Search USGSSearch

USGS · 70216329

Use of landscape simulation modeling to quantify resilience for ecological applications

Abstract

Goals of fostering ecological resilience are increasingly used to guide U.S. public land management in the context of anthropogenic climate change and increasing landscape disturbances. There are, however, few operational means of assessing the resilience of a landscape or ecosystem. We present a method to evaluate resilience using simulation modeling. In this method, we use historical conditions (e.g., in North America, prior to European settlement), quantified using simulation modeling, to provide a comparative reference for contemporary conditions, where substantial departures indicate loss of resilience. Contemporary ecological conditions are compared statistically to the historical time series to create a resilience index, which can be used to prioritize landscapes for treatment and inform possible treatments. However, managing for resilience based on historical conditions is tenuous in the Anthropocene, which is characterized by rapid climate change, extensive human land use, altered disturbance regimes, and exotic species introductions. To account for the future variability of ecosystems resulting from climate and disturbance regime shifts, we augment historical simulations with simulations of ecosystem dynamics under projected climate and land use changes to assess the degree of departure from benchmark historical conditions. We use a mechanistic landscape model (FireBGCv2) applied to a large landscape in western Montana, USA, to illustrate the methods presented in this paper. Spatially explicit ecosystem modeling provides the vehicle to generate the historical and future time series needed to quantify potential resilience conditions associated with past and potential future conditions. Our methods show that given selection of a useful set of metrics, managers could use simulations like ours to evaluate potential future management directions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert Keane, Rachel A. Loehman, Lisa M. Holsinger, Donald A. Falk, Phil E Higuera, Sharon Hood, Paul F. Hessburg. 2018-09-13. Use of landscape simulation modeling to quantify resilience for ecological applications. https://doi.org/10.1002/ecs2.2414

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Evaluating harvest liberalization strategies on population dynamics of southern latitude temperate-breeding Canada geese

The recovery of the Canada goose ( Branta canadensis ) is one of North America's greatest conservation success stories. Today, continental abundance of temperate-breeding Canada geese (those breeding in southern Canada and lower 48 states in the United States) greatly exceeds historical levels. As a result of increased abundance, human–goose conflicts have also increased, ranging from private and agricultural property damage to human health concerns. Managers have primarily attempted to lower Canada goose populations using hunter harvest via liberalized hunting regulations (increased bag limits and total hunting days). To evaluate the effectiveness of harvest strategies for temperate-breeding Canada geese, managers need a better understanding of how liberalized hunting regulations affect population dynamics. We estimated survival and harvest probabilities, abundance, and recovery distribution of temperate-breeding Canada geese banded in Arkansas, USA, during 2005–2020. We found that adult harvest probabilities declined overall during the study and adult survival probabilities increased. Annual abundances of juvenile geese (≤1 year old) declined during the study, whereas there was no detectable trend in abundance of the adult population over time. Most Canada geese breeding in Arkansas were shot in Arkansas, indicating potential for population dynamics to be influenced largely by state-specific harvest regulations. However, our results suggest that harvest liberalization as a management tool may have limited capacity to further influence population dynamics of Canada geese. Declines in productivity and recruitment likely had a greater influence on the similar observed declines in juvenile and adult abundances, respectively, during our study. More research is needed to better understand the ecological mechanism affecting the population dynamics of Canada geese in both urban and rural environments.

Arkansas

Predicted habitat use for reintroduced grizzly bears in the transboundary North Cascades ecosystem

Grizzly bears ( Ursus arctos ) were once numerous in the North Cascades transboundary region of Washington State (United States) and British Columbia (Canada); however, few remain today. To support ongoing reintroduction evaluations, we used simulations based on movement models developed in the Northern Rocky Mountains to predict habitat use by a small founding group of grizzly bears in the North Cascades during the early stages of reintroduction. These simulations represent movements across a spatially explicit landscape based on individual-specific selection and movement parameters. We first evaluated predictive performance in three nearby populations of grizzly bears in the Squamish-Lillooet, McGillvary Mountains, and North Stein-Nahatlatch regions of the Coast Mountains, British Columbia. After applying an elevation-based calibration, predicted habitat use showed strong agreement with GPS location data from 73 collared bears. Across population–sex subgroups, Spearman rank correlations were ≥0.95, with 69.2%–83.3% of locations occurring within the top five habitat classes (covering 50% of mapped habitat) and 17.1%–36.2% in the top class (representing 10%). Overall, 24.5% and 77.9% of locations fell within the top class and top five classes, respectively. Seasonal validation showed strongest predictive performance from May to late summer or fall. Model predictions for the North Cascades indicate that habitat use during early reintroduction is likely to be concentrated in the central and northern mountainous portions of the ecosystem along the US–Canada border. These maps can guide recovery planning when no local bear data are yet available in the North Cascades transboundary region.

British Columbia, Washington

Holistic understanding of uncertainty for collaborative and proactive global change decision making

Global change is accelerating and pushing the planet's ecosystems beyond the range of historical observations, creating increasing uncertainty in future system conditions. Despite general agreement that proactive environmental action is warranted, environmental decision conversations often end by identifying additional data needed to reduce uncertainty before taking novel action. Given the inherent uncertainty in complex issues such as global change, quantitative data alone are likely insufficient to support proactive environmental action. Holistic understanding of uncertainty includes scientific quantification of uncertainty paired with emotional responses and transcendental grounding to help people work together toward proactive action in uncertain decision contexts. Holistic understanding arises from the four ways in which humans perceive the world, termed the Four Realms: Physical (e.g., how I observe), Mental (e.g., how I think), Emotional (e.g., how I feel), and Transcendental (e.g., how I connect to greater meaning or purpose). Environmental scientists and decision makers are generally trained in Physical and Mental Realm observation and analysis, but not in how to apply Emotional and Transcendental Realm understanding. Emotional and Transcendental processing occurs in scientists and decision makers whether it is acknowledged or not and contributes to different people interpreting the same information in different ways. Thus, when the role of Emotional and Transcendental Realms in an individual's interpretation process is not understood, it can derail conversations and perpetuate the status quo. Explicitly recognizing all Four Realms can bring people together across differences and inspire shared, novel decision making even in increasing uncertainty. To illustrate the benefits of holistic understanding, we share stories from our experiences in environmental decision contexts. Because accessing the Four Realms requires experiential and embodied techniques, while still relying on core scientific tenets of observation and analysis, we also present techniques for readers to learn to feel their own emotional understanding and connect to their own transcendental understanding. Holistic understanding can enhance data-driven decisions by recognizing that human responses to uncertainty inherently include interactions between emotions, thoughts, transcendental connections, and behavior. Ultimately, holistic understanding can help anchor data-driven decisions in intra- and interpersonal connections, inspiring action in the face of uncertainty.

Ecosphere