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Results for “Modeling and Using Context”

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At least 523 records · Page 29Linked to original sources

USGS2011

Surface complexation modeling for predicting solid phase arsenic concentrations in the sediments of the Mississippi River Valley alluvial aquifer, Arkansas, USA

The potential health impact of As in drinking water supply systems in the Mississippi River Valley alluvial aquifer in the state of Arkansas, USA is significant. In this context it is important to understand the occurrence, distribution and mobilization of As in the Mississippi River Valley alluvial aquifer. Application of surface complexation models (SCMs) to predict the sorption behavior of As and hydrous Fe oxides (HFO) in the laboratory has increased in the last decade. However, the application of SCMs to predict the sorption of As in natural sediments has not often been reported, and such applications are greatly constrained by the lack of site-specific model parameters. Attempts have been made to use SCMs considering a component additivity (CA) approach which accounts for relative abundances of pure phases in natural sediments, followed by the addition of SCM parameters individually for each phase. Although few reliable and internally consistent sorption databases related to HFO exist, the use of SCMs using laboratory-derived sorption databases to predict the mobility of As in natural sediments has increased. This study is an attempt to evaluate the ability of the SCMs using the geochemical code PHREEQC to predict solid phase As in the sediments of the Mississippi River Valley alluvial aquifer in Arkansas. The SCM option of the double-layer model (DLM) was simulated using ferrihydrite and goethite as sorbents quantified from chemical extractions, calculated surface-site densities, published surface properties, and published laboratory-derived sorption constants for the sorbents. The model results are satisfactory for shallow wells (10.6 m below ground surface), where the redox condition is relatively oxic or mildly suboxic. However, for the deep alluvial aquifer (21–36.6 m below ground surface) where the redox condition is suboxic to anoxic, the model results are unsatisfactory.

Arkansas↗

USGS2014

Mountain landscapes offer few opportunities for high-elevation tree species migration

Climate change is anticipated to alter plant species distributions. Regional context, notably the spatial complexity of climatic gradients, may influence species migration potential. While high-elevation species may benefit from steep climate gradients in mountain regions, their persistence may be threatened by limited suitable habitat as land area decreases with elevation. To untangle these apparently contradictory predictions for mountainous regions, we evaluated the climatic suitability of four coniferous forest tree species of the western United States based on species distribution modeling (SDM) and examined changes in climatically suitable areas under predicted climate change. We used forest structural information relating to tree species dominance, productivity, and demography from an extensive forest inventory system to assess the strength of inferences made with a SDM approach. We found that tree species dominance, productivity, and recruitment were highest where climatic suitability (i.e., probability of species occurrence under certain climate conditions) was high, supporting the use of predicted climatic suitability in examining species risk to climate change. By predicting changes in climatic suitability over the next century, we found that climatic suitability will likely decline, both in areas currently occupied by each tree species and in nearby unoccupied areas to which species might migrate in the future. These trends were most dramatic for high elevation species. Climatic changes predicted over the next century will dramatically reduce climatically suitable areas for high-elevation tree species while a lower elevation species, Pinus ponderosa, will be well positioned to shift upslope across the region. Reductions in suitable area for high-elevation species imply that even unlimited migration would be insufficient to offset predicted habitat loss, underscoring the vulnerability of these high-elevation species to climatic changes.

Arizona;California;Colorado;Idaho;Kansas;Montana;N↗

USGS2006

Emerging concepts for management of river ecosystems and challenges to applied integration of physical and biological sciences in the Pacific Northwest, USA

Integration of biological and physical concepts is necessary to understand and conserve the ecological integrity of river systems. Past attempts at integration have often focused at relatively small scales and on mechanistic models that may not capture the complexity of natural systems leaving substantial uncertainty about ecological responses to management actions. Two solutions have been proposed to guide management in the face of that uncertainty: the use of “natural variability” in key environmental patterns, processes, or disturbance as a reference; and the retention of some areas as essentially unmanaged reserves to conserve and represent as much biological diversity as possible. Both concepts are scale dependent because dominant processes or patterns that might be referenced will change with scale. Context and linkages across scales may be as important in structuring biological systems as conditions within habitats used by individual organisms. Both ideas view the physical environment as a template for expression, maintenance, and evolution of ecological diversity. To conserve or restore a diverse physical template it will be important to recognize the ecologically important differences in physical characteristics and processes among streams or watersheds that we might attempt to mimic in management or represent in conservation or restoration reserves.

International Journal of River Basin Management↗

USGS2019

A stratigraphic approach to inferring depositional ages from detrital geochronology data

With the increasing use of detrital geochronology data for provenance analyses, we have also developed new constraints on the age of otherwise undateable sedimentary deposits. Because a deposit can be no older than its youngest mineral constituent, the youngest defensible detrital mineral age defines the maximum depositional age of the sampled bed. Defining the youngest `defensible' age in the face of uncertainty (e.g., analytical and geological uncertainty, or sample contamination) is challenging. The current standard practice of finding multiple detrital minerals with indistinguishable ages provides confidence that a given age is not an artifact; however, we show how requiring this overlap reduces the probability of identifying the true youngest component age. Barring unusual complications, the principle of superposition dictates that sedimentary deposits must get younger upsection. This fundamental constraint can be incoporated into the analysis of depositional ages in sedimentary sections through the use of Bayesian statistics, allowing for the inference of bounded estimates of true depositional ages and uncertainties from detrital geochronology so long as some minimum age constraints are present. We present two approaches for constructing a Bayesian model of deposit ages, first solving directly for the ages of deposits with the prior constraint that the ages of units must obey stratigraphic ordering, and second describing the evolution of ages with a curve that represents the sediment accumulation rate. Using synthetic examples we highlight how this method preforms in less-than-ideal circumstances. In an example from the Magallanes Basin of Patagonia, we demonstrate how introducing other age information from the stratigraphic section (e.g., fossil assemblages or radiometric dates) and formalizing the stratigraphic context of samples provides additional constraints on and information regarding depositional ages or derived quantities (e.g., sediment accumulation rates) compared to isolated analysis of individual samples.

Frontiers in Earth Science↗

USGS2017

The effect of urban growth on landscape-scale restoration for a fire-dependent songbird

A landscape-scale perspective on restoration ecology has been advocated, but few studies have informed restoration with landscape metrics or addressed broad-scale threats. Threats such as urban growth may affect restoration effectiveness in a landscape context. Here, we studied longleaf pine savanna in the rapidly urbanizing southeastern United States where a habitat-specialist bird, Bachman's sparrow ( Peucaea aestivalis ), is closely associated with savanna vegetation structure and frequent fire. Our objectives were to construct a species distribution model for Bachman's sparrow, determine the relationship between fire and urbanization, quantify the urban growth effect (2010–2090), identify potential restoration areas, and determine the interaction between restoration potential and urban growth by 2050. Number of patches, patch size, and isolation metrics were used to evaluate scenarios. The species distribution model was 88% accurate and emphasized multiscale canopy cover characteristics, fire, and percent habitat. Fires were less common <600 m from urban areas, and this fire suppression effect exacerbated urban growth effects. For restoration scenarios, canopy cover reduction by 30% resulted in nearly double the amount of habitat compared to the prescribed fire scenario; canopy cover reduction resulted in larger patch sizes and less patch isolation compared to current conditions. The effect of urban growth on restoration scenarios was unequal. Seventy-four percent of restoration areas from the prescribed fire scenario overlapped with projected urban growth, whereas the canopy cover reduction scenario only overlapped by 9%. We emphasize the benefits of simultaneously considering the effects of urban growth and landscape-scale restoration potential to promote a landscape with greater patch sizes and less isolation.

North Carolina↗

USGS2007

The roles of physical habitat in reproduction and survival of pallid sturgeon and shovelnose sturgeon in the Lower Missouri River, progress 2005–06: Chapter D in Factors affecting the reproduction, recruitment, habitat, and population dynamics of pallid sturgeon and shovelnose sturgeon in the Missouri River

This report documents progress on three related components of habitat assessments in the Lower Missouri River during 2005–06. The habitat-use component links this research directly to sturgeon ecology research described in other chapters. The habitat availability and habitat dynamics assessments provide physical context for the ecological research. Results from 2005 to 2006 indicate that the methods developed to assess habitat use, quality, quantity, and dynamics are appropriate and sufficiently accurate to address critical questions about sturgeon habitat on the Lower Missouri River. Preliminary analysis of habitats occupied by adult female shovelnose sturgeon indicates that migrating sturgeon do not select for depth but seem to select for lower than reach-averaged velocities and higher than reach-averaged velocity gradients. Data collected to compile, calibrate, and validate multidimensional hydraulic models in probable spawning reaches appear to be sufficient to support the modeling objectives. Monitoring of selected channel cross sections and long profiles multiple times during the year showed little change at the upstreammost reach over the range of flows measured during 2006, likely because of channel stability associated with an armored bed. Geomorphic changes documented at monitoring cross sections increased with distance downstream. Hydroacoustic substrate-class parameters documented systematic changes with discharge and with hydraulic environment across the channel. Similarly, bed velocity varied predictably with discharge and hydraulic environment, indicating its potential as an indicator of bedload sediment transport. Longitudinal profiles showed substantial downstream movement of dunes over the monitored discharges, as well as substantial within-year variability in dune size. Observations of geomorphic change during the moderate flow range of 2006 support the hypothesis that the magnitude of flow modifications under consideration on the Lower Missouri River will be sufficient to transport sediment and potentially modify spawning habitats.

Lower Missouri River↗

USGS2023

Context-dependent representation of within- and between-model uncertainty: Aggregating probabilistic predictions in infectious disease epidemiology

Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.

Journal of the Royal Society Interface↗

USGS2020

Simulating strategic implementation of the CRP to increase Greater prairie-chicken abundance

The Conservation Reserve Program (CRP) has the potential to influence the distribution and abundance of grasslands in many agricultural landscapes, and thereby provide habitat for grassland-dependent wildlife. Greater prairie-chickens ( Tympanuchus cupido pinnatus ) are a grassland-dependent species with large area requirements and have been used as an indicator of grassland ecosystem function; they are also a species of conservation concern across much of their range. Greater prairie-chicken populations respond to the amount and configuration of grasslands and wetlands in agriculturally dominated landscapes, which in turn can be influenced by the CRP; however, CRP enrollments and enrollment caps have declined from previous highs. Therefore, prioritizing CRP reenrollments and new enrollments to achieve the greatest benefit for grassland-dependent wildlife seems prudent. We used models relating either lek density or the number of males at leks to CRP enrollments and the resulting landscape structure to predict changes in greater prairie-chicken abundance related to changes in CRP enrollments. We simulated 3 land-cover scenarios: expiration of existing CRP enrollments, random, small-parcel (4,040 m 2 ) addition of CRP grasslands, and strategic, large-parcel (80,000 m 2 ) addition of CRP grasslands. Large-parcel additions were the average enrollment size in northwestern Minnesota, USA, within the context of a regional prairie restoration plan. In our simulations of CRP enrollment expirations, the abundance of greater prairie-chickens declined when grassland landscape contiguity declined with loss of CRP enrollments. Simulations of strategic CRP enrollment with large parcels to increase grassland contiguity more often increased greater prairie-chicken abundance than random additions of the same area in small parcels that did not increase grassland contiguity. In some cases, CRP enrollments had no or a negative predicted change in greater prairie-chicken abundance because they provided insufficient grassland contiguity on the landscape, or increased cover-type fragmentation. Predicted greater prairie-chicken abundance increased under large-parcel and small-parcel scenarios of addition of CRP grassland; the greatest increases were associated with large-parcel additions. We suggest that strategic application of the CRP to improve grassland contiguity can benefit greater prairie-chicken populations more than an opportunistic approach lacking consideration of the larger landscape context. Strategic implementation of the CRP can benefit greater prairie-chicken populations in northwestern Minnesota, and likely elsewhere in landscapes where grassland continuity may be a limiting factor.

Minnesota↗

USGS2024

Population genetics of museum specimens indicate decreasing genetic resiliency: The case of two bumble bees of conservation concern

Genetic resiliency is the likelihood that populations retain sufficient genetic diversity to respond to environmental change. It is rarely examined through time in conservation genetic studies due to challenges of acquiring and sequencing historical specimens. Focusing on populations of two sibling bumble bee species of conservation concern with different recent patterns of decline, we used museum specimens collected between 1960 and 2020 and 15 microsatellite markers to assess genetic resiliency (allelic richness, expected heterozygosity, and inbreeding) through time and across geographic space. We find evidence of decreasing allelic richness through time, starting at least 30 years before observed abundance declines in one species and at least 20 years before present in a species with apparently stable abundance. We also found increasing expected heterozygosity through time, indicating increased inbreeding, in the putatively stable species. We demonstrate that genetic measurements taken from specimens collected through time can be used to detect population decline in imperiled species before decreases in abundance are detected. We also demonstrate the importance of interpreting population genetic metrics within the context of historical patterns to assess species' conservation statuses. Finally, we discuss the limitations of currently available population genetic methods, including the influence of isolation by distance and sampling density on measurements of genetic structure, and the influence of demographic characteristics and choice of genetic markers on estimates of genetic diversity and structure. We call for further development of individual-based modeling methods to measure genetic structure, as opposed to commonly applied population-based metrics, to overcome these limitations.

Biological Conservation↗

USGS2008

Uncertainty and sensitivity issues in process-based models of carbon and nitrogen cycles in terrestrial ecosystems

Many process-based models of carbon (C) and nitrogen (N) cycles have been developed for northern forest ecosystems. These models are widely used to evaluate the long-term decisions in forest management dealing with effects like particulate pollution, productivity and climate change. Regarding climate change, one of the key questions that have sensitive political implications is whether northern forests will sequester atmospheric C or not. Whilst many process-based models have been tested for accuracy by evaluating or validating against observed data, few have dealt with the complexity of the incorporated procedures to estimate uncertainties associated with model predictions or the sensitivity of these predictions to input factors in a systematic, inter-model comparison fashion. In general, models differ in their underlying attempts to match natural complexities with assumed or imposed model structure and process formulations to estimate model parameters, to gather data and to address issues on scope, scale and natural variations. Uncertainties may originate from model structure, estimation of model parameters, data input, representation of natural variation and scaling exercises. Model structure relates to the mathematical representation of the processes modelled and the type of state variables that a model contains. The modelling of partitioning among above- and below-ground C and N pools and the interdependence among these pools remain a major source of uncertainty in model structure and error propagation. For example, most soil C models use at least three state variables to represent the different types of soil organic matter (SOM). This approach results in creating three artificial SOM pools, assuming that each one contains C compounds with the same turnover rate. In reality, SOM consists of many different types of C compounds with widely different turnover rates. Uncertainty in data and parameter estimates are closely linked. Data uncertainties are associated with high variations in estimating forest biomass, productivity and soil organic matter and their estimates may be incomplete for model initialisation, calibration, validation and sensitivity analysis of generalised predictor models. The scale at which a model is being used also affects the level of uncertainty, as the errors in the prediction of the C and N dynamics differ from site to landscape levels and across climatic regions. If the spatial or temporal scale of a model application is changed, additional uncertainty arises from neglecting natural variability in system variables in time and space. Uncertainty issues are also intimately related to model validation and sensitivity analysis. The estimation of uncertainties is needed to inform decision processes in order to detect the possible corridor of development. Uncertainty in this context is an essential measure of quality for stakeholder and decision makers.

Book chapter↗

USGS2019

Spatially explicit modelling of floodplain forest succession: Interactions among flood inundation, forest successional processes, and other disturbances in the Upper Mississippi River floodplain, USA

Simulation models are often used to identify hydrologic regimes suitable for different riparian or floodplain tree species. However, most existing models pay little attention to forest successional processes or other disturbances that may interact with the hydrologic regime of river systems to alter forest dynamics in space and time. In this study, we introduce a flood disturbance module to the LANDIS-II forest succession modelling framework to enable investigations into how inundation interacts with other disturbances and successional processes to alter floodplain forest cover and community dynamics. We illustrate the functionality of the model using a case study with multiple scenarios in the Upper Mississippi River floodplain, USA. We found that model predictions of total forest cover and the abundance of specific forest community types were generally related to uncertainty in the susceptibility of different species and age classes to inundation. By simulation year 100, increases or decreases in total forest cover and forest type distributions were roughly proportional to the initial differences in the susceptibility of species and age classes to inundation. The largest decrease in total forest cover was associated with a scenario that included disturbance by the emerald ash borer ( Agrilus planipennis ) and when using susceptibility parameters corresponding to the weakest flood tolerance. In contrast, changes in the composition of aboveground biomass were not sensitive to differences in susceptibility, and generally showed shifts toward later successional species with higher shade tolerance and longer lifespans for all scenarios. Our findings suggest that flood inundation interacts with other disturbances (e.g., insect outbreaks) and forest successional processes to alter forest abundance, distribution, and species composition in this system. Our modelling framework should allow for future studies that examine such interactions in other systems, and in the context of alternative hydrologic scenarios and other disturbance regimes.

Minnesota, Wisconsin↗

USGS2024

Shift in piscivory by salmonids following invasion of a minnow in an oligotrophic reservoir

Predation can play an important role in structuring ecological communities, and predator–prey dynamics can be altered following the introduction of new species. An unauthorized introduction of redside shiner ( Richardsonius balteatus ) into reservoirs in the Upper Skagit River, Washington, USA created concern that a consequent shift in predator–prey dynamics in the reservoirs could reduce recruitment and production of native salmonids in the basin. We estimated predation mortality in Ross Lake on nonnative redside shiner and juvenile native salmonids to evaluate the potential role of predation in regulating these populations and limiting survival of native species of concern. We used bioenergetics modelling and stable isotope analysis combined with directed field measurements of growth, seasonal diet and thermal experience of piscivorous salmonids to quantify their consumption demand on prey fishes to evaluate the relative magnitude of predation mortality on invasive redside shiners and native salmonids. While redside shiner are the dominant prey fish species in Ross Lake, the modest biomass of native salmonids consumed could translate into substantial mortality, the magnitude of which depended on the timing and size at which prey fishes were eaten. This information provides important context for how nonnative species may indirectly impact native species through shared predation (apparent competition) and can inform conservation decisions surrounding nonnative species control, sustainability of native salmonids and introductions of anadromous fishes.

Ecology of Freshwater Fish↗

USGS2019

The Laurentian Great Lakes: A case study in ecological disturbance and climate change

Climate change effects are already significant, but can also magnify other ecological problems. This can be clearly seen in the Laurentian Great Lakes, which have suffered habitat degradation, fishery overharvest and dramatic alterations by invasive species. Thermal changes are expected to cause extensive loss of suitable fish habitat, and changing precipitation patterns will aggravate the problems with our highly modified lotic and lentic systems. A brief summary of the historic ecological context provided by the Great Lakes case is presented, followed by the descriptions of selected tools that help to understand and evaluate both ecological and climate change problems. Species distribution models and habitat classification combined with climate change predictions can identify the distribution and extent of optimal habitats, and identify which are most vulnerable to climate change. Ecological flow modelling can help to identify when critical flow changes are likely. Mechanistic simulation modelling specifies understanding of how aquatic systems function and can reveal cause and effect relationships. These tools can be used to help managers to protect optimal habitat, resist climate change effects to other habitats and adapt cultural systems to climate‐altered aquatic systems.

Great Lakes↗

USGS2019

Geographic context affects the landscape change and fragmentation caused by wind energy facilities

Wind energy generation causes transformation of landscapes as new roads, pads, and transmission lines are constructed. Limiting the landscape change and fragmentation caused by these facilities likely minimizes impacts to biodiversity and sensitive wildlife species. We examined the effects of wind energy facilities’ geographic context on changes in landscape patterns. We digitized the footprints of 39 wind facilities and the surrounding land cover using high-resolution imagery. We then measured landscape pattern before and after facility construction using 3 metrics associated with species responses to habitat loss and fragmentation (amount, core area, and connectivity of undeveloped land) within 1km around newly constructed turbines and roads. New facilities decreased the amount of undeveloped land, on average, by just 1.8% while changes in metrics of landscape pattern were relatively much larger (50 to 140%). Statistical models indicated levels of pre-construction development were a key factor explaining the impact of new wind facilities on landscape metrics, with pre-construction road networks, turbine spacing, and topography having smaller influences. As the proportion of developed land around facilities increased, a higher proportion of the facility utilized pre-construction developed land and a lower density of new roads were built, resulting in smaller impacts to undeveloped landscapes. Building of new road networks was also a predictor of landscape fragmentation. Density of new roads was higher in places with little pre-existing development and may be influenced by facility design. Utilizing existing development and carefully placing turbines may provide opportunities to minimize the impacts of new wind energy facilities.

PeerJ↗

USGS2015

Context-dependent survival, fecundity and predicted population-level consequences of brucellosis in African buffalo

Chronic infections may have negative impacts on wildlife populations, yet their effects are difficult to detect in the absence of long-term population monitoring. Brucella abortus , the bacteria responsible for bovine brucellosis, causes chronic infections and abortions in wild and domestic ungulates, but its impact on population dynamics is not well understood. We report infection patterns and fitness correlates of bovine brucellosis in African buffalo based on (1) 7 years of cross-sectional disease surveys and (2) a 4-year longitudinal study in Kruger National Park (KNP), South Africa. We then used a matrix population model to translate these observed patterns into predicted population-level effects. Annual brucellosis seroprevalence ranged from 8·7% (95% CI = 1·8–15·6) to 47·6% (95% CI = 35·1–60·1) increased with age until adulthood (>6) and varied by location within KNP. Animals were on average in worse condition after testing positive for brucellosis ( F = −5·074, P < 0·0001), and infection was associated with a 2·0 (95% CI = 1·1–3·7) fold increase in mortality (χ 2 = 2·039, P = 0·036). Buffalo in low body condition were associated with lower reproductive success ( F = 2·683, P = 0·034), but there was no association between brucellosis and pregnancy or being observed with a calf. For the range of body condition scores observed in the population, the model-predicted growth rate was λ = 1·11 (95% CI = 1·02–1·21) in herds without brucellosis and λ = 1·00 (95% CI = 0·85–1·16) when brucellosis seroprevalence was 30%. Our results suggest that brucellosis infection can potentially result in reduced population growth rates, but because these effects varied with demographic and environmental conditions, they may remain unseen without intensive, longitudinal monitoring.

Kruger National Park↗

USGS2013

Adjustment of the San Francisco estuary and watershed to decreasing sediment supply in the 20th century

The general progression of human land use is an initial disturbance (e.g., deforestation, mining, agricultural expansion, overgrazing, and urbanization) that creates a sediment pulse to an estuary followed by dams that reduce sediment supply. We present a conceptual model of the effects of increasing followed by decreasing sediment supply that includes four sequential regimes, which propagate downstream: a stationary natural regime, transient increasing sediment supply, transient decreasing sediment supply, and a stationary altered regime. The model features characteristic lines that separate the four regimes. Previous studies of the San Francisco Estuary and watershed are synthesized in the context of this conceptual model. Hydraulic mining for gold in the watershed increased sediment supply to the estuary in the late 1800s. Adjustment to decreasing sediment supply began in the watershed and upper estuary around 1900 and in the lower estuary in the 1950s. Large freshwater flow in the late 1990s caused a step adjustment throughout the estuary and watershed. It is likely that the estuary and watershed are still capable of adjusting but further adjustment will be as steps that occur only during greater floods than previously experienced during the adjustment period. Humans are actively managing the system to try to prevent greater floods. If this hypothesis of step changes occurring for larger flows is true, then the return interval of step changes will increase or, if humans successfully control floods in perpetuity, there will be no more step changes.

California↗

USGS2023

Blue carbon in a changing climate and a changing context

Blue carbon, a convenient term to encompass the climate mitigation value of coastal carbon dynamics, has received global policy attention and growing datasets to support management actions. Carbon stock assessments in mangroves, seagrass, and tidal marshes document significant carbon storage in soils. Models illustrate significant downward fluxes of carbon dioxide and limited methane emissions, making tidal wetland preservation and restoration notably potent for carbon dioxide removal (CDR). Natural variation in different carbon stocks and fluxes has led to prioritization efforts to characterize coastal lands across physical and biological gradients. However, a larger concern beyond upscaling carbon dynamics is the resilience of these stocks and fluxes with global changes. Data-informed models have greatly improved our assessments of the vulnerability of soil and biomass stocks, greenhouse gas (GHG) balance, and spatial extents. Accelerated sea-level rise is increasingly concerning, but its impacts vary by resilience context, as very few coastal lands are without direct human impact. As the landscape context has changed, blue carbon fluxes have also shifted in terms of importance and distribution. New incentives for tidal ecosystem management are expanding boundaries to include algal carbon and tidal transport of alkalinity, which bring additional co-benefits to coastal waters. Using examples from the conterminous USA on blue carbon stocks, radiative balance, and extent, this chapter explores timelines of physical and biogeochemical stressors and their application to past, current, and future climate mitigation functions of coastal ecosystems.

Book chapter↗

USGS2006

Dealing with uncertainty and sensitivity issues in process-based models of carbon and nitrogen cycles in northern forest ecosystems

Many process-based models on carbon (C) and nitrogen (N) cycles have been developed for northern forest ecosystems. These models are widely used to evaluate the long-term decisions in forest management dealing with effects like particulate pollution, productivity and climate change. Regarding climate change, one of the key questions that have sensitive political implications is whether northern forests will sequester atmospheric C or not. Whilst many process-based models have been tested for accuracy by evaluating or validating against observed data, few have dealt with the complexity of the incorporated procedures to estimate uncertainties associated with model predictions or the sensitivity of these predictions to input factors in a systematic, inter-model comparison fashion. In general, models differ in their underlying attempts to match natural complexities with assumed or imposed model structure and process formulations to estimate model parameters, to gather data and to address issues on scope, scale and natural variations. Uncertainties may originate from model structure, estimation of model parameters, data input, representation of natural variation and scaling exercises. Model structure relates to the mathematical representation of the processes modelled and the type of state variables that a model contains. The modelling of partitioning among above- and below-ground C and N pools and the interdependence among these pools remain a major source of uncertainty in model structure and error propagation. Most soil C models use at least three state variables to represent the different types of soil organic matter (SOM). This approach results in creating three artificial SOM pools, assuming that each one contains C compounds with same turnover rate. In reality, SOM consists of many different types of C compounds with widely different turnover rates. Uncertainty in data and parameter estimates are closely linked. Data uncertainties are associated with high variations in estimating forest biomass, productivity and soil organic matter and may be incomplete for model initialization, calibration, validation and sensitivity analysis of generalized predictor models. The scale at which a model is being used also affects the level of uncertainty, as the errors in the prediction of the C and N dynamics differ from the site to the landscape levels and across climatic regions. If the spatial or temporal scale of a model application is changed, additional uncertainty arises from neglecting natural variability in system variables in time and space. Uncertainty issues are also intimately related to model validation and sensitivity analysis. The estimation of uncertainties is needed to inform decision process, in order to detect the possible corridor of development. Uncertainty in this context is an essential measure of quality for stakeholder and decision makers.

Conference Paper↗