Lecture 14 Models II Principles of Landscape Ecology March 31, 2005 Mechanistic detail Spatially dynamic Raster models Patch models Individual tree models Gap models X LANDIS-II A system of cell networks or grids Cells interact with neighborhood Each cell adopts one of m (m may be infinite) possible states Transition rules for each state can be simple, deterministic, or stochastic. Transition rules ~ f(abiotic constraints, biotic interactions, disturbances) Cellular Models What is a cellular landscape model? Run iteratively over time The landscape has a memory of previous events Equilibrium doesnt apply in the sense of analytical models Cellular landscape models simulate change through time in response to endogenous processes (growth, competition) and exogenous forcing (disturbance, climate change, etc). Spatially explicit and spatially interactive GIS used to store/display data. Entities have map coordinates Include spatial processes: seed dispersal, disturbance Model Example: LANDIS-II Spatially interactive cellular model Each site exchanges information (seeds) and energy (disturbances: chemical and mechanical) with neighboring sites. Species have unique life history attributes -shade tolerance, fire tolerance -longevity, maturity age -seed dispersal capabilities Species presence in age cohorts. Example: sugar maple 11-20, basswood 61-70, m FORESTED LANDSCAPE WINDTHROW FIRE HARVESTING INSECTS / DISEASE DISPERSAL SPECIES ESTABLISHMENT LIVING BIOMASS DEAD BIOMASS CLIMATE Multiple disturbances and interactions Scales tree growth up to the landscape scale Model Example: LANDIS-II Model Application Example: Climate change effects in northern Wisconsin Goal: Estimate the effects of climate change and disturbance on forest composition and biomass. Potential Great Lakes Forests Spp composition Aboveground - Biomass CLIMATE CHANGE Current Great Lakes Forests Why Species Composition and Biomass? Of concern to management Integrating variables Forest tree communities Disturbance Dispersal Ecosystem Processes Scenarios Simulation of climate change in northern Wisconsin Climate change effects modeled: tree species germination and establishment tree spp growth rates and competitive ability Climate change effects not modeled: changes in disturbance regimes potential CO 2 fertilization changes in soil properties Other processes not modeled: herbivory exotic species Simulation of climate change in northern Wisconsin Climate Scenarios 3 climate scenarios: Current Climate Hadley Centre for Climate Prediction +3.8C and +38cm ppt Canadian Centre for Climate Modelling +5.8C and +20cm ppt Simulate forest change over 200 years Years includes climate change Constant climate from year climate averages Disturbance Scenarios Two Disturbance Scenarios: No Disturbance Wind + Harvesting Wind equal to historic frequency Clearcutting Selective cutting Heavy thinning LANDIS-II Input Data 23 tree species with life history attributes Probability of establishment calculated from a forest gap model (0.1 ha) Growth and decomposition rates Establishment, growth, and decomposition varied among ecoregions due to climate and soils Wind and HarvestingNo Disturbance Total Aboveground Live Biomass Canadian GCM ANIMATION > 325 Mg/ha Aboveground Live Biomass Wind and HarvestingNo Disturbance Total Aboveground Live Biomass Canadian GCM > 325 Mg/ha Aboveground Live Biomass Results: Biomass change Hadley Climate Current Climate Canadian Climate Without Disturbance With Wind & Harvesting constant climate begins Results: Change in community composition Without Disturbance: The landscape becomes dominated by sugar maple Few opportunities for southern species to migrate north With Wind and Harvesting: Shift toward southern oak and hickory if climate changes, although the shift is small Why isnt there a larger shift toward southern species? Interactions between climate change and fragmentation Wisconsin As climate changes, we expect northward migration of some tree species. Illinois At the same time, many species will be displaced. Interactions between climate change and fragmentation However, species migration limited by: distance-limited seed dispersal the priority effect - occupancy by current species Other limits not considered: generational lags herbivory Wisconsin Illinois Interactions between climate change and fragmentation Wisconsin Illinois Fragmentation also reduces migration: fewer available colonization sites fewer seed sources Interactions between climate change and fragmentation Consequences: Spp richness reduced. aboveground live biomass Decline in productivity and aboveground live biomass. Why? Realized niche fundamental niche. The species best adapted to new climate are not widely dispersed. Wisconsin Illinois Interactions between climate change and fragmentation Our Questions: How will seed dispersal limitations affect aboveground live biomass? Is seed dispersal limited by fragmentation? Is seed dispersal limited by existing species? Wisconsin Illinois Estimation of the effects of seed dispersal aboveground live biomass How do we measure the effect that seed dispersal has on aboveground live biomass (B)? Estimate B from identical scenarios with distance limited seed dispersal and without seed dispersal distance limitations. Calculate Difference Total B no distance limit - Total B distance limited = Total B seed dispersal effect Estimation of the effects of seed dispersal Hadley Climate Canadian Climate Disturbance is a strong determinant of future community composition under climate change. But, five important tree species will be extirpated and landscape diversity will be reduced if the climate warms. Climate Change Conclusions The northward migration of many species is limited by seed dispersal. Aboveground live biomass is limited by seed dispersal. Management will need to balance carbon storage, maintenance of diversity, and the reality of species loss. Climate Change Conclusions Only two possible climate change scenarios out of dozens. We did not include many critical processes, e.g. herbivores. Our results are unvalidated. Question: What, if any, value does this research have for management? Caveats: Modeling Conclusions Preparing input data is the most arduous task. Garbage in, garbage out (?). Replication is not always helpful - depends on the size of stochastic events. You can never include everything. Always focus on the questions first, tools last. Modeling Conclusions Technical limitations remain Increase in computer capability in past decade is not a panacea. Challenge of appropriate complexity in spatial models remains Spatial data availability Spatial and temporal scale limitations ResolutionExtent tradeoff Model caveats Building model confidence: data validation Traditional validation: compare model data with empirical data. However, there is rarely independent landscape data collected at same scales. Data solutions include: Fine-scale data Problem: wrong scale Space-for-time e.g. southern forests ~ climate change Problem: different initial conditions, multiple changes Reconstruct past responses Problem: unknown starting conditions lack of human behavior model lack of climate data Compare to other models e.g. GCMs Problem: few other FLSMs applied at regional scale Both models wrong or right? Model autocorrelation. Building model confidence: alternatives to validation Landscape validation is not always possible - need to judge by different standards. Process validation Independent application, assessment, and review Indirect Corroborating evidence Development over time Model transparency: open code generous comments Building model confidence: Summary Model acceptance - + Model development time Confidence from: application review development mistakes! Doubts from increasing complexity