1. CH11: Modeling Species and Ecosystem
Response

2. FIGURE 11.1 Schematic of an SDM.
Species distribution modeling begins with the selection of a study area (left). The study area is usually selected to be large enough to include the complete ranges of the species of interest to ensure that data sampling the entire climate space the species can tolerate are included. Climate variables and other factors constraining species distribution (shaded layers on right) are then correlated with known occurrences of the species of interest (layer with points). This statistical relationship can be projected geographically to simulate the species’ range (bottom shaded area). Repeating this process using GCM-generated future climate variables allows simulation of range shifts in response to climate change. Source: Lishke et al. (1998). Copyright Massachusetts Institute of Technology, by permission of MIT Press.

3. FIGURE 11.2 Global and regional vegetation simulation of a DGVM.
The global distribution of PFTs (top) can be simulated in a coarse-scale DGVM. The same DGVM run at finer resolution can simulate PFT distribution with many local features resolved (bottom left). Driving the DGVM with projected future climates from a GCM provides a simulation of the change in PFT distribution due to climate change at either global or regional (bottom right) scale. Source: Neilson et al. (2005). Courtesy of USDA Forest Service.

4. FIGURE 11.3 Gap model.
Styized simulation of trees in a 30m x 30m forest plot, similar to a gap model, is illustrated in this computer-generated schematic. Source: Figure courtesy of H Sato, Japan Ministry of Marine-Earth Science and Technology. Reproduced with permission JAMSTEC.

5. FIGURE 11.4 DGVM intercomparison.
Outputs of six different first-generation DGVMs (top six panels) compared to the composite of all six (bottom left) and the PFT distribution classified from satellite imagery (bottom right). Source: Cramer et al. (2001).

6. FIGURE 11.5 Climate and CO2 DGVM simulations.
DGVM global change simulations for high emissions (A1 scenario) and high climate sensitivity GCM (Hadley) (a) and low emissions (B2 scenario) and low climate sensitivity GCM (ECHAM) (b). Source: IPCC.

7. FIGURE 11.6 Generation of a future climatology for species distribution modeling.
Because SDMs often require climatologies with horizontal resolutions much finer than those offered by GCMs, techniques are needed for generating downscaled future climatologies from GCM outputs. One approach commonly used applies the difference between GCM simulations of the present and the future to a current fine-scale climatology. This is done because GCM fidelity to current climate may be imperfect. The use of a historical fine-scale climatology ensures reasonable reproduction of major climatic features. The GCM difference (future–present) simulates future warming. Courtesy of Karoleen Decatro, Ocean o’Graphics.

8. FIGURE 11.7 SDM result correlation to climate variables.
The scattergrams indicate relationships between species loss (%) and anomalies of moisture availability and growing-degree days in Europe. The colors correspond to the climate change scenarios indicated in the key. Source: Thuiller et al. (2005). Copyright National Academy of Sciences.

9. FIGURE 11.8 Example of SDM output.
SDM output for a protea (pictured) from the Cape Floristic region of South Africa. Current modeled range is shown in red, and future modeled range is shown in blue. Known occurrence points for the species are indicated by black circles. Source: Figure courtesy Guy Midgley, Stellenbosch University.

10. FIGURE 11.9 Backward and forward modeling of the eastern mole (Scalopus aquaticus).
(a) SDM created from known Pleistocene occurrences predicts present distribution. (b) SDM created from known current distribution predicts known fossil occurrences. Source: Martinez-Meyer et al. (2004).

11. FIGURE 11.10 Model of suitable climate for glassy-winged sharpshooter in the United States.
The potential spread of insect pests such as the glassy-winged sharpshooter can be predicted using SDMs. Source: Venette and Cohen (2006).

12. FIGURE 11.11 Habitat suitability for Australian rain forests.
Suitability for Australian rain forests is shown for three past climates and the present. The modeling tool used was Bioclim, an early SDM software. Like other SDMs, Bioclim can be used to simulate suitability for biomes or vegetation types as well as species. End panels indicate genetic similarity of forest fragments (right) and areas that are stable in all modeled time slices (left). Source: Hugall et al. (2002).

13. FIGURE 11.12 SDM time-slice analysis.
SDM simulation of present and future range for the pygmy spotted skunk of western Mexico. This model was constructed in 10-year time slices to allow pathways of contiguous habitat (present to future) to be identified. Source: Hannah et al. (2007). Reproduced with permission from the Ecological Society of America.

14. FIGURE 11.13 Gap model output.
This gap model of forest composition in Switzerland under climate change shows an early peak in oak abundance, giving way to a mixed fir–beech forest with little oak. Source: Redrawn from Bugmann (2001).

15. FIGURE 11.14 Lake habitat suitability profiles under double-CO2 scenarios.
With warming, lake habitat suitability is very close with depth and over time. Simulated habitat suitability is shown for double-CO2 scenarios for lakes in Duluth, Minnesota, and Austin, Texas. In Duluth, uninhabitable surface water extends deeper and lasts longer in the climate change scenario. In Austin, a summer window of habitability in the middepths closes, making the entire lake uninhabitable by late summer. Source: Stefan et al. (2001).

16. FIGURE 11.15 Maximum monthly sea surface temperatures.
(a) Observed, (b) 2000–2009 projected, (c) 2020–2029 projected, (d) 2040–2049 projected, and (e) 2060–2069 projected. Warmer temperatures cause bleaching that threatens persistence of coral reefs. Source: Guinotte et al. (2003). With kind permission from Springer 1 Business Media.

17. FIGURE 11.16 Aragonite saturation state of seawater.
Red/yellow, low-marginal; green, adequate/optimal. (a) Preindustrial (1870), (b) 2000–2009 projected, (c) 2020–2029 projected, (d) 2040–2049 projected, and (e) 2060–2069 projected. Low saturation states unsuitable for coral reefs collapse in toward the equator as the century progresses. Source: Guinotte et al. (2003). With kind permission from Springer 1 Business Media.

18. FIGURE 11.17 Areas in which temperature and aragonite saturation state combine to stress corals.
(a) Observed, (b) 2000–2009 projected, (c) 2020–2029 projected, (d) 2040–2049 projected, and (e) 2060–2069 projected (see legend in panel e). Source: Guinotte et al. (2003). With kind permission from Springer 1 Business Media.

19. FIGURE 11.18 Output of a marine ecosystem model.
Simulated relative percentage change in phytoplankton production is shown for the region surrounding Australia, based on a nutrient–phytoplankton–zooplankton model. Production generally increases with warmer water temperatures, although note the exception in a region off southeastern Australia. The change is calculated as the percentage difference between the 2000–2004 mean and the 2050 mean. Source: Brown et al. (2010).

20. FIGURE 11.19 EcoSim food web model results.
Based on the production changes shown in Figure 11.18, the EcoSim food web model simulates change in abundance of species of conservation interest. Following increases in production, biomass abundance increases for most species. However, abundance of some species may decline owing to trophic effects (changed food web relationships). Source: Brown et al. (2010).