An Alternative Futures Approach to Understanding Landscape Dynamics and Services
Today I would like to take some time to describe for you a process – one that focuses on developing plausible representations of what future landscapes may look like, and how effective those future landscapes may be at providing the ecosystem services needed to sustain the system. I’ll begin with an overview of a particular approach to this problem, then describe a modeling framework that has shown significant utility in this regard. The talk will finish with a set of examples designed to outline the kinds of projections that can be developed to better understand the dynamics which define socio-ecological systems.
First, I wanted to take a little bit of time to describe how I became interested in the idea of socio-ecological modeling. I am trained as a hydrologist, with a particular expertise in rainfall-runoff modeling. Much of my work focuses on trying to understand the implications of a changing landscape, and a changing climate, on hydrologic processes – in particular, developing process-based simulations to quantify potential changes.As I began to consider this type of work, it quickly became apparent that while hydrology is a key component of natural systems, it is only a very small piece of a much larger puzzle. As soon as one begins to consider landscape change, it is very difficult to not consider anthropogenic drivers of that change, and to capture such changes goes well beyond the field of hydrology.As it became clearer how relevant the built environment, natural ecosystems, as well as agricultural and forestry dynamics were to hydrology…and even more importantly how each is dependant upon the other, I began to learn more and more about the dynamics of socio-ecological modeling, and how it can be incorporated into studies of change.
The term ‘‘biocomplexity’’ is used to describe the complex structures, interactions and dynamics of a diverse set of biological and ecological systems, often operating at multiple spatial and temporal scales. The study of biocomplexity reflects an intention to understand fundamental principles governing global behavior of these systems, expressed in terms of biological, physical, ecological and human dimensions, in terms of the interactions and resulting patterns and structures that collectively define system responsesThe scientific community is being asked to bring to bear these advances in our collective understanding of systems impacted by anthropogenic influences to improve management and planning of these systems, resulting in the need for new approaches to incorporating human behavior as an important component of ecological and environmental systemsbehaviors. The modeling community is developing new approaches to representation and analysis that are allowing exploration of complex systems in ways that are beginning to answer questions about how these systems interact, evolve, and transition to new, often unexpected, behaviors.And a significant challenge is how to make these ideas operational.
In parallel to the emergence of biocomplexity as an analysis paradigm, a number of studies have recently focused on alternative futures analyses. This has resulted largely from a need and desire to utilize analytical approaches, generally using process-level models synthesizing multiple landscape elements, to predict a particular set of responses of the target landscape to a particular set of perturbations reflecting alternative landscape management. These efforts generally incorporate stakeholder involvement in determining the nature, pattern and scale of the perturbation(s) considered, and resulting modeled landscapes or landscape trajectories are used to assess the outcome behaviors. While these efforts can be very effective for moving models into the policy and management arena and can provide insight into the implications of specific management strategies, they raise a number of issues related to our ability to effectively model the myriad of potential interactions and behaviors that may (or may not) lead to surprising and unforeseen results. While opening the door for modelers to interject current understanding of important processes and interactions into the management of coupled human/natural systems, alternative futures analyses can place additional burdens on the modeler, particularly related to identifying and incorporating interactions across multiple processes, possibly across multiple spatial and temporal scales.
There are a number of requirements of modeling tools that are designed to aid in the alternative futuring process. As computer software designed for use for non-programming scientists, the work must present a well-defined, stable user interface, and should be able to present results to users in a format that is useful. Additionally, given the complexities of socio-ecological systems, the modeling tools need to be spatially and temporally explicitAs decision support tools, the software must be flexible enough to recognize that science is continually developing, and that the system itself must be able to incorporate changing ideas and methods. In this sense, it must be growable through time and it should deal directly with currently accepted data and models
The system I use is called Envision, and in this figure the software is essentially defined by the gray horizontal bar in the center of the figure. This Analysis Framework is designed to facilitate the processes, all related to socio-ecological modeling and biocomplexity, that are listed at the top.Elements at the bottom of the figure represent key strategies and datasets that are needed to develop an alternative futuring project within Envision. Throughout the rest of the talk I will outline more clearly additional details of each of these elements, but briefly note that we need a set of spatial and temporal datasets, and that the framework uses Goals, Policies, and Stressors to develop a set of plausible landscape alternatives that can be evaluated for their ability to provide ecosystem services.
Landscape change modeling is at the core of most alternative futures analyses, and the last decade had seen considerable activity in this area (see Parker et al., 2003 for an excellent review). This activity is in part a result of the widespread availability of GIS-based platforms and datasets, complimented by a rapid increase in computing power and sophistication of representational tools for software development resulting from a convergence of approaches derived from individual-based modeling and complexity analysis. In particular, actor-based approaches have become a commonly- used tool for representing human interactions driving landscape change, as well as many other types of systems in which collective behavior arises from collective behavior. Actor- based models typical explicitly represent: 1) a landscape as a collection of decision units, defined by spatial properties and attributes relevant to the decision making criteria relevant to the task addressed by the modeler; and 2) entities that make decisions and/or take actions that result in landscape change. An appeal of an actor-based approach for landscape change modeling is that modeled actors can be based in large part on actual actors contributing to behaviors of the real system which the model is attempting to capture, increasing the realism of the model. Simulated actors may be based on individual decision makers, collections of individuals acting as a homogeneous entity (i.e. an institution), or as abstractions with no specific real-world counterpart (e.g. organizational structures reflecting collective actions that are not captured in specific real-world organizations). From a modeling perspective, the task of the modeler involves determining an appropriate set of characteristics that represent the attributes of the actor relevant to the model, and a set of actor behaviors that capture the decisions or actions of the actors in the system. While the set of necessary actor attributes is highly dependent on the problem being addressed, behaviors typically consist of some form of decision rules that related site and/or system characteristics to a particular actor action and resulting landscape change.
Envision provides a framework for representing: 1) a landscape consisting of a set of spatial containers, or integrated decision units (IDU’s), modeled as a set of polygon-based geographic information system (GIS) coverages containing spatially-explicit depictions of landscape attributes and patterns; 2) a set of actors operating on a landscape, defined in terms of a value system that couples actor behavior to global and local production metrics and in part determine policies the actor will select for decision making; 3) a set of policies that constrain actor behavior and whose selection and application results in a set of outcomes modifying landscape attributes; 4) a set of autonomous process descriptions that model non-policy driven landscape change; and 5) a set of landscape evaluators modeling responses of various landscape production metrics to landscape attribute changes resulting from actor decision making. Envision provides a general-purpose architecture for representing landscape change within a general paradigm incorporating actors, policies, spatially explicit landscape depictions, landscape feedback, and adaptation; application-specific components are ‘‘plugged in’’ to Envision as required to model particular processes.Taken together, these elements provide a basic platform for assembling actor-based models of landscape change. Because many of these elements are ‘‘pluggable’’ software components, the basic Envision platform can be used with application-specific actor definitions, policy sets, autonomous process descriptions, and landscape evaluators.
Envision is characterized by the explicit relationships between actor values, the policies that they enact, and the capacity of the landscape to achieve defined goals. The goals are represented by evaluative models that characterize the services associated with the landscape. These services may focus on economics, ecosystems, or other culture aspects of the environment.We defined actors previously, and the next couple of slides will more clearly define policies and the models that can be used to evaluate the level of service associated with different landscapes.
In Envision, policies represent decisions or plans of actions designed to accomplish desired outcomes. Policies are developed by many different groups, and we often turn to planning documents, of which the three on the right are representative examples, to help develop policy sets for Envision.
More specifically, policies are statements that describe potential actions that are available to actors.In Envision, policies typically are made up of three features. The first is a description of the environmental conditions that define where the policy might be applied. This is incorporated as a spatial query. The second is a score that is used to define the effectiveness of the policy at addressing the goals that have been established for the Envision application. The last feature is a set of possible outcomes associated with the selection and application of a particular policy.This is an example of these key features. The first part of the policy is a statement defining the outcome: to purchase conservation easements, which means paying the owner of the land NOT to develop it. In this case, the policy also results in revegetation of degraded habitat. The second part, in magenta, represents a spatial query which subsets the overall landscape into only those areas where the policy might be applicable. The last part of this policy outlines the fact that the policy is effective at addressing a particular goal. In this case that goal is provide good habitat for native fish.The key thing to remember is that an Envision application may have hundreds of these kinds of policy statements, and that the actor based decision-making selects a policy for application at any one time and place, based on the actors values and the scarcity of metrics that the policy addresses.
Envision uses a combination of actor based decision making, landscape data, and policy statements to produce an evolving set of future scenarios. The last piece of the puzzle are the plug-in models that perform two specific tasks. The first talk, by what we call autonomous processes, represents landscape changes that occur independent of human decision-making. Examples include a changing climate, vegetation succession, forest growth, fire, flooding and many others. The second type of model, which we refer to as evaluative models, summarize how well a landscape is doing at producing metrics of interest. Ecosystem, economic, and cultural services fall into this modeling category. Examples include carbon sequestration, habitat production, availability of land for development.In both cases, the list of models included is entirely up to the developers of the particular Envision application. While the framework does include a number of standard models, it also includes a detailed mechanism for writing and plugging in user-defined alternatives.
So far I have described the idea of alternative futures analysis and provided some detail about a particular tool that we find useful in the regard: Envision.I’ll now a switch gears and outline two examples to give you a better idea of the types of basic results we are attempting to capture. Both of these examples are from the Pacific Northwest of the United States. The first example is an application to a forested site and looks at patterns of development, timber harvest, as well as a simple fisheries indicator under a changing climate. The second example focuses more on development and nearshore modifications over a relatively long coastline.The next slide zooms in on the rectangle in the upper right.
The study areas are outlined in red, and I’ll begin with a description of the southern area, which is outlined by the black box.
This oblique air photo is taken from about the middle of the study area, and looks to the east. Outlined in blue are a series of gauged catchments that make up the HJ Andrews experimental forest, which is part of the LTER network. Of particular interest in the picture is that the landscape is a mosaic of different forested landcovers, which ranges from mature old growth, to newly clearcut.
This slide shows both entire study area, and a much closer look at a small portion of the landscape. Here we have a shaded map, rather than the airphoto, which outlines the kind of spatial data that is require for the model. In this case, the dark green are older forest, the light green is younger forest, the red represents developed areas, and blues are wetlands and aquatic areas. A road network is shown in white. The faint lines on the zoomed in map outline the polygon borders which define the areas over which actors manage this landscape.The study area is 195 km2, and over a 25 year simulation period, the human population will grow from 10000 to about 18500 people.
The Envision application for the forest was developed to look at how management of timberland may interact with climate change to alter the ability of the landscape to produce different ecosystem services. As an initial step in this direction, we developed 4 different scenarios which are combinations of the current and a hypothetical future climate, and different strategies to house people in the landscape.
Presenting particular models that make use of envision often begin with a slide like this, which is designed to highlight the very generalized procedure of model development. All Envision applications include these key elements, with the gray bar in the middle outlining the Envision software, its ability to deal with spatial data, and allow for Multi-actor decisionmaking through the selection of landscape policies.And while Autonomous Process Models, and Evaluative models are listed, the details are not yet filled in, because they are entirely application dependent.In this example, we include autonomous processes to simulate residential population development in this forested region, vegetation succession, and climate change. The Evaluative models are designed to provide a set of production metrics that are of interest in this area. These include a set that is related to exaction of timber, and have a very economic basis…and a set of metrics that look at fish habitat suitability, and also a the protection of natural resource areas. Taken together, this set of data, actors, policies and models was used in this application
[Note: Animation won’t work in the emailed version – the movie file is too big to email]Here is a view of two of the scenarios which is updated once every year for the 25 year simulation period. Greens represent forested areas and red represents residential lands. The two scenarios treat development very differently, and the landscape that results in each scenario show very divergent patterns.In the next couple of slides we will look at time series output from the evaluative models which compare across all 4 scenarios (including climate change) the capacity of the region to provide 1. Forest Carbon 2. The production of lumber, or harvested trees, and 3. An index of biological integrity that takes into account near stream and upstream landcover to approximate the habitat suitability for native fish.
Forest carbon represents the amount of carbon stored in the system in stems, branches, and rooting systems. The temporal patterns are functions of the carbon accounting model and of the model of vegetation succession which is a simple vegetation state transfer model that ages the landscape. To account for changes in the succession under a changing climate, the transfer matrices were modified based upon expert judgement
Forest product extraction represents the amount of timber that was removed from the landscape each year. The amount of timber available for removal was different under the two scenarios, with much less available under the conservation scenario than under the development scenario. But the amount available for removal was, in both cases, tied to the total amount available. Because the forest was simulated to grow faster under the climate change scenario, even with a set of conservation practices in place, and limited harvest, the total amount exacted under the changed climate was for both development AND conservation, higher than without conservation.Of course, this is a single realization of a stochastic model, using a set of simplifying assumptions. We are in the process of evaluating this kind of result more carefully, but nevertheless note that these kinds of patterns and feedbacks are typical of socio-economic systems, and can be captured with tools like Envision.
The Fish IBI is a simple empirical model that uses the amount development and forest both near each stream reach and upslope of each stream reach, to provide an estimate of the habitat suitability for native fish. In this case, the IBI under the conservation scenario is high and does not change. This is a result of policies that did not allow any development or harvest near the stream channel. The IBI declines under the development scenario because of increased development near streams – the policies that precluded development were not available, or of interest, to actors under this scenario.It is also interesting that for both of the climate change scenarios, the IBI declines more rapidly than under current climate. The model assumed that the spatially distributed stream network would decline significantly in length during the late summer because of decreased snowpack and increased rates of evaporation and transpiration, given the warmer conditions. The total available habitat area is a component of the IBI, and estimates declines in stream area were enough to negatively impact habitat, even with very conservative land management practices.
For the second example, I’ll move north to the Puget Sound watershed. This is a very large basin, over which we ran Envision simulations over a 60 year period from the year 2000 to the year 2060. During that period of time, we used a linear model of population growth with suggests that the 4.2 million people living in the region in 2000 will swell to about 7 million by 2060. Many entities are interested to better understand where the additional millions will live, what the pattern of development needed to support them will look like, and how this development will impact the sensitive Puget Sound waterway. While I will not present all of the results from this study, we will look at landscape development and a small set of indicator models which have bearing on a number of ecosystem services that are provided by the nearshore region Puget Sound itself.
We looked at the region through the lense of 3 scenarios, focused on maintaining current growth patterns, managing those growth patterns with a set of urban containment policy choices, and, alternatively, opening up currently undeveloped lands to increased development pressure.
As with the first example, we start the presentation Puget Sound Envision with an unfilled framework, that includes the key features: the model, landscape data, policy sets, and agent descriptors. The elements of the simulation that proceeded without actor decision making (autonomous process models) include the population growth, a Rural/Urban development model that spatially modified the allowable number of dwelling units within each piece of the landscape, and a model that captured potential dynamics associated with the expansion of near shore structures (docks, marinas, shoreline armouring).The evaluative models were developed to evaluate the implications of the simulated development patterns. These include a model of the overall impervious area (which changes with development), a regression based water quality model, a nearshore habitat model, and a simple Carbon model derived from the INVEST ecosystem services project.Additionally, a model was developed to provide statistics related to the amount of resource lands that were protected from development and the amount of land area that was available for residential development.Again, I won’t go into the results for all of these elements, but have selected a small subset of results, focused just on landcover projections, to provide an idea of the model functionality.
But first, here is a screen shot from the Envision software, outlining the Puget Sound and classified to display landuselandcover. As with other maps, Green represents forested areas, and Red represents developed areas.In the next slide we zoom in to the are inside the yellow box.
This slide provide a little more detail, with the city of Seattle outlined on the right side of Puget Sound (which here is gray).Now we zoom in a little bit more, just to provide an idea of the level of detail of the data behind the modeling.
The South Central Watershed is represents about 20% of the overall study area, and includes the city of Seattle (as well as surrounding development), which I pointed out earlier. The map at the upper left represent current conditions for landuse/landcover in this watershed. The other 3 represents possible future landscapes in the year 2050. These future landscapes include about 3 million more people than the 2000 baseline landscape, but the placement of that population varies considerably depending upon the different scenario of development.We are currently in the process of running hundreds of stochastically different simulations, under the 4 different scenarios, to better understand the resilience of the landscape, with particular reference to nearshore ecosystem function, but it is the variable pattern of growth, depicted in the this picture, that is a key strength of the actor based alternative future approach.The next slide is an animated sequence that outlines these same results, with a flight path that begins over the volcano (Mt. Rainier, labelled in the lower right figure) that is depicted at the bottom of the South Central Watershed in this slide. The flight will take us over the mountain, then to the east of Seattle, and north along the coastline to the border with Canada. It then back tracks across the landscape, changing the potential landscape with each pass.
In summary, I’d like to point out a number of points that these type of alternative future analyses need to keep in mind.Alternative future assessments are fundamentally place-based and client-dependent: Each application is different.Commonalities do exist and should be exploited within an extensible, adaptable DSS frameworkInteractions between population growth, landscape development and ecosystem services drive socio-ecological systems, and need to be accommodatedEngagement with stakeholders is critical to define decision processes, desired outcomes endpoints
And I’ll leave you with a picture of the Envision website, the address of that site, and a specific acknowledgement of the primary developer of the Envision software, Dr. John Bolte who led the development of Envision and case studies I’ve presented this evening.Thank you for your time and attention.
ENVISION Y El Modelamiento Del Paisaje - En Ingles
An Alternative Futures Approach to Understanding Landscape Dynamics and Services<br />Kellie Vache, PhD. Biological & Ecological Engineering Oregon State University<br />
Today’s Discussion<br />Overview of alternative futures approach to socio-ecological modeling<br />Description of one approach using Envision<br />Example applications<br />Andrews Forest<br />Puget Sound<br />
To Start - A Definition of Biocomplexity<br />Term used to describe complex structures, interactions, adaptive capabilities and dynamics<br />diverse set of biological and ecological systems <br />multiple spatial and temporal scales<br /><ul><li>Many Approaches!!! Some focusing on capturing richness of system dynamics, some on complex adaptive systems approaches
Challenge – How to make these operational?</li></li></ul><li>Alternative Futures Projects<br />Examine multiple scenarios of trends and assumptions about future conditions, generally using one or more models of change, <br />Assist in incorporating stakeholder interactions to define goals, constraints, trajectories, drivers, outcomes<br />Allow visualization of the results <br />Ultimately are intended to assist in improving land management decision-making<br />
Software-based Alternative Futures<br />A mechanism to include biocomplexity in alternative Futures – to do so requires:<br />Easy to use interface<br />Present results in a format useful to end users<br />Spatially and temporally explicit<br />Extensible to incorporate evolving “best” science<br />Internal feedback<br />
Envision Components<br />Site Selection and Characterization<br />Aggregate Evaluation of Management Alternatives<br />Detailed Evaluation of Individual Services<br />Alternative Scenario Selection<br />Analysis Framework and Architecture<br />Alternatives<br />Datasets<br /> Landscape Production Evaluators<br />Visualizations<br />Goals<br />Water Quality<br />Policies<br />Stressors<br />Carbon<br />…<br />Other ESE’s<br />Drivers<br />
Approach: Multi-Agent Modeling<br />Model the behavior and actions agents (actors)<br />represents land management decisions of actors with authority over parcels of land<br />Actor decisions implemented through policies that guide & constrain potential actions<br />Ecosystem Services (e.g. forest succession, wetland function) can be simultaneously modeled<br />
Envision – Conceptual Structure<br />Multiagent Decision-making<br />Ecosystem Service Models<br />Generating Landscape Metrics Reflecting Landscape Productions<br />LandscapeFeedbacks<br />Select policies and generate land management decision affecting landscape pattern<br />Actors<br /> Decision-makers managing the landscape by selecting policies responsive to their objectives<br />Landscape<br /> Spatial Container in which landscape changes, ES Metrics are depicted<br />Scenario<br />Definition<br />Policies<br /> Fundamental Descriptors of constraints and actions defining land use management decisionmaking<br />Autonomous Change Processes<br />Models of Non-anthropogenic Landscape Change<br />
Socio-cultural Services</li></ul>Provide a common frame of reference<br />for actors, policies and landscape productions<br />Landscapes<br />Service Metrics<br />
Policy Definition<br />Landscape policies are decisions or plans of action for accomplishing desired outcomes.<br /> from:<br />Lackey, R.T. 2006. Axioms of ecological policy. Fisheries. 31(6): 286-290. <br />
Policies in ENVISION<br />Policies are a decision or plan of action for accomplishing a desired outcome; they are a fundamental unit of computation in Envision<br />Describe actions available to actors<br />Primary Characteristics:<br />Applicable Site Attributes (Spatial Query)<br />Effectiveness of the Policy at addressing goals<br />Outcomes (possible multiple) associated with the selection and application of the Policy<br />Example: [Purchase conservations easement to allow revegetation of degraded riparian areas] in [areas with no built structures and high channel migration capacity] when [native fish habitat becomes scarce]<br />
Models in ENVISION<br />Models are “plug-ins” of two types:<br />Autonomous Processes: Represent processes causing landscape changes independent of human decision-making – e.g. climate change, vegetative succession, fire, flooding, ??? <br />Evaluative Models – Generate production statistics and report back how well the landscape is doing a producing metrics of interest – e.g. carbon sequestration, habitat production, land availability, ???<br />
Lessons Learned<br />Alternative future assessments are fundamentally place-based and client-dependent: Each application is different.<br />Commonalities do exist and should be exploited within an extensible, adaptable DSS framework<br />Interactions between population growth, landscape development and ecosystem services drive socio-ecological systems, and need to be accommodated<br />Engagement with stakeholders is critical to define decision processes, desired outcomes endpoints<br />
Thanks to Dr. John Bolte<br />and the Envision Development Team<br />
Muchas Gracias!more info at:http://envision.bioe.orst.edu<br />