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Arctic Report Card: Update for 2015

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Community-based Observing Network Systems for Arctic Change Detection and Response

L. Alessa1,2,3, A. Kliskey1,2, D. Forbes4, D. Atkinson5,
D. Griffith1, T. Mustonen6, P. Pulsifer7

1Center for Resilient Communities, University of Idaho, Moscow, ID, USA
2International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA
3Department of Homeland Security Arctic Domain Awareness Center, Anchorage, AK, USA
4Geological Survey of Canada, Natural Resources Canada, Dartmouth, NS, Canada
5University of Victoria, Department of Geography, Victoria, BC, Canada
6Snowchange Cooperative, Selkie, Finland
7National Snow and Ice Data Center, University of Colorado Boulder, Boulder, CO, USA

An Anatomy of Community-based Monitoring

Community-based monitoring (CBM) is a broad set of approaches that engage the capacity of community residents in observing and monitoring of a region, e.g., the Arctic (Arctic Council 2015; Johnson et al. 2015). CBM encompasses a continuum of approaches from community-based observing network systems (CBONS), citizen science and observer blogs (Table 11.1).

Table 11.1. Comparison of community based monitoring types (CBONS: community-based observation networks and systems; CBM: community-based monitoring)
  CBONS Citizen
Science
Generic
CBM
Observer
Blog
Pre-determined variables Yes Yes Yes No
Observer types Domain experts General population General population General population
Observers trained - instruments Yes Sometimes Sometimes No
Observers trained - scientific method Yes Sometimes No No
Observers trained - QA-QC Yes No Sometimes No
Standardized intakes Yes No Often No
Context based application Yes No Sometimes Sometimes
Systems based approach Yes No Sometimes Sometimes
Coordinated / networked Yes Sometimes Sometimes Yes
Interoperable Yes Yes Sometimes No
Validated Yes No Sometimes No
Archived Yes Yes Yes Yes
Quality assured Yes No Sometimes No

CBONS consist of distributed networks of skilled residents in communities throughout a region who systematically observe and document their environment on a regular basis in the context of hunting, fishing or other livelihood activities (Alessa et al. 2015a). A key feature of CBONS is that data collection protocols and the resulting observing data are quality-assured and quality-controlled (QA-QC).

Citizen science is the engagement of volunteers from the public in the systematic measurement or observation of specified phenomena, typically using physical instrumentation or a standard scientific protocol (Alessa et al. 2015a). Citizen science provides important opportunities for the participation of the general public in scientific data collection leading to valuable educational outcomes. Many citizen science projects attempt to include quality assurance, although in some cases observer bias can be difficult to control.

Observer blogs are online portals that provide a two-way communication mechanism for members of the public to report environmental observations from their local area and to receive reciprocal feedback from the portal manager. Such observer blogs have the benefit of being open conduits for anyone to report observations. Observer blog entries may be vetted by a moderator, but there is little or no QA-QC. Examples of observer blogs include the Local Environment Observer (LEO) Network facilitated by the Alaska Native Tribal Health Consortium, and CitizenSky hosted by the American Association of Variable Star Observers.

CBONS are built on a strategic network of communities where the observations from multiple groups can be scaled up to provide a regional-level perspective and inter-community sharing on, for example, changes in common species or issues. Citizen science projects vary widely in scale and in the nature of the phenomena observed or measured. Some citizen science project efforts may involve a network of individuals or a sampling network of multiple individuals who coordinate observations by their community, although this is a feature of the sampling protocol rather than an explicit effort to network communities or observers. Observer blogs receive observations via an open, self-selected range of individuals leading to the possibility of regional coverage and two-way communication.

Both citizen science and CBONS involve observers who have received some professional development or training in the use of specific equipment. For citizen science this might include training in the use of instrumentation and the application of a measurement or observing protocol. CBONS is based on full partnerships with communities who internally select and support a cohort of specialized observers, i.e., long-term residents whose awareness of the environment and quality of observing is high. In this type of observing network the community is a key driver of the variables to be monitored, complete with co-developed protocols and data protections under their control. Observer blogs are open to any interested individual with access to the portal or social media; training is not necessary.

Each system has its individual strengths and weaknesses (false positives, false negatives, predictive capability, timeliness, relevance to need, costs, etc.), but a diversity of systems (different approaches) can enhance the overall strength of the suite of arctic observing efforts.

Place-based and Local Knowledge

Traditional ecological knowledge (TEK) has been defined as:

… a systematic way of thinking and knowing that is elaborated and applied to phenomena across biological, physical, cultural and linguistic systems. Traditional Knowledge is owned by the holders of that knowledge, often collectively, and is uniquely expressed and transmitted through indigenous languages. It is a body of knowledge generated through cultural practices, lived experiences including extensive and multigenerational observations, lessons and skills. It has been developed and verified over millennia and is still developing in a living process, including knowledge acquired today and in the future, and it is passed on from generation to generation. (Arctic Council 2015)

TEK and its more generic form, traditional local knowledge (TLK), typically refer to Indigenous societies and communities. The counterpart to this type of knowing among multi-generational, though transplanted, people is local knowledge (LK). Collectively these are referred to as place-based local knowledge (PBLK): the cumulative and transmitted knowledge, experience and wisdom of human communities with a long-term attachment to the land and sea (Kliskey et al. 2009). The expression of PBLK through CBM generally, and CBONS more specifically, represents one way in which collective cultural histories enable environmental change to be placed into societal contexts. However, while this form of knowledge is particularly essential in arctic communities, where rapid economic, climatic and technological changes require adaptive responses unique to place, it is critical that it be place, not race, bound.

A Snapshot of Current Networks

The Community-based Observation Network for Adaptation and Security (CONAS) is an example of a long-standing, quality-assured and effective CBONS. CONAS, and its predecessor the Bering Sea Sub Network (BSSN, www.bssn.net), was created in 2007 as a partnership between Arctic indigenous communities and scientists. CONAS, an official project under the Arctic Council's Conservation of Arctic Flora and Fauna (CAFF) program, utilizes distributed human observers as intelligent sensors across the Bering Sea (Fig. 11.1) in both Alaska and the Russian Far East to systematically observe and document over 40 factors of Arctic environmental and globalization changes through co-developed surveys and questionnaires (Alessa et al. 2015b; Arctic Council 2015). All observations, ranging from weather to marine traffic, are subject to QA-QC, meaning they are verified, validated and vetted by community and academic practitioners and scientists.

Fig. 11.1. (a, top) Map showing the eight Bering Sea communities that comprise CONAS (Community-based Observing Network for Adaptation and Security), an example of an operational CBON. (b, bottom) Example of community-based observing network data, in this case from CONAS and the village of Togiak, used to support decision-making with respect to walrus habitat.

The Inuvialuit Settlement Region Community-based Monitoring Program (ISR-CBMP) is a CBM effort that supports environmental monitoring linked with Inuvialuit knowledge of Arctic wildlife, environment and biological productivity (Johnson et al. 2015). The objective of ISR-CBMP is to inform and support decisions by resource managers, Inuvialuit organizations and co-management boards.

The Snowchange Cooperative is a network of Saami, Yukaghir and Chukchi communities across Finland, Norway and the Russian North that documents oral history archives of the traditional knowledge, stories, handicraft and fishing and hunting traditions (Roop et al. 2015). The Circumpolar Arctic Coastal Communities Observatory Network (CACCON) is a pan-Arctic network of community-engaged, integrative coastal community observatories with a focus on environmental and social change. These represent a collection of CBM programs, including several CBONS, with pan-Arctic coverage.

Data Management and Interoperability: A Critical Aspect of Community-based Observing

CBONS data range from numerical, quantified values, open-ended interview questions that provide qualitative data, and location surveys that generate geospatially explicit data. To derive an integrated understanding of arctic social-ecological systems, CBONS data need to be synthesized. This, by necessity, requires addressing data interoperability and developing approaches for synthesizing qualitative, quantitative and spatial data (Fig. 11.2). To support the co-production of knowledge it is essential to have established data management protocols, e.g., those established by the Exchange for Local Observations and Knowledge of the Arctic (ELOKA: Pulsifer et al. 2014). CBONS data are interoperable with geospatial frameworks such as NOAA's Emergency Response Management Application (ERMA).

Fig. 11.2. Diagram showing the process of co-development of a community-centered, regional early warning and response system based on interoperable data from community-based observing networks and systems.

The premise for CBONS data is that the network communities, via community research associates (CRAs), in consultation with science and data management members of the network, decide on and approve data sharing protocols. CBONS data management aims to maintain and share observational data, to protect the cultural property rights and confidentiality of participant individuals and communities, and to share metadata, summarized data and raw data (following community approval) with the broader scientific community. Since data are viewed as community-controlled or community-owned this involves a constant balance between maintaining data access and protecting sensitive data. As important is ensuring the integrity of data and knowledge when they are applied and used in a policy-making or decision-making arena (Fig. 11.3); control of and responsibility for ensuring the validity of the interpretation of knowledge needs to be articulated.

Community-based Observing and a Systems Approach for Responding to Change

When considering a framework for responding to change it is necessary to integrate social components, including policies, laws and governance, the biogeophysical components (including the inherent types and rates of change in ecosystems), and the technological components, which include the range of technologies that are both driving socio-environmental change as well as available to respond to them. To do this, there must be systematic observation of change, placement of these observations of change in both a situational and anticipatory context for forecasting critical events, and then targeted preparedness such that response actions can occur quickly with the best likelihood of success (Figs. 11.2 and 11.3). CBONS are essential to this systems approach to responding to change.

Fig. 11.3. Diagram showing the relationship between observation, preparation, and response activities, in the context of early-warning systems, with corresponding system components for the Arctic: CBONS allow observations to be placed in a situational context; arctic natural and social sciences provide input to the forecasting system, and; an integrated response framework allows targeted preparedness, training and equipment to be mobilized.

Ultimately, CBONS allow observations to be placed in a situational and social context best suited to the locale in which observations, and responses, are made. The vast array of arctic natural and social sciences can provide input to a forecasting system, and an integrated response framework allows targeted preparedness, training and equipment to be mobilized in partnership with responding agencies (Alessa et al. 2015c). Such a framework could better enable local and regional responses around an "Observe-Prepare-Respond" paradigm, ensuring that communities on the ground gain control of not only anticipating change as it happens but also responding successfully to it.

References

Alessa, L., G. Beaujean, L. Bower, I. Campbell, O. Chemenko, M. Copchiak, M. Fidel, U. Fleener, J. Gamble, A. Gundersen, V. Immingan, L. Jackson, A. Kalmakoff, A. Kliskey, S. Merculief, D. Pungowiyi, O. Sutton, E. Ungott, J. Ungott, and J. Veldstr, 2015a: Bering Sea Sub-Network II: Sharing Knowledge, Improving Understanding, Enabling Response - International community-based environmental observation alliance for a changing Arctic. Conservation of Arctic Flora and Fauna, 61 pp.

Alessa, L., A. Kliskey, J. Gamble, M. Fidel, G. Beaujean, and J. Gosz, 2015b: The role of Indigenous science and local knowledge in integrated observing systems: Moving toward adaptive capacity indices and early warning systems. Sustainability Science. doi: 10.1007/s11625-015-0295-7.

Alessa, L., A. Kliskey, P. Williams and G. Beaujean 2015c: Incorporating Community Based Observing Networks and Systems: Toward a Regional Early Warning System For Enhanced Responses to Marine Arctic Critical Events. Washington J. Environmental Law and Policy, in press.

Arctic Council, 2015: Ottawa Traditional Knowledge Principles. Arctic Council NCR#6642168, 3 pp.

CAFF (Conservation of Arctic Flora and Fauna), 2015: Traditional Knowledge and Community-based Monitoring. Conservation of Arctic Flora and Fauna, 4 pp.

Johnson, N., L. Alessa, C. Behe, F. Danielsen, S. Gearhead, V. Gofman, A. Kliskey, E. Krummel, A. Lynch, T. Mustonen, P. Pulsifer, and M. Svoboda, 2015: The contributions of community-based monitoring and traditional knowledge to Arctic observing networks: Reflections on the state of the field. Arctic, 68, doi: http://dx.doi.org/10.14430/arctic4447.

Kliskey, A., L. Alessa, and B. Barr, 2009: Integrating local and traditional ecological knowledge for marine resilience. Managing for resilience: New directions for marine ecosystem-based management, K. McLeod and H. Leslie, Eds., Island Press Publishers, 145-161.

Pulsifer, P., H. Huntington, and G. Pecl, 2014: Introduction: local and traditional knowledge and data management in the Arctic. Polar Geog., 37, 1-4, doi: 10.1080/1088937X.2014.894591.

Roop, S., L. Alessa, A. Kliskey, M. Fidel, and G. Beaujean, 2015: "We didn't cross the border; the border crossed us": Informal Social Adaptations to Formal Governance and Policies by Communities across the Bering Sea Region in the Russian Far East and United States. Washington J. Environmental Law and Policy, 5, 69-96.

November 25, 2015

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