AK EPSCoR – Integration Core Kalb Stevenson, RAM Group, UAA & INE, UAF Lilian Alessa, RAM Group, UAA Andrew Kliskey, RAM Group UAA Mark Altaweel RAM Group UAA & University College London

Resilience and Adaptive Management Group Physical Home: 6 Core Faculty, 6 Disciplinary Fields, 3 Ph.D. students, 2 Postdoctoral Fellows, 5 Undergraduate Researchers, 2 Technicians, 1 Program Coordinator. No funds or support ever received from University of Alaska Anchorage. RAM is the only computational social science center in Alaska; focus is on place- based sustainability and integrating local, indigenous knowledge with western datasets, emphasis on understanding the human hydrological system. University of Alaska Anchorage Bering Sea Sub Network: As part of the NSF Arctic Observing Network program. Communities in both Alaska and the Russian Far East. Science, survey design and data management occurs through Community Coordinators, equal partners in BSSN. Aleut International Association and 14 Tribal Entities COMSES net: OpenABM, Community for Modeling of Social Ecological Systems, an NSF Research Coordination Network. 4 large NSF projects, including first Coupled Natural Human (CNH) project ever awarded to Alaska. Exchange of personnel and regular collaboration on SES-related efforts. Hi’iaka Network ASU, U New Hampshire, U Chicago, U Hawai’i and U Kansas Cross appointed at Institute of Northern Engineering. Ph.D. students: UAF Interdisciplinary Graduate Program. Work closely with Vice Chancellor for Research to develop research trajectories and programs. co Principal Investigator and a primary author on Alaska EPSCoR (submission currently under review with NSF). University of Alaska Fairbanks 2

Outline Social Ecological Systems (SES) – Characterizing SES (SES typology) – Mapping SES (SES hotspots) – Modeling SES (Forecasting Environmental Resilience in Arctic Landscapes: FERAL coupled model) – Synthesis for SES (Architecture for Integrated and Dynamic Data Analysis: “AIDA”)

The challenge for understanding SES SES transitions due to land use, climate and other change drivers are poorly understood. Few models examine cumulative processes from the bottom up and even fewer incorporate critical social data. Several emergent tools and theories now exist to improve our understanding.

Existing SES models involve “neat” examples, rather than “messy” or complex systems. This includes the totality of human settlement, including social organization, technologies and movements of water, energy, materials and people, along with distribution systems. How do different communities approach resilience? What is the ability of a system to withstand perturbation while maintaining function (short- vs. long-term adaptive strategies?)

SES types charts Two Axes: Community Size and Resilience (proximity to a threshold). Assessing trajectory: Is it towards resilience? Towards vulnerability or collapse? Towards transition?

Mapping Values In any given resource issue there are multiple and sometimes competing values We rarely map these values. As a consequence, there is often a difficulty in making management decisions that are sustainable or that don’t result in great conflict Overlapping landscape values on the Kenai Peninsula: assessment using surveys of values of different communities and detecting overlapping social-ecological “hotspots”

Mapping Values SES hotspots adopted by USGS in their SoLVES tool SES hotspots adopted by Chugach National Forest in their Online Mapping Tool Assessment of important values in landscape (historical, cultural, recreational, economic, etc.)

Projecting Change Seward Peninsula and Fish River watershedVulnerability mapping (Social + Ecological data): Two middle figures are 100 years out

Modeling Consequences Forecasting Environmental Resilience in Arctic Landscapes (FERAL): coupled biophysical and social monitoring Proposed modeling and simulation approach that assesses social ecological change at variable speeds and spatial scales Used to address issues affecting community water supply (Seward Peninsula study) – the paper demonstrates the need to develop tools addressing issues at the community level, addressing human-hydrology interactions and policy decisions

Modeling Consequences Step 1: Assess water source selection process with observed trends and determine consequences of water selection choices. Environmental + Social modeling Uses agent-based modeling w/ agents as programed elements

We combine models of social dynamics, which are strong stand alone models, with models of hydrological change, which are also strong stand alone models. The key in approaches such as this is in acquiring organizing and sorting the social data such that they are interoperable with the biophysical data. The goal is to understand the consequences of various change regimes and of different policy and management decisions. This type of engagement allows you to advance the fundamental understanding of a system, how variables interact, how they change over time and what kinds of trade offs can be expected. Modeling Consequences

Acquiring, Organizing and Synthesizing (AOS) Social Science Data: AIDA Undertaking of “sentiment analysis” - Relationship of sentiments towards concepts

Architecture for Integrated and Dynamic Data Analysis (AIDA) Captures social drivers and unseen factors affecting social-ecological change Social monitoring through an “information infusion approach”

Applying AIDA

Summary Social Ecological Systems (SES) – Characterizing SES (SES typology) – Mapping SES (SES hotspots) – Modeling SES (FERAL coupled model) – Synthesis for SES (AIDA)