Geology topics

Ross K. Meentemeyer

Publications and source records attributed to Ross K. Meentemeyer.

12 recordsLinked to original sources

USGS2024

Projecting the long-term effects of large-scale human influence on the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome

Decades of human activities and fire suppression have adversely affected longleaf pine ( Pinus palustris ) ecosystems, which are home to high levels of diversity and endemism. These iconic ecosystems also now face challenges from urbanization and climate change, which will alter conservation outcomes over the remainder of the 21st century. To explore how long-term, large-scale human influences could affect the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome, we extracted a set of 2400 longleaf pine patches ≥40 ha in size from the Florida Longleaf Pine Ecosystem Geodatabase. Projections from the FUTURES urban growth model and the Florida 2070 project indicate that development will lead to losses of existing longleaf pine habitat, reductions in longleaf pine patch size, and patches that are predominantly located in close proximity to developed areas. Finer-scale patterns of longleaf pine loss in three focal landscapes highlighted differences in land protection, ecological setting, and development pressure and the value of using of multiple urbanization iterations. The occurrence of suitable conditions to conduct prescribed fires, a crucial tool for maintaining, improving, and restoring longleaf pine ecosystems, is projected to decrease seasonally throughout the study area. As a result, the functional persistence of ecosystems is at risk due to climate changes that increase barriers to the safe and reliable application of intentional fire. The long-term viability of this critical ecosystem will warrant the evaluation of adaptive strategies that explicitly account for the individual and compounding effects of urban development and changing fire management conditions when considering options for ecosystem protection, management, and restoration.

Florida↗

USGS2023

Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk

Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%–24%, depending on flood hazard zone (50%–0.2% annual probability). We simulated various “what-if” scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.

South Carolina↗

USGS2022

Integrating principles and tools of decision science into value-driven watershed planning for compensatory mitigation

Several environmental policies strive to restore impaired ecosystems and could benefit from a consistent and transparent process — co-developed with key stakeholders — to prioritize impaired ecosystems for restoration activities. The Clean Water Act, for example, establishes reallocation mechanisms to transfer ecosystem services from sites of disturbance to compensation sites to offset aquatic resource functions that are unavoidably lost through land development. However, planning for the prioritization of compensatory mitigation areas is often hampered by decision-making processes that fall into a myopic decision frame because they are not co-produced with stakeholders. In this study, we partnered with domain experts from the North Carolina Division of Mitigation Services (NCDMS) to co-develop a real-world decision framework to prioritize catchments by potential for the development of mitigation projects following principles of a structured decision-making process and knowledge co-production. Following an iterative decision analysis cycle, domain experts revised foundational components of the decision framework and progressively added complexity and realism as they gained additional insights or more information became available. Through the course of facilitated in-person and remote interactions, the co-development of a decision framework produced three main ‘breakthroughs’ from the perspective of the stakeholder group: a) recognition of the problem as a multi-objective decision driven by several values in addition to biogeophysical goals (e.g., functional uplift; restoring or enhancing lost functionality of ecosystems), b) that the decision comprises a linked and sequential planning-to-implementation process, and c) future risk associated with land-use and climate change must be considered. We also present an interactive tool for ‘on-the-fly’ assessment of alternatives and tradeoff analysis, allowing domain experts to quickly test, react to, and revise prioritization strategies. The decision framework described in this study is not limited to the prioritization of compensatory mitigation activities across North Carolina, but rather serves as a framework to prioritize a wide range of restoration, conservation, and resource allocation activities in similar environmental contexts across the nation.

Ecological Applications↗

USGS2022

Predicting flood damage probability across the conterminous United States

Floods are the leading cause of natural disaster damages in the United States, with billions of dollars incurred every year in the form of government payouts, property damages, and agricultural losses. The Federal Emergency Management Agency oversees the delineation of floodplains to mitigate damages, but disparities exist between locations designated as high risk and where flood damages occur due to land use and climate changes and incomplete floodplain mapping. We harnessed publicly available geospatial datasets and random forest algorithms to analyze the spatial distribution and underlying drivers of flood damage probability caused by excessive rainfall and overflowing water bodies across the conterminous United States. From this, we produced the first spatially complete map of flood damage probability for the nation, along with spatially explicit standard errors for four selected cities. We trained models using the locations of historical reported flood damage events ( n = 71,434) and a suite of geospatial predictors (e.g., flood severity, climate, socio-economic exposure, topographic variables, soil properties, and hydrologic characteristics). We developed independent models for each hydrologic unit code level 2 watershed and generated a flood damage probability for each 100-m pixel. Our model classified damage or no damage with an average area under the curve accuracy of 0.75; however, model performance varied by environmental conditions, with certain land cover classes (e.g., forest) resulting in higher error rates than others (e.g., wetlands). Our results identified flood damage probability hotspots across multiple spatial and regional scales, with high probabilities common in both inland and coastal regions. The highest flood damage probabilities tended to be in areas of low elevation, in close proximity to streams, with extreme precipitation, and with high urban road density. Given rapid environmental changes, our study demonstrates an efficient approach for updating flood damage probability estimates across the nation.

Environmental Research Letters↗

USGS2021

Tools and technologies for quantifying spread and impacts of invasive species

The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash ( Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass ( Bromus tectorum ) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap. 10 are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap. 10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.

Book chapter↗

USGS2020

Forecasting water demand across a rapidly urbanizing region

Urban growth and climate change together complicate planning efforts meant to adapt to increasingly scarce water supplies. Several studies have independently examined the impacts of urban planning and climate change on water demand, but little attention has been given to their combined impact. Here we forecast urban water demand using a Geographically Weighted Regression model informed by socio-economic, environmental and landscape pattern metrics. The purpose of our study is to evaluate how future scenarios of population densities and climate warming will jointly affect water demand across two rapidly growing U.S. states (North Carolina and South Carolina). Our forecasts indicate that regional water demand by 2065 will increase by 37%–383% relative to the baseline in 2010, across all scenarios of change. Our results show future water demand will increase under rising temperatures, but could be ameliorated by policies that promote higher density development and urban infill. These water-efficient land use policies show a 5% regional reduction in water demand and up to 25% reduction locally for counties with the highest expected population growth by 2065. For rural counties experiencing depopulation, the land use policies we considered are insufficient to significantly reduce water demand. For expanding communities seeking to increase their adaptive capacity to changing socio-environmental conditions, our framework can assist in developing sustainable solutions.

North Carolina, South Carolina↗

USGS2019

Modeling landowner interactions and development patterns at the urban fringe

Population growth and unrestricted development policies are driving low-density urbanization and fragmentation of peri-urban landscapes across North America. While private individuals own most undeveloped land, little is known about how their decision-making processes shape landscape-scale patterns of urbanization over time. We introduce a hybrid agent-based modeling (ABM) – cellular automata (CA) modeling approach, developed for analyzing dynamic feedbacks between landowners’ decisions to sell their land for development, and resulting patterns of landscape fragmentation. Our modeling approach builds on existing conceptual frameworks in land systems modeling by integrating an ABM into an established grid-based land-change model – FUTURES. The decision-making process within the ABM involves landowner agents whose decision to sell their land to developers is a function of heterogeneous preferences and peer-influences (i.e., spatial neighborhood relationships). Simulating landowners’ decision to sell allows an operational link between the ABM and the CA module. To test our hybrid ABM-CA approach, we used empirical data for a rapidly growing region in North Carolina for parameterization. We conducted a sensitivity analysis focusing on the two most relevant parameters—spatial actor distribution and peer-influence intensity—and evaluated the dynamic behavior of the model simulations. The simulation results indicate different peer-influence intensities lead to variable landscape fragmentation patterns, suggesting patterns of spatial interaction among landowners indirectly affect landscape-scale patterns of urbanization and the fragmentation of undeveloped forest and farmland.

North Carolina↗

USGS2018

Quantifying the visual-sensory landscape qualities that contribute to cultural ecosystem services using social media and LiDAR

Landscapes are increasingly recognized for providing valuable cultural ecosystem services with numer- ous non-material benefits by serving as places of rest, relaxation, and inspiration that ultimately improve overall mental health and physical well-being. Maintaining and enhancing these valuable benefits through targeted management and conservation measures requires understanding the spatial and tem- poral determinants of perceived landscape values. Content contributed through mobile technologies and the web are emerging globally, providing a promising data source for localizing and assessing these land- scape benefits. These georeferenced data offer rich in situ qualitative information through photos and comments that capture valued and special locations across large geographic areas. We present a novel method for mapping and modeling landscape values and perceptions that leverages viewshed analysis of georeferenced social media data. Using a high resolution LiDAR (Light Detection and Ranging) derived digital surface model, we are able to evaluate landscape characteristics associated with the visual- sensory qualities of outdoor recreationalists. Our results show the importance of historical monuments and attractions in addition to specific environmental features which are appreciated by the public. Evaluation of photo-image content highlights the opportunity of including temporally and spatially vari- able visual-sensory qualities in cultural ecosystem services (CES) evaluation like the sights, sounds and smells of wildlife and weather phenomena.

North Carolina↗

USGS2017

Bayesian methods to estimate urban growth potential

Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.

Landscape and Urban Planning↗

USGS2017

Payments for carbon sequestration to alleviate development pressure in a rapidly urbanizing region

The purpose of this study was to determine individuals' willingness to enroll in voluntary payments for carbon sequestration programs through the use of a discrete choice experiment delivered to forest owners living in the rapidly urbanizing region surrounding Charlotte, North Carolina. We examined forest owners' willingness to enroll in payments for carbon sequestration policies under different levels of financial incentives (annual revenue), different contract lengths, and different program administrators (e.g., private companies versus a state or federal agency). We also examined the influence forest owners' sense of place had on their willingness to enroll in hypothetical programs. Our results showed a high level of ambivalence toward participating in payments for carbon sequestration programs. However, both financial incentives and contract lengths significantly influenced forest owners' intent to enroll. Neither program administration nor forest owners' sense of place influenced intent to enroll. Although our analyses indicated that payments from carbon sequestration programs are not currently competitive with the monetary returns expected from timber harvest or property sales, certain forest owners might see payments for carbon sequestration programs as a viable option for offsetting increasing tax costs as development encroaches and property values rise.

Georgia, North Carolina, Tennessee, Virginia, Sout↗

USGS2016

Changing disturbance regimes, ecological memory, and forest resilience

Ecological memory is central to how ecosystems respond to disturbance and is maintained by two types of legacies – information and material. Species life-history traits represent an adaptive response to disturbance and are an information legacy; in contrast, the abiotic and biotic structures (such as seeds or nutrients) produced by single disturbance events are material legacies. Disturbance characteristics that support or maintain these legacies enhance ecological resilience and maintain a “safe operating space” for ecosystem recovery. However, legacies can be lost or diminished as disturbance regimes and environmental conditions change, generating a “resilience debt” that manifests only after the system is disturbed. Strong effects of ecological memory on post-disturbance dynamics imply that contingencies (effects that cannot be predicted with certainty) of individual disturbances, interactions among disturbances, and climate variability combine to affect ecosystem resilience. We illustrate these concepts and introduce a novel ecosystem resilience framework with examples of forest disturbances, primarily from North America. Identifying legacies that support resilience in a particular ecosystem can help scientists and resource managers anticipate when disturbances may trigger abrupt shifts in forest ecosystems, and when forests are likely to be resilient.

Frontiers in Ecology and the Environment↗

USGS2011

Forest species diversity reduces disease risk in a generalist plant pathogen invasion

Empirical evidence suggests that biodiversity loss can increase disease transmission, yet our understanding of the 'diversity-disease hypothesis' for generalist pathogens in natural ecosystems is limited. We used a landscape epidemiological approach to examine two scenarios regarding diversity effects on the emerging plant pathogen Phytophthora ramorum across a broad, heterogeneous ecoregion: (1) an amplification effect exists where disease risk is greater in areas with higher plant diversity due to the pathogen's wide host range, or (2) a dilution effect where risk is reduced with increasing diversity due to lower competency of alternative hosts. We found evidence for pathogen dilution, whereby disease risk was lower in sites with higher species diversity, after accounting for potentially confounding effects of host density and landscape heterogeneity. Our results suggest that although nearly all plants in the ecosystem are hosts, alternative hosts may dilute disease transmission by competent hosts, thereby buffering forest health from infectious disease.

Ecology Letters↗