Multi-scale Integrated Models of Ecosystem Services

On October 30-31,2007 members of the MIMES team met up with members of the EBM team at the University of New Hampshire in Durham, NH to define ways MIMES can be used in the EBM Pilot Project.

Co-hosted by: COMPASS, Census of Marine Life, Stellwagen Bank National Marine Sanctuary, MA Ocean Partnership Fund, Department of Fisheries & Oceans Canada, MIMES – University of Vermont, Boston University

Location: University of New Hampshire

Background:

The Gulf of Maine is central to one of the world’s most productive coastal and marine regions. The area faces increasing pressure from a growing coastal population, coastal development, global climate change, shifting demand and markets for seafood, new ocean and coastal technologies, and an influx of invasive species. The future and sustainability of coastal communities and the region’s natural heritage will depend on bold initiatives today. Stakeholders could benefit by addressing management and science issues through a comprehensive strategic vision that encouraged and supported coordinated planning. Policy-relevant science can play an integral part in shaping a more ecosystem-based approach to management.

Ecosystem-based-management approaches can be used to document our scientific understanding about the interaction between ecology and human activities in several spatially explicit places.

We seek to do this by supporting pilot projects in both Canada and the United States. This effort will likely require strong and effective partnerships with existing regional entities working in these areas as well as among them to facilitate learning and accelerate progress.

Given the large geography, we will need to create some site selection criteria to ensure we make progress in a reasonable period of time. Possible criteria include: the area has enough organized and pertinent information/data and the existing capacity to be a pilot. The area is representative of different sectoral activities, human uses and impacts and there are resources that can be leveraged to support any work there.

Goal
To learn about the implementation of EBM by understanding how best to manage the interactions between ecology and human activities in the pilot project areas

Objectives
1. Create and apply techniques/approaches that enhance our scientific understanding about these interactions;
2. Develop, apply and evaluate best practices that coordinate the development and dissemination of science for EBM when working under different governance structures in the U.S. and Canada;
3. Assemble natural and socio-economic baselines;
4. Develop and apply tools to visualize the interactions between human activities and ecosystem services and evaluate tradeoffs between human uses of the ecosystem

Examples of Possible Products:
• Natural and socio-economic baselines;
• Synthetic maps and models of ecosystem properties and species distributions as well as human uses and ecosystem impacts. The products might include a human use atlas, an ecosystem properties atlas, and a threats analysis.

Potential Pilot Sites
1. Biodiversity Discovery Corridor (Bay of Fundy to the seamounts)
2. Stellwagen Bank (could include region around this area as well)
3. Massachusetts Bay
4. Coastal Maine (e.g., Muscongus Bay, Taunton Bay, etc.)
5. Great Bay
6. Southwest New Brunswick Marine Initiative
7. Bay of Fundy
8. Scotian Shelf

From L. Shawn Matott, Ph.D.
U.S. Environmental Protection Agency
Ecosystems Research Division

Modeling frameworks such as MIMES and FRAMES can be leveraged to conduct integrated environmental modeling in support of the ERP core focus on ecosystems services. For such tools to be useful in a policy-relevant context, elucidating model uncertainty is a challenging but vital task.

To start off the discussion, let’s consider a few definitions and related concepts:

1. Uncertainty may be classified into two ‘types’: aleatory, i.e. irreducible and stemming from natural variability, and epistemic, i.e. reducible and stemming from lack of knowledge. Aleatory and epistemic uncertainties have different ramifications for policy decisions.

2. Uncertainty may also be classified in terms of its ’sources’, which include uncertain model input (e.g. amount of rainfall), uncertain model parameters (e.g. various rate constants), and uncertain model structure (e.g. the degree to which the model equations represent the real world).

3. Formal uncertainty analysis (UA) methods propagate sources of uncertainty through the model to generate statistical moments or complete probability distributions for various model outputs. Traditional UA schemes wrap a stochastic Monte Carlo shell around one or more linked (and possibly calibrated) models. More recently, there has been increased emphasis on Hierarchical Bayesian approaches, which utilize sampling procedures that explicitly condition model output on available data.

4. Less formal methods, such as scenario analysis and alternative futures, can also be important tools for uncertainty analysis; albeit in a more qualitative sense. Along these same lines, the NUSAP (Numeral, Unit, Spread, Assessment, Pedigree) system of Funtowicz and Ravetz can serve as an important tool for assessing and assuring quality throughout the modeling process.

5. Sensitivity analysis (SA) studies the degree to which model output is influenced by changes in model input. By measuring model input importance, SA methods enable identification of critical areas where knowledge or data are lacking. In this way, SA can assist decision-makers in prioritizing future research and data collection activities.

Given these definitions and concepts, we can discuss what (if any) infrastructure is available within the MIMES and FRAMES systems to support the UA/SA needs of the ERP.

I am speaking for myself here as a member of the Willamette project who has been following the DSS and valuation issues for the project. This past summer, at the encouragement of our division director, we ran a Stella exercise, in which about half a dozen of the project members were variously involved, and that resulted in an initial model combining hydrology, carbon, and nitrogen processing for the basin (attached below). It is only conceptual at this point because there are no data entered except for Corps dam operational curves. We were much aided in this effort by Nico Sayavedra, an undergrad at RPI, who did all the Stella programming, and who will be in Burlington. Part of the inspiration for this was GUMBO; some of us have read the Boumans, Costanza et al. paper. Nico got familiar with GUMBO, and he and another person gave a demo of GUMBO to some 8th graders in Corvallis as a world futures gaming experiment. The question is where to go with this kind of activity, whether to maybe use the Stella modeling as a planning tool for designing field experiments, or to actually attempt to build simulation capability.

On the other hand, an alternative approach of interest here is an integrated model that could be inspired by the Evoland modeling system (http://evoland.bioe.orst.edu/) of John Bolte and colleagues at Oregon State. Evoland is a stimulating example of a plug and play design into which valuation and policy modules as well as environmental process modules can be inserted.

So what I was hoping would happen at Burlington is a thorough examination of the philosophy, capabilities, development environment, and transferability of MIMES and its host language SIMILE, as either an alternative to the other directions that have been explored for the Willamette, or as another example of ideas that could be adapted to the needs here.

Denis White
US EPA
Corvallis, Oregon

From Daniel J. McGarvey, Ph.D.
U.S. Environmental Protection Agency
Ecosystems Research Division

It seems to me that one potential hurdle to a large-scale implementation of MIMES could be its spatially-explicit structure. As I understand it (and please clarify if I am wrong), MIMES is designed to calculate any number of endpoints for each cell within a gridded landscape (much like its ‘precursor’, the Spatial Modeling Environment (SME) of the Landscape Modeling Framework (LMF)). Since we’re looking to be able to make predictions at the national scale, we’ll obviously need to be able to adjust model parameters (e.g. species lists) within distinct cells, depending upon the locations of those cells. This is certainly do-able (though it will likely be a huge time investment).

I’m wondering if a more systemic problem might be the use of
fundamentally different models within cells. Imagine, for example, that fish assemblage structure in one region (i.e. cluster of cells) is
mediated by deterministic (or quasi-deterministic) mechanisms, such as critical habitat availability; BASS might be a good way to model and ultimately predict the structure and productivity of such assemblages.

Now, imagine that fish assemblage structure in another region has more to do with stochastic immigration-extinction dyamics (re-colonization following flood events, for example); in this case, we might want to implement a literal Island Biogeography model. Given the radically different structures of these two (hypothetical) models, I’m skeptical that we could get away with simply adjusting model parameters; I think we’d need a routine that is capable of calling independent sub-procedures/models on demand (regardless of whether the call is automated or manual). This might be a trival issue to the high-powered comp. sci. folks, but it seems like a potentially signficant challenge to a lowly ecologist, such as myself. . .

Also, I’m wondering if MIMES can synchronously run model procedures at two different scales? That is, can we use model endpoints calculated for one size of grid cell to feed or parameterize model procedures within smaller, nested cells? Here’s an example of what I mean: there’s a lot of good evidence to suggest that species-area relationships (or species-discharge relationships in aquatic systems), though entirely correlative and empirical, are a robust means of predicting total community-level richness. Noting this, I can imagine using a species-area relationship to predict total species richness on a per-basin or per-region basis, then using a process model (again, I use BASS as an example) to predict particular species occurances and densities within each of the region’s cells. This “top-down THEN bottom-up” approach might provide robust predictions - I’m just not sure how easy it would be to automate.

Hopefully, the GUND meeting will provide answers. . .