Online Resource Climatic Change

Online Resource – Climatic Change Development of risk matrices for evaluating climatic change responses of forested habitats

Louis R. Iverson1, Stephen N. Matthews1,2, Anantha M. Prasad1, Matthew P. Peters1, Gary Yohe3

Corresponding author: Louis Iverson, Phone 740-368-0097, Fax 740-368-0152, email:[email protected], web site:www.nrs.fs.fed.us/people/liverson

Online Resource 1: Overview of Study Area - Vegetation, Land Use, and Climate of Northern Wisconsin Forests

Northern Wisconsin lies at the convergence of three major biomes – eastern deciduous forest, tall grass prairie, and boreal forest. These biomes are distributed across a wide variety of glaciated landscapes in the region; thus the overall landscape is very diverse. The southern boundary is also a well-defined climatic zone, referred to as the Tension Zone, which runs through central Wisconsin from the southeast to the northwest part of the state (Curtis, 1959). The geology and soils of the region are dominated by glacial activity of Pleistocene, leaving a high concentration of lakes and an abundance of wetlands (WDNR, 2009). The natural communities of the region result from its intersection of two biomes: the eastern deciduous forests and the boreal forests. In this paper, we selected one species (black ash) and a forest type (spruce-fir) from the boreal component, and three species (white oak, yellow poplar, sugar maple) from the deciduous forest as demonstration species. The landscape of northern Wisconsin is currently dominated by forest (46%), followed by agricultural land (21%), wetlands (17%) and other land uses (16%) (WDNR, 2009). The vegetation is also quite different as it crosses the Tension Zone, with the agriculture-dominated (formerly prairie and oak savanna) zone to the south blending to deciduous- coniferous forests to the north, where the forest type groups represented include maple-beech-birch (28%), aspen-birch (25%), oak-hickory (13%), spruce-fir (12%), elm-ash-cottonwood (9%), white/red/jack pine (8%), oak/pine (3%), and other (1%) (Miles, 2010).

Profound changes to the ecosystems of northern Wisconsin occurred between the 1850s and the early 1930s. Logging of white pine and other species began as early as the 1830s and peaked in the late 1800s; as a result of logging and its slash, intense and catastrophic fires affected almost all of northern Wisconsin. By the 1930s, nearly all of the primary forest had been harvested or burned (WDNR, 2009). Besides fire, severe wind events also are responsible for the vegetation patterning in northern Wisconsin (Schulte and Mladenoff, 2005). More recently, non-native insect, disease, and invasive plant outbreaks have occurred at an increasing frequency as a consequence of the introduction and establishment of these agents in a global economy (e.g., emerald ash borer, gypsy moth, oak wilt, butternut canker). As mentioned previously, some of the increasing disturbances presented above have been linked to climate change (e.g., Allen et al., 2010). More detail on the threats and vulnerabilities to the individual forest types in the region are presented in Swanston et al. (2011), but the selected species demonstrated in this paper all have a series of stress factors which are important to consider in light of climate change. Thus our process of including modification factors (see Matthews et al. 2011b) in these risk matrices is important to especially understand the potential impacts of climate change on these species/forest type habitats.

The climate of Wisconsin has been changing. Data from weather stations across Wisconsin from 1950- 2006 indicate that (1) nighttime low temperatures have increased by 0.6 to 2.2 oC; (2) days with a minimum temperature below -18 oC are becoming much less frequent (up to 18 days fewer per winter in the north); (3) the date of the last spring freeze is 6-20 days earlier and the date of the first autumn freeze is 3-18 days later across most of the state (most change in northwest part of state), thus the growing season length has increased substantially; and (4) when defined by climatic variables, the Tension Zone has shifted north and northeast by 15-20 km (Kucharik et al., 2010).

Models of future climate change in northern Wisconsin indicate that, by 2100, May-September temperatures may increase by 2.2-7.6 oC, according to the Parallel Climate Model, B1 scenario (PCMlo = mild scenario) and the Hadley CM3 model, A1fi scenario (Hadhi = harsh scenario), respectively (Swanston et al., 2011). This range represents a full range of possibilities according to models and emission storylines projected by the Intergovernmental Panel on Climate Change (IPCC, 2007; Nakicenovic et al., 2000). Precipitation is not projected to change nearly so much, with Hadhi slightly lower and PCMlo slightly higher precipitation estimates for 2100 as compared to current. Nonetheless, with such large increases in growing season temperatures, coupled with the usual lower available moisture levels in the later summer and early autumn, there is a large potential increase in water stress during this period in the future, especially under the Hadhi scenario (Swanston et al., 2011).

Online Resource 2: Basis for the risk matrix for potential changes in tree species habitats - the x axis

Data used for these risk matrices are based on a long history of habitat modeling and species vulnerability assessments. We have used a series of species distribution models (SDMs) to assess habitat suitability for 134 tree species across the eastern United States, both for the current environmental conditions and for those conditions estimated for three periods into the future: those decades around 2040, 2070, and 2100. The methods used for these models, called DISTRIB, have been well documented (e.g., Iverson et al., 2011; Iverson et al., 2008; Prasad et al., 2009; Prasad et al., 2007), but to briefly summarize, the procedure is as follows: (1) collect data on the forests via over 100,000 Forest Inventory and Analysis plots (Miles et al., 2001) and 38 predictors including 7 climate, 5 elevation, 9 soil class, 12 soil property, and 5 land use and fragmentation variables; (2) aggregate all data to conform to 20x20 km grid across the eastern U.S., including estimates of importance based on numbers and sizes of 134 different tree species; (3) run a decision tree ensemble method of statistical modeling (including regression tree analysis, bagging, and random forests) to estimate importance values (IV), based on basal area and number of stems, of each species currently (Prasad et al., 2006); (4) use a series of future climate scenarios, varying according to emission scenarios and global circulation models, to swap the seven climate variables in the models with potential future climatic conditions at each of the three future time steps; and (5) map the outputs and calculate the ratio of future suitable habitat (for 30-year periods ending in 2040, 2070 and 2100) to current suitable habitat for any species and for any region of interest.

Online Resource 3: Basis for the risk matrix for potential impacts on tree species based on disturbance and biological characteristics - the y axis

We adopt a literature-based scoring system, called Modification Factors, to capture the species’ response to changes in climate that cannot be adequately captured by the DISTRIB model. For a complete discussion and explanation of the system, the reader is referred to Matthews et al. (2011). This literature-based approach was used to assess the capacity for each species to adapt to twelve disturbance types, and to assess nine different biological characteristics related to the species adaptability (Table 1). The biological characteristics attempt to assess the species’ capacity to adapt to conditions expected in the future, e.g., higher capacities to regenerate after fire, regenerate vegetatively, or disperse are all positively associated with adaptability to expected climate changes. Similarly, the disturbance characteristics assess the resilience or capacity to withstand disturbances (e.g., drought, fire, flood, etc.). Many of the disturbances are expected to increase with climate change or other human-influenced stresses and some are indeed already showing detectable signals of such change (e.g., Allen et al., 2010; Breshears et al., 2005; Westerling, 2006). Evidence and models show that the future will bring more drought-related stress, more fire, more flooding events, more wind damage, more ice damage (in northern locations), more air pollutants, more disease, insects, and herbivory, more invasive plants, and more timber harvests (at least in some locations).

For both biological and disturbance factors, we conducted a literature assessment; with the primary sources being the USDA Forest Service Silvics Manual (Burns and Honkala, 1990a, b) , USDA National Resources Conservation Service Plants Database (http://plants.usda.gov/), USDA Forest Service Climate Change Tree Atlas (http://www.nrs.fs.fed.us/atlas), and USDA Forest Service Fire Effects Information System (http://www.fs.fed.us/database/feis/plants/tree). These sources were largely compiled from the primary literature, and thus represent a broad assessment of species’ characteristics. For some species, additional literature was used to fill in gaps, but we could not do a complete literature review for each species.

The scoring system involved developing a standardized set of variables and variable definitions (Table 1) and then compiling pertinent information for each species based primarily on the four sources above. Once these data were collected for each species, each biological or disturbance characteristic was given three scores. In developing the scoring criteria, we evaluated standard social science protocol, (e.g., Bright et al., 2000) and developed several scores from the compiled literature. First, and foremost, a “literature” score was assigned on a bi-polar Likert-style score, ranging from -3 to 3 (strongly negative to strongly positive, following Bright et al., 2000) on the species’ capacity to withstand the disturbance or tolerate a biological condition. This score establishes the direction and magnitude of the likely characteristic impact on the species. Two multipliers, to account for uncertainty in a characteristic’s influence and the likely relevance of the characteristic under projected climate change, were then applied to modify this score. The uncertainty score was assigned which could only reduce or maintain the initial score; its only possible values were 0.5, 0.75, or 1. Therefore, as this value increases, the certainty around the initial score was higher (e.g. the information content from the sources was adequate, clear, and consistent as to the direction and magnitude of a characteristic’s influence on the species). When adequate information was not found for a characteristic, estimates were made based on a species’ closest known relatives, and the uncertainty multiplier score was either 0.5 or 0.75. The future relevance score, which ranged from 1 to 4, was used as a multiplier to account for the expectation that the variable will be more or less relevant with respect to climate change will affect the variable in the future, i.e., how much will the variable change due to climate changes?, and ranged from 1 to 4. The four-point range was used to allow finer distinction of the characteristics. For example, increased drought, insect pests, and fire activity have already been attributed to climate change (Westerling, 2006); and these variables are projected to be more prevalent on the landscape and thus receive high future relevance scores (Table 2).

For each characteristic (Table 1) and for all 134 species, author scoring was used for the three scores (literature, uncertainty, and future relevance) based on the surveyed literature. Though the scoring necessarily was largely subjective, the evaluation of the scoring criteria was calibrated using repeated independent evaluations followed by review until consensus among the authors was achieved. We also preserve among the authors and the score rational recorded for each characteristic to serve as an archive. For example, the biological characteristic of seedling establishment has high future relevance and thus was assigned a 4 (Table 2) because the ability of a species to occur in the face of shifting habitats will hinge on its ability to establish in new areas. In the case of Acer rubrum, statements found in Burns and Honkala (1990a) and other sources point to the species’ broad germination niche, resulting in a +3 seedling establishment score with little uncertainty (assigned 1). In the case of Abies balsamea, there were inconsistencies across sources but a more negative statement such as low seed viability with variation contingent on age and light, resulting in an assigned -1 score with high uncertainty (0.5). However, managers can (and should) change these scores as they consider local conditions for each of these characteristics. With local knowledge of site-specific processes, managers will be better suited to interpret the potential suitable habitat models after considering these ModFacs. In addition, this approach encourages decision-makers’ to include silvicultural knowledge and to take an active role in managing tree habitats under projected future climatic conditions.

Table 1. The following explains the biological and disturbance characteristics that make up the factor scores. For each of the characteristics, default values are assigned for each species as a beginning place in deliberations (Table 2) and then results of the literature assessment are used to arrive at distribution wide values, ranging from -3 to +3 ( a seven-point scale ranging from a negative to positive influence). The uncertainty score represents the consistency and clarity of a characteristics influence on the species. The future relevance score accounting for the expectation that climate change will affect the variable in the future. BIOLOGICAL FACTORS:

CO2/ productivity – Literature shows an increase in productivity for some young trees under elevated CO2; however, this increase in productivity has been shown to be not sustained over many years, or for older trees. Therefore there is a slight positive effect by default but with high uncertainty assigned to this factor.

CO2/ water use efficiency – Plants under higher CO2 levels need not have their stomates open as long during the day, thereby increasing water use efficiency and reduce the projected increased drought stress.

Shade tolerance - The tolerance of a species towards light. Does the species grow better in shade, partial shade, or full sun? These values depend on species tolerance level; species intolerant to shade receive -3, Intermediate either -1, 0, 1 , Tolerant species have scores of +3.

Edaphic specificity - The specific soil requirements (e.g., pH, texture, organic content, horizon thickness, permeability) for a species to survive in a suitable habitat. Includes long-term soil moisture capacities of the soil. Species with general requirements have positive scores, while species with specific requirements have negative scores. Unsuitable soils north of the current range of a species can be a barrier to migration.

Environmental habitat specificity - Considers the range of non-edaphic environmental characteristics (e.g., slope, aspect, topographic position, climatic modulation, specific associates) that the species requires. The score, also, considers whether the species may be able to survive a changed climate in relatively small refugia (e.g., coves, north-facing slopes). Generalist species receive positive scores while specialist species receive negative scores.

Dispersal - The species ability to effectively produce and distribute seeds; considers viability, production, production intervals, seed banking, dispersing agents (even humans), and other attributes related to moving seeds across the landscape.

Seedling establishment - The ability of the species to regenerate with seeds to maintain future populations; considers the conditions required for establishment of seedlings and survival rates for seedlings, but not necessarily to the sapling stage. Because this is an expected mechanism of successful migration, it merits a high future relevance value. Vegetative reproduction – The ability of the species to regenerate by means of stump sprouts or cloning (not necessarily growing into sapling sizes). Species that can reproduce vegetatively have positive defaults and those that cannot have negative defaults. Species with good vegetative reproduction will be good at maintaining themselves in refugia under a changed climate (southern boundary will not readily migrate).

Fire regeneration – The capability of the species to be enhanced in regeneration through fire, usually surface fires. This score will never be < 0 as it is only used if there is an extra benefit in fire to regenerate the species, above seedling establishment and vegetation reproduction.

DISTURBANCE FACTOR:

Disease - Accounts for the number and severity of known pathogens that attack a species. If a species is resistant to many pathogens, it is assumed that it will continue to be in the future. If the mortality rate is low, it is assumed that the species is not greatly affected by diseases.

Insect pests - Accounts for the number and severity of insects that may attack the species. If a species is resistant to attacks from known insect pests now or is adapted to cope with them, then it is assumed to be at least partially resistant in the future. This factor, although highly uncertain in overall effects, is likely to be very important over the next 50 years.

Browse - The extent to which browsing has on the species, either positive by promoting growth or by effective strategies for herbivory avoidance, or negative by over browsing.

Invasive plants - The effects of invasive plants on the species, either through competition for nutrients or as a pathogen. This factor is not yet well researched as to effects on individual tree species, but could be very important in the future as invasives are usually more readily adapted to changing environments, and can form monotypic stands that restrict regeneration. Therefore, it rates as high uncertainty but with high future relevance.

Drought - Extended periods without sufficient access to water. Certain species are better adapted to drier condition allowing them to survive more frequent or prolonged droughts. Flood - Frequent or prolonged periods of standing water. Species adapted to sustained flooding will be positively affected while species vulnerable to flooding will be negatively affected by the assumed greater flooding exposures under climate change.

Ice - The damaging effects of ice storms and potential to ice heaving on a species. This factor can be regionally important, but not especially important to potential range changes.

Wind – The damaging effects of wind storms and uprooting potential (and top breakage) of a species.

Fire topkill - The effects of fire or fire suppression on the larger stems of a species (poles and sawtimber). Species adapted to fire will be positively affected by the assumed greater fire exposure under climate change, while species vulnerable to fire will be negatively affected. As a first approximation, bark thickness relates directly to this characteristic.

Harvest – If the species is harvested using best management practices, is the species generally enhanced or diminished through time? If the best management practice includes replanting (as in some pines), that is included in the ranking. If the species is not a target species currently being managed within a harvest context, consider how the species responds when it is an incidental species in harvested stands.

Temperature gradients - The effects of variation in the temperature gradient associated with a species. Species that currently occupy regions with a diverse range of temperatures are assumed to be better adapted to warmer and highly variable climates than species occupying regions with a small range of temperatures.

Pollution - Airborne pollutants that affect, mostly negatively, a species’ growth, health, and distribution. Includes acid rain.

Table 2. Default modification factor scores, including the literature score, uncertainty score, and future relevance.

Modification Literatur Future Factor Characteristics e Score Uncertainty Relevance Biological Disease -1 0.75 2 Insect pests -2 0.50 4 Browse -1 0.75 1 Invasive plants -2 0.50 4 Drought -1 0.75 4 Flood -1 0.75 1 Ice -1 0.50 1 Wind -1 0.75 2 Fire topkill -1 0.75 3 Harvest 1 0.50 2 Temperature gradients 1 0.75 2 Pollution -1 0.75 2 Disturbance CO2/ PROD 1 0.50 1 CO2/ WUE 1 0.75 2 Competition-light 0 0.75 3 Edaphic specificity 0 0.75 2 Environmental habitat specificity 0 0.75 3 Dispersal 1 0.50 3 Seedling establishment 1 0.75 4 Vegetative reproduction 1 0.75 2 Fire regeneration 0 0.75 2

Online Resource 4: Combining species into forest types

Forest-type maps were developed for current climate and each future scenario based on rules that sum the importance values of most of the key species defining the forest type. The US Forest Service and the Society of American Foresters have defined the types (e.g., Eyre, 1980; Miles et al., 2001), and individual species were assigned to a particular forest type (or types) based on associations according to Miles et al. (2001). Then, each 20x20 km cell was given an IV summation score for each of 10 possible forest types, with the final forest type class given to the class with the highest IV sum for the cell (Iverson and Prasad, 2001, Iverson et al. 2011). This same classification procedure was conducted with the future scenarios of climate to produce potential suitable habitat maps for particular forest types through this century.

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