Humans have protected natural areas—national parks, refuges, reserves, and wilderness—over the past century for several reasons, not the least of which is to protect species that are charismatic or imperiled due to human activities. Moreover, monitoring of protected areas (PA), like those in Yellowstone, are centered on an explicit or implicit value of the species they support, and nearly all monitoring and management programs are focused on the persistence of those populations as both an ecological goal and as part of a set of legislated mandates like the Endangered Species Act of 1973. A century earlier in 1872, the U.S. Congress established Yellowstone National Park which ushered in the modern era of governmental creation of PAs that catalyzed a global movement to protect and monitor natural areas. Today, approximately 13% of terrestrial environments are designated as protected and a tremendous variety of monitoring programs and conservation planning efforts are underway to protect and sustain species and their habitats. Despite these achievements, species population declines, extinctions, habitat degradation, pathogen/invasive spread, and state-change events are occurring at unprecedented rates. These impacts are exacerbated by continued changes in human land-use activities and their interactions of the direct and indirect effects of climate change and disruption. It is clear that designation and monitoring of PAs are critically necessary but insufficient to effectively maintain biodiversity.
The good news—or potential good news—is that the global movement of PAs prompted the monitoring of species populations, and we now have invaluable long-term datasets on the species they aim to protect. For example, Yellowstone National Park has counted trumpeter swans annually since 1872. Such long-term legacy data are of unique value because they provide insight into the mechanisms that support or degrade the desired attributes of the PA ecosystems. E.O. Wilson writes extensively about why understanding species (inventory and monitoring) are the key to sustaining all life (biodiversity) on earth and calls for a deeper understanding of species’ roles while setting aside 50% of the earth is some form of PA status. There is also more potential good news: Practitioners such as agency biologists and managers commonly fund and conduct species monitoring programs, while scientists seek access to those data to investigate the cause and consequence of population increase or decline. This provides a common ground for collaboration and bridging of the gap between scientists and practitioners—a golden opportunity for action.
Trumpeter swans—at 15 to 30 pounds the largest waterfowl species in North America—have been monitored in Yellowstone National Park since it was founded.
In 2006, YERC met with the Science Advisor of the U.S. Fish and Wildlife Service, National
Park Service officials, and the NASA Biodiversity and Ecological Forecasting Program to
design and launch a new program to analyze these long-term datasets. However, we jointly
identified several major roadblocks. First and foremost was a major void in the environmental
datasets needed to explain the possible causes in the annual population change of species
over time. Without being able to match annual species observations (direct counts, sightings,
or sign) with annual data on impacts such as climate, habitat loss, competitors, invasives
and human mortality, practitioners and scientists were left without knowing the ‘cause’, and
thereby the solutions to recover species. Second, there was a lack of user-friendly tools and
techniques to allow practitioners access and understanding of the analysis methods and models
leading to solutions—on-the- ground conservation action and formation of sound policies.
Lastly, there was a much needed trust relationship between the stakeholders handling and
interpreting the data and conducting the analysis.
We responded with solutions to all three obstacles and proposed what we called RRSC (Risk-Reward Spatial Capacity) population models that provide a deep understanding of the cause of population increase/decrease over time and across space with an emphasis on ‘risk’ factors that are often ignored and thereby renders the results biased. We also developed a set of user- friendly, open-access tools and techniques to support the development of predictions to guide conservation decision-making called EAGLES (Ecosystem Assessment, Geospatial analysis and Landscape Evaluation System). Finally we wrapped the EAGLES tools and RRSC models in a formalized end-to- end program called the Adaptive Impact Modeling (AIM) process that was designed to build ‘trust’ among shareholders by providing a collaborative framework that is standardized, transparent, repeatable, and defensible (STRD) to forge sustainable policies leading to population recovery. As Roger Pielke, Jr. said, “Science and decision making should go hand in hand, because they both measure success by their ability to predict the consequences of actions”.
2.) Long-term, time-series data sets for two indicator species: breeding mallards across North America (red) and elk on Yellowstone National Park’s Northern Range (green). Significant variation in response to climate and other environmental factors occurred during the 2000-2009 decade.
The AIM process is really about providing a deep understanding of the environmental ‘change’ agents or impacts that affect species’ population fluctuations over time. It was patterned after the North American Waterfowl Management Plan (NAWMP), arguably one of the greatest conservation success stories on planet earth. Agencies and organizations formed working groups across the continent that today includes dozens of shareholder groups that review the scientific basis for managing waterfowl harvest on an annual cycle. It applies Adaptive Harvest Models (AHMs) to what is considered the best long-term dataset in the world—standardized aerial surveys of North American waterfowl breeding pair counts since 1955—to adaptively set harvest limits and guide wetland restoration. YERC worked with the NAWMP team to add satellite-derived information on changing water and climate conditions to improve decision-making. From this collaborative work, we then created the AIM process by generalizing the AHM approach to include not just mortality but multiple environmental impacts—both anthropogenic (e.g., land-use, habitat management, invasive spread, etc.) and natural (e.g., disturbance, climate, predation, drought)—for any species’ observation dataset.
The Adaptive Impact Modeling (AIM) process. This annual cycle is adapted from the Strategic Habitat Conservation (SHC) framework to incorporate the AIM process. The merged approach integrates human and natural impacts to further inform the decision-making process using predictive models and thus sustain resilient species populations.
Based on our work we obtained over 30 long-term datasets from our agency partners. We are now starting to apply the AIM process to these invaluable, legacy datasets, but we need support to further provide science-based guidance on how to recover and sustain these species. In doing so we’ll provide the deep insight into how biodiversity interacts with ecosystem processes, the life support system for all species including humankind.
This map assesses the 11-year change in Net Primary Productivity for major regions in the Central Flyway Ecoregion.
Although we have produced RRSC models for many of the datasets (see bison for example), we showcase one species: Mountain Fox in the GYC demonstrate RRSC with a habitat suitability model that basically says they select habitat with high prey biomass but also attempt to avoid heavy snows (a risk factor). A similar analysis in Yellowstone additionally showed that fox avoid coyotes. This analysis, although common sense, quantifies the strength by which fox are attracted to, or avoid, a particular impact factor.