Geology topics

Donald R. Schoolmaster

Publications and source records attributed to Donald R. Schoolmaster.

32 records · Page 2Linked to original sources

USGS2017

Development of a multimetric index for integrated assessment of salt marsh ecosystem condition

Tools for assessing and communicating salt marsh condition are essential to guide decisions aimed at maintaining or restoring ecosystem integrity and services. Multimetric indices (MMIs) are increasingly used to provide integrated assessments of ecosystem condition. We employed a theory-based approach that considers the multivariate relationship of metrics with human disturbance to construct a salt marsh MMI for five National Parks in the northeastern USA. We quantified the degree of human disturbance for each marsh using the first principal component score from a principal components analysis of physical, chemical, and land use stressors. We then applied a metric selection algorithm to different combinations of about 45 vegetation and nekton metrics (e.g., species abundance, species richness, and ecological and functional classifications) derived from multi-year monitoring data. While MMIs derived from nekton or vegetation metrics alone were strongly correlated with human disturbance ( r values from −0.80 to −0.93), an MMI derived from both vegetation and nekton metrics yielded an exceptionally strong correlation with disturbance ( r = −0.96). Individual MMIs included from one to five metrics. The metric-assembly algorithm yielded parsimonious MMIs that exhibit the greatest possible correlations with disturbance in a way that is objective, efficient, and reproducible.

Estuaries and Coasts↗

USGS2017

Land area change in coastal Louisiana (1932 to 2016)

Coastal Louisiana wetlands are one of the most critically threatened environments in the United States. These wetlands are in peril because Louisiana currently experiences greater coastal wetland loss than all other States in the contiguous United States combined. The analyses of landscape change presented here have utilized historical surveys, aerial, and satellite data to quantify landscape changes from 1932 to 2016. Analyses show that coastal Louisiana has experienced a net change in land area of approximately -4,833 square kilometers (modeled estimate: -5,197 +/- 443 square kilometers) from 1932 to 2016. This net change in land area amounts to a decrease of approximately 25 percent of the 1932 land area. Previous studies have presented linear rates of change over multidecadal time periods which unintentionally suggest that wetland change occurs at a constant rate, although in many cases, wetland change rates vary with time. A penalized regression spline technique was used to determine the model that best fit the data, rather than fitting the data with linear trends. Trend analyses from model fits indicate that coastwide rates of wetland change have varied from -83.5 +/- 11.8 square kilometers per year to -28.01 +/- 16.37 square kilometers per year. To put these numbers into perspective, this equates to long-term average loss rates of approximately an American football field’s worth of coastal wetlands within 34 minutes when losses are rapid to within 100 minutes at more recent, slower rates. Of note is the slowing of the rate of wetland change since its peak in the mid- 1970s. Not only have rates of wetland loss been decreasing since that time, a further rate reduction has been observed since 2010. Possible reasons for this reduction include recovery from lows affected by the hurricanes of 2005 and 2008, the lack of major storms in the past 8 years, a possible slowing of subsidence rates, the reduction in and relocation of oil and gas extraction and infrastructure since the peak of such activities in the late 1960s, and restoration activities. In addition, many wetlands in more exposed positions in the landscape have already been lost. Most notable of the factors listed above is the lack of major storms over the past 8 years. The observed coastwide net “stability” in land area observed over the past 6–8 years does not imply that loss has ceased. Future disturbance events such as a major hurricane impact could change the trajectory of the rates. Sea-level rise is projected to increase at an exponential rate, and that would also expedite the rate of wetland loss.

Louisiana↗

USGS2017

Causal mechanisms of soil organic matter decomposition: Deconstructing salinity and flooding impacts in coastal wetlands

Coastal wetlands significantly contribute to global carbon storage potential. Sea-level rise and other climate change-induced disturbances threaten coastal wetland sustainability and carbon storage capacity. It is critical that we understand the mechanisms controlling wetland carbon loss so that we can predict and manage these resources in anticipation of climate change. However, our current understanding of the mechanisms that control soil organic matter decomposition, in particular the impacts of elevated salinity, are limited, and literature reports are contradictory. In an attempt to improve our understanding of these complex processes, we measured root and rhizome decomposition and developed a causal model to identify and quantify the mechanisms that influence soil organic matter decomposition in coastal wetlands that are impacted by sea-level rise. We identified three causal pathways: 1) a direct pathway representing the effects of flooding on soil moisture, 2) a direct pathway representing the effects of salinity on decomposer microbial communities and soil biogeochemistry, and 3) an indirect pathway representing the effects of salinity on litter quality through changes in plant community composition over time. We used this model to test the effects of alternate scenarios on the response of tidal freshwater forested wetlands and oligohaline marshes to short- and long-term climate-induced disturbances of flooding and salinity. In tidal freshwater forested wetlands, the model predicted less decomposition in response to drought, hurricane salinity pulsing, and long-term sea-level rise. In contrast, in the oligohaline marsh, the model predicted no change in response to sea-level rise, and increased decomposition following a drought or a hurricane salinity pulse. Our results show that it is critical to consider the temporal scale of disturbance and the magnitude of exposure when assessing the effects of salinity intrusion on carbon mineralization in coastal wetlands. Here we identify three causal mechanisms that can reconcile disparities between long-term and short-term salinity impacts on organic matter decomposition.

Ecology↗

USGS2017

Soils mediate the impact of fine woody debris on invasive and native grasses as whole trees are mechanically shredded into firebreaks in piñon-juniper woodlands

To stem wildfires, trees are being mechanically shredded into firebreaks with the resulting fine woody debris (FWD) potentially exerting immense control over soil and plants. We linked FWD-induced changes in microbial activity and nutrient availability to the frequency of Bromus tectorum and three native, perennial grasses across 31 piñon-juniper woodlands, UT, USA. Using a series of mixed models, we found that FWD increased the frequency of three of the four grasses by at least 12%. Deep, as opposed to shallow, soils mediated frequencies following FWD additions but only partially explained the variation in Bromus and Pseudoroegneria spicata . Although fertile areas associated with tree-islands elicited no response, FWD-induced increases in nitrogen mineralization in deep soils (15–17 cm) caused the frequency of the exotic and Pseudoroegneria to rise. Higher phosphorus availability in FWD-covered surface soils (0–2 cm) had no impact on grasses. FWD altered deep soil respiration, and deep and shallow microbial biomass structuring Pseudoroegneria frequencies, suggesting that microorganism themselves regulated Pseudoroegneria . The positive effects of FWD on grass frequencies intensified over time for natives but diminished for Bromus . Our results demonstrate that microorganisms in deeper soils helped mediate species-specific responses to disturbance both facilitating exotic invasion and promoting native establishment.

Journal of Arid Environments↗

USGS2016

A landscape-scale assessment of above- and belowground primary production in coastal wetlands: Implications for climate change-induced community shifts

Above- and belowground production in coastal wetlands are important contributors to carbon accumulation and ecosystem sustainability. As sea level rises, we can expect shifts to more salt-tolerant communities, which may alter these ecosystem functions and services. Although the direct influence of salinity on species-level primary production has been documented, we lack an understanding of the landscape-level response of coastal wetlands to increasing salinity. What are the indirect effects of sea-level rise, i.e., how does primary production vary across a landscape gradient of increasing salinity that incorporates changes in wetland type? This is the first study to measure both above- and belowground production in four wetland types that span an entire coastal gradient from fresh to saline wetlands. We hypothesized that increasing salinity would limit rates of primary production, and saline marshes would have lower rates of above- and belowground production than fresher marshes. However, along the Northern Gulf of Mexico Coast in Louisiana, USA, we found that aboveground production was highest in brackish marshes, compared with fresh, intermediate, and saline marshes, and belowground production was similar among all wetland types along the salinity gradient. Multiple regression analysis indicated that salinity was the only significant predictor of production, and its influence was dependent upon wetland type. We concluded that (1) salinity had a negative effect on production within wetland type, and this relationship was strongest in the fresh marsh (0–2 PSU) and (2) along the overall landscape gradient, production was maintained by mechanisms at the scale of wetland type, which were likely related to plant energetics. Regardless of wetland type, we found that belowground production was significantly greater than aboveground production. Additionally, inter-annual variation, associated with severe drought conditions, was observed exclusively for belowground production, which may be a more sensitive indicator of ecosystem health than aboveground production.

Estuaries and Coasts↗

USGS2016

Barrier displacement on a neutral landscape: Towards a theory of continental biogeography

Macroevolutionary theory posits three processes leading to lineage diversification and the formation of regional biotas: dispersal (species geographic range expansion), speciation (species lineage splitting), and extinction (species lineage termination). The Theory of Island Biogeography (TIB) predicts species richness values using just two of these processes; dispersal and extinction. Yet most species on Earth live on continents or continental shelves, and the dynamics of evolutionary diversification at regional and continental scales are qualitatively different from those that govern the formation of species richness on biogeographic islands. Certain geomorphological processes operating perennially on continental platforms displace barriers to gene flow and organismal dispersal, and affect all three terms of macroevolutionary diversification. For example, uplift of a dissected landscape and river capture both merge and separate portions of adjacent areas, allowing dispersal and larger geographic ranges, vicariant speciation and smaller geographic ranges, and extinction when range sizes are subdivided below a minimum persistence threshold. The TIB also does not predict many biogeographic and phylogenetic patterns widely observed in continentally distributed taxa, including: 1, power function-like species-area relationships; 2, log-normal distribution of species geographic range sizes, in which most species have restricted ranges (are endemic) and few species have broad ranges (are cosmopolitan); 3, mid-domain effects with more species towards the geographic center, and more early-branching, species-poor clades towards the geographic periphery; 4, exponential rates of net diversification with log-linear accumulation of lineages through geological time; and 5, power function-like relationships between species-richness and clade diversity, in which most clades are species-poor and few clades are species-rich. Current theory does not provide a robust mechanistic framework to connect these seemingly disparate patterns. Here we present SEAMLESS (Spatially-Explicit Area Model of Landscape Evolution by SimulationS) that generates clade diversification by moving geographic barriers on a continuous, neutral landscape. SEAMLESS is a neutral Landscape Evolution Model (LEM) that treats species and barriers as functionally equivalent with respect to model parameters. SEAMLESS differs from other model-based biogeographic methods (e.g. Lagrange, GeoSSE, BayArea, BioGeoBEARS) by modeling properties of dispersal barriers rather than areas, and by modeling the evolution of species lineages on a continuous landscape, rather than the evolution of geographic ranges along branches of a phylogeny. SEAMLESS shows how dispersal is required to maintain species richness and avoid clade-wide extinction, demonstrates that ancestral range size does not predict species richness, and provides a unified explanation for the suite of commonly observed biogeographic and phylogenetic patterns listed above. SEAMLESS explains how a simple barrier-displacement mechanism affects lineage diversification under neutral conditions, and is advanced here towards the formulation of a general theory of continental biogeography.

Systematic Biology↗

USGS2016

Do the rich get richer? Varying effects of tree species identity and diversity on the richness of understory taxa

Understory herbs and soil invertebrates play key roles in soil formation and nutrient cycling in forests. Studies suggest that diversity in the canopy and in the understory are positively associated, but these studies often confound the effects of tree species diversity with those of tree species identity and abiotic conditions. We combined extensive field sampling with structural equation modeling to evaluate the simultaneous effects of tree diversity on the species diversity of understory herbs, beetles, and earthworms. The diversity of earthworms and saproxylic beetles was directly and positively associated with tree diversity, presumably because species of both these taxa specialize on certain species of trees. Tree identity also strongly affected diversity in the understory, especially for herbs, likely as a result of interspecific differences in canopy light transmittance or litter decomposition rates. Our results suggest that changes in forest management will disproportionately affect certain understory taxa. For instance, changes in canopy diversity will affect the diversity of earthworms and saproxylic beetles more than changes in tree species composition, whereas the converse would be expected for understory herbs and detritivorous beetles. We conclude that the effects of tree diversity on understory taxa can vary from positive to negative and may affect biogeochemical cycling in temperate forests. Thus, maintaining high diversity in temperate forests can promote the diversity of multiple taxa in the understory.

Ecology↗

USGS2016

Scaling relationships among drivers of aquatic respiration from the smallest to the largest freshwater ecosystems

To address how various environmental parameters control or constrain planktonic respiration (PR), we used geometric scaling relationships and established biological scaling laws to derive quantitative predictions for the relationships among key drivers of PR. We then used empirical measurements of PR and environmental (soluble reactive phosphate [SRP], carbon [DOC], chlorophyll a [Chl- a )], and temperature) and landscape parameters (lake area [LA] and watershed area [WA]) from a set of 44 lakes that varied in size and trophic status to test our hypotheses. We found that landscape-level processes affected PR through direct effects on DOC and temperature and indirectly via SRP. In accordance with predictions made from known relationships and scaling laws, scale coefficients (the parameter that describes the shape of a relationship between 2 variables) were found to be negative and have an absolute value 1, others <1). We also found evidence of a significant relationship between temperature and SRP. Because our dataset included measurements of respiration from small pond catchments to the largest body of freshwater on the planet, Lake Superior, these findings should be applicable to controls of PR for the great majority of temperate aquatic ecosystems.

Inland Waters↗

USGS2015

Structural equation modeling: Building and evaluating causal models

Scientists frequently wish to study hypotheses about causal relationships, rather than just statistical associations. This chapter addresses the question of how scientists might approach this ambitious task. Here we describe structural equation modeling (SEM), a general modeling framework for the study of causal hypotheses. Our goals are to (a) concisely describe the methodology, (b) illustrate its utility for investigating ecological systems, and (c) provide guidance for its application. Throughout our presentation, we rely on a study of the effects of human activities on wetland ecosystems to make our description of methodology more tangible. We begin by presenting the fundamental principles of SEM, including both its distinguishing characteristics and the requirements for modeling hypotheses about causal networks. We then illustrate SEM procedures and offer guidelines for conducting SEM analyses. Our focus in this presentation is on basic modeling objectives and core techniques. Pointers to additional modeling options are also given.

Book chapter↗

USGS2014

Connecting the dots: a collaborative USGS-NPS effort to expand the utility of monitoring data

The Natural Resource Challenge (National Park Service 1999) was a call to action. It constituted a mandate for monitoring based on the twin premises that (1) natural resources in national parks require active management and stewardship if we are to protect them from gradual degradation, and (2) we cannot protect what we do not understand. The intent of the challenge was embodied in its original description: We must expand existing inventory programs and develop efficient ways to monitor the vital signs of natural systems. We must enlist others in the scientific community to help, and also facilitate their inquiry. Managers must have and apply this information to preserve our natural resources. In this article, we report on ongoing collaborative work between the National Park Service (NPS) and the US Geological Survey (USGS) that seeks to add to our scientific understanding of the ecological processes operating behind vital signs monitoring data. The ultimate goal of this work is to provide insights that can facilitate an understanding of the systems and identify potential opportunities for active stewardship by NPS managers (Bennetts et al. 2007; Mitchell et al. 2014). The bulk of the work thus far has involved Acadia and Rocky Mountain national parks, but there are plans for extending the work to additional parks. Our story stats with work designed to consider ways of assessing the status and condition of natural resources and the potential for historical or ongoing influences of human activities. In the 1990s, the concept of "biotic integrity" began to take hold as an aspiration for developing quantitative indices describing how closely the conditions at a site resemble those found at pristine, unimpacted sites. Quantitative methods for developing indices of biotic integrity (IBIs) and elaborations of that idea (e.g., ecological integrity) have received considerable attention and application of these methods to natural resources has become widespread (Karr 1991; Barbour et al. 1999; Stoddard et al. 2008). Despite widespread use, many questions remain about how metrics are combined to form effective indices and about how to interpret both. Scientists and natural resource specialists within NPS and USGS have joined forces to critique the current analysis methods, with the collaboration involving the Rocky Mountain and Northeast Temperate NPS Inventory and Monitoring (I & M) networks, along with others, and USGS scientists from the National Wetlands Research Center and Patuxent Wildlife Research Center. Funding that initiated the project was from a joint-partnership fund managed by the USGS Ecosystems Program for National Park Monitoring research and the work was focused at Acadia National Park and Rocky Mountain National Park. Here we present synopses of two major issues addressed by the group.

The George Wright Forum↗

USGS2013

An algorithmic and information-theoretic approach to multimetric index construction

The use of multimetric indices (MMIs), such as the widely used index of biological integrity (IBI), to measure, track, summarize and infer the overall impact of human disturbance on biological communities has been steadily growing in recent years. Initially, MMIs were developed for aquatic communities using pre-selected biological metrics as indicators of system integrity. As interest in these bioassessment tools has grown, so have the types of biological systems to which they are applied. For many ecosystem types the appropriate biological metrics to use as measures of biological integrity are not known a priori. As a result, a variety of ad hoc protocols for selecting metrics empirically has developed. However, the assumptions made by proposed protocols have not be explicitly described or justified, causing many investigators to call for a clear, repeatable methodology for developing empirically derived metrics and indices that can be applied to any biological system. An issue of particular importance that has not been sufficiently addressed is the way that individual metrics combine to produce an MMI that is a sensitive composite indicator of human disturbance. In this paper, we present and demonstrate an algorithm for constructing MMIs given a set of candidate metrics and a measure of human disturbance. The algorithm uses each metric to inform a candidate MMI, and then uses information-theoretic principles to select MMIs that capture the information in the multidimensional system response from among possible MMIs. Such an approach can be used to create purely empirical (data-based) MMIs or can, optionally, be influenced by expert opinion or biological theory through the use of a weighting vector to create value-weighted MMIs. We demonstrate the algorithm with simulated data to demonstrate the predictive capacity of the final MMIs and with real data from wetlands from Acadia and Rocky Mountain National Parks. For the Acadia wetland data, the algorithm identified 4 metrics that combined to produce a -0.88 correlation with the human disturbance index. When compared to other methods, we find this algorithmic approach resulted in MMIs that were more predictive and comprise fewer metrics.

Ecological Indicators↗

USGS2013

A causal examination of the effects of confounding factors on multimetric indices

The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach.

Ecological Indicators↗

USGS2012

Guidelines for a graph-theoretic implementation of structural equation modeling

Structural equation modeling (SEM) is increasingly being chosen by researchers as a framework for gaining scientific insights from the quantitative analyses of data. New ideas and methods emerging from the study of causality, influences from the field of graphical modeling, and advances in statistics are expanding the rigor, capability, and even purpose of SEM. Guidelines for implementing the expanded capabilities of SEM are currently lacking. In this paper we describe new developments in SEM that we believe constitute a third-generation of the methodology. Most characteristic of this new approach is the generalization of the structural equation model as a causal graph. In this generalization, analyses are based on graph theoretic principles rather than analyses of matrices. Also, new devices such as metamodels and causal diagrams, as well as an increased emphasis on queries and probabilistic reasoning, are now included. Estimation under a graph theory framework permits the use of Bayesian or likelihood methods. The guidelines presented start from a declaration of the goals of the analysis. We then discuss how theory frames the modeling process, requirements for causal interpretation, model specification choices, selection of estimation method, model evaluation options, and use of queries, both to summarize retrospective results and for prospective analyses. The illustrative example presented involves monitoring data from wetlands on Mount Desert Island, home of Acadia National Park. Our presentation walks through the decision process involved in developing and evaluating models, as well as drawing inferences from the resulting prediction equations. In addition to evaluating hypotheses about the connections between human activities and biotic responses, we illustrate how the structural equation (SE) model can be queried to understand how interventions might take advantage of an environmental threshold to limit Typha invasions. The guidelines presented provide for an updated definition of the SEM process that subsumes the historical matrix approach under a graph-theory implementation. The implementation is also designed to permit complex specifications and to be compatible with various estimation methods. Finally, they are meant to foster the use of probabilistic reasoning in both retrospective and prospective considerations of the quantitative implications of the results.

Ecosphere↗

USGS2012

A general theory of multimetric indices and their properties

1. Stewardship of biological and ecological resources requires the ability to make integrative assessments of ecological integrity. One of the emerging methods for making such integrative assessments is multimetric indices (MMIs). These indices synthesize data, often from multiple levels of biological organization, with the goal of deriving a single index that reflects the overall effects of human disturbance. Despite the widespread use of MMIs, there is uncertainty about why this approach can be effective. An understanding of MMIs requires a quantitative theory that illustrates how the properties of candidate metrics relates to MMIs generated from those metrics. 2. We present the initial basis for such a theory by deriving the general mathematical characteristics of MMIs assembled from metrics. We then use the theory to derive quantitative answers to the following questions: Is there an optimal number of metrics to comprise an index? How does covariance among metrics affect the performance of the index derived from those metrics? And what are the criteria to decide whether a given metric will improve the performance of an index? 3. We find that the optimal number of metrics to be included in an index depends on the theoretical distribution of signal of the disturbance gradient contained in each metric. For example, if the rank-ordered parameters of a metric-disturbance regression can be described by a monotonically decreasing function, then an optimum number of metrics exists and can often be derived analytically. We derive the conditions by which adding a given metric can be expected to improve an index. 4. We find that the criterion defining such conditions depends nonlinearly of the signal of the disturbance gradient, the noise (error) of the metric and the correlation of the metric errors. Importantly, we find that correlation among metric errors increases the signal required for the metric to improve the index. 5. The theoretical framework presented in this study provides the basis for understanding the properties of MMIs. It can also be useful throughout the index construction process. Specifically, it can be used to aid understanding of the benefits and limitations of combining metrics into indices; it can inform selection/collection of candidate metrics; and it can be used directly as a decision aid in effective index construction.

Methods in Ecology and Evolution↗