Geology topics

Leonardo Frid

Publications and source records attributed to Leonardo Frid.

14 recordsLinked to original sources

USGS2022

Operational assessment tool for forest carbon dynamics for the United States: A new spatially explicit approach linking the LUCAS and CBM-CFS3 models

Background Quantifying the carbon balance of forested ecosystems has been the subject of intense study involving the development of numerous methodological approaches. Forest inventories, processes-based biogeochemical models, and inversion methods have all been used to estimate the contribution of U.S. forests to the global terrestrial carbon sink. However, estimates have ranged widely, largely based on the approach used, and no single system is appropriate for operational carbon quantification and forecasting. We present estimates obtained using a new spatially explicit modeling framework utilizing a “gain–loss” approach, by linking the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). Results We estimated forest ecosystems in the conterminous United States stored 52.0 Pg C across all pools. Between 2001 and 2020, carbon storage increased by 2.4 Pg C at an annualized rate of 126 Tg C year −1 . Our results broadly agree with other studies using a variety of other methods to estimate the forest carbon sink. Climate variability and change was the primary driver of annual variability in the size of the net carbon sink, while land-use and land-cover change and disturbance were the primary drivers of the magnitude, reducing annual sink strength by 39%. Projections of carbon change under climate scenarios for the western U.S. find diverging estimates of carbon balance depending on the scenario. Under a moderate emissions scenario we estimated a 38% increase in the net sink of carbon, while under a high emissions scenario we estimated a reversal from a net sink to net source. Conclusions The new approach provides a fully coupled modeling framework capable of producing spatially explicit estimates of carbon stocks and fluxes under a range of historical and/or future socioeconomic, climate, and land management futures.

Continental United States↗

USGS2022

Coupling process-based and empirical models to assess management options to meet conservation goals

Conservation lands face a mounting threat of ecosystem transformation and the loss of biodiversity from the invasion of fire-prone perennial and annual grasses. Managers must make difficult decisions to find efficient ways to expend limited resources to manage large and complex landscapes amidst substantial uncertainty regarding effective treatment strategies, climates, and invader-induced novel processes. We developed a state-and-transition simulation model coupled with a fire behavior model to study impacts to native biodiversity and fire regimes in a national park invaded by a perennial grass. We evaluated resources required to meet management objectives, and how to spatially allocate available resources efficiently and effectively. Management strategies and ecological scenarios strongly influenced the ability to minimize potential impacts; the inclusion of a new management strategy, aerial precision spot spraying targeting low cover levels in remote regions, was needed to supplement current treatments that can only target large, dense patches. Adding this new treatment method may be enough to conserve the desert ecosystem from small scale transformation through invasive competition and from broad functional transformations through invasive-induced fire regime changes. Spot spraying may also be highly beneficial if wetter monsoonal conditions create faster growth rates, decreasing the ability to contain the invasion even with unlimited management resources. Given current annual budgets for control, and the new treatment option, invasion control and preservation of native biodiversity may be possible regardless of spatial prioritization. Coupled empirical and process-based models efficiently simulated the effects of management activities, quantifying potential management costs and ecological impacts, while considering a wide range of possible future uncertainties associated with climate, spread rates, and wildfires lacking historical precedent. These techniques could be applied to other situations to evaluate the feasibility of conservation goals and to determine actions that would be most efficient and effective in meeting those goals.

Arizona↗

USGS2022

Integrating climate considerations into grazing management programs in national parks

The National Park Service (NPS) is responsible for managing livestock grazing in nearly 100 parks, and several park grazing management planning efforts are currently underway. However, there is a recognized need to update grazing management practices to be responsive and adaptive to future climate change. As a step toward developing a process to address this need, this project worked with Dinosaur National Monument to consider climate change in its grazing management planning process. In this project, we convened researchers, managers, subject-matter experts, and climate change adaptation specialists through a participatory climate change scenario planning workshop to develop and apply a small set of challenging, plausible, relevant, and divergent scenarios that qualitatively assessed how grazing resources and management may be affected under climate change.

Final Project Report↗

USGS2021

A new approach for representing agent-environment feedbacks: Coupled agent-based and state-and-transition simulation models

Context Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavior, and mobility; and agent-environment interactions. STSMs are flexible and intuitive stochastic landscape models that can track scenarios and integrate diverse data. Both can be run spatially and track metrics of management success. Objectives Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns. Methods We developed analytical techniques and software tools to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrated the capabilities and value of this coupled approach through a proof-of-concept case study of bison-vegetation interactions in Badlands National Park. Results The coupled ABM-STSM: (1) streamlined handling of model inputs and outputs; (2) allowed representation of processes at multiple temporal scales; (3) minimized assumptions; and (4) generated spatial and temporal patterns that better reflected agent-environment interactions. Conclusions These developments constitute a new approach for representing agent-environment feedbacks; modelers can now use output from an ABM to dictate landscape changes within an STSM that in turn influence agents. This facilitates experimentation across domains (agent and environment) and creation of more realistic and management-relevant projections, and opens new opportunities for communicating models and linking to other methods.

Landscape Ecology↗

USGS2020

Assessing ecological uncertainty and simulation model sensitivity to evaluate an invasive plant species’ potential impacts to the landscape

Ecological forecasts of the extent and impacts of invasive species can inform conservation management decisions. Such forecasts are hampered by ecological uncertainties associated with non-analog conditions resulting from the introduction of an invader to an ecosystem. We developed a state-and-transition simulation model tied to a fire behavior model to simulate the spread of buffelgrass ( Cenchrus ciliaris ) in Saguaro National Park, AZ, USA over a 30-year period. The simulation models forecast the potential extent and impact of a buffelgrass invasion including size and frequency of fire events and displacement of saguaro cacti and other native species. Using simulation models allowed us to evaluate how model uncertainties affected forecasted landscape outcomes. We compared scenarios covering a range of parameter uncertainties including model initialization (landscape susceptibility to invasion) and expert-identified ecological uncertainties (buffelgrass patch infill rates and precipitation). Our simulations showed substantial differences in the amount of buffelgrass on the landscape and the size and frequency of fires for dry years with slow patch infill scenarios compared to wet years with fast patch infill scenarios. We identified uncertainty in buffelgrass patch infill rates as a key area for research to improve forecasts. Our approach could be used to investigate novel processes in other invaded systems.

Scientific Reports↗

USGS2019

Developing an expert elicited simulation model to evaluate invasive species and fire management alternatives

Invasive species can alter ecosystem properties and cause state shifts in landscapes. Resource managers charged with maintaining landscapes require tools to understand implications of alternative actions (or inactions) on landscape structure and function. Simulation models can serve as a virtual laboratory to explore these alternatives and their potential impacts on a landscape. To be useful, however, managers need to participate in model development to ensure that model structure can evaluate the response of key resources to plausible actions. Here, we detail development of a state‐and‐transition simulation model (STSM) to evaluate buffelgrass ( Cenchrus ciliaris L. syn Pennisetum ciliare (L.) Link) in Saguaro National Park (SNP), Arizona, USA, through collaboration between managers and researchers. We integrate expert knowledge and research to create and parameterize a stochastic, spatially explicit STSM to evaluate specific management objectives. We also develop a dynamic link between the STSM and a fire behavior model to allow exploration of potential novel processes introduced to the ecosystem by buffelgrass invasion. Our projections show that buffelgrass can be expected to increase on the landscape and that the integration of fire into the model accelerates the projected rate of invasion and increases degradation of resources of management concern. We highlight the benefit of engaging end users in the modeling process so that the model is targeted to evaluate management objectives, in this case retention of saguaro cacti ( Carnegiea gigantea (Engelm.) Britton & Rose) on the landscape. Being able to integrate an external model that can help address the unique characteristics of a problem such as the introduction of fire into the SNP desert ecosystem increases the ability of simulations to address complex ecological and management questions.

Ecosphere↗

USGS2018

Integrating continuous stocks and flows into state-and-transition simulation models of landscape change

State-and-transition simulation models (STSMs) provide a general framework for forecasting landscape dynamics, including projections of both vegetation and land-use/land-cover (LULC) change. The STSM method divides a landscape into spatially-referenced cells and then simulates the state of each cell forward in time, as a discrete-time stochastic process using a Monte Carlo approach, in response to any number of possible transitions. A current limitation of the STSM method, however, is that all of the state variables must be discrete. Here we present a new approach for extending a STSM, in order to account for continuous state variables, called a state-and-transition simulation model with stocks and flows (STSM-SF). The STSM-SF method allows for any number of continuous stocks to be defined for every spatial cell in the STSM, along with a suite of continuous flows specifying the rates at which stock levels change over time. The change in the level of each stock is then simulated forward in time, for each spatial cell, as a discrete-time stochastic process. The method differs from the traditional systems dynamics approach to stock-flow modelling in that the stocks and flows can be spatially-explicit, and the flows can be expressed as a function of the STSM states and transitions. We demonstrate the STSM-SF method by integrating a spatially-explicit carbon (C) budget model with a STSM of LULC change for the state of Hawai'i, USA. In this example, continuous stocks are pools of terrestrial C, while the flows are the possible fluxes of C between these pools. Importantly, several of these C fluxes are triggered by corresponding LULC transitions in the STSM. Model outputs include changes in the spatial and temporal distribution of C pools and fluxes across the landscape in response to projected future changes in LULC over the next 50 years. The new STSM-SF method allows both discrete and continuous state variables to be integrated into a STSM, including interactions between them. With the addition of stocks and flows, STSMs provide a conceptually simple yet powerful approach for characterizing uncertainties in projections of a wide range of questions regarding landscape change.

Hawai'i↗

USGS2017

Co-producing simulation models to inform resource management: a case study from southwest South Dakota

Simulation models can represent complexities of the real world and serve as virtual laboratories for asking “what if…?” questions about how systems might respond to different scenarios. However, simulation models have limited relevance to real-world applications when designed without input from people who could use the simulated scenarios to inform their decisions. Here, we report on a state-and-transition simulation model of vegetation dynamics that was coupled to a scenario planning process and co-produced by researchers, resource managers, local subject-matter experts, and climate change adaptation specialists to explore potential effects of climate scenarios and management alternatives on key resources in southwest South Dakota. Input from management partners and local experts was critical for representing key vegetation types, bison and cattle grazing, exotic plants, fire, and the effects of climate change and management on rangeland productivity and composition given the paucity of published data on many of these topics. By simulating multiple land management jurisdictions, climate scenarios, and management alternatives, the model highlighted important tradeoffs between grazer density and vegetation composition, as well as between the short- and long-term costs of invasive species management. It also pointed to impactful uncertainties related to the effects of fire and grazing on vegetation. More broadly, a scenario-based approach to model co-production bracketed the uncertainty associated with climate change and ensured that the most important (and impactful) uncertainties related to resource management were addressed. This cooperative study demonstrates six opportunities for scientists to engage users throughout the modeling process to improve model utility and relevance: (1) identifying focal dynamics and variables, (2) developing conceptual model(s), (3) parameterizing the simulation, (4) identifying relevant climate scenarios and management alternatives, (5) evaluating and refining the simulation, and (6) interpreting the results. We also reflect on lessons learned and offer several recommendations for future co-production efforts, with the aim of advancing the pursuit of usable science.

South Dakota↗

USGS2017

Using state-and-transition modeling to account for imperfect detection in invasive species management

Buffelgrass, a highly competitive and flammable African bunchgrass, is spreading rapidly across both urban and natural areas in the Sonoran Desert of southern and central Arizona. Damages include increased fire risk, losses in biodiversity, and diminished revenues and quality of life. Feasibility of sustained and successful mitigation will depend heavily on rates of spread, treatment capacity, and cost–benefit analysis. We created a decision support model for the wildland–urban interface north of Tucson, AZ, using a spatial state-and-transition simulation modeling framework, the Tool for Exploratory Landscape Scenario Analyses. We addressed the issues of undetected invasions, identifying potentially suitable habitat and calibrating spread rates, while answering questions about how to allocate resources among inventory, treatment, and maintenance. Inputs to the model include a state-and-transition simulation model to describe the succession and control of buffelgrass, a habitat suitability model, management planning zones, spread vectors, estimated dispersal kernels for buffelgrass, and maps of current distribution. Our spatial simulations showed that without treatment, buffelgrass infestations that started with as little as 80 ha (198 ac) could grow to more than 6,000 ha by the year 2060. In contrast, applying unlimited management resources could limit 2060 infestation levels to approximately 50 ha. The application of sufficient resources toward inventory is important because undetected patches of buffelgrass will tend to grow exponentially. In our simulations, areas affected by buffelgrass may increase substantially over the next 50 yr, but a large, upfront investment in buffelgrass control could reduce the infested area and overall management costs.

Arizona↗

USGS2016

State-and-transition simulation models: a framework for forecasting landscape change

Summary A wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features. We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states. We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years. State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of landscape dynamics.

Methods in Ecology and Evolution↗

USGS2016

State-and-transition models: Conceptual versus simulation perspectives, usefulness and breadth of use, and land management applications

State-and-Transition Simulation Modeling (STSM) is a quantitative analysis method that can consolidate a wide array of resource management issues under a “what-if” scenario exercise. STSM can be seen as an ensemble of models, such as climate models, ecological models, and economic models that incorporate human dimensions and management options. This chapter presents STSM as a tool to help synthesize information on social–ecological systems and to investigate some of the management issues associated with exotic annual Bromus species, which have been described elsewhere in this book. Definitions, terminology, and perspectives on conceptual and computer-simulated stochastic state-and-transition models are given first, followed by a brief review of past STSM studies relevant to the management of Bromus species. A detailed case study illustrates the usefulness of STSM for land management. As a whole, this chapter is intended to demonstrate how STSM can help both managers and scientists: (a) determine efficient resource allocation for monitoring nonnative grasses; (b) evaluate sources of uncertainty in model simulation results involving expert opinion, and their consequences for management decisions; and (c) provide insight into the consequences of predicted local climate change effects on ecological systems invaded by exotic annual Bromus species.

Book chapter↗

USGS2015

Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change

State-and-transition simulation models (STSMs) are known for their ability to explore the combined effects of multiple disturbances, ecological dynamics, and management actions on vegetation. However, integrating the additional impacts of climate change into STSMs remains a challenge. We address this challenge by combining an STSM with species distribution modeling (SDM). SDMs estimate the probability of occurrence of a given species based on observed presence and absence locations as well as environmental and climatic covariates. Thus, in order to account for changes in habitat suitability due to climate change, we used SDM to generate continuous surfaces of species occurrence probabilities. These data were imported into ST-Sim, an STSM platform, where they dictated the probability of each cell transitioning between alternate potential vegetation types at each time step. The STSM was parameterized to capture additional processes of vegetation growth and disturbance that are relevant to a keystone species in the Greater Yellowstone Ecosystem—whitebark pine ( Pinus albicaulis ). We compared historical model runs against historical observations of whitebark pine and a key disturbance agent (mountain pine beetle, Dendroctonus ponderosae ), and then projected the simulation into the future. Using this combination of correlative and stochastic simulation models, we were able to reproduce historical observations and identify key data gaps. Results indicated that SDMs and STSMs are complementary tools, and combining them is an effective way to account for the anticipated impacts of climate change, biotic interactions, and disturbances, while also allowing for the exploration of management options.

AIMS Environmental Science↗

USGS2015

Simulating long-term effectiveness and efficiency of management scenarios for an invasive grass

Resource managers are often faced with trade-offs in allocating limited resources to manage plant invasions. These decisions must often be made with uncertainty about the location of infestations, their rate of spread and effectiveness of management actions. Landscape level simulation tools such as state-and-transition simulation models (STSMs) can be used to evaluate the potential long term consequences of alternative management strategies and help identify those strategies that make efficient use of resources. We analyzed alternative management scenarios for African buffelgrass ( Pennisetum ciliare syn. Cenchrus ciliaris ) at Ironwood Forest National Monument, Arizona using a spatially explicit STSM implemented in the Tool for Exploratory Landscape Scenario Analyses (TELSA). Buffelgrass is an invasive grass that is spreading rapidly in the Sonoran Desert, affecting multiple habitats and jurisdictions. This invasion is creating a novel fire risk and transforming natural ecosystems. The model used in this application incorporates buffelgrass dispersal and establishment and management actions and effectiveness including inventory, treatment and post-treatment maintenance. We simulated 11 alternative scenarios developed in consultation with buffelgrass managers and other stakeholders. The scenarios vary according to the total budget allocated for management and the allocation of that budget between different kinds of management actions. Scenario results suggest that to achieve an actual reduction and stabilization of buffelgrass populations, management unconstrained by fiscal restrictions and across all jurisdictions and private lands is required; without broad and aggressive management, buffelgrass populations are expected to increase over time. However, results also suggest that large upfront investments can achieve control results that require relatively minimal spending in the future. Investing the necessary funds upfront to control the invasion results in the most efficient use of resources to achieve lowest invaded acreage in the long-term.

Arizona↗

USGS2015

An integrated approach to modeling changes in land use, land cover, and disturbance and their impact on ecosystem carbon dynamics: a case study in the Sierra Nevada Mountains of California

Increased land-use intensity (e.g. clearing of forests for cultivation, urbanization), often results in the loss of ecosystem carbon storage, while changes in productivity resulting from climate change may either help offset or exacerbate losses. However, there are large uncertainties in how land and climate systems will evolve and interact to shape future ecosystem carbon dynamics. To address this we developed the Land Use and Carbon Scenario Simulator (LUCAS) to track changes in land use, land cover, land management, and disturbance, and their impact on ecosystem carbon storage and flux within a scenario-based framework. We have combined a state-and-transition simulation model (STSM) of land change with a stock and flow model of carbon dynamics. Land-change projections downscaled from the Intergovernmental Panel on Climate Change’s (IPCC) Special Report on Emission Scenarios (SRES) were used to drive changes within the STSM, while the Integrated Biosphere Simulator (IBIS) ecosystem model was used to derive input parameters for the carbon stock and flow model. The model was applied to the Sierra Nevada Mountains ecoregion in California, USA, a region prone to large wildfires and a forestry sector projected to intensify over the next century. Three scenario simulations were conducted, including a calibration scenario, a climate-change scenario, and an integrated climate- and land-change scenario. Based on results from the calibration scenario, the LUCAS age-structured carbon accounting model was able to accurately reproduce results obtained from the process-based biogeochemical model. Under the climate-only scenario, the ecoregion was projected to be a reliable net sink of carbon, however, when land use and disturbance were introduced, the ecoregion switched to become a net source. This research demonstrates how an integrated approach to carbon accounting can be used to evaluate various drivers of ecosystem carbon change in a robust, yet transparent modeling environment.

California↗