Northeastern Naturalist 490 T. Patterson, et al. 22001166 NORTHEASTERN NATURALIST 2V3(o4l). :2439,0 N–5o0. 04 Climate–Growth Relationships of Pinus rigida (Mill.) at the Species’ Northern Range Limit, Acadia National Park, ME Thomas Patterson1,*, R. Stockton Maxwell2, Grant L. Harley3, Joshua S. Oliver3, James H. Speer4, Savannah Collins5, Madison Downe6, Benjamin Gannon7, Lan Ma8, Chance Raso9, Cody Russell10, and Aaron Teets11 Abstract - This study examined climate–tree growth relationships of a G2 globally rare Pinus rigida (Pitch Pine) barren located at the species’ northern range limit in Acadia National Park, ME. Our tree-ring chronologies spanned the period 1804–2014 CE and included 50 dated tree-ring series from 33 trees. We found significant (P < 0.05) positive correlations in all chronologies between each year’s tree growth and previous October through April temperature, as well as with August precipitation. Additionally, we found negative correlations between our chronologies and previous July precipitation. Moving interval correlation analysis showed temporal instability of all climate–growth relationships except for April temperature and August precipitation for the total width and latewood chronologies. Our results corroborate previous findings that suggest tree species at their northern range limit respond positively to winter temperature. We posit warmer winter temperatures and enhanced late-summer precipitation indicate a maritime influence that positively influenced radial growth at our site. Introduction Factors limiting primary and secondary tree-growth usually change throughout the range of each species. In theory, temperature should control growth at a species’ northern range limit (NRL; Fritts 1976). A positive response between radial growth and winter temperature has been shown for numerous Pinus spp. at their NRL including P. taeda L. (Loblolly Pine; Cook et al. 1998), P. palustris Mill. (Longleaf Pine; Bhuta et al. 2009), P. sylvestris L. (Scots Pine; Babst et al. 2013), and P. rigida Mill. (Pitch Pine; Cook and Cole 1991, Pederson et al. 2004). Little is known about the influence that maritime climate might have on the growth 1Department of Geography, University of North Carolina at Greensboro, Greensboro, NC 27412. 2Department of Geospatial Science at Radford University, Radford, VA 24142. 3Department of Geology and Geography, University of Southern Mississippi, Hattiesburg, MS 39406. 4Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809 5Department of Geography, University of Tennessee, Knoxville, Knoxville, TN 37996. 6Department of Geography, University of Guelph, Guelph, ON N1G 2W1, Canada. 7Colorado Forest Restoration Institute, Colorado State University, Fort Collins, CO 80523. 8Department of Earth and Climate Sciences, San Francisco State University, San Francisco, CA 94132. 9Department of Geography, Virginia Tech University, Blacksburg, VA 24061. 10Department of Biology, California State University, Northridge, Northridge, CA 91330. 11School of Forest Resources, University of Maine, Orono, ME 04469. *Corresponding author - twpatter@uncg.edu. Manuscript Editor: David Orwig Northeastern Naturalist Vol. 23, No. 4 T. Patterson, et al. 2016 491 response of species at their NRL. Maritime, high-latitude populations provide the opportunity to examine how climate influences radial growth, and the potential for positive growth benefits to impending winter warming caused by humaninduced climate change (IPCC 2014). Pitch Pine is native to eastern North America, where it is distributed from central Tennessee to southeastern Maine. Acadia National Park, ME, is representative of the NRL of Pitch Pine. Annual temperature has increased 1.7 °C throughout Maine from 1895–2015 CE and is expected to increase an additional 1.7–2.8 °C by 2050 CE (Fernandez et al. 2015). Similarly, Fernandez et al. (2015) report total precipitation has increased 15.25 cm since 1895 CE and is expected to increase an additional 8 cm by 2050 CE. Winter warming has outpaced that of all other seasons throughout New England (Kunkel et al. 2013), and mean annual sea-surface temperatures in the Gulf of Maine have warmed faster than the global average since 1982 (Pershing et al. 2015). At present, no study has examined how these changing climate conditions have influenced coastal tree-ring growth. Previous dendroclimatic studies revealed positive correlations between Pitch Pine radial growth and annual precipitation and temperature in New York (Abrams and Orwig 1995). Significant negative correlations between radial growth and previous November–December temperature and previous July precipitation were found in southern Virginia (Copenheaver et al. 2002). Closer to the current study site, Pederson et al. (2004) found positive correlations between Pitch Pine radial growth and winter temperature in the Hudson River Valley, NY. Most recently, Leland et al. (2016) found a negative correlation between Pitch Pine radial growth and maximum January temperature and a positive correlation with July precipitation in central New Jersey. An introduced population of Pitch Pine on the Baltic coast of Poland expressed positive, significant correlations with January–March temperature (Cedro et al. 2013). To understand climate drivers for Pitch Pine growing at its NRL, this research: (1) developed total width, earlywood (spring/early summer), and latewood (late summer) tree-ring–width chronologies for a coastal Pitch Pine barren in Acadia National Park, ME, and (2) explored the spatiotemporal variability of climate–growth relationships for Pitch Pine growing at its NRL. Herein, we present the results of this study and discuss their implications within the context of the increased warming trend over coastal Maine. Site Description We sampled Pitch Pine individuals growing in a coastal pine barren along the Wonderland Trail (hereafter Wonderland) in Acadia National Park, ME. The site is located within the Laurentian Plains and Hills ecoregion of central Maine, where Pitch Pine trees are found below 500 m elevation in barrens or along the rocky coastline. Wonderland is located less than 0.5 km from the Atlantic Ocean and 5.5 km south of Southwest Harbor on Mount Desert Island, ME (44°13'53"N, 68°18'53"W). The site is a 5.5-ha Pitch Pine/Corema conradii Torr (Broom Crowberry) barren spanning 12–18 m above sea-level (masl; Lubinski et al. 2003, Tierney et al. 2012). The Pitch Pine/Broom Crowberry woodland is classified as a G2 (imperiled) Northeastern Naturalist 492 T. Patterson, et al. 2016 Vol. 23, No. 4 community, with only an estimated 20–30 occurrences of this community worldwide (NatureServe 2016). The Pitch Pine woodland community is also classified as S3 (state rare) in Maine (Gawler and Cutko 2010). At the time of sampling, Pitch Pine were the dominant, uneven-aged tree species growing on the Wonderland barren. Broom Crowberry was ubiquitous throughout the site aside from small patches of exposed bedrock and where game trails existed. We found no evidence of major disturbances or recent fire activity, yet both Pitch Pine and Broom Crowberry are fire-adapted species. Methods We sampled from individuals that displayed old-age characteristics including smooth bark, flat crowns, and twisted/sinuous boles (Pederson 2010; Fig. 1), yet were stunted in height, presumably due to a combination of thin soil, proximity to coast, and site harshness. We removed 2 core samples from 55 Pitch Pine trees at 0.3 m height (due to short stem height [less than 3 m], Fig. 1). For each tree, we recorded diameter at coring height and geolocation. Care was taken to sample evenly throughout the site. We prepared samples using standard dendrochronological techniques (Stokes and Smiley 1968). The samples were visually crossdated using skeleton plotting, measured with 0.001 mm accuracy using WinDENDRO (Regent Instruments Canada, Inc. 2012) software, and compiled to produce total-width, earlywood (spring/early summer), and latewood (late summer) chronologies. Visual crossdating accuracy for each chronology was verified using the program COFECHA (Grissino-Mayer 2001, Holmes 1983). We detrended our chronologies with a cubic-smoothing spline with a frequency response of 50% at two-thirds the length of each series using the program ARSTAN (Cook and Figure 1. Typical landscape at the Wonderland study site, a maritime Pitch Pine woodland in Acadia National Park, ME. Northeastern Naturalist Vol. 23, No. 4 T. Patterson, et al. 2016 493 Holmes 1996). Detrending is necessary to remove juvenile growth trends and growth releases/suppressions that may be a function of forest-stand dynamics. The resulting time series are indices with a mean of 1. We used the Arstan chronology for all analyses because it retains some autocorrelation that may be related to climate (Cook 1985). We obtained climatic data from the National Climatic Data Center (NOAA 2015) for Maine Climate Division 3 (coastal zone) for the years 1895–2014 including monthly mean minimum/maximum/mean temperature, total precipitation, and Palmer drought severity index (PDSI; Palmer 1965). We investigated the relationship between the 3 chronologies (henceforth known as total width, earlywood, and latewood) and climate data for a period that included previous May–current year October using stationary (all years) and moving (36-year moving average) correlation functions in the program DendroClim2002 (Biondi and Waikul 2004). The 36-year moving-average window was selected in DendroClim2002 as the minimum window length to show where changes in climate/radial-growth relationships existed. Finally, we analyzed regional climate correlations using KNMI Climate Explorer (Royal Netherlands Meteorological Institute 2016, Trouet and van Oldenborgh 2013) with the aforementioned climate variables (gridded data from the Climate Research Unit, CRU TS3.23; Harris et al. 2014) to test the influence of regional climate patterns. Results We developed 3 chronologies based on 50 increment cores from 33 trees that spanned the period 1804–2014 CE (Fig. 2). Interseries correlation ranged from 0.491 for earlywood to 0.612 for total width and 0.723 for latewood. Mean sensitivity for these chronologies ranged from 0.266 for total width to 0.285 for earlywood and 0.478 for latewood. The interseries correlation is a measure of the accuracy and quality of dating, whereas mean sensitivity is a measure of the yearto- year variability in the growth series (Speer 2010). We identified locally absent growth rings on 8 of the 50 core samples, with as many as 5 missing rings on 2 of the cores. Sample-depth reliability exceeded the widely accepted 0.85 Expressed Population Signal (EPS) threshold (Wigley et al. 1984) beginning in year 1901, 1907, and 1889 CE for total width, early width, and latewood, respectively. EPS values range from 0–1, with higher values indicating greater signal to noise ratio, which is dependent on sample depth (Wigley et al. 1984). The stationary correlation analysis revealed the 3 chronologies principally correlated with the temperature variables during winter months (previous December– current March) and into early spring (April) (Table 1). Only the latewood chronology correlated with PDSI (Table 1). The strongest statistical correlations (r > 0.30) for temperature were between April mean and maximum temperature and total width (both r = 0.33), and April mean (r = 0.35), maximum (r = 0.34) and minimum (r = 0.32) temperature and latewood (Table 1). The strongest statistical correlation for total precipitation was between August total precipitation and latewood (r = 0.39); however, previous June and July total precipitation were also Northeastern Naturalist 494 T. Patterson, et al. 2016 Vol. 23, No. 4 correlated with the 3 chronologies (Table 1). Moving-interval correlation analyses indicated stable (i.e., consistent positive) April mean temperature and total August precipitation relationships from 1895–2014 for latewood (Fig. 3). Temporal Figure 2. (a) Total width, (b) earlywood, and (c) latewood Wonderland P. rigida ARSTAN chronologies spanning the period 1804–2014 CE. Northeastern Naturalist Vol. 23, No. 4 T. Patterson, et al. 2016 495 Table 1. Correlation coefficients between total width, earlywood, and latewood chronologies and monthly mean, minimum, and maximum temperature, precipitation, and Palmer drought severity index (PDSI) from Maine Climate Division 3 for years 1895–2014. The lowercase “p” in front of the month indicates the previous year. All presented correlations are significant at P < 0.05. NA = not available (no significant correlation). Chronology Mean temp Max temp Min temp Precipitation PDSI Total width pDec (0.19) pDec (0.20) pDec (0.18) pJul (–0.24) NA Jan (0.18) Feb (0.24) Jan (0.20) Aug (0.22) Feb (0.24) Apr (0.33) Feb (0.23) Apr (0.33) Mar (0.18) Apr (0.28) Oct (0.16) Earlywood width pDec (0.20) pDec (0.20) PDec (0.19) pJun(–0.14) NA Jan (0.19) Feb (0.19) Jan (0.21) pJul (–0.25) Feb (0.20) Apr (0.29) Feb (0.19) Apr (0.28) Apr (0.22) Latewood width pDec (0.18) pNov (0.16) pOct (0.23) pJul (–0.20) Aug (0.22) Feb (0.26) pDec (0.20) Feb (0.24) Aug (0.39) Mar (0.23) Feb (0.26) Mar (0.25) Apr (0.35) Mar (0.19) Apr (0.32) Oct (0.20) Apr (0.34) Oct (0.22) Figure 3. Moving-interval correlation analysis showing both persistent and non-stationary climate–growth relationships between the 3 chronologies and total precipitation and average temperature for a 36-year window during the period 1895–2014. Grey bars indicate significant (P < 0.05) negative correlations and black bars indicate significant (P < 0.05) positive correlations. Examples of persistent, significant correlations include latewood and August precipitation (f) and latewood and April average temperature (e). Northeastern Naturalist 496 T. Patterson, et al. 2016 Vol. 23, No. 4 instability of the climate/radial-growth relationships was observed for the other significant winter and early spring temperature variables (as shown in the stationary analysis) for all 3 chronologies throughout the 20th century (Fig. 3). Additionally, the earlywood chronology expressed the most temporal instability, where significant correlations identified in the stationary analysis over the 119-year record were never persistent for more than ~30 years in the moving interval analysis (Fig. 3). Regional climate–growth correlation analysis revealed that latewood correlated with mean April temperature broadly throughout New England and into southern Quebec, New Brunswick, and Nova Scotia (Fig. 4a). Additionally, the focus of the strongest August precipitation correlation resided over coastal Maine (Fig. 4b). Discussion and Conclusion We present baseline information for climate sensitivity analysis of a maritime Pitch Pine woodland at its NRL and contribute to a limited body of literature regarding this species. As expected, our results from the stationary analysis confirm the work of others (Babst et al. 2013, Bhuta et al. 2009, Cook and Cole 1991, Cook et al. 1998, Pederson et al. 2004) that species at their NRL respond positively to winter temperature. Locally absent rings were detected in 16% of our core samples but at a lower rate than in Leland et al.’s (2016) study, which had missing rings in over 80% of their samples. Missing rings are a physiological adaptation of trees during severe environmental stress (Fritts 1976), yet no consistent anomalies were detected in our climate data during the most common years for missing rings at Wonderland. We analyzed earlywood/latewood measurements for the species and found that latewood had the strongest climate–growth relationships, possibly due to higher mean sensitivity than the total width or earlywood chronologies. We demonstrated temporally unstable climate–growth relationships during winter months Figure 4. Regional spatial correlations between the Wonderland Pitch Pine latewood chronology and (a) April mean temperature and (b) total August precipitation during the period 1895–2014. Both maps are scaled to 40–49°N, 63–74°W and were created using KNMI Climate Explorer (Trouet and van Oldenborgh 2013). Asterisk indicates study-site location. Northeastern Naturalist Vol. 23, No. 4 T. Patterson, et al. 2016 497 (previous December–current March) for the 3 chronologies (Fig. 3) that we posit were influenced by the observed warming trend in winter temperature (Fernandez et al. 2015, Kunkel et al. 2013). The stable climate–growth relationships for latewood width also indicate that changing climate may affect tree growth differently depending on the season of growth, with tree growth during the early period of the growing season being more susceptible to alteration from changing climate conditions. However, the changing climate–growth response requires further investigation. The latewood correlation with April average temperature might also be influenced by warmer winters, yet latewood growth is initiated during late summer (Speer 2010), indicating a lag effect where growing vigor persists through the growing season. Our findings most closely agree with those of Pederson et al. (2004), who noted a positive correlation between Pitch Pine radial growth and winter temperature. Specifically, Pederson et al. (2004) found significant positive correlations for Pitch Pine total width and November–March maximum and minimum temperature at 2 locations in New York that spanned 85–120 masl; however, they found no correlation with winter temperature at a third Pitch Pine rock outcrop at 365 masl. Additionally, our results agree with Cedro et al. (2013), who described a positive correlation between Pitch Pine radial growth and January–March temperature at a coastal dune site ~10° latitude north of Wonderland along the Baltic Sea in Poland. However, our results are contrary to the findings of Cedro et al. (2013), who found a negative correlation with April precipitation. The differences between our results and those of Cedro et al. (2013) are perhaps due to disparities in broad-scale climate patterns and growth seasonality between North America and Europe. In addition, our results are in disagreement with those of Leland et al. (2016), who found significant negative correlations with January maximum temperature and July precipitation for a New Jersey Pine Barrens provenance experiment site with 36-year-old trees compared to climate data from 1980–2009. Lastly, with regard to temperature our results contradict those of Copenheaver et al. (2002), who found negative correlations between Pitch Pine radial growth and previous November and December temperature, but confirm their negative correlation with previous July precipitation at 2 locations (350, 520 masl) in Southwest Virginia. The regional climate analysis indicated that Pitch Pine was responding to a broader climate pattern than just coastal Maine (Fig. 4). The April mean temperature correlations signal the end of winter for the northeastern US and the hastening of the growing season as indicated by their broad spatial footprint. The August precipitation map showed a coastal signal that could be linked to the source of moisture from Atlantic Ocean storms, where the effect is most immediate in proximity to the coast. Maritime climate in Maine is punctuated by a moderating effect of the ocean on temperature and both more precipitation and a higher frequency of extremeprecipitation events during summer and fall (Fernandez et al. 2015). Our results show that warmer winter temperatures (Kunkel et al. 2013), coupled with a maritime effect that is enhanced by warming sea-surface temperatures (Pershing Northeastern Naturalist 498 T. Patterson, et al. 2016 Vol. 23, No. 4 et al. 2015), positively influence ring growth of Pitch Pine at its NRL. Additionally, this population may also benefit from the predicted increase in August precipitation (Fernandez et al. 2015), yet the causal mechanism for heavy latesummer precipitation (i.e., Atlantic Ocean storms) may harm individual trees. We documented that Pitch Pine is a long-lived (>200 years) and climate-sensitive species at its NRL, and stress the importance of continued community-level monitoring by the National Park Service, especially during this era of impending climate change and associated impact on natural resources. Our results may be useful to the National Park Service in meeting the goals for their Climate Change Response Strategy by incorporating the best available scientific data to support mitigation and adaptation strategies to climate change (National Park Service 2010), especially in globally rare communities. 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