Landscape Analysis

Overview

For land management purposes, a landscape may be considered an area larger than a forest stand and smaller than a region. Climate changes will affect forest resources at a spatial scale of landscapes, crossing traditional organizational boundaries.

Landscape analysis involves the evaluation of patterns across the landscape, the linkage of these patterns to underlying environmental characteristics and processes, and the feedbacks and interactions between patterns and processes. For example, landscape analysis might describe how vegetation patterns are related to climate, fire and other disturbances, and landforms. These pages provide some examples of how climate-species interactions are being studied through landscape analysis, and how the results are addressing important forest management questions.

An archived version of this topic section is available

Climate Change and Species Distribution

Synthesis

Synthesis: 

Preparers

Louis Iverson, Northern Research Station; Don McKenzie, Pacific Northwest Research Station

Issues

Why are species located where they are? This is a primary question ecologists have long been asking, and the classic answer is that vegetation depends on climate, parent material, organisms, disturbance, topography, and time (1). Prehistoric records show that the climate has changed through time, as have the roles of organisms (including humans) and disturbances such as fire. In present times, however, the climate is changing rapidly. Human-caused greenhouse gas emissions have raised peak levels of atmospheric carbon dioxide above 400 ppm for the first time in at least 3 million years. The changing climate is affecting species distributions via changes in growth, reproduction, and mortality, with increasing likelihood of more marked changes in the coming decades. Climate changes can act to directly influence species distributions (e.g., drought, floods, wind) as well as indirectly (e.g., temperature and weather related changes in patterns of wildfire, insects, and disease outbreaks).

Likely Changes

Some species ranges have shifted in recent decades, very likely in response to climate changes. For example, a meta-analysis of 764 species (mostly arthropods) found an average rate of poleward migration of 16.9 km/decade (2). An earlier meta-analysis, using 99 species of birds, butterflies, and alpine herbs, reported an average poleward migration of 6.1 km/decade (3). For tree species, direct evidence of latitudinal shifts is more limited. Indirect evidence is apparent in some studies in the eastern US (4, 5, 6) and in the far north at the treeline ecotones of black spruce (7) and white spruce (8, 9) or Siberian pine (10). Even though suitable tree habitats appear to be changing, actual tree range shifts can take decades or more to detect. In the past however, we know that tree ranges shifted polewards in response to warming climate. The pollen-estimated rates for tree migration during the last late glacial period 10,000 -20,000 years ago show movement of about 1-10 km/decade (11, 12), rates much faster than experiments and mechanistic models have been able to account for (Reid’s paradox, 13). The explanations for rapid post-glacial colonization thus far include rare long-distance dispersal and recolonization from persistent isolated populations (14). Post-glacial migration occurred when species were not slowed by forest fragmentation, which can reduce expected migration rates by more than half (15, 16).

A major concern today is that tree migration may not be able to keep up with rates of climate change. The average velocity of changing global temperatures for 2050-2100, for an ensemble of middle range emissions (A1B) scenarios, is estimated to be 3.5 km/decade for the temperate broadleaf and mixed forests, 1.1 km/decade for temperate coniferous forests, and 4.3 km/decade for boreal forests and taiga (17). Terrain can have a significant effect on required migration rates. Forests in flat terrain must migrate ~145 km in latitude to reach similar zones with a 1oC temperature difference, whereas forests in mountainous terrain need only migrate ~167 m in altitude (18). These and many other indicators suggest that it is unlikely that trees and other organisms will be able to migrate fast enough to keep up with the rate of climate change, without human assistance.

Models provide useful methods to estimate potential changes in species distributions, as long as the caveats in interpreting the models are considered (“all models are wrong, some are useful”!). Predictive models of vegetation change are often divided into two categories: process-based models, which are usually simulations of vegetation dynamics at the taxonomic resolution of species or life forms, and empirical models (often called species distribution models, or SDMs) that establish statistical relationships between species or life forms and (often numerous) predictor variables. More and more there are hybrid models, extensions to SDMs that include elements of process models that provide additional scope and power to take advantage of the best of both worlds. However, there always will be trade-offs between using complex mechanistic models versus the simpler empirical models to associate changes in species habitats with forecasts of environmental change (19, 20). A further discussion on the merits of each category of model can be found in the next sections.

Management Options

Management under climate change increases complexity, but the basic toolbox is the same as for current management. As discussed elsewhere, forest management under climate change can be categorized as mitigation, adaptation, or both. Some specific actions when considering how species may shift in response to climate include 1. Encourage increased connectivity for species modeled to increase with climate change; 2. Evaluate potential for assisted migration; 3. Encourage retention of refugia which may allow persistence of species modeled to decline under climate change; 4. Prepare for additional costs likely required to maintain forest health due to increased stress and disturbances (e.g., insect pests, diseases, fire, ice, drought); and 5. Identify species likely to be especially vulnerable.

 

References: 
  1. Major, J. 1951. A functional, factorial approach to plant ecology. Ecology. 32:392-412.
  2. Chen, I.C.; Hill, J.K.; Ohlemuller, R.; Roy, D.B.; Thomas, C.D. 2011. Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science. 333:1024-1026.
  3. Parmesan C.; Yohe, G. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 421:37-42.
  4. Schuster, W.S.F.; Griffin, K.L.; Roth, H.; Turnbull, M.H.; Whitehead, D.; Tissue, D.T. 2008. Changes in composition, structure and aboveground biomass over seventy-six years (1930–2006) in the Black Rock Forest, Hudson Highlands, southeastern New York State. Tree Physiology. 28:537-549.
  5. Woodall, C.; Oswalt, C.M.; Westfall, J.A.; Perry, C.H.; Nelson, M.D.; Finley, A.O. 2009. An indicator of tree migration in forests of the eastern United States. Forest Ecology and Management. 257:1434-1444.
  6. Treyger, A.L.; Nowak, C.A. 2011. Changes in tree sapling composition within powerline corridors appear to be consistent with climatic changes in New York State. Global Change Biology. 17:3439-3452.
  7. Gamache, I.; Payette, S. 2005. Latitudinal response of subarctic tree lines to recent climate change in eastern Canada. Journal of Biogeography. 32:849-862.
  8. Caccianiga, M.; Payette, S. 2006. Recent advance of white spruce (Picea glauca) in the coastal tundra of the eastern shore of Hudson Bay (Québec, Canada). Journal of Biogeography. 33:2120-2135.
  9. 9. Lloyd, A.H.; Fastie,C.L. 2003. Recent changes in treeline forest distribution and structure in interior Alaska [pdf]. Ecoscience. 10:176-185.
  10. Kharuk, V.; Ranson, K.; Dvinskaya, M. 2007. Evidence of evergreen conifer invasion into larch dominated forests during recent decades in central Siberia. Eurasian Journal of Forest Research. 10-2:163-171.
  11. Pearson, R.G. 2006. Climate change and the migration capacity of species. Trends in Ecology & Evolution. 21:111-113.
  12. Davis, M.B. 1989. Lags in vegetation response to greenhouse warming. Climate Change. 15:75-82.
  13. Clark, J.S.; Fastie, C.; Hurtt, G.; Jackson, S.T.; Johnson, C.; King, G.A.; Lewis, M.; Lynch, J.; Pacala, S.; Prentice, C.;Schupp, E. W.; Webb, T. I.; Wychoff, P. 1998. Reid's paradox of rapid plant migration. Bioscience. 48:13-24.
  14. McLachlan, J.S.; Clark, J.S.; Manos, P.S. 2005. Molecular indicators of tree migration capacity under rapid climate change. Ecology. 86:2007-2017.
  15. Schwartz, M.W. 1993. Modelling effects of habitat fragmentation on the ability of trees to respond to climatic warming. Biodiversity and Conservation. 2:51-61.
  16. Iverson, L.R.; Schwartz, M.W.; Prasad, A. 2004. How fast and far might tree species migrate under climate change in the eastern United States? Global Ecology and Biogeography. 13:209-219.
  17. Loarie, S.R.; Duffy, P.B.; Hamilton, H.; Asner, G.P.; Field, C.B.; Ackerly, D.D. 2009. The velocity of climate change. Nature. 462:1052-1055.
  18. Jump, A.S.; Matyas, C.; Penuelas, J. 2009. The altitude-for-latitude disparity in the range retractions of woody species. Trends in Ecology & Evolution. 24:694-701.
  19. Thuiller, W.; Albert, C.; Araújo, M.B.; Berry, P.M.; Cabeza, M.; Guisan, A.; Hickler, T.; Midgley, G.F.; Paterson, J.; Schurr, F.M.; Sykes, M.T.; Zimmermann, N.E. 2008. Predicting global change impacts on plant species' distributions: Future challenges. Perspectives in Plant Ecology Evolution and Systematics. 9:137-152.
  20. Littell, J.S.; McKenzie, D.; Kerns, B.K.; Cushman, S.A.; Shaw, C.B. 2011. Managing uncertainty in climate-driven ecological models to inform adaptation to climate change. Ecosphere. 2(9):102.
How to cite: 

Iverson, L.; McKenzie, D. (February, 2014). Climate Change and Species Distribution. U.S. Department of Agriculture, Forest Service, Climate Change Resource Center. www.fs.usda.gov/ccrc/topics/species-distribution

Reading

Recommended Reading: 

Franklin, J. 2009. Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge, UK.

Iverson, L. and D. McKenzie. 2013. Tree-species range shifts in a changing climate - detecting, modeling, assisting. Landscape Ecology 28:879-889.

Research

Research: 

There are many Forest Service research projects related to species distribution and climate. Please visit the Research Roundup and search under Analysis and Assessments -> Vegetation distribution for a list of projects.

Using forest inventory analysis to detect tree migration in response to climate change

Synthesis

Synthesis: 

Preparers

Christopher Woodall, Northern Research Station; Gretchen Moisen, Rocky Mountain Research Station; Louis Iverson, Northern Research Station; Nicholas Crookston, Rocky Mountain Research Station

Issues

The spatial contraction, expansion, and persistence of tree ranges in response to climate change is species-specific and can be collectively referred to as tree migration. Past migration of tree species across long time scales is well-documented, especially since the last ice age (1). However, given recent changes in climate and projected future conditions, the ability of tree species to migrate at the pace of rapid climate change is unlikely (2). Since tree species will respond individually to climate change, changes in the spatial co-occurrence of some species are expected (3, 4, 5), potentially resulting in future North American biomes for which there is no contemporary analog (6). The rapid rate of climate change combined with the response of individual tree species could lead to extirpation, or regional loss of certain tree species. This in turn could lead to a loss in biodiversity (7), loss or gain of forest area, or all of these. For example, forests may be converted to a different ecosystem such as grassland, or alpine tundra could be converted to forest.

Scientists are actively engaged in efforts to both model potential future tree ranges (for example see 8) and monitor current tree species distributions. Monitoring of current shifts in tree ranges is often limited by the lack of consistent data over the 1900’s with the first national atlas of tree distributions not developed until the 1970’s (9). Due to this relatively short temporal scale, evaluating range boundaries across the multitude of tree species in US forests is problematic. Novel indicators of tree range dynamics have been developed to assist, for example comparing seedlings to adult tree locations (10).

Likely Changes

To date, contemporary tree migration has been documented in a portion of the eastern US along elevational gradients, (e.g., 11) and observed globally (12). Tree migration up elevation gradients usually affects relatively small geographic areas, and therefore may occur more readily than tree migration latitudinally, which concerns broad regions especially in flat terrain (13, 14). For example, a 1˚C increase in mean annual temperature may correspond to 100 to 200 m of elevation in contrast with 150 km of latitude (Fig. 1 in 13).

Tree range studies have been initiated across latitudinal scales for much of the eastern US forested area as more inventory data have become available. Current research suggests that tree ranges in the eastern US may be fairly static along their margins, but with tree seedlings for some species present at higher densities in the northern part of their range causing a slight northward shift in their mean latitudinal location compared to their adult counterparts (10). If the margins of tree ranges remain static while regeneration fails within the range such as along southern range boundaries, range contraction will occur (15). Emerging work has suggested such a dynamic may be currently occurring for certain species (2). The vast presence of invasive plant species across US forests (16) coupled with the advanced stage of stand development and stocking of forests (17) suggests there will be difficulty in recruiting tree regeneration in the future, especially for native tree species subjected to deer herbivory. Even over the past decade, the abundance of tree regeneration in eastern US forests has decreased, potentially as a result of these factors (18).

If climate change continues or accelerates, one would expect to see more rapid loss of tree species with narrow ecological niches along their southern range boundaries. One would also expect to see tree regeneration failure and the loss of small trees in these zones. Some tree species have genetically distinct subpopulations that have become adapted to local conditions over time, and climate change could lead to their maladaptation. Maladaptation and potential mortality will impact forest stand composition in species and tree size. Therefore the predicted sequestration of carbon, yields of wood volume, and provision of other ecosystem services from forests will differ greatly from those forest managers would expect without climate change (19). For example, in the western US, climate change is expected to cause locally adapted Douglas-fir to become maladapted (20, 21) leading to growth loss (22). On the other hand, some populations may benefit; certain subpopulations of western larch may be more genetically suited to future climates than others and could serve as seed sources for management interventions (23). Emerging research suggests that range contraction may be expected in the more montane areas of the west where regeneration may fail to match adult tree distributions (24).

Beyond climate change itself, regional land use legacies can compound the complexity of future tree range dynamics. For example, populations of piñons and junipers across the interior western US have been highly dynamic over the last two centuries, undergoing an overall expansion but punctuated with regional mortality. These species have areas of long-term persistence relative to their centurial (piñons) and millennial (junipers) life spans which in turn reflect differing land management legacies.

Management Options

It is important to recognize that under future climates there will be species-specific zones of contraction, expansion, and persistence across the landscape. Management practices that are developed for certain species under certain conditions cannot necessarily be extrapolated to different species under disparate conditions.

Expected shifts in tree ranges mean that the healthy forests of the future may not look exactly like the healthy forests of today. The following considerations may help managers retain forests and the benefits they provide even as the areas they manage become more or less suitable for specific species.

  • Consider rotation periods that match the time trees growing on a site will likely remain genetically attuned to the environment. This will often require the use of shorter rotations.
  • For currently growing trees of a given species, introduce trees of the same species that are more climatically-adapted to projected future climate (e.g., from a different subpopulation).
  • Establish species and seed imported from a wide array of locations to increase the genetic diversity at a location, with the expectation that some portion of the trees will be viable at the site as the climate changes.

Finally, it is important to remember that tree regeneration is a complex dynamic, mediated by numerous processes in addition to climate. Tree regeneration and mortality are affected by site factors (e.g., soils and elevation), climate (e.g., precipitation and temperature regimes), herbivory (i.e., deer browse), mast periodicity, management actions, stochastic disturbances, forest succession, and competing vegetation (e.g., invasives). This complicates both tree range monitoring and the development of adaptive management responses to climate. Managing tree ranges, whether through assisted migration or species selection during silvicultural operations, will need to take into account the fact that more than climate will be affecting tree ranges in any given region of the US.

References: 
  1. Clark, J.S.; Fastie, C.; Hurtt, G.; Jackson, S.T.; Johnson, C.; King, G.A.; Lewis, M.; Lynch, J.; Pacala, S.; Prentice, C.; Schupp, E.W.; Webb, T.; Wyckoff, P. 1998. Reid’s paradox of rapid plant migration: Dispersal theory and interpretation of paleoecological records. Bioscience. 48: 13-24.
  2. Zhu, K.; Woodall, C.W.; Clark, J.S. 2012. Failure to migrate: lack of tree range expansion in response to climate change. Global Change Biology. 18:1042-1052.
  3. Gibson, J. 2011. Individualistic responses of piñon and juniper distributions to projected climate change. Unpublished M.S. Thesis, Utah State University, Logan, Utah, USA.
  4. Gibson, J.; Moisen, G.G.; Frescino, T.S.; Edwards, T.C. Jr. 2012. Expansion and contraction tension zones in western pinyon-juniper woodlands under projected climate change. Proceedings of the 2012 FIA Symposium. In: Morin, R.S.; Liknes, G.C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station.[CD-ROM]: 115-118.
  5. Gibson, J., G.G. Moisen, T.S. Frescino, T.C. Edwards, Jr. [In press.] Public vs. true FIA plot coordinates as initial conditions in current and forecast climate-driven models of species distribution. Ecosystems.
  6. Rehfeldt, G.E.; Crookston, N.L.; Saenz-Romero, C.; Campbell, E.M. 2012. North American vegetation model for land-use planning in a changing climate: a solution to large classification problems. Ecological Applications. 22(1): 119-141.
  7. Betancourt 1990
  8. Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, M. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management. 254:390-406.
  9. Little, E.L. 1971. Atlas of United States trees. Volume I. Conifers and important hardwoods. U.S. Department of Agriculture Forest Service. Miscellaneous Publication 1146.
  10. Woodall, C.W.; Oswalt, C.M.; Westfall, J.A.; Perry, C.H.; Nelson, M.D.; Finley, A.O. 2009. An indicator of tree migration in forests of the eastern United States. Forest Ecology and Management. 257: 1434-1444.
  11. Beckage, B.; Osborne, B.; Gavin, D.G.; Pucko, C.; Siccama, T.; Perkins, T. 2008. A rapid upward shift of a forest ecotone during 40 years of warming in the Green Mountains of Vermont. Proceedings of the National Academy of Sciences of the United States of America. 105:4197-4202.
  12. Harsch, M.A.; Hulme, P.E.; McGlone, M.S.; Duncan, R.P. 2009. Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecology Letters. 12, 1040-1049.
  13. Jump, A.S.; Matyas, C.; Penuelas, J. 2009. The altitude-for-latitude disparity in the range retractions of woody species. Trends in Ecology & Evolution. 24:694-701
  14. Loarie, S.R.; Duffy, P.B.; Hamilton, H.; Asner, G.P.; Field, C.B.; Ackerly, D.D. 2009. The velocity of climate change. Nature. 462:1052-1055.
  15. Woodall, C.W.; Zhu, K.; Westfall, J.A.; Oswalt, C.M.; D’Amato, A.W.; Walters, B.F.; Lintz, H.E. 2013a. Assessing the stability of tree ranges and influence of disturbance in eastern US forests. Forest Ecology and Management. 291: 172-180.
  16. Schulz, B.K.; Gray, A.N. 2013. The new flora of northeastern USA: quantifying introduced plant species occupancy in forest ecosystems. Environmental Monitoring and Assessment. 2012; 185 (5): 3931.
  17. Woodall, C.W.; Perry, C.H.; Miles, P.D. 2006. Relative density of forests in the United States. Forest Ecology and Management. 226: 368-372.
  18. Woodall, C.W.; Westfall, J.A.; Zhu, K.; Johnson, D.J. 2013b. Assessing the effect of snow/water obstructions on the measurement of tree seedlings in a large-scale temperate forest inventory. Forestry. 86: 421-427.
  19. Crookston, N.L.; Rehfeldt, G.E.; Dixon, G.E.; Weiskittel, A.R. 2010. Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics. Forest Ecology and Management. 260: 1198-1211.
  20. St Clair, J.B.; Mandel, N.L.; Vance-Borland, K.W. 2005. Genecology of Douglas Fir in Western Oregon and Washington. Annals of Botany. 96: 1199-1214.
  21. St Clair, J.B.; Howe, G.T. 2007. Genetic maladaptation of coastal Douglas-fir seedlings to future climates. Global Change Biology. 13: 1441-1454.
  22. Leites, L.P.; Robinson, A.P.; Rehfeldt, G.E.; Marshall, J.D.; Crookston, N.L. 2012. Height-growth response to climatic changes differs among populations of Douglas-fir: a novel analysis of historic data. Ecological Applications. 22: 154-165.
  23. Rehfeldt, G.E.; Jaquish, B.C. 2010. Ecological impacts and management strategies for western larch in the face of climate change. Mitigation and Adaptation Strategies for Global Change. 15: 283-306.
  24. Bell, D.M.; Bradford, J.B.; Lauenroth, W.K. 2014. Early indicators of change: divergent climate envelopes between tree life stages imply range shifts in the western United States. Global Ecology and Biogeography. 23: 168-180.

     

How to cite: 

Woodall, C.; Moisen, G.; Iverson, L.; Crookston, N. (February, 2014). Using forest inventory analysis to detect tree migration in response to climate change. U.S. Department of Agriculture, Forest Service, Climate Change Resource Center. www.fs.usda.gov/ccrc/topics/inventory-analysis

Reading

Recommended Reading: 

Woodall, C.W.; Oswalt, C.M.; Westfall, J.A.; Perry, C.H.; Nelson, M.D.; Finley, A.O. 2009. An indicator of tree migration in forests of the eastern United States. Forest Ecology and Management. 257: 1434-1444.

Rehfeldt, G.E.; Ferguson, D.E.; Crookston, N.L. 2009. Aspen, climate, and sudden decline in western USA. Forest Ecology and Management. 258. 2353-2364.

An Overview of Vegetation Models for Climate Change Impacts

Data Basin

Synthesis

Synthesis: 

Preparers

Becky Kerns, Pacific Northwest Research Station and David W. Peterson, Pacific Northwest Research Station,

Issues

Climate change is expected to alter plant species distributions; plant community composition and diversity; vegetation structure (e.g., biomass, leaf area); and ecosystem processing of carbon, nutrients, and water. These vegetation responses to climate change will be the result of many lower-level vegetation responses, including changes in net plant carbon uptake, plant water use, plant growth and biomass allocation, competitive interactions, and responses to disturbances.

The complexity of climatic influences on vegetation and the time it takes for the responses to become clear makes it difficult to project potential vegetation responses to future climatic changes based solely on theory or on laboratory and field experiments. Computer simulation models are often used to integrate theory and experimental results to project vegetation responses to changing CO2 (carbon dioxide) and climate. The ideal vegetation model for use in developing climate change adaptation strategies would simulate the full range of climatic and other environmental conditions under which plant species could establish, grow, reproduce, and persist (1). It would also account for the effects of biotic interactions and disturbances on plant species distributions and plant performance. In projecting vegetation changes over time, an ideal model would not only describe the end result, but also indicate the rates of change, intermediate conditions, and the mechanisms producing change. Such ideal vegetation models do not currently exist, though some models are approaching this ideal (e.g. a coupled  BioGeochemical Cycles – species level model). Instead, most current vegetation models focus on one or more – but not all - aspects of the overall problem. They describe environmental niches, simulate competition, or examine spatial processes while greatly simplifying other aspects of the problem through assumptions, theory-based approximations, or simply purposefully ignoring them in order to focus on the necessary and sufficient parts of the problem in the model. Many types of vegetation models can and have been used to assess potential impacts of climate change on terrestrial vegetation. Many different types of models have been developed, and continue to be developed, to examine how vegetation may change with future climate change, but their utility is often not made clear to decision makers.

Types of Models

Vegetation models are generally either process based and simulate some or many of the underlying physiological processes, ecosystem processes, disturbance processes, and climatic and biotic interactions that drive changes in vegetation, or they are statistically based and quantify relationships between species occurrence data and corresponding environmental descriptors. Admittedly this dichotomy is a simplification, as the lines between models are often blurred, and some models do not fall easily into one category or the other (e.g. state and transition models). However, the dichotomy is useful for broadly understanding the major types of models that are used to examine climate impacts on vegetation.

Ecological process models focus on the “how” and “when” aspects of vegetation and ecosystem responses. Process models are of many different types, such as gap models, biogeochemical models, and dynamic global vegetation models. Gap models are used to examine species interactions and vegetation change at very fine spatial scales (plots the size of an individual canopy gap or small stand of trees) over daily to annual time steps (2). But simulated dynamics over many stands and cells are possible. Biogeochemical models are process-based models that simulate carbon, water, and mineral (nutrient) cycles in terrestrial ecosystems, including forests and rangelands. Biogeochemical models are commonly used to simulate ecosystem net primary productivity and carbon flux and storage, and this is their primary usage in climate change research (3). Dynamic global vegetation models project changes in vegetation properties (e.g., leaf area and phenology) at broad spatial scales (thousands of square kilometers, although more fine-scale projections are now available, e.g., 4) over annual to decadal time steps.

Statistical species distribution models (SDM), also known as niche models or bioclimatic envelope models, are used to describe the range of environmental conditions (niche) under which species occur by quantifying relationships between species occurrence data and corresponding environmental descriptors. The SDMs have arisen out of decades of ecological inquiry and analyses related to species-environment relationships (e.g., 5) and share a common philosophy and theoretical foundation with indirect gradient analysis, in that they attempt to relate observed spatial variations in species frequency to associated underlying environmental gradients. Although correlations between species occurrence and environmental conditions can suggest causal mechanisms, the models themselves are purely descriptive and do not directly include processes or mechanisms, although an approach to doing so has been proposed recently (6).

As noted above, not all models fall neatly into the blurry process vs. statistical dichotomy, and others share components of both. For example landscape models have a spatial component and incorporate aspects of either process model or statistical models or both (7). State-and-transition models are based on transition matrix methods that simulate vegetation dynamics using transition probabilities (deterministic and stochastic) between vegetation states. Yet another category of models are growth and yield models. Growth and yield models are used in forest management to estimate future growth and yield of forest stands based on site characteristics (e.g., site index) and mathematical or statistical representations of tree growth through time (8). Hybrid models that bridge categories are also increasingly being developed.

Options for Management

An often-repeated characterization of models is that they will always be wrong to some degree, but may be useful anyway. In applying vegetation models to inform management adaptations to climate change, it is important to match the spatial and temporal scale of the model with that of the question being asked. It is also important to note that models are developed based on assumptions that are usually reasonable for the original scale and purpose of the model, but may be completely unreasonable when models are applied to a different problem or at a different spatial scale.

There is really no “best” modeling approach – all approaches have different strengths and weaknesses. Therefore using an ensemble approaches is important. Ensemble approaches are commonly used when assessing climate projections with global climate models (GCMs). Such ensemble projections using multiple GCMs and multiple types of vegetation models can be used to find the projections common to multiple scenarios, which may be more likely to be closer to future outcomes (9, 10). In addition, the differences among vegetation model projections given their climatic drivers are themselves useful to frame discussions regarding uncertainty In general, agreement among models with different underlying principles and different conceptual approaches when climate inputs are similar (e.g., same scenarios), or across multiple climate models and multiple ecological models, suggests scientific agreement (if not consensus) on sensitivity of the vegetation response to climate. However, model development, lineages, and model input data are sometimes shared (e.g., sibling models), so it may be important to note that if there is agreement, the models need to be independent in their assumptions and parameterizations for potential reductions in uncertainty to hold (11). For example, process models from different broad categories (e.g. gap model vs. DGVM) tend to differ considerably in their conceptual approaches and development and be truly independent of each other. Therefore agreement among several different types of process models may be potentially more meaningful than agreement among several species distribution models. When models disagree and uncertainty is large, local knowledge, monitoring, observations of historical patterns, and knowledge from other sources may help decrease uncertainty. Even when models agree 1)there is a need for local knowledge, especially when translating the results into management actions, and 2) users must understand that many unknown variables and higher order interactions remain such that true outcomes might be radically different than even those derived from ensemble sets with great agreement.

Models may be best used to 1) alert us to the potential magnitude of the effects of climate change, and provide insight into mechanisms of those effects, 2) inform strategic decision making or land-use planning, rather than tactical stand management decisions, and 3) clarify policy concerns and begin to identify processes that are likely to be important (1). It is important to note that the role of models in this context is not to predict the future, but rather to help manage uncertainty by narrowing the possible range of futures to a subset of plausible futures that pertain directly to vulnerabilities for specific resources and management objectives (11). This is why the term “projection” is often used in reference to simulated model output about the future. A broad and comprehensive approach may be useful where multiple model output is first evaluated, other data are considered (e.g. paleoecological information, plant response studies, etc.), and then more specific relationships and potential outcomes based on these other data, local knowledge, finer-scale conditions, and expert opinion are used to evaluate potential outcomes.

 

Publication date: 
Thu, 05/15/2014
References: 
  1. Peterson, David W.; Kerns, Becky K.; Dodson, Erich K. 2013. Climate change effects on vegetation in the Pacific Northwest: a review and synthesis of the scientific literature and simulation model projections. Gen. Tech. Rep. PNW-GTR-XXX. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. XXX p.
  2. Busing, R.T.; Solomon, A.M.; McKane, R.B.; Burdick, C.A. 2007. Forest dynamics in Oregon landscapes: evaluation and application of an individual-based model. Ecological Applications. 17: 1967–1981.
  3. Waring R.H.; Running, S.W. 1998. Forest ecosystems: analysis at multiple scales. 2nd edition. San Diego, CA: Academic Press. 370 p.
  4. Rogers, B.M.; Neilson, R.P.; Drapek, R.; Lenihan, J.M.; Wells, J.R.; Bachelet, D.; Law, B.E. 2011. Impacts of climate change on fire regimes and carbon stocks of the U.S. Pacific Northwest. Journal of Geophysical Research. 116: G03037.
  5. MacArthur, R.H. 1972. Geographical ecology: patterns in the distribution of species. Princeton, NJ: Princeton University Press. 269 p.
  6. Kearney, M.; Porter, W. 2009. Mechanistic niche modelling: combining physiological and spatial data to predict species ranges. Ecology Letters. 12: 334–350.
  7. Keane, R.E.; Cary, G.J.; Davies, I.D.; Flannigan, M.D.; Gardner, R.H.; Lavorel, S.; Lenihan, J.M.; Li, C.; Rupp, T.S. 2004. A classification of landscape fire succession models: spatial simulations of fire and vegetation dynamics. Ecological Modelling. 179: 3–27.
  8. Crookston, N.L.; Rehfeldt, G.E.; Dixon, G.E.; Weiskittel, A.R. 2010. Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics. Forest Ecology and Management. 260: 1198–1211.
  9. Araújo, M.B.; New, M. 2007. Ensemble forecasting of species distributions. Trends in Ecology and Evolution. 22: 42–47.
  10. Bradley, B.A. 2010. Assessing ecosystem threats from global and regional change: hierarchical modeling of risk to sagebrush ecosystems from climate change, land use and invasive species in Nevada, USA. Ecography. 33: 198–208.
  11. Littell, J.S.; McKenzie, D.; Kerns, B.K.; Cushman, S.; Shaw, C.G. 2011. Managing uncertainty in climate-driven ecological models to inform adaptation to climate change. Ecosphere. 2(9): 102.
How to cite: 

Kerns, B.; Peterson, D.W. (May, 2014). An Overview of Vegetation Models for Climate Change Impacts. U.S. Department of Agriculture, Forest Service, Climate Change Resource Center. www.fs.usda.gov/ccrc/topics/overview-vegetation-models

Reading

Recommended Reading: 

Aber, J.D.; Ollinger, S.V.; Federer, C.A.; Reich, P.B.; Goulden, M.L.; Kicklighter, D.W.; Melillo, J.M.; Lathrop, R.G., Jr. 1995. Predicting the effects of climate change on water yield and forest production in the Northeastern U.S. Climate Research. 5: 207–222.

Bachelet, D.; Lenihan, J.M.; Daly, C.; Neilson, R.P.; Ojima, D.S.; Parton, W.J. 2001a. MC1: a dynamic vegetation model for estimating the distribution of vegetation and associated ecosystem fluxes of carbon, nutrients, and water—technical documentation. Version 1. Gen. Tech. Rep. PNW-GTR-508. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 95 p.

Beale, C.M.; Lennon, J.J. 2012. Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions of the Royal Society Biology. 367: 247–258.

Boisvenue, C.; Running, S.W. 2010. Simulations show decreasing carbon stocks and potential for carbon emissions in Rocky Mountain forests over the next century. Ecological Applications. 20: 1302–1319.

Bugmann, H. 2001. A review of forest gap models. Climatic Change. 51: 259–305.

Elith, J.; Leathwick, J.R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology Evolution and Systematics. 40: 677–697.

Guisan, A.; Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters. 8: 993–1009.

Halofsky, J.E., M.A. Hemstrom, D.R. Conklin, J.S. Halofsky, B.K. Kerns, and D. Bachelet. 2013. Assessing potential climate change effects on vegetation using a linked model approach. Ecological Modelling 266: 131-143.

Hickler, T.; Smith, B.; Sykes, M.T.; Davis, M.B.; Sugita, S.; Walker, K. 2004. Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA. Ecology. 85: 519–530.

Kerns, B. K., Hemstrom, M., Conklin, D., Yospin, G., Johnson, B., Brigham, S.  2012.  Approaches to incorporating climate change effects in vegetation state and transition simulation models.  In: The First State- and-Transition Landscape Simulation Modeling Conference Proceedings.  PNW-GTR-869. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, pp 161-171.

Links

Tree Habitat Shifts - Species Distribution Models

K. Marcinkowski

Synthesis

Synthesis: 

Preparers

Louis Iverson, Northern Research Station; William Hargrove, Southern Research Station

Issues

Species distribution models (SDMs) project future species habitats based on statistical associations between species occurrence or abundance and environmental (predictor) variables thought to influence habitat suitability (1). Such models often examine how species habitats are related to a combination of soil characteristics, elevation, climate, and land fragmentation. For example, environmental variables were statistically related to sugar maple abundance to map its abundance across the eastern United States (2). Using current or projected future climate information in these models allows the prediction and mapping of suitable habitat, though not whether or not a species will reach those habitats. SDMs thus predict potential ranges based on the realized niche rather than the fundamental niche; that is, the niche into which the species is “squeezed” by predators, parasitoids and competitors. Numerous statistical methods have been used to build SDMs. Some of the more recent methods, such as Random Forests (5), stochastic gradient boosting (6), or maximum entropy (7) have been shown to perform better in prediction than most other methods.

SDMs have some advantages compared with other modeling techniques; they are purely empirical, computationally less demanding, and allow occurrence patterns to emerge freely from the available survey or census data. SDMs can approximate potential species habitat for scores of species using multiple future scenarios of climate and human-based decisions, and thus can be useful for conservation and management, especially since ignoring the inevitability of future changes in a rapidly changing climate is not a realistic option (8).

SDMs have limitations, however, including assumptions that the selected variables reflect the niche requirements of a species, that species are in equilibrium with their suitable habitat, that predictions can be made into novel climates and land covers, that the effects of adaptation and evolution are minimal over the modeled timeframe, and that the effects of biotic interactions (including human interactions) are minimal (9, 10, 4, 11 ). It is also important to realize that not all SDMs are equal – within a series of species run with a particular methodology, some models will perform much better than others, as a function of the quantity and quality of input data. And SDM methods and subsequent outcomes can also vary widely with various algorithms and input variables so one must carefully scrutinize the methods and reliability of the models (12).

Likely Changes

Several groups have been using SDMs to project potential changes in habitat for the trees of North America. The “ForeCASTS” project of Hargrove and Potter (13) uses 17 climatic, soil-related (edaphic), and topographic variables to model environmental niches and geographic ranges for more than 200 tree species under current and future climates, nationally and globally. ForeCASTS uses the PCM and HadleyCM3 climate models and A1 and B1 emissions scenarios to project future climates and uses multivariate spatial clustering as the statistical prediction method. Each tree species range is predicted with and without using elevation as an environmental factor. For example, in the A1 emissions scenario, the prediction for red spruce (Picea rubens) including elevation describes habitat gains through 2050, but then losses from 2050-2100. ForeCASTS partitions climatic change risk into three separate metrics: percent change in suitable area under future climates, percent overlap of present and future suitable ranges, and average nonzero Minimum Required Migration (MRM) distance. Potter et al. (13) estimated the straight-line MRM distance from each 4 km2 grid cell in a species' current suitable habitat to the nearest favorable future habitat. The greater this distance, the less likely that the species will be able to reach the nearest refuge, and the more likely that the species will become locally extirpated without intervention. Less than a dozen of the ~225 tree species evaluated in ForeCASTS were predicted to have an increase in suitable habitat under the Hadley B1 combination by 2050. Eastern species were predicted to experience a greater decline in suitable area and had less range stability than western species, although predicted MRM distance did not differ between the regions. Eastern species were more likely, on average, to be habitat generalists. ForeCASTS lists all 17 environmental factors in order of their relative importance for each tree species.

McKenney and colleagues (14) (15) used heat and moisture climatic variables to model tree species, and projected an average northward movement of the climate habitat for 130 North American tree species of roughly 700 km, across three scenarios, by end of century.

Crookston and colleagues (16) modified the Forest Vegetation Simulator to assess presence/absence for 74 tree species of the western United States and to modify site index to account for expected climate effects under various GCM/emission scenarios. This was done by modifying Forest Inventory and Analysis plot data with outputs from the Random Forests classification tree (3).

The TreeAtlas uses 38 environmental variables including seven climate variables to assess potential future habitat changes in eastern U.S. trees, through Random Forest modeling (2). For 134 tree species, the ‘mean centers’ of habitat were predicted to move northeastward by 2100; distances ranged from up to 400 km for the less CO2-sensitive model (PCM) with high energy-resource efficiency (B1) to 800 km in the more sensitive (HadleyCM3) model with a ‘business as usual’ scenario (A1F1). Under HadleyCM3 A1F1, 66 species would gain and 54 species would lose at least 10% of their suitable habitat under climate change. A lower emission pathway would reduce both losers and gainers. The iconic sugar maple would lose most of its habitat across the east central U.S. under the most severe scenario (17, 2), but it would still maintain a reduced presence throughout. These species models have been enhanced by 21 species-specific Modification Factors, which include considerations such as species capacities to compete for light or endure fire, drought, flood, or browsing 18). A large vulnerability assessment for northern Wisconsin using these models 19) found that the most vulnerable species were those in the northern part of their range. However, though these species’ habitats decline drastically, refugia are expected to remain in places like coves or low-lying north-facing slopes.

Many studies have used SDMs (see for example, some additional descriptions of studies worldwide in (12). Although the above four studies differed in data and methods, there was generally a consistent pattern of projections of poleward movement of habitat throughout this century. For example, both Crookston et al. (16) and ForeCASTS predict areas in Colorado that are suitable for whitebark pine, Pinus albicaulis, even though these areas are not included in Little’s present range polygons. The outputs in the TreeAtlas are somewhat different than the rest in that they are based on abundance, not a binary presence/absence, and the Modification Factors add a level of interpretability by including information on disturbances and biological characteristics not readily included in SDMs (20). The combined TreeAtlas-Modification Factors have also been used to create risk matrices for various species for the National Climate Assessment (21, 22). In an evaluation of several SDMs, (23) asserted that for conservation purposes, there is, at least sometimes, an inherent bias for models to over-estimate climate-driven vulnerability to extirpation, and are therefore generally more useful in estimating future habitat (poleward or upslope) than in estimating where current habitat will no longer exist under future states.

Management Options

A key element for managers to remember is that outputs from SDMs are best used as guidance in a general way; they are not refined sufficiently for stand-level prescriptions. However, they do provide an idea on which species may have habitat gaining in the region, and which species may lose habitat in the future due to added pressures. If multiple models, especially models using different methodologies like SDMs and process models, have a tendency to agree on the general outcomes, one can have increased confidence in the guidance. Additionally, the fingerprint of potential climate change impacts for individual tree species may suggest potential adaptation strategies; a species having little overlap between present and future ranges, and with a large average minimum required migration distance may be a candidate for human-assisted migration (discussed elsewhere on this site), for example. This general information, along with local knowledge, should provide managers a base from which to start in developing strategies to respond to the impacts of climate change.

Some important examples can be found within the Climate Change Response Framework as developed by the Northern Institute of Applied Climate Science. The Framework is an integrated set of tools, partnerships, and actions to support climate smart conservation, and it provides a collaborative approach to incorporate climate change into forest management. The Framework covers three large regions across the eastern United States: the Northwoods, the Central Hardwoods, and the Central Appalachians. Each regional project interweaves four components - science and management partnerships, vulnerability assessments, adaptation resources, and demonstration projects. Example outputs from these projects include vulnerability assessments (e.g. 19, 24, 25, 26) and adaptation guidelines (27).

As mentioned above, it is important that managers interpret any outputs from SDMs as general guidelines and not specific instructions for the forest stand of interest. Fine-scale topography, soil, other threats, and management history play a huge role in final outcomes of most management actions. It is also important to remember that trees growing in the central parts of their ranges are very often also affected (positively or negatively) when the northern and southern extents of their ranges are shifting northward.

Publication date: 
Tue, 02/18/2014
References: 
  1. Franklin, J. 2009. Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge, UK.
  2. Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, M. 2008. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. Forest Ecology and Management. 254:390-406.
  3. Breiman, L. 2001. Random forests. Machine Learning. 45:5-32.
  4. Prasad, A.M.; Iverson, L.R.; Liaw, A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems. 9:181-199.
  5. Cutler, D.R.;  Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. 2007. Random forests for classification in ecology. Ecology. 88:2783-2792.
  6. Friedman, J. H. 2002. Stochastic gradient boosting. Computational Statistics and Data Analysis. 38:367-378.
  7. Elith, J.; Phillips, S.; Hastie, J.T.; Dudik, M.; Chee, Y.E.; Yates, C.J. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions. 17:43-57.
  8. Wiens, J.A.; Stralberg, D.; Jongsomjit, D.;Howell, C.A.; Snyder, M.A. 2009. Niches, models, and climate change: Assessing the assumptions and uncertainties. Proceedings of the National Academy of Sciences. 106:19729-19736.
  9. Ibanez, I.; Clark, J.S.; Dietze, M.C.; Felley, K.; Hersh, M.; LaDeau, S.; McBride, A.; Welch, N.E.; Wolosin, M.S. 2006. Predicting biodiversity change: outside the cliimate envelope, beyond the species-area curve. Ecology. 87:1896-1906.
  10. Pearson, R.G.; Thuiller, W.; Araújo, M.B.; Martinez-Meyer, E.; Brotons, L.; McClean, C.; Miles, L.; Segurado, P.; Dawson, T.P.; Lees, D.C. 2006. Model-based uncertainty in species range prediction. Journal of Biogeography. 33:1704-1711.
  11. Fitzpatrick, M.C.; Hargrove, W.W. 2009. The projection of species distribution models and the problem of non-analog climate. Biodiversity and Conservation. 18:2255-2261.
  12. Iverson, L.; McKenzie, D. 2013. Tree-species range shifts in a changing climate - detecting, modeling, assisting. Landscape Ecology. 28:879-889.
  13. Potter, K.M.; Hargrove, W.W.; Koch, F.H. 2010. Predicting climate change extirpation risk for central and southern Appalachian forest tree species.  In Rentch, J.S.; Schuler, T.M., eds. Proceedings from the conference on the ecology and management of high-elevation forests in the central and southern Appalachian Mountains. Gen. Tech. Rep. NRS-P-64. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 179-189. 
  14. McKenney, D.W.; Pedlar, J.H.; Hutchinson, M.F.; Lawrence, K.; Campbell, K. 2007. Potential impacts of climate change on the distribution of North American trees. Bioscience. 57:939-948.
  15. McKenney, D.W.; Pedlar, J.H.; Rood, R.B.; Price, D. 2011. Revisiting projected shifts in the climate envelopes of North American trees using updated general circulation models. Global Change Biology. 17:2720-2730.
  16. Crookston, N.L.; Rehfeldt, G.E.; Dixon, G.E.; Weiskittel, A.R. 2010. Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics. Forest Ecology and Management. 260:1198-1211.
  17. Lovett, G.M.; Mitchell, M.J. 2004. Sugar maple and nitrogen cycling in the forests of eastern North America. Frontiers in Ecology and the Environment. 2:81-88.
  18. Matthews, S.N.; Iverson, L.R.; Prasad, A.M.; Peters, M.P.; Rodewald, P.G. 2011. Modifying climate change habitat models using tree species-specific assessments of model uncertainty and life history factors. Forest Ecology and Management. 262:1460-1472.
  19. Swanston, C.; Janowiak, M.; Iverson, L.; Parker, L.; Mladenoff, D.; Brandt, L.; Butler, P.; St. Pierre, M.; Prasad, A.M.; Matthews, S.; Peters, M.; Higgins, D. 2011. Ecosystem vulnerability assessment and synthesis: a report from the Climate Change Response Framework Project in northern Wisconsin. Gen. Tech. Rep. NRS-82. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 142 p.
  20. Iverson, L.; Prasad, A.M.; Matthews, S.; Peters, M. 2011. Lessons learned while integrating habitat, dispersal, disturbance, and life-history traits into species habitat models under climate change. Ecosystems. 14:1005-1020.
  21. Iverson, L.; Matthews, S.; Prasad, A.; Peters, M.; Yohe, G. 2012. Development of risk matrices for evaluating climatic change responses of forested habitats. Climatic Change. 114:231-243.
  22. Vose, J.M.; Peterson, D.L.; Patel-Weynand, T. 2012. Effects of Climatic Variability and Change on Forest Ecosystems:A Comprehensive Science Synthesis for the U.S. Forest Sector. General Technical Report PNW-GTR-870. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 265 p.
  23. Schwartz, M.W. 2012. Using niche models with climate projections to inform conservation management decisions. Biological Conservation. 155:149-156.
  24. Brandt, L.; He, H.; Iverson, L.; Thompson, F.; Butler, P.; Handler, S.; Janowiak, M.; Swanston, C.; Albrecht, M.; Blume-Weaver, R.; Dijak, B.; Deizman, P.; DePuy, J.; Dinkel, G.; Fei, S.; Jones-Farrand, T.; Leahy, M.; Matthews, S.; Nelson, P.; Oberle, B.; Perez, J.;  Peters, M.; Prasad, A.; Schneiderman, J.E.; Shuey, J.;  Smith, A.B.; Studyvin, C.; Tirpak, J.; Walk, J.; Wang, W.; Watts, L.; Weigel, D.; Westin, S. 2014. Central Hardwoods Ecosystem Vulnerability Assessment and Synthesis: A report from the Central Hardwoods Climate Change Response Framework. Gen. Tech. Rep. NRS-124. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station.
  25. Handler, S.; Duveneck, M.; Iverson, L.; Peters, E.; Scheller, R.; Wythers, K.; Brandt, L.; Butler, P.; Janowiak, M.; Swanston, C.; Kolka, R.; McQuiston, C.; Palik, B.; Turner, C.; White, M.; Adams, C.; Barrett, K.; D'Amato, A.; Hagell, S.; Johnson, R.; Johnson, P.; Larson, M.; Matthews, S.; Montgomery, R.; Olsen, S.; Peters, M.; Prasad, A.; Rajala, J.; Reich, P.; Shannon, P.D.; Daley, J.; Davenport, M.; Emery, M.; Fehringer, D.; Hoving, C.; Johnson, G.; Johnson, L.; Neitzel, D.; Rissman, A.; Rittenhouse, C.; Ziel, R. 2014. Minnesota Forest Ecosystem Vulnerability Assessment and Synthesis: A report from the Northwoods Climate Change Response Framework. Gen. Tech. Rep. NRS-133. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station.
  26. Handler, S.; Duveneck, M.J.; Iverson, L.; Peters, E.; Scheller, R.; Wythers, K.; Brandt, L.; Butler, P.; Janowiak, M.; Swanston, C.; Clark-Eagle, A.; Cohen, J. G.; Corner, R.; Reich, P.B.; Baker, T.; Chhin, S.; Clark, E.; Fehringer, D.; Fosgitt, J.; Gries, J.; Hall, K.; Hall, C.; Heyd, R.; Hoving, C.L.; Ibanez, I.; Kuhr, D.; Matthews, S.; Muladore, J.; Nadelhoffer, K.; Neumann, D.; Peters, M.; Prasad, A.; Sands, M.; Swaty, R.; Wonch, L.; Daley, J.; Davenport, M.; Emery, M.R.; Johnson, G.; Johnson, L.; Neitzel, D.; Rissman, A.; Rittenhouse, C.; Ziel, R. 2014. Michigan Forest Ecosystem Vulnerability Assessment and Synthesis: A report from the Northwoods Climate Change Response Framework. Gen. Tech. Rep. NRS-129. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station.
  27. Swanston, C.W.; Janowiak, M.K. 2012. Forest Adaptation Resources: Climate change tools and approaches for land managers. Gen. Tech. Rep. NRS-87. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 121 p.
How to cite: 

Iverson, L.; Hargrove, W. (February, 2014). Tree Habitat Shifts - Species Distribution Models. U.S. Department of Agriculture, Forest Service, Climate Change Resource Center. www.fs.usda.gov/ccrc/topics/species-distribution-models

Reading

Recommended Reading: 

Franklin, J. 2013. Species distribution models in conservation biogeography: developments and challenges. Diversity and Distributions 19:1217-1223.

Franklin, J. 2012. Back of the envelope: climate change and species distribution modelling Bulletin of the British Ecological Society 43:28-30

Research

Research: 

Assessing forest tree risk of extinction and genetic degradation from climate change
Scientists are using spatial models of future environmental conditions to predict and map the location and quality of habitat for several hundred North American forest tree species. Known as the Forecasts of Climate-Associated Shifts in Tree Species (ForeCASTS) project, scientists are also determining where each species, within its current range, is most susceptible to extinction as a result of climate change. Learn more about the tool at http://www.fs.fed.us/ccrc/tools/forecasts.shtml.
Contact: Kevin Potter

North American Vegetation Model for Land-Use Planning in a Changing Climate
Researchers used modeling techniques to examine potential changes in the distribution of 46 North American biomes under future climates. The resulting maps can help land managers identify areas where projections are more or less certain, and where land management programs may be most successful.
Contact: Jerry Rehfeldt, Nicholas Crookston

 Modeling potential future habitats for trees and birds in the eastern U.S.
The Landscape Change Research Group, from the Delaware, OH lab of the Northern Research Station, has been modeling potential changes in suitable habitat for trees and birds of the eastern US. These maps are available online at www.nrs.fs.fed.us/atlas. We also look at dispersal potentials through another modeling toolset, and work with modification factors to understand more about the factors not readily modeled.
Contact: Louis Iverson

 

Links

Predicting Changes in Forest Composition and Dynamics – Landscape-scale Process Models

Synthesis

Synthesis: 

Preparers

Eric Gustafson, Institute for Applied Ecosystem Studies, Northern Research Station; Robert Keane, Missoula Fire Sciences Laboratory, Rocky Mountain Research Station

Issues

Natural resource managers need predictions of how climate change will affect landscape-level tree community composition and ecosystem function.  Ecologically based process models are critical tools for exploring vegetation dynamics under climate change. 

There are four general categories of process models distinguished primarily by the scale and detail at which they operate: forest gap models, ecosystem models, forest landscape models and dynamic global vegetation models (DGVMs). 

Forest gap models were developed to simulate the effects of ecophysiological drivers, such as water, nutrient, and light availability, on the rates of establishment, growth, and mortality among competing species of trees within a relatively homogenous forest stand (1, 2).  Early gap models were not spatially explicit, but some later gap models simulated spatial interactions among trees at fine scales (3: SORTIE, 4: FM) and the effects of specific disturbances (5: FORSKA; 6: FORCLIM).  Most gap models simulate process for a plot or stand so they cannot incorporate effects of exogenous disturbance regimes and spatial ecological processes such as dispersal, hydrology, and topography.

Ecosystem process models are similar to forest gap models in that they simulate the effects of biogeochemical process (e.g., fluxes of energy and mass) on ecological dynamics (e.g., forest growth rates, carbon accumulation, decomposition), but they emphasize biogeochemical and water dynamics for coarsely defined vegetation types or life forms, rather than individual trees or species (7).  Ecosystem process models have been applied at both fine and broad-scales, typically using land-cover datasets from remotely-sensed imagery, with each pixel representing a site.  However, similar to gap models spatially-explicit interactions among landscape-scale processes are rarely simulated in ecosystem models (8).

Forest Landscape Models (FLMs), the main subject of this page, are used to simulate forest generative processes (establishment, development, aging) and degenerative processes (disturbance, senescence) over appropriate spatial (landscape) and temporal (centuries) scales.  They explicitly model spatial processes such as seed dispersal and disturbance spread, and account for interactions in both time and space.  FLMs include stochastic or ‘random’ elements that cannot easily be predicted, such as sudden disturbances.  They are used to study how abiotic environmental factors (including changing climate), disturbances and management activities interact to affect forest dynamics such as tree species and age composition, spatial pattern and other ecosystem attributes.

FLMs are primarily intended to simulate forest disturbance and successional processes, as well as their interactions, across broad spatial and temporal scales.  FLMs also generally provide spatially continuous projections of disturbance and vegetation dynamics that are valuable for determining key drivers of landscape-level forest composition or structure or disturbance behavior.  Within this framework, the diverse FLM family of models can be further classified based on ability to simulate multiple processes or operate at fine temporal resolutions (9), or whether community change is static or dynamic, with the former determined by a priori successional stages and the latter by the life history attributes, behavior (e.g., seed dispersal), and physiological requirements of individual species (8).  Some FLMs directly or indirectly incorporate the influence of biogeochemical process on forest productivity and can be coupled with gap or ecosystem process models to derive inputs representing climate effects on species establishment probabilities or productivity (e.g., 10 using PnET-II).  Unlike DGVMs (discussed below), FLMs do not incorporate feedback loops with climate as simulated by General Circulation Models (GCMs), and applications are currently not feasible at continental to global scales. 

DGVMs are similar to terrestrial biogeochemical models, but simulate competition among vegetation types (e.g., biomes) and are directly coupled to GCMs, allowing feedbacks to climate at regional to global scales (11).  DGVMs have the potential to simulate climate change effects on tree establishment and mortality via mechanistic plant responses to biogeochemical and hydrological dynamics (e.g., 12: SEIB–DGVM), but, because DGVMs operate at very broad scales, many processes are highly simplified and some are omitted.  For example, DGVMs are unable to simulate important species- and plant-level ecological responses to disturbances.

Forest Landscape Models

FLMS operate at a scale that is highly relevant to strategic forest management planning and decision-making.  They are useful to describe the landscape and long-term context for more tactical planning tools such as FVS, by integrating global drivers (e.g. climate) and landscape disturbance processes with potential management actions.

FLMs are designed using either a mechanistic or a phenomenological approach, or more often, a combination.

Mechanistic models (sometimes called process-based) take a reductionist approach by explicitly representing the mechanisms that lead from cause to effect.  For example, tree growth, is simulated using the ecophysiological processes of photosynthesis and respiration.

Phenomenological models (sometimes called empirical or statistical) take a more holistic approach by using information about how a system has typically behaved in the past to predict how causes produce effects (phenomena).  That is, the outcome of the process is predicted using surrogates for the mechanism.  Tree growth in this case might be predicted as a change in diameter increment calculated from data about how trees have grown under various climate, shade, and nutrient conditions. The use of the term “empirical” for this type of model is somewhat confusing because mechanistic models often estimate mechanistic functions using empirical data.

Given a rapidly changing world, it may be increasingly difficult to rely on analysis of past conditions to predict the future because the relationships used by the models may no longer hold.  Key questions include:

  • Will successional pathways (sequence, timing and likelihood) remain the same as the climate changes?  Will the species that comprise successional communities change? 
  • How will rapidly changing factors such as CO2-fertilization, increased temperatures, reduced rainfall, physiological changes in water use efficiency of trees and changes in cloudiness and solar radiation interact to affect tree species growth rates and species competition? 
  • How will disturbance regimes (rotation intervals, event size, severity and frequency) change?  How will the complex interactions among disturbances (fire, pests, grazing, harvests) and with climate, vegetation, fuels, and topography dictate future landscape conditions?

New landscape behaviors and conditions may not be predictable if we only look at how systems behaved in the past.  For these reasons, forest landscape modelers are gradually moving toward a more mechanistic approach to simulate landscape dynamics under changing climate conditions.  Mechanistic modeling comes at a cost, though.  Mechanistic models are difficult to build and run because quantitative physical relationships may not be available or parameterized for many ecological processes; model uncertainty and instability usually increases with model complexity; and operation of the model usually requires more computing resources, higher levels of expertise, and greater amounts of input data.

Modeling Tree Community Changes

There are approximately 50-150 FLMs that have been published in the peer-reviewed literature and each one was designed for specific ecosystems or simulation objectives (for reviews see 13; 14; 15; 8; 9; 16).  We present two models that the authors have used in their studies.

LANDIS-II is a grid-cell FLM that simulates forest development processes of establishment, growth, competition and the forest degenerative processes of senescence and disturbances such as fire, wind, insect outbreaks and timber harvesting (17).  The model tracks living and dead biomass within cohorts of species on each cell.  LANDIS-II encapsulates distinct ecological or physical processes that act on the biomass of cohorts within cells on the landscape, interacting with one another to produce overarching forest dynamics across the landscape.

FireBGCv2 is a mechanistic, individual-tree succession model containing stochastic properties implemented on grid cells at landscape scales (18).  Tree growth, organic matter decomposition, litterfall, and many other ecological processes are simulated using detailed physical biogeochemical relationships.  Tree establishment and mortality are modeled probabilistically using empirically estimated probabilities.  FireBGCv2 also includes a spatial simulation of fire, where fire spreads across the landscape based on slope and wind information from a fire start ignited based on fuel moisture and loading.  Fire effects are simulated by computing fire intensity, then estimating fuel consumption, tree mortality, and soil heating using algorithms in the embedded FOFEM model (19).  Daily weather inputs drive primary canopy processes (e.g., transpiration, photosynthesis and respiration) and are also used to ignite and spread fire across the landscape.  Carbon allocation to the stem of a tree is used to calculate diameter and height growth.  Material from trees (fallen needles, leaves, and branches) are added to the fuelbed and eventually decompose based on available water, nitrogen, and light.  Cone crops are stochastically generated and subsequent seed dissemination is spatially modeled across the landscape using empirical dispersal relationships representing wind and bird dispersal.

The following examples offer a glimpse into the ability of landscape models to project how climate change can interact with many ecosystem processes and properties to profoundly impact forested ecosystems.

LANDIS-II: Gustafson et al. (20) simulated scenarios of climate change and harvest of virgin forests in Siberia (Russia).  They found that the direct effects of climate change on forest composition and landscape pattern was relatively modest, but that indirect effects, specifically the arrival of a cold-limited defoliating insect, dramatically changed composition and pattern.  This in turn affected the fire regime.  A strength of the modeling approach was that the interactions among the novel conditions (climate, insects, harvesting) and existing disturbances (fire, wind) emerged from the simulations because each process independently operated on the ecosystem vegetation in a mechanistic way.

FireBGCv2: Keane et al. (18) found that under warming climates, the pattern and composition of landscapes in the US northern Rockies will result from a complex net of interactions between disturbance, vegetation, fuel, genetics, and spatial scale.  The set of disturbances and ecological processes included in the model dictate subsequent landscape behavior.  Wildland fire, for example, will create significantly different landscapes under warming climates depending on whether they are suppressed or allowed to burn.  Mountain pine beetle and the exotic white pine blister rust will also influence future landscapes as they interact with fire, fuels, and vegetation (21)

Management Options

Landscape process models are complex, but managers can work with researchers to achieve robust and defensible results.  A collaborative, iterative approach involving researchers, management decision makers and local resource experts is a promising model for using landscape models to inform management decisions (see 22).

FLMs are simplifications of reality, so results may be somewhat inaccurate but highly precise.  Model results should not be interpreted as specific predictions, but as expected behavior.  Because of this it is most useful to compare scenarios or management alternatives because the magnitude of differences between scenarios is more reliable than the specific values of predictions.  FLMs are great tools to conduct “what-if” landscape experiments.  Like ecological systems, FLM’s have a random component.  Therefore replicated model runs give a sense of the range and variation that can occur for the modeled ecosystem dynamics.  The value of FLM results greatly depends on the quality of the input data; accurate model parameters and initial conditions will reduce the uncertainty in simulation results.

Although FLMs can be a valuable tool for guiding management decisions, certain precautions are helpful in interpreting and using model results.

  • FLMs are best viewed as general learning or insight tools rather than specific predictive tools.  Although they can make increasingly robust predictions, uncertainty is probably higher than managers would like.  Scenarios and “what-if” experiments should be designed to maximize insight and learning rather than to simply try to predict an uncertain future.
  • Extrapolating results simulated for one landscape to others is not recommended.  Simulation results may be landscape-specific because each landscape has a unique set of biophysical characteristics.  While general trends may be the same, the specifics of modeled outcomes may be significantly different.

Despite the limitations of FLMs, they are extremely powerful tools to integrate a great number of complex and interacting factors, account for spatial processes, make projections at landscape spatial and temporal scales and produce quantitative and comparable projections of many ecosystem characteristics of interest to managers.  They have tremendous potential to provide critical information that can improve strategic management decisions in the face of multiple global changes.

Publication date: 
Tue, 04/15/2014
References: 
  1. Botkin, D.B.; Janak, J.F.; Wallis, J.R. 1972. Ecological consequences of a computer model of forest growth. Journal of Ecology. 60:849-872.
  2. Shugart, H.H. 1984. A theory of forest dynamics: the ecological implications of forest succession models. Springer-Verlag, New York.
  3. Pacala, S.W.; Canham, C.D.; Silander, J.A.J. 1993. Forest models defined by field measurements: I. The design of a northeastern forest simulator. Canadian Journal of Forest Research. 23:1980-1988.
  4. Miller, C.; Urban, D.L. 2000. Modeling the effects of fire management alternatives on Sierra Nevada mixed-conifer forests. Ecological Applications. 10:85-94.
  5. 5. Prentice, C.I.; Sykes, M.T.; Cramer, W. 1993. A simulation model for the transient effects of climate change on forest landscapes. Ecolological Modeling. 65:51-70.
  6. Bugmann, H.; Cramer, W. 1998. Improving the behaviour of forest gap models along drought gradients. Forest Ecology and Management. 103:247-263.
  7. Cushman, S.A.; McKenzie, D.; Peterson, D.L.; Littell, J.; McKelvey, K.S. 2007. Research agenda for integrated landscape modeling. RMRS-GTR-194. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO.
  8. Scheller, R.M.; Mladenoff, D.J. 2007. An ecological classification of forest landscape simulation models: tools and strategies for understanding broad-scale forested ecosystems.  Landscape Ecology. 22:491-505.
  9. He, H.S.; Keane, R.E.; Iverson, L.R.  2008.  Forest landscape models, definition, characterization, and classification. [pdf]  Forest Ecology and Management. 254:484-498.
  10. Xu, C.; Gertner, G.Z.; Scheller, R.M. 2009. Uncertainty in the response of a forest landscape to global climatic change.  Global Change Biology. 15:116–13.
  11. Medlyn, B.E.; Duursma, R.A.; Zeppel, M.J.B. 2011. Forest productivity under climate change: a checklist for evaluating model studies. Wiley Interdisciplinary Reviews: Climate Change. 2:332-355.
  12. Sato, H.; Itoh, A.; Kohyama, T. 2007. SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach. Ecological Modelling. 200:279-307.
  13. Gardner, R.H.; Romme, W.H.; Turner, M.G. 1999.  Predicting forest fire effects at landscape scales. In: Mladenoff, D.J.; Baker, W.L. (eds.). Spatial modeling of forest landscape change: approaches and applications. Cambridge University Press, Cambridge, United Kingdom, pp 163-185.
  14. Keane, R.E.; Cary, G.; Davies, I.D.; Flannigan, M.D.; Gardner, R.H.; Ra, L.F.; Lenihan, J.M.; Chao, L.H.  2004.  A classification of landscape fire succession models: spatially explicit models of fire and vegetation dynamics. Ecological Modelling. 256:3-27
  15. Perry, G.L.W.; Enright, N.J. 2006. Spatial modelling of vegetation change in dynamic landscapes: a review of methods and applications. Progress in Physical Geography. 30:47-72.
  16. Baker, W.L. 1989. A review of models of landscape change. Landscape Ecology. 2:111-133
  17. Scheller, R.M.; Mladenoff, D.J. 2004.  A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application.  Ecological Modelling. 180:211-229
  18. Keane, R.E.; Loehman, R.A.; Holsinger, L.M. 2011. The FireBGCv2 landscape fire and succession model: a research simulation platform for exploring fire and vegetation dynamics.  USDA Forest Service Gen. Tech. Rep. RMRS-255.  Rocky Mountain Research Station, Fort Collins, Colorado, USA.
  19. Reinhardt, E.; Keane, R.E. 1998. FOFEM - a First Order Fire Effects Model. Fire Management Notes. 58(2), 25-28.
  20. Gustafson, E.J.; Shvidenko, A.Z.; Sturtevant, B.R.; Scheller, R.M. 2010. Predicting global change effects on forest biomass and composition in south-central Siberia. Ecological Applications. 20:700-715.
  21. Loehman, R.A.; Clark, J.A.; Keane, R.E. 2011. Modeling Effects of Climate Change and Fire Management on Western White Pine (Pinus monticola) in the Northern Rocky Mountains, USA. Forests. 2(4):832-860
  22. Gustafson, E.J.; Sturtevant, B.R.; Fall, A. 2006. A collaborative, iterative approach to transfer modeling technology to land managers.  In Perera, A.H.; Buse, L.; Crow, T.R. (eds.). Forest landscape ecology: Transferring knowledge to practice.  Cambridge Press, London, UK.  pp 43-64.
How to cite: 

Gustafson, E.; Keene, R. (April, 2014). Predicting Changes in Forest Composition and Dynamics – Landscape-scale Process Models. U.S. Department of Agriculture, Forest Service, Climate Change Resource Center. www.fs.usda.gov/ccrc/topics/process-models

Reading

Recommended Reading: 

Canham, C.D.; Cole, J.J.; Lauenroth, W.K. 2004. Models in Ecosystem Science. Princeton University Press, Princeton, New Jersey, USA.

Gustafson, E.J. 2013. When the past can’t be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world.  Landscape Ecology. 28(8):1429-1437.

Gustafson, E.J.; Sturtevant, B.R.; Fall, A.  2006.  A collaborative, iterative approach to transfer modeling technology to land managers.  In Perera, A.H.; Buse, L.; Crow, T.R. (eds).  Forest landscape ecology: Transferring knowledge to practice.  Cambridge Press, London, UK.  p. 43-64.

Green, D.G.; Sadedin, S. 2005.  Interactions matter—complexity in landscapes and ecosystems. Ecological Complexity 2:117-130

LeBauer, D.S.; Wang, D.; Richter, K.T.; Davidson, C.C.; Dietze, M.C. 2013. Facilitating feedbacks between field measurements and ecosystem models. Ecological Monographs. 83:133-154

Scheller, R.M.; Mladenoff, D.J. 2007. An ecological classification of forest landscape simulation models: tools and strategies for understanding broad-scale forested ecosystems.  Landscape Ecology. 22:491-505.

Seidl, R.; Fernandes, P.M.; Fonseca, T.F. et al. (2011)  Modelling natural disturbances in forest ecosystems: a review. Ecological Modelling 222:903-924

Shifley, S.R.; Thompson, F.R.; Dijak, W.D.; Fan, Z. (2008)  Forecasting landscape-scale, cumulative effects of forest management on vegetation and wildlife habitat: A case study of issues, limitations, and opportunities.  Forest Ecology and Management. 254:474-483

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