values had high agreement (FIT ϭ 0.863) with the responses lineatum decreased 25% for every 100 m increase in elevation. There compared to a FIT of 0.633 for the fixed-effects component of the was a significant interaction between elevation and the precipitation model (Figure 2). In the context of a spatial model the fixed-effects zones (except the damp precipitation zone (P4) [P value ϭ 0.17]). components of the model may be thought of as describing the large- This implies that the influence of elevation on counts of adult T. scale variability in counts, while the random effects component is lineatum varied among the precipitation zone. In percentage terms, describing the local variability in counts across sites. counts of adult T. lineatum in the damp precipitation zone (P4)

312 Forest Science • April 2014 precipitation zone (P3) counts actually increased 34% for every 100-m increase in elevation. Since there were only two sample sites in this precipitation zone (Figure 3) the results may not reflect the true trend in the counts for this precipitation zone. One would have expected counts in this precipitation zone to have decreased about 21% for every 100-m increase in elevation. The number of adult T. lineatum trapped also varied significantly among the precipitation zones (P value Ͻ 0.09) with the highest counts in damp regions (P4) (P value ϭ 0.05). In percentage terms, counts of adult T. lineatum were 631% higher in the damp regions (P4) and 435% higher in the wettest regions (P5) compared to the drier regions (P2) (the excluded precipitation zone). In the moist regions (P3), counts were 99.9% lower than counts in the drier zones (P2). Again, this may be a sample size problem since there were only two sample sites in this precipitation zone. One would have expected counts in this precipitation zone to be 318% higher than counts in the drier regions. The number of adult T. lineatum trapped were jointly significant at the 5% level with the sample period and temperature zones. The coefficients for the sample periods S2, S3, and S4 suggest a curvilinear relationship in the counts. Counts increased in sample period S2 (␤ ϭ 1.0674) and then decreased in sample periods S3 Figure 5. Influence of temperature and precipitation on the sea- (␤ ϭϪ0.5677) and S4 (␤ ϭϪ1.4099). The coefficients, however, sonal activity of T. lineatum caught in Lindgren funnel traps along were insignificant except possibly in sample period S4 (P value ϭ a 1,100-km longitudinal transect that runs south (point 1) to north 0.06). This curvilinear relationship follows the general trend in sum- (point 43) through the interior and southern Alaska in 2009. mer temperatures in Alaska. Average summer temperatures in Alaska are increasing in June (S2, June 16–July 1), reaching a max- Table 3. Estimated coefficients and level of significance for the imum in July (S2, July 2–July 20), and then begin to decline (S3, negative binomial regression model with fixed effects (elevation, sample period, temperature, and precipitation zone) and random July 21–Aug. 2) with the coldest average temperatures occurring in effects (sites) fitted to counts of ambrosia beetle along a 1,100-km August (S4, Aug. 3–Aug. 20). latitudinal transect in Alaska. Examining the coefficients associated with the interaction be-

a tween the sample period and temperature zones suggests that within Variable Coefficient P value a given sample period, counts followed somewhat of a curvilinear Intercept 7.1876 Ͻ 0.0001 relationship across temperature zones. In general, counts increased Ϫ Ͻ Elev 0.0029 0.0001 from the cold (T2) to the mild (T3) temperature zones and then P3 Ϫ7.9314 0.0176 P4 1.9889 0.0485 decreased through the warm (T4) and hot (T5) temperature zones. P5 1.6763 0.0830 In sample period S1, this trend was not significant, but the signifi- S2 1.0674 0.1172 cance of this trend increased over the sample periods. For example, S3 Ϫ0.5677 0.4207 S4 Ϫ1.4099 0.0582 in sample period S4, the coefficient for the mild (T3) temperature Elev * P3 0.0058 0.0039 zone was positive and significant (␤ ϭ 1.6193) implying that Elev * P4 0.0011 0.1719 counts of adult T. lineatum were 405% higher than those in the cold Elev * P5 0.0018 0.0465 S1 * T3 0.5327 0.4553 (T2) temperature zone (the excluded temperature zone). In sample S1*T4 Ϫ0.5804 0.4662 periods S4 and S5, the coefficients were negative and significant, S1*T5 Ϫ0.9209 0.3169 ␤ ϭϪ2.9594 and ␤ ϭϪ3.7718, respectively. Counts were 95% S2*T3 Ϫ0.1571 0.8218 S2 * T4 2.0065 0.0094 lower in the warm temperature (T4) zone when compared to the S2*T5 Ϫ3.6248 Ͻ 0.0001 cold temperature zone (T2) and 98% lower in the hottest (T5) S3*T3 Ϫ0.7981 0.2717 temperature zone. S3*T4 Ϫ2.4225 0.0028 S3*T5 Ϫ3.6813 Ͻ 0.0001 S4 * T3 1.6193 0.0401 Spatial-Temporal Trends in Abundance of T. lineatum in Alaska S4*T4 Ϫ2.9594 0.0009 In early July the negative binomial regression model predicted Ϫ Ͻ S4*T5 3.7718 0.0001 the highest numbers for T. lineatum throughout the southern re- Dispersion parameter 1.560 FIT–Model 0.863 gions of the state and some areas in the northern parts of the Con- FIT–Prediction 0.633 tinental Zone, which is just south of the Brooks Range (Figure 6A). a Elev–elevation (m); P3 (moist), P4 (damp), P5 (wet)–precipitation zones; T3 While these areas are colored the same on the maps, the predicted (mild),T4 (warm),T5 (hot)–temperature zones; S1 (June 16–July 1), S2 (July numbers in the southern portion of the state was more than three 2–20), S3 (July 21–Aug. 2), S4 (Aug. 3–20) sample periods. times higher than those predicted in the northern regions (i.e., 3,000 versus 1,000). To help interpret the maps, contour lines rep- decreased 17% for every 100 m increase in elevation, while in the resenting the probability of observing spruce forests were overlaid on wettest precipitation zone (P5) counts of adult T. lineatum de- the predicted surfaces. The contour lines were derived from kernel creased 10% for every 100 m increase in elevation. In the moist estimates of the density of spruce forests derived from the binary

Forest Science • April 2014 313 Figure 6. Expected endemic levels of T. lineatum in the state of Alaska based on a mixed effects negative binomial regression model for sample periods: (A) July 2, 2009; (B) July 17, 2009; (C) July 31, 2009; and (D) Aug. 18, 2009. The probability of observing spruce forests is overlaid as contour lines. surface of the presence-absence of spruce forests in the state. By one life cycle. The methods used in this paper demonstrate the mid-July the predicted number of adult beetles decreased through- utility of such surveys and, if implemented on a long-term basis, out the state with the largest decreases occurring in the southern would be able to provide estimates of yearly and seasonal trends. regions of the state (Figure 6B). In late July, the number of adult Predicting the status of forest insects, such as T. lineatum, in beetles was consistent with that predicted in the previous sample remote areas of Alaska is a major challenge. Results of this study period but at much reduced levels (Figure 6C). By mid-August, the suggest that reasonably accurate predictions of the occurrence and spatial distribution of the number of adult beetles continued to abundance of at least some insects in remote regions could be de- decrease throughout the state. The highest counts were predicted in rived using information associated with a latitudinal transect that the southern regions of the state (Figure 6D). crosses a range of environmental and site conditions and that the information derived from this transect can be used to extrapolate to Discussion more remote areas. Monitoring stations established in many regions In this study, counts of T. lineatum ambrosia beetles were ob- of Alaska generate reasonably reliable data for temperature and pre- tained once every 2 weeks for 8 weeks, and the data used to charac- cipitation. Many of these monitoring stations have been operating terize the inﬂuence of temperature zones, precipitation zones, and for decades, and much of these data are readily accessible. Partition- elevation on the spatial-temporal dynamics of the T. lineatum over ing large geographic areas of Alaska using climatic zones appears to

314 Forest Science • April 2014 offer a logical approach for predicting insect activity in remote areas, dinal transect with much lower numbers elsewhere. This general which are difficult or impossible to visit for visual assessments of pattern has been found during other less intense surveys along a insect pest damage during routine aerial surveys. similar transect conducted over three years previous to this study. In T. lineatum seems particularly suitable for this study because it this regard, it seems to be a consistent characteristic of T. lineatum, occurs across a wide geographic region, its populations show spatial although certainly does not ensure it. This pattern suggests a com- and temporal variations in the number of beetles trapped, and mon synchronizing cause among different populations of T. linea- were correlated with large-scale patterns of temperature and precip- tum across space. In this regard, south central Alaska has seen more itation. Similar results across a more limited geographic range were rapid residential growth and development than northern regions. found in a companion study using aspen leaf miner Phyllocnistis Human activities like land clearing, timber harvesting and other populiella (Chambers) (Lepidoptera: Gracillariidae) and the willow silvicultural applications, housing construction, and road building leaf blotch miner Micruraphteryx salicifoliella (Chambers) (Lepidop- can also have a major impact on abundance and distribution of host tera: Gracillariidae) (Reich et al. 2013). This approach would prob- material, which might explain why this insect is more abundant in ably not work for introduced species, such as the amber-marked the southern reaches of the transect (R. Kelsey, USDA Forest Ser- birch leaf miner (Profenusa thomsoni) (Hymenoptera: Tenthredini- vice, pers. comm.). dae), which has a limited geographic distribution in Alaska and Another possible explanation for the large-scale patterns of abun- spreads primarily by anthropogenic movements of hitchhiking in- dance could be subtle changes in the vulnerability of living hosts dividuals or infested material along road networks. caused by shifting climate conditions. In this regard, the location of The model developed here shows the relationship between am- host stands can influence beetle abundance. In this study, T. linea- brosia beetle counts and large-scale and long-term averages in tem- tum was found to be more abundant in the warmer and wetter perature and precipitation and elevation. In this regard, a number of regions of the state, whereas spruce occurred more frequently in the large-scale trends were evident: (1) The largest counts occurred in cooler and drier regions of the state. The highest abundance of T. mid-July; (2) counts decreased with increasing elevation; (3) greater lineatum occurred in those climate zones in which the host tree had numbers of beetles coincided with climate zones characterized as a low frequency of occurrence. These areas may represent marginal having the highest average annual temperatures and highest levels of sites in which the environmental factors may not be optimal for tree average annual precipitation; and (4) precipitation moderated the growth, and the trees growing there may be under increasing abnor- effects of elevation on the number of beetles trapped. In general, mal stress. On marginal sites the host populations may be more data collected along the latitudinal transect were useful in predicting vulnerable to establishment of beetle populations. The existence of the less-variable, large-scale background conditions of beetle abun- such zones is an important factor that should not be overlooked dance and less able to predict the location of local “hotspots,” or when analyzing or predicting long-term and/or long-scale trends in epicenters, of local concentration of abundance. the abundance T. lineatum in the state. In addition to temperature and precipitation, the availability and abundance of suitable host material impacts the population dynam- Endnote ics of T. lineatum (Daterman and Overhulser 2002). Suitable host 1. Data obtained from the USGS Alaska Science Center, High Altitude Climate material can take many forms and might help explain site-to-site Transects was accessed at http://alaska.usgs.gov/science/geographic/history/ variation in population density. In this regard, recent dead and history.html. stressed trees, coarse woody debris, stumps, and unhealthy parts of Literature Cited healthy trees serve as its primary host material (Wood 1982). Nat- ACHARYA, T., AND A.K. RAY. 2005. Image processing: Principles and appli- ural events like wildfire, severe wind storms, root diseases and other cations. John Wiley and Sons, New York. 425 p. agents of stress, breakage, or mortality influence concentrations and BERMAN, M., G.P. JUDAY, AND R. BURNSIDE. 1999. Climate change and distributions of suitable host materials (Furniss and Carolin 1977). Alaska’s forests: People, problems, and policies. In Proc. of a workshop, The composition of host stands, age, stem density, and size of trees Assessing the consequences of climate change for Alaska and the Bering Sea within stands can influence beetle abundance (Hindmarch and Reid region, Weller, G., and P.A. Anderson (eds.). Fairbanks, Oct. 29–30, 2001). Infested wood colonized by ambrosia fungi impacts repro- 1998, Center for Global Change and Arctic System Research, Univer- ductive success of T. lineatum (Helio¨vaara and Peltonen 1999). sity of Alaska Fairbanks. Ku¨hnholz et al. (2001) suggested that under some conditions, am- BERRYMAN, A.A. 1986. Forest insects principles and practice of population brosia beetles can even attack living trees. management. Plenum Press, New York. 278 p. In addition, trap efficiency and trap apparency have been shown BORDEN, J.H. 2004. Chemical ecology and management of forest insects. to have a significant effect on catch abundance. The influence of trap NSERC Cooperative Research and Development Grant No. size, type, color, and location and the interactions of these factors CRDPJ247613–01, and BC Forestry Innovation Investment Grant with pheromone concentrations, enantiomer composition, and No. R04–055. Available online at www.for.gov.bc.ca/hfd/library/ pheromone combinations are not well understood (Hoover et al. FIA/HTML/FIA2004MR207.htm; last accessed July 18, 2013. 2000). Consequently, “attractiveness” of a trap can vary among BOYCE, J.S. 1961. Forest pathology. McGraw-Hill Book Co., New York. 572 p. locations. Trapping, for instance, might be more efficient where BRIGHT, D.E., AND R.W. STARK. 1973. The bark and ambrosia beetles of temperatures cause greater release rates of attractants or where traps California (Coleoptera: Scolytidae and Platypodidae). Bull. Calif. Insect are less obscured by trees or other vegetation. Determining the cause Survey 16. 169 p. of beetle distribution is not trivial and is more likely due to a com- BRIGHT, D.E. 1976. The bark beetles of Canada and Alaska. The insects plex of interacting factors of which temperature and precipitation and arachnids of Canada, Part 2. Can. Dept. Agric., Pub. 1576. 241 p. play only a part (Campbell and Borden 2009). 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