United States Geological Survey
  • Reston, Colorado, United States
Recent publications
Most wildfires are started by humans, however, geographic variation of potential ignition sources is not often explicitly accounted for in wildfire simulation modelling or risk assessments. In this study, we investigated how patterns of human and lightning ignitions can influence modelled fire simulations and demonstrate how these data can be used to assess post-fire flooding and sediment transport.Weusedhistoricalignitiondata(1992–2015) to characterize ignition patterns for thirteen mountain ranges in southern Arizona, United States, and developed FlamMap burn probability (BP) models for three scenarios: human ignition, lightning ignition, and random ignition. We then developed a watershed-scale case study assessing the impacts of ignition scenarios on post-fire hydrology using the KINEROS2 model that simulates runoff and erosion. BP models illustrated considerable differences in landscape fire risk between the three ignition scenarios. Results from the watershed model indicate the greatest impacts from the post-fire human ignition scenario, with a 10-fold increase in sediment discharge and four-fold increase in peak flow compared to pre-fire conditions. Our results show that consideration of ignition source and location is important for assessing fire risk, and our modelling approach provides a planning mechanism to identify locations most at risk to fire-induced flood hazards, where prevention and mitigation activities can be focused.
Background China has committed to achieving peak CO2 emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics. Methods We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes. Results 1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 Mg·ha‒1) and 64 years (232.04 Mg·ha‒1), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 Tg C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest. Conclusions This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO2 emissions and reach carbon neutrality.
Background Deep-sea mussels in the subfamily Bathymodiolinae have unique adaptations to colonize hydrothermal-vent and cold-seep environments throughout the world ocean. These invertebrates function as important ecosystem engineers, creating heterogeneous habitat and promoting biodiversity in the deep sea. Despite their ecological significance, efforts to assess the diversity and connectivity of this group are extremely limited. Here, we present the first genomic-scale diversity assessments of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by Gigantidas childressi and Bathymodiolus heckerae . Results A Restriction-site Associated DNA Sequencing (RADSeq) approach was used on 177 bathymodiolines to examine genetic diversity and population structure within and between seep sites. Assessments of genetic differentiation using single-nucleotide polymorphism (SNP) data revealed high gene flow among sites, with the shallower and more northern sites serving as source populations for deeper occurring G. childressi . No evidence was found for genetic diversification across depth in G. childressi , likely due to their high dispersal capabilities. Kinship analyses indicated a high degree of relatedness among individuals, and at least 10–20% of local recruits within a particular site. We also discovered candidate adaptive loci in G. childressi and B. heckerae that suggest differences in developmental processes and depth-related and metabolic adaptations to chemosynthetic environments. Conclusions These results highlight putative source communities for an important ecosystem engineer in the deep sea that may be considered in future conservation efforts. Our results also provide clues into species-specific adaptations that enable survival and potential speciation within chemosynthetic ecosystems.
Coastal wetlands provide numerous ecosystem services; yet these ecosystems are increasingly vulnerable to climate change stressors, especially excessive flooding from sea-level rise and storm events. This study highlights the important contribution of vegetation belowground biomass to marsh stability and identifies loss of vegetation as a critical driver of marsh collapse. We investigated the shear strength of salt marshes and unvegetated interior ponds using a modified cone penetrometer along a chronosequence of wetland marsh collapse (0 to 21 + years following pond formation) to characterize changes in the structural integrity of the marsh soil. Following conversion from vegetated marsh to open water pond, the surficial soils experienced a dramatic loss in shear strength resulting from the loss of vegetation and compaction of soil pore space. The Cone Penetrometer Testing (CPT) data indicate that higher shear strength in the surficial layers of the vegetated marsh sites were never recovered, up to 21 + years following marsh collapse. Coupled with significant elevation loss from marsh collapse, additional sea-level rise, deep subsidence, and reduced sedimentation may contribute to conditions that can exceed critical flooding thresholds, making recovery from marsh collapse difficult or impossible. Therefore, characterizing mechanisms and thresholds of marsh collapse are critical for identifying those coastal marshes that are vulnerable to collapse before conversion from vegetated marsh to open water occurs.
Biological ocean science has a long history; it goes back millennia, whereas the related data services have emerged in the recent digital era of the past decades. To understand where we come from—and why data services are so important—we will start by taking you back to the rise in the study of marine biology—marine biodiversity—and its key players, before immersing ourselves in the data life cycle, past and present joint global initiatives, and systems that allow(ed) scientists to more easily access biological data, online services through some simple keyboard strokes, and the many challenges we still encounter on a daily basis when dealing with these types of data.
As anthropogenic influences push ecosystems past tipping points and into new regimes, complex management decisions are complicated by rapid ecosystem changes that may be difficult to reverse. For managers who grapple with how to manage ecosystems under novel conditions and heightened uncertainty, advancing our understanding of regime shifts is paramount. As part of an ecological resilience assessment, researchers and managers have collaborated to identify alternate regimes and build an understanding of the thresholds and factors that govern regime shifts in the Upper Mississippi River System. To describe the management implications of our assessment, we integrate our findings with the recently developed resist-accept-direct (RAD) framework that explicitly acknowledges ecosystem regime change and outlines management approaches of resisting change, accepting change, or directing change. More specifically, we developed guidance for using knowledge of desirability of current conditions, distance to thresholds, and general resilience (that is, an ecosystem’s capacity to cope with uncertain disturbances) to navigate the RAD framework. We applied this guidance to outline strategies that resist, accept, or direct change in the context of management of aquatic vegetation, floodplain vegetation, and fish communities across nearly 2000 river kilometers. We provide a case study for how knowledge of ecological dynamics can aid in assessing which management approach(es) are likely to be most ecologically feasible in a changing world. Continued learning from management decisions will be critical to advance our understanding of how ecosystems respond and inform the management of ecosystems for desirable and resilient outcomes.
The transition towards renewable energy requires increasing quantities of nonfuel mineral commodities, including tellurium used in certain photovoltaics. While demand for tellurium may increase markedly, the potential to increase tellurium supply is not well-understood. In this analysis, we estimate the quantity of tellurium contained in anode slimes generated by electrolytic copper refining by country between 1986 and 2018, including uncertainties. For 2018, the results indicate that 1930 (1500-2700, 95% confidence interval) metric tons of tellurium were contained in anode slimes globally. This is nearly quadruple the reported tellurium production for that year. China has the greatest potential to increase tellurium supplies. However, most of the tellurium potentially recoverable by Chinese refineries appears to come from copper mined elsewhere. Further research into the business decisions associated with tellurium recovery may help translate the physical availability of tellurium into economic availability. The methodology presented here can be applied to other byproduct elements.
Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll-a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon, and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.
Detailed mineralogical and geochemical analysis of drill core samples from three previously unstudied localities (Sheps Lake, Lac Ritchie, Hayot Lake) of the ca. 1.88 Ga Sokoman continental margin-type iron formation (IF) was undertaken to better understand tectonically stable, shallow-marine environments and surface redox conditions during the late Paleoproterozoic. Suboxic (Fe-oxide-rich including paragenetically early hematite) and anoxic (Fe-silicate/carbonate-rich) mineral paragenetic pathways operated during IF deposition. Post-depositional alteration beyond late diagenesis/metamorphism was negligible, based on petrographic examination and analysis of bulk Fe(III)/Fe(II) ratios. High-precision trace element (TE) data of the Sokoman IF, in the context of new analyses of IF/iron ore reference materials (IOC-1, FeR-3, FeR-4), reveal similarities to contemporaneous continental margin-type IF. However, both the analytical approach and integration of chemostratigraphic variations in detrital element, rare earth element and yttrium (REE + Y), and other TE (Cr, V, U, Ni, Co, Zn) parameters with a previously published sequence-stratigraphic framework provides refined insight into the ca. 1.88 Ga marine surface environment. Specifically, this study dissects new details on the effects of base-level fluctuations, terrigenous input, basin redox stratification, and microbial activity that are collectively captured within the mineralogically and texturally complex units of the Sokoman IF. The REE + Y signature of the Sokoman IF is confirmed to have developed during deposition/early diagenesis through a comparison of geochemical signatures of chert (jasper) intraclasts and surrounding bulk IF. Furthermore, the Sokoman IF REE + Y data show patterns reminiscent of modern seawater (LREE depletion, small negative Ce anomalies, small positive La, Gd, and Y anomalies), but in some cases also strong positive Ce anomalies. Modelling of hyperbolic trends in Ce/Ce*-Pr/Pr* plots, preserved despite varying detrital admixtures, provides supporting evidence for interaction of dissolved REE + Y with marine Fe- and Mn-(oxyhydr)oxides, and quantitatively constrains the amount of detritus required to overprint Ce anomalies. The co-existence of positive and negative bulk-rock Ce anomalies, similar to those of other ca. 1.88 Ga IF, implies the presence of a shallow marine redoxcline at that time. However, the absence of any strong covariations between these Ce anomalies and (1) Mn- or Fe-enrichments, (2) Y anomalies, (3) LREE/HREE ratios, or (4) tetrad coefficients (τ) is best explained by the separation of a shallow Mn-redoxcline from a slightly deeper and more diffuse Fe-redoxcline inferred here to be controlled by cyanobacteria and photoferrotrophs, respectively. Combined plots of chemostratigraphic and TE/ΣFe vs. enrichment factors highlight variable input/scavenging of different TEs within the Sokoman IF; authigenic TE enrichment is more readily captured in deeper, suboxic to anoxic units relative to shallower, nearshore units where even low amounts of continental detritus can obscure low-magnitude, authigenic redox signatures. This approach confirms the low magnitude and limited range of authigenic enrichments in redox-sensitive and nutrient-type TEs in the Sokoman IF as being similar to those of other ca. 1.88 Ga IF localities, but reveals which depositional environments best capture specific authigenic signatures (e.g., Cr-V-U-P enrichments). Detritus-poor samples record highly fractionated Nb/Ta and Zr/Hf ratios (m/m; Nb/Ta: median 56.4, range 15.5–680; Zr/Hf: median 97.8, range 42.9–409) that exceed those observed in the modern hydrosphere, and are interpreted to reflect greater interaction of the “dissolved” load of these elements with abundant marine Fe/Mn colloids/fine-particulates. Detritus-rich samples have Nb/Ta and Zr/Hf ratios converging towards crustal values similar to those of shales within the Sokoman basin; both datasets support a model for a predominantly felsic (Archean plutonic/metamorphic rock) source. New inferences from our data on the Sokoman IF support a close link between atmosphere–ocean oxygenation and microbial ecosystems via continental weathering under an oxygen-poor atmosphere (aided locally by arid conditions). In this model, such conditions limited the terrestrial supply of redox-sensitive and nutrient-type elements (most notably P) into the ocean, largely restricting the spatial extent of primary productivity to the photic zone of coastal regions. These processes are consistent with collective evidence from other ca. 1.88 Ga IF deposits that suggest low-O2 and nutrient-limited Earth surface conditions relative to preceding time intervals in the Paleoproterozoic.
Plain Language Summary In 2018 Kı̄lauea volcano erupted so much lava that, based on long‐term magma supply rates, one might have anticipated a decade long quiescence. Yet Kı̄lauea erupted in December 2020 less than 2.5 years later. Volcano deformations are used to infer pressure changes within magma systems. However, large inelastic deformations during Kı̄lauea's 2018 caldera collapse prohibit this approach. Here, we utilize diverse observations to infer the pressure history within the magma system during the inter‐eruptive period. Analysis of magma surges following caldera collapse events suggests that the pressure available to drive flow was very low at the end of the 2018 eruption. Due to unique observations of lava lake drainage in 2018 eruption, we were able to use Global Positioning System displacements to estimate the pressure increase between the end of the 2018 eruption and the start of the 2020 eruption. Given that eruption was most likely between the bottom of the 2018 pit and the top of the largest down‐dropped block, we show that there was a high probability that reservoir pressure was sufficient to bring magma to the surface well before the 2020 eruption. Our approach could aid future eruption forecasting.
Plain Language Summary We present a method for automated identification of two distinct types of electrical activity from explosive volcanic eruptions. Explosive eruptions produce lightning, just like thunderstorms. In addition, they also produce small (<4 m) spark‐like electrical discharges at the vent of a volcano, which are called vent discharges. These vent discharges occur for relatively long durations compared to the duration of a typical lightning flash (seconds vs. hundreds of milliseconds) and are thus easily distinguishable in very high frequency (30–300 MHz) electric field measurements. We use logistic regression to classify an electric field impulse as either being part of a lightning flash or a vent discharge. The classifier uses the number of peaks in the electric field signal in 1 ms time windows before and after an electric field impulse. The accuracy of the classifier is 97.9%. We explain that the classifier could be used on a low‐power lightning sensor to automatically identify that an explosive eruption had occurred. We discuss how this capability would enable a new era of volcanic lightning monitoring that would allow for new research into understanding the physical mechanisms of vent discharges to learn how they can be used during the response to an eruption.
The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52
Recent literature has demonstrated the sensitivity of mayflies to environmental contaminants. However, to date, there are no EPA‐approved methods for using sensitive insects like mayflies in Whole Effluent Toxicity or receiving water toxicity tests. The parthenogenetic mayfly Neocloeon triangulifer has been shown to be amenable to continuous culture in the laboratory and methods have been described for its use in both acute and chronic toxicity studies. The goal of the present study was to investigate aspects of N. triangulifer testing and culturing methods that might require adjustment so that they are applicable for testing effluents and receiving waters in a short‐term exposure. To this end, the influence of organism age, test duration, and test temperature on sensitivity to NaCl as a reference toxicant were tested (concentrations ranging from 182 to 2,489 mg/L). Further studies were conducted to assess the utility of commercially available diets and the influence of nutrient amendment of water on organism growth and sensitivity. Seven‐d NaCl tests started with <24‐h‐old larvae were similar in sensitivity to 14‐d and full life chronic tests, and much more sensitive than those started with 7‐d old organisms. Reducing test temperature from 25 °C to 22 °C had minor influence on culture timing, and little impact on sensitivity to NaCl. In other experiments reconstituted test water supplemented with nutrients to potentially improve in‐test food quality had minimal effect on growth at 7‐d and did not significantly alter acute sensitivity to NaCl relative to un‐amended reconstituted water. A suitable commercially available, ready‐to‐feed diet substitute for cultured diatoms was not found. Testing N. triangulifer in effluents or receiving waters with methods recommended here will complement similar methods for Ceriodaphnia dubia. This article is protected by copyright. All rights reserved.
Latest climate models project conditions for the end of this century that are generally outside of the human experience. These future conditions affect the resilience and sustainability of ecosystems, alter biogeographic zones, and impact biodiversity. Deep-time records of paleoclimate provide insight into the climate system over millions of years and provide examples of conditions very different from the present day, and in some cases similar to model projections for the future. In addition, the deep-time paleoecologic and sedimentologic archives provide insight into how species and habitats responded to past climate conditions. Thus, paleoclimatology provides essential context for the scientific understanding of climate change needed to inform resource management policy decisions. The Pliocene Epoch (5.3–2.6 Ma) is the most recent deep-time interval with relevance to future global warming. Analysis of marine sediments using a combination of paleoecology, biomarkers, and geochemistry indicates a global mean annual temperature for the Late Pliocene (3.6–2.6 Ma) ∼3°C warmer than the preindustrial. However, the inability of state-of-the-art climate models to capture some key regional features of Pliocene warming implies future projections using these same models may not span the full range of plausible future climate conditions. We use the Late Pliocene as one example of a deep-time interval relevant to management of biodiversity and ecosystems in a changing world. Pliocene reconstructed sea surface temperatures are used to drive a marine ecosystem model for the North Atlantic Ocean. Given that boundary conditions for the Late Pliocene are roughly analogous to present day, driving the marine ecosystem model with Late Pliocene paleoenvironmental conditions allows policymakers to consider a future ocean state and associated fisheries impacts independent of climate models, informed directly by paleoclimate information.
Context Seagrasses are submerged marine plants that have been declining globally at increasing rates. Natural resource managers rely on monitoring programs to detect and understand changes in these ecosystems. Technological advancements are allowing for the development of patch-level seagrass maps, which can be used to explore seagrass meadow spatial patterns. Objectives Our research questions involved comparing lacunarity, a measure of landscape configuration, for seagrass to assess cross-site differences in areal coverage and spatial patterns through time. We also discussed how lacunarity could help natural resource managers with monitoring program development and restoration decisions and evaluation. Methods We assessed lacunarity of seagrass meadows for various box sizes (0.0001 ha to 400.4 ha) around Cat Island and Ship Island, Mississippi (USA). For Cat Island, we used seagrass data from 2011 to 2014. For Ship Island, we used seagrass data for seven dates between 1963 and 2014. Results Cat Island, which had more continuous seagrass meadows, had lower lacunarity (i.e., denser coverage) compared to Ship Island, which had patchier seagrass beds. For Ship Island, we found a signal of disturbance and path toward recovery from Hurricane Camille in 1969. Finally, we highlighted how lacunarity curves could be used as one of multiple considerations for designing monitoring programs, which are commonly used for seagrass monitoring. Conclusions Lacunarity can help quantify spatial pattern dynamics, but more importantly, it can assist with natural resource management by defining fragmentation and potential scales for monitoring. This approach could be applied to other environments, especially other coastal ecosystems.
Ecological forecasting provides a powerful set of methods for predicting short‐ and long‐term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.
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2,894 members
Thomas J. Smith III
  • Wetland and Aquatic Research Center
Lillian Rose Ostrach
  • Astrogeology Science Center
Luke Iwanowicz
  • Eastern Ecological Science Center
Carole Mcivor
  • Wetland and Aquatic Research Center
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Denver Federal Center, 80225-0046, Reston, Colorado, United States
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