Five common conifer species occur on the northern range of the Archipelago: western hemlock , mountain hemlock , yellow-cedar, Sitka spruce , and shore pine . These coastal forests are simple in composition yet often complex in age and tree structure . Yellow-cedar occurs across a soil-drainage gradient from poorly drained bogs to well-drained soils on steeper slopes that often support more productive stands . This study occurs in the northern portion of the yellow-cedar population distribution and at the current latitudinal limits of forests affected by decline. We centered our investigation on protected lands in four inlets in the Alexander Archipelago on the outer coast of the West Chichag of-Yakobi Wilderness on Chichagof Island in the Tongass National Forest and Glacier Bay National Park and Preserve . Aerial surveys were conducted in 2010 and 2011 to assess the presence of affected forests and to identify the edge of yellow-cedar dieback that occurs south of GLBA on Chichagof Island. Aside from a brief history of small-scale gold mining that occurred in several areas on Chichagof Island between 1906 and 1942, plastic planters there is little evidence of human impact on these lands, making them ideal for studying ecological dynamics.
Drawing upon previous studies that estimated the time-since-death for five classes of standing dead yellow-cedar trees at various stages of deterioration , our plot selection consisted of sequential steps, in the field, to sample forests representative from a range of time-since-death. Not all yellow-cedar trees in a forest affected by mortality die at once; mortality is progressive in forests experiencing dieback . Highly resistant to decay, these trees remain standing for up to a century after their death . As a result, they offer the opportunity to date disturbance, approximately, and to create a long-term chronosequence. First, we stratified the study area coastline into visually distinguishable categories of ‘‘cedar decline status’’ by conducting boat surveys and assessing cedar decline status across 121.1 km of coastline in June 2011 and 2012. We traveled the coastline and made visual observations of live and dead yellow-cedar trees and their snag classes. We assigned cedar decline status to coastal forests at 100 m increments using a GPS Garmin 60 CSx . Next, using the ArcGIS 10.2 Geographic Information System software , we randomly generated plot locations in forests categorized during the coastline survey as follows: live, unaffected by mortality; recent mortality; mid-range mortality; and old mortality. Lastly, we controlled for basal area and key biophysical factors, including elevation and aspect via methods described.
Plots were restricted to elevations less than 150 m, excluding northeast facing plots, to sample from low-elevation plots representative of conditions where yellow-cedar decline commonly occurs at this latitude . Plots were randomly located between 0.1 and 0.5 km of the mean high tide to avoid sampling within the beach fringe area, and on slopes ,72% to limit risk of mass movement . We excluded plots with a total basal area ,35 m2 /ha to avoid sampling below the optimal niche of yellow cedar . This control was performed in the field by point sampling to estimate basal area using a prism with a basal area factor 2.5 . Plots dominated by the presence of a creek bed or other biophysical disturbance were eliminated from plot selection, due to the confounding influence of disturbance on the number of trees standing and species abundance. By restricting our sampling to these controls, our study was designed to examine the process of forest development post-decline in low-elevation coastal forests with plot conditions typical for yellow-cedar mortality excluding bog wetlands, where yellow-cedar may co-occur sparsely with shore pine. After controlling for biophysical factors, 20 plots were sampled in live forests and 10 plots in each of the affected cedar status categories for a total of 50 plots across the study area .Data were collected in fixed, circular nested plots to capture a wide range of tree diameters and in quadrats within each plot to account for spatial variability in understory vegetation.
Forty plots were established and measured during the 2011 field season and 10 plots during the 2012 field season, through the seasonal window of mid-June to mid-August. Nested circular plots were used to sample trees and saplings as follows: a 10.3 m fixed radius plot for trees 25.0 cm diameter at breast height , a 6.0 m fixed radius plot for saplings ,2.5 cm dbh and 1 m height, and trees 2.5–24.9 cm dbh. We counted live saplings of each species to analyze the population dynamics for individuals that survive to this size class. For each tree, we recorded species, dbh to the nearest 0.1 cm, height to the nearest 0.01 m, dead or live, and for dead trees snag classes I–V. Eight quadrats at each plot were utilized to record understory plants and tree seedling densities. To provide an additional longterm view of species changes, we recorded counts for smaller conifer seedlings , identifying western hemlock and mountain hemlock to genus, and other conifers to species. We noted presence/ absence of each conifer species 10–99 cm, but did not sample this size class for individual counts. We recorded maximum height and percentage cover of each plant species observed according to the Daubenmire method on a continuous scale . In unique cases where consistent identification to species was difficult Salisb.; Vaccinium ovalifolium Sm., and V. alaskaense Howell, we combined observations but noted both species presence for total richness across the study area. Blueberries, V. ovalifolium and V. alaskaense, are similar in appearance and often synonymized . Mosses and liverworts were recorded together as bryophytes within the quadrat. Sedges were recorded together but distinguished from true grasses . We used hemispherical photography to assess canopy cover at each plot. Photographing from plot center at dbh camera height, we captured imagery in relatively uniform, overcast skies and consistently avoided any mid-day sun conditions . To prevent diminished sharpness associated with consumer-grade cameras , we used a Sigma 4.5mm fish-eye lens on a professional grade Canon 7D camera . Full-view images were processed using Gap Light Analyzer to yield percentages of canopy openness per plot as a proxy for light in understory analyses .Clustering plots by cedar decline status.—To rigorously account for the timing of mortality relative to the coarse visual cedar decline status categorizations made by boat, we performed kmeans clustering analyses on the yellow-cedar population observed across the chronosequence by partitioning 50 plots into those affected by mortality and live ‘‘controls’’ for subsequent stages of analysis. Using observations of dead and live yellow-cedar trees at each of the 50 plots , we stratified the plots into two groups for unaffected and affected forests. We then performed a k-means clustering analysis with the categorical snag classifications observed at the resulting 30 plots affected by mortality, plastic plant nursery pot assigning the a priori k ¼ 3 for three affected status categories sampled: recent mortality, midrange mortality, and old mortality. We restricted this analysis to yellow-cedar trees .10 cm dbh because the methods of dating time-since-death for yellow-cedar trees rely upon standing, larger trees . We analyzed the cluster stability by computing the Jaccard coefficient to measure similarity between resulting clusters, assessed by the bootstrap distribution of the Jaccard coefficient for each cluster compared with the most similar cluster in the bootstrapped datasets .
Post hoc Fisher’s exact tests further clarified differences in the numbers of observed class I, II, and III snags between recent and mid-range mortality clusters ; observed class II, III, and IV snags between recent and mid-range mortality clusters , and between mid-range and old mortality clusters ; and observed class III, IV, and V snags between mid-range and old mortality clusters . These analyses were performed in R using the GCLUS and FPC packages. This post-field methodology for plot stratification enabled us to refine the visual cedar decline status assigned in the boat surveys by clustering according to the observed populations of live yellow-cedar trees from the plot data. Stand structure and regeneration.—We calculated the importance value for live conifers in the overstory as the sum of relative density, frequency, and basal area per species to characterize the stand structure and conifer composition within each cedar decline status resulting from clustering analyses, and to make comparisons across the chronosequence of cedar decline status. For each species in three size classes , we computed the following variables: density , frequency , and dominance , and with the relative values of these three parameters, the importance value was calculated as IV ¼ DR þ FR þ DoR . Thus, the cumulative value for all tree species per size class in each cedar decline status was 300%. In assessing regeneration, we focused analysis on seedling counts and saplings to consider established plants. We used Krukal–Wallis tests and performed permutation tests on the measure of central tendency to examine differences in mean seedling and sapling abundance across the four cedar decline status categories. Using presence/absence sapling data, we calculated the probabilities of finding each individual conifer species in the sapling life stage in each cedar decline status and generated binomial confidence intervals to estimate uncertainty using the Wilson score interval. We used a two-part modeling approach to determine the probability of species’ occurrence in cedar decline status and to test for significant effects of cedar decline status on each species’ abundance in the sapling stage. This method was selected to account for over dispersion in zeros in the individual abundance data for the conifer species in the sapling life stage . In the first step, the data were considered as zeros versus non-zeros and a binomial model was used to model the probability of observing a zerovalue; in the second step, non-zero observations were modeled with a zero-altered Poisson model . Canopy openness and cedar decline status were included in the models as explanatory variables to predict species presence/absence and sapling abundance. Best models were selected based upon AIC values. These analyses were performed in R using the PSCL, MHURDLE, and BINOM packages. We determined the IV for saplings in each cedar decline status on the basis of relative density and relative frequency , such that the IV of all species would sum 200%. To compare the persistence of saplings to treelets in the early stages of stand development, we calculated the ratio of saplings to live treelets per hectare at each plot and tested for significant differences between live ; recent mortality and live ; mid-range mortality using Wilcoxon rank sum tests. Probabilities calculated for species occurrence in the size class 10–99 cm in each cedar status were used for comparison with seedling and sapling results to assess trends in survival.The changes observed across the chronosequence provide strong evidence that this species dieback associated with climate change can result in a temporally dynamic forest community distinguished by the diminished importance of yellow-cedar, an increase in graminoid abundance in the early stages of stand development, and a significant increase in shrub abundance and volume over time. Tree mortality timing and intensity, as characterized by our stratified sampling of cedar decline status, played an important role in determining the understory community composition and overstory processes of stand re-initiation and development. Our results highlight the ways in which widespread mortality of one species can create opportunities for other species and underscores the importance of considering long-term temporal variation when evaluating the effects of a species dieback associated with climate change. Methods for predicting future changes in species distributions, such as the climate envelope approach, rely upon statistical correlations between existing species distributions and environmental variables to define a species’ tolerance; however, a number of critiques point to many factors other than climate that play an important role in predicting the dynamics of species’ distributions . Given the different ecological traits among species, climate change will probably not cause entire plant communities to shift en masse to favorable habitat . Although rapid climatic change or extreme climatic events can alter community composition , a more likely scenario is that new assemblages will appear . As vulnerable species drop out of existing ecosystems, resident species will become more competitive and new species may arrive through migrations . Individual species traits may also help explain the process of forest development in forests affected by widespread mortality, as the most abundant species may be those with traits that make them well-adapted to changing biotic and abiotic conditions .