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USGS2026

Genetic structure in a previously extirpated population of gray wolves following reintroduction and natural recolonization

Genetic structuring in wildlife populations is driven by barriers that restrict gene flow as well as the history of population demography. Mechanisms driving genetic structuring can be nuanced in group-living species, such as gray wolves ( Canis lupus ). Behavioral factors, such as social affiliation and resistance, natal habitat imprinting, and trade-offs between dispersal from natal packs and territorial biding, affect habitat selection of wolves despite landscape barriers providing little resistance to their extensive dispersal capabilities. Wolves were previously extirpated from Idaho, USA, and current populations are the result of both reintroductions in 1995 and 1996 and natural dispersal from Canada. In this context we examined genetic structure of wolves in Idaho using 101 individuals genotyped at 18 nuclear DNA microsatellite loci and a subset of 38 individuals genotyped at 1019 single nucleotide polymorphism markers. We hypothesized panmictic (i.e., random mating) genetic structure in Idaho due to the long-distance dispersal abilities of gray wolves. Contrary to our hypothesis, we found three genetic clusters of gray wolves in Idaho, primarily supported by SNP markers. Microsatellite data suggested similar patterns, but permutation tests indicated these differences were not statistically significant. The extent of differentiation and evidence of gene flow, however, suggests that the three genetic clusters are not wholly isolated from one another. The distinctions between clusters spatially align with areas of reintroduction into central Idaho and Yellowstone National Park, as well ongoing natural recolonization from adjacent populations in Canada and Montana. Wolves at the periphery of analysis areas showed more admixture than those in the core, consistent with territoriality and mating behaviors contributing to genetic structuring. We demonstrate how management history, including reintroduction efforts, and animal behavior may interact and contribute to patterns of genetic structure in wild populations.

Idaho, Montana, Wyoming↗

USGS2019

Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems

Widespread land use change in coastal ecosystems has led to a decline in the amount of habitat available for fish and wildlife, lower production of ecosystem goods and services, and loss of recreational and aesthetic value. This has prompted global efforts to restore the natural hydrologic regimes of developed shorelines, especially resource-rich estuaries, but the resilience of these restored ecosystems in the face of accelerated sea-level rise (SLR) remains uncertain. We implemented a Mo nitoring-based S imulation of A ccretion i n C oastal E s tuaries (MOSAICS) in R statistical software to address uncertainty in the resilience of modified estuarine habitats, using the Nisqually River Delta in the Pacific Northwest USA as a case study. MOSAICS is a spatially explicit model with a numerical foundation that uses empirical monitoring datasets to forecast habitat change in response to rising tidal levels. Because it accounts for the crucial ecomorphodynamic feedbacks between tidal inundation, vegetative growth, and sediment accretion, MOSAICS can be used to determine whether alternative management scenarios, such as enhanced sediment inputs, will bolster estuarine resilience to SLR. Under moderate SLR (0.62 m), the model predicted that a two-fold increase in mean daily suspended sediment during the rainy season was sufficient to maintain Nisqually’s emergent marshes through 2100, but under high SLR (1.35 m) MOSAICS indicated that greater sediment additions would be necessary to prevent submergence. A comparison between a restored marsh with subsided and high-elevation areas and a relict marsh demonstrated that the subsided restoration area was highly susceptible to SLR. Findings from the MOSAICS model highlight the importance of a site’s initial elevation, capacity for producing above and belowground biomass, and suspended sediment availability when considering management actions in estuaries and other coastal ecosystems.

Washington↗

USGS2016

Assessing the relationship between groundwater nitrate and animal feeding operations in Iowa (USA)

Nitrate-nitrogen is a common contaminant of drinking water in many agricultural areas of the United States of America (USA). Ingested nitrate from contaminated drinking water has been linked to an increased risk of several cancers, specific birth defects, and other diseases. In this research, we assessed the relationship between animal feeding operations (AFOs) and groundwater nitrate in private wells in Iowa. We characterized AFOs by swine and total animal units and type (open, confined, or mixed), and we evaluated the number and spatial intensities of AFOs in proximity to private wells. The types of AFO indicate the extent to which a facility is enclosed by a roof. Using linear regression models, we found significant positive associations between the total number of AFOs within 2 km of a well (p trend < 0.001), number of open AFOs within 5 km of a well (p trend < 0.001), and number of mixed AFOs within 30 km of a well (p trend < 0.001) and the log nitrate concentration. Additionally, we found significant increases in log nitrate in the top quartiles for AFO spatial intensity, open AFO spatial intensity, and mixed AFO spatial intensity compared to the bottom quartile (0.171 log(mg/L), 0.319 log(mg/L), and 0.541 log(mg/L), respectively; all p < 0.001). We also explored the spatial distribution of nitrate-nitrogen in drinking wells and found significant spatial clustering of high-nitrate wells (> 5 mg/L) compared with low-nitrate (≤ 5 mg/L) wells ( p = 0.001). A generalized additive model for high-nitrate status identified statistically significant areas of risk for high levels of nitrate. Adjustment for some AFO predictor variables explained a portion of the elevated nitrate risk. These results support a relationship between animal feeding operations and groundwater nitrate concentrations and differences in nitrate loss from confined AFOs vs. open or mixed types.

Iowa↗

USGS2021

Movement of synthetic organic compounds in the food web after the introduction of invasive quagga mussels (Dreissena bugensis) in Lake Mead, Nevada and Arizona, USA

Introductions of dreissenid mussels in North America have been a significant concern over the last few decades. This study assessed the distribution of synthetic organic compounds (SOCs) in the food web of Lake Mead, Nevada/Arizona, USA and how this distribution was influenced by the introduction of invasive quagga mussels. A clear spatial gradient of SOC concentrations in water was observed between lake basins downstream of populated areas and more rural areas. Within the food web, trophic magnification factors (TMF) indicated statistically significant biomagnification for nine, and biodilution for two, of 22 SOCs examined. The highest value recorded was for PCB 118 (TMF, 5.14), and biomagnification of methyl triclosan (TMF, 3.85) was also apparent. Biodilution was observed for Tonalide® (0.06) and Galaxolide® (0.38). Total SOC concentration in quagga mussels was higher than in three pelagic fishes. Also, 19 of 20 SOC examined in Largemouth Bass ( Micropterus salmoides ) had substantially lower concentrations in 2013, when quagga mussels had become well established, than in 2007/08, soon after quagga mussels were introduced. Estimates of SOC concentrations in the water column and quagga mussels suggest that a considerable portion (~10.5%) of the SOC mass in the lake has shifted from the pelagic to the benthic environments due to quagga mussel growth. These observations suggest that benthic species, such as the endangered Razorback Sucker, may be experiencing increased risk of SOC exposure. In addition, stable isotope analysis (carbon and nitrogen) indicated a decrease in the nutritional value of zooplankton to consumers (e.g., Razorback Sucker larvae) since quagga mussels became established. These changes could affect Razorback Sucker larval survival and recruitment. Results from this study strongly suggest that the introduction of quagga mussels has greatly altered the dynamics of SOCs and other processes in the food web of Lake Mead.

Arizona, Nevada↗

USGS2011

Shallow lithological structure across the Dead Sea Transform derived from geophysical experiments

In the framework of the DEad SEa Rift Transect (DESERT) project a 150 km magnetotelluric profile consisting of 154 sites was carried out across the Dead Sea Transform. The resistivity model presented shows conductive structures in the western section of the study area terminating abruptly at the Arava Fault. For a more detailed analysis we performed a joint interpretation of the resistivity model with a P wave velocity model from a partially coincident seismic experiment. The technique used is a statistical correlation of resistivity and velocity values in parameter space. Regions of high probability of a coexisting pair of values for the two parameters are mapped back into the spatial domain, illustrating the geographical location of lithological classes. In this study, four regions of enhanced probability have been identified, and are remapped as four lithological classes. This technique confirms the Arava Fault marks the boundary of a highly conductive lithological class down to a depth of ∼3 km. That the fault acts as an impermeable barrier to fluid flow is unusual for large fault zone, which often exhibit a fault zone characterized by high conductivity and low seismic velocity. At greater depths it is possible to resolve the Precambrian basement into two classes characterized by vastly different resistivity values but similar seismic velocities. The boundary between these classes is approximately coincident with the Al Quweira Fault, with higher resistivities observed east of the fault. This is interpreted as evidence for the original deformation along the DST originally taking place at the Al Quweira Fault, before being shifted to the Arava Fault.

Dead Sea↗

USGS2009

Climatic extremes improve predictions of spatial patterns of tree species

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D 2 , +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Conference Paper↗

USGS2014

Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)

The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km 2 basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks. The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive metrics such as the mean and variance and an evaluation of rare and sustained events. In general, precipitation and streamflow quantities were negatively biased in the downscaled GCM outputs, and results indicate that the downscaled GCM simulations consistently underestimate the largest precipitation events relative to the GSD. The KS test results indicate that ARRM-based air temperatures are similar to GSD at the daily time step for the majority of the ACFB, with perhaps subweekly averaging for stream temperature. Depending on GCM and spatial location, ARRM-based precipitation and streamflow requires averaging of up to 30 days to become similar to the GSD-based output. Evaluation of the model skill for historical conditions suggests some guidelines for use of future projections; while it seems correct to place greater confidence in evaluation metrics which perform well historically, this does not necessarily mean those metrics will accurately reflect model outputs for future climatic conditions. Results from this study indicate no “best” overall model, but the breadth of analysis can be used to give the product users an indication of the applicability of the results to address their particular problem. Since results for historical conditions indicate that model outputs can have significant biases associated with them, the range in future projections examined in terms of change relative to historical conditions for each individual GCM may be more appropriate.

Florida, Georgia↗

USGS2018

Turing-style tests for UCERF3 synthetic catalogs

Epidemic-Type Aftershock Sequence (ETAS) catalogs generated from the 3rd Uniform California Earthquake Rupture Forecast (UCERF3) model are unique in that they are the first to combine a complex, fault-based long-term forecast with short-term earthquake clustering statistics. We present Turing-style tests to examine whether these synthetic catalogs can successfully imitate observed earthquake behavior in California. We find that UCERF3-ETAS is more spatially diffuse than the observed historic catalog in California and that it is lacking quiet periods that are present in the real catalog. While mean aftershock productivity of the observed catalog is matched closely by UCERF3-ETAS, the real catalog has more inter-sequence productivity variability and small mainshocks have more foreshocks. In sum, we find that UCERF3-ETAS differs from the observed catalog in ways that are foreseeable from its modeling simplifications. The tests we present here can be used on any model which produces suites of synthetic catalogs; as such, in addition to providing avenues for future improvements to the model, they could also be incorporated into testing platforms such as Collaboratory for the Study of Earthquake Predictability (CSEP).

Bulletin of the Seismological Society of America↗

USGS2023

Declining American Goshawk (Accipiter atricapillus) nest site habitat suitability in a timber production landscape: Effects of abiotic, biotic, and forest management factors

Conservation of the American Goshawk ( Accipiter atricapillus ; hereafter goshawk) has been contentious in relation to forest management. Higher quality goshawk nesting habitat is generally considered to consist of contiguous tracts of mature forest, due to goshawks' large home ranges, territoriality, and food requirements. The large trees of mature forest have the greatest economic value to timber companies. We used long-term (1965–2019) data from 281 goshawk nest site locations in the Black Hills National Forest (BHNF), South Dakota, and Wyoming, USA, to evaluate (1) abiotic and biotic factors associated with goshawk nest site habitat suitability (hereafter habitat suitability); (2) changes in habitat suitability over time; and (3) the effect of anthropogenic activities and natural disturbances on habitat suitability. We evaluated forest attributes across five spatial scales relevant to goshawks, used information-theoretic methods to rank and select models, and assessed the predictive capability of the best-approximating models using the concordance statistic. The best-approximating model had excellent predictive capability (concordance = 0.821). Forest attributes at the 12-ha scale were a better predictor of goshawk habitat suitability than covariates evaluated at the point or >12-ha scales, indicating the importance of managing goshawk habitat beyond the nest tree, but within the nest stand. Goshawk habitat suitability was positively related to mean percent canopy cover and median canopy base height, and negatively related to variability in canopy base height within 12 ha of the location. As mean percent canopy cover within 12 ha of a location increased, goshawk habitat suitability increased more slowly in burned compared to unburned areas. Commercial thinning treatments were more likely to occur in closed canopy forest that already had a higher likelihood of goshawk nesting, and we documented a positive relationship between habitat suitability and the interaction of canopy cover with commercial thinning. Goshawk habitat suitability was negatively related to slope and distance to drainage bottoms, and positively related to distance to ridges, which may be related to microclimatic factors. Our results indicate goshawk habitat suitability decreased across the BHNF over the past three decades and much high-quality nesting habitat was lost during this period due to a combination of unsustainable timber harvest and natural disturbances. Minimizing forest management activities that decrease canopy cover and canopy base height, and increase variability in canopy base height in areas of high- and medium-quality goshawk habitat are likely to slow the loss of higher-quality habitat and allow development of future nesting habitat. In addition to informing management, this study demonstrates the value of using existing long-term legacy datasets in conjunction with time series of remotely sensed habitat attributes to evaluate changes in habitat suitability for raptors in heavily managed landscapes with extensive natural disturbances.

South Dakota, Wyoming↗

USGS2002

Measurement of Turbulence with Acoustic Doppler Current Profilers - Sources of Error and Laboratory Results

Acoustic Doppler current profilers (ADCPs) provide a promising method for measuring surface-water turbulence because they can provide data from a large spatial range in a relatively short time with relative ease. Some potential sources of errors in turbulence measurements made with ADCPs include inaccuracy of Doppler-shift measurements, poor temporal and spatial measurement resolution, and inaccuracy of multi-dimensional velocities resolved from one-dimensional velocities measured at separate locations. Results from laboratory measurements of mean velocity and turbulence statistics made with two pulse-coherent ADCPs in 0.87 meters of water are used to illustrate several of inherent sources of error in ADCP turbulence measurements. Results show that processing algorithms and beam configurations have important effects on turbulence measurements. ADCPs can provide reasonable estimates of many turbulence parameters; however, the accuracy of turbulence measurements made with commercially available ADCPs is often poor in comparison to standard measurement techniques.

Conference Paper↗

USGS2025

Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change

1. Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce. 2. Here, we present a successful case study demonstrating both space-for-time and time-for-space substitutions using emperor penguins (Aptenodytes forsteri) as the focal species. Using abundance-based species distribution models (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long-term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average fledgling abundance across 50 colonies. 3. Our analysis reveals that distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. aSDM’s space-for-time substitution performance, as measured by Pearson correlation coefficient was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar time-for-space substitution performance when predicting abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58. 4. We posit that such space-time equivalence arises because: 1) emperor penguins colonies conform to their existing fundamental niche; 2) there is not yet any environmental novelty when comparing the spatial vs temporal variation of distance to nearest open water; and 3) models of more specific components of life histories, such as fledgling abundance, rather than occurrence or total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.

Journal of Animal Ecology↗

USGS2004

N-mixture models for estimating population size from spatially replicated counts

Spatial replication is a common theme in count surveys of animals. Such surveys often generate sparse count data from which it is difficult to estimate population size while formally accounting for detection probability. In this article, I describe a class of models (n-mixture models) which allow for estimation of population size from such data. The key idea is to view site-specific population sizes, n, as independent random variables distributed according to some mixing distribution (e.g., Poisson). Prior parameters are estimated from the marginal likelihood of the data, having integrated over the prior distribution for n. Carroll and Lombard (1985, Journal of American Statistical Association 80, 423-426) proposed a class of estimators based on mixing over a prior distribution for detection probability. Their estimator can be applied in limited settings, but is sensitive to prior parameter values that are fixed a priori. Spatial replication provides additional information regarding the parameters of the prior distribution on n that is exploited by the n-mixture models and which leads to reasonable estimates of abundance from sparse data. A simulation study demonstrates superior operating characteristics (bias, confidence interval coverage) of the n-mixture estimator compared to the Caroll and Lombard estimator. Both estimators are applied to point count data on six species of birds illustrating the sensitivity to choice of prior on p and substantially different estimates of abundance as a consequence.

Biometrics↗

USGS2006

Chlorophyll a and inorganic suspended solids in backwaters of the upper Mississippi River system: Backwater lake effects and their associations with selected environmental predictors

The Long Term Resource Monitoring Program (LTRMP) uses a stratified random sampling design to obtain water quality statistics within selected study reaches of the Upper Mississippi River System (UMRS). LTRMP sampling strata are based on aquatic area types generally found in large rivers (e.g., main channel, side channel, backwater, and impounded areas). For hydrologically well-mixed strata (i.e., main channel), variance associated with spatial scales smaller than the strata scale is a relatively minor issue for many water quality parameters. However, analysis of LTRMP water quality data has shown that within-strata variability at the strata scale is high in off-channel areas (i.e., backwaters). A portion of that variability may be associated with differences among individual backwater lakes (i.e., small and large backwater regions separated by channels) that cumulatively make up the backwater stratum. The objective of the statistical modeling presented here is to determine if differences among backwater lakes account for a large portion of the variance observed in the backwater stratum for selected parameters. If variance associated with backwater lakes is high, then inclusion of backwater lake effects within statistical models is warranted. Further, lakes themselves may represent natural experimental units where associations of interest to management may be estimated.

Open-File Report↗

USGS2012

Social values for ecosystem services (SolVES): Documentation and user manual, version 2.0

In response to the need for incorporating quantified and spatially explicit measures of social values into ecosystem services assessments, the Rocky Mountain Geographic Science Center (RMGSC), in collaboration with Colorado State University, developed a geographic information system (GIS) application, Social Values for Ecosystem Services (SolVES). With version 2.0 (SolVES 2.0), RMGSC has improved and extended the functionality of SolVES, which was designed to assess, map, and quantify the perceived social values of ecosystem services. Social values such as aesthetics, biodiversity, and recreation can be evaluated for various stakeholder groups as distinguished by their attitudes and preferences regarding public uses, such as motorized recreation and logging. As with the previous version, SolVES 2.0 derives a quantitative, 10-point, social-values metric, the Value Index, from a combination of spatial and nonspatial responses to public attitude and preference surveys and calculates metrics characterizing the underlying environment, such as average distance to water and dominant landcover. Additionally, SolVES 2.0 integrates Maxent maximum entropy modeling software to generate more complete social value maps and to produce robust statistical models describing the relationship between the social values maps and explanatory environmental variables. The performance of these models can be evaluated for a primary study area, as well as for similar areas where primary survey data are not available but where social value mapping could potentially be completed using value-transfer methodology. SolVES 2.0 also introduces the flexibility for users to define their own social values and public uses, model any number and type of environmental variable, and modify the spatial resolution of analysis. With these enhancements, SolVES 2.0 provides an improved public domain tool for decisionmakers and researchers to evaluate the social values of ecosystem services and to facilitate discussions among diverse stakeholders regarding the tradeoffs among different ecosystem services in a variety of physical and social contexts ranging from forest and rangeland to coastal and marine.

Open-File Report↗

USGS2018

Increasing connectivity between metapopulation ecology and landscape ecology

Metapopulation ecology and landscape ecology aim to understand how spatial structure influences ecological processes, yet these disciplines address the problem using fundamentally different modeling approaches. Metapopulation models describe how the spatial distribution of patches affects colonization and extinction, but often do not account for the heterogeneity in the landscape between patches. Models in landscape ecology use detailed descriptions of landscape structure, but often without considering colonization and extinction dynamics. We present a novel spatially explicit modeling framework for narrowing the divide between these disciplines to advance understanding of the effects of landscape structure on metapopulation dynamics. Unlike previous efforts, this framework allows for statistical inference on landscape resistance to colonization using empirical data. We demonstrate the approach using 11 yr of data on a threatened amphibian in a desert ecosystem. Occupancy data for Lithobates chiricahuensis (Chiricahua leopard frog) were collected on the Buenos Aires National Wildlife Refuge (BANWR), Arizona, USA from 2007 to 2017 following a reintroduction in 2003. Results indicated that colonization dynamics were influenced by both patch characteristics and landscape structure. Landscape resistance increased with increasing elevation and distance to the nearest streambed. Colonization rate was also influenced by patch quality, with semi‐permanent and permanent ponds contributing substantially more to the colonization of neighboring ponds relative to intermittent ponds. Ponds that only hold water intermittently also had the highest extinction rate. Our modeling framework can be widely applied to understand metapopulation dynamics in complex landscapes, particularly in systems in which the environment between habitat patches influences the colonization process

Ecology↗

USGS2007

Paleoecology of the Late Pennsylvanian-age Calhoun coal bed and implications for long-term dynamics of wetland ecosystems

Quantitative plant assemblage data from coal balls, miospores, megaspores, and compression floras from the Calhoun coal bed (Missourian) of the Illinois Basin (USA) are used to interpret spatial and temporal changes in plant communities in the paleo-peat swamp. Coal-ball and miospore floras from the Calhoun coal bed are dominated strongly by tree ferns, and pteridosperms and sigillarian lycopsids are subdominant, depending on geographic location within the coal bed. Although the overall composition of Calhoun peat-swamp assemblages is consistent both temporally and spatially, site-to-site differences and short-term shifts in species dominance indicate local topographic and hydrologic control on species composition within the broader context of the swamp. Statistical comparison of the Calhoun miospore assemblages with those from other Late Pennsylvanian coal beds suggests that the same basic species pool was represented in each peat-swamp landscape and that the relative patterns of dominance and diversity were persistent from site to site. Therefore, it appears that the relative patterns of proportional dominance stayed roughly the same from one coal bed to the next during Late Pennsylvanian glacially-driven climatic oscillations.

International Journal of Coal Geology↗

USGS2018

Landscape-scale variation in canopy water content of giant sequoias during drought

Recent drought (2012–2016) caused unprecedented foliage dieback in giant sequoias ( Sequoiadendron giganteum ), a species endemic to the western slope of the southern Sierra Nevada in central California. As part of an effort to understand and map sequoia response to droughts, we studied the patterns of remotely sensed canopy water content (CWC), both within and among sequoia groves in two successive years during the drought period (2015 and 2016). Our aims were: (1) to quantify giant sequoia responses to severe drought stress at a landscape scale using CWC as an indicator of crown foliage status, and (2) to estimate the effect of environmental correlates that mediate CWC change within and among giant sequoia groves. We utilized airborne high fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) data from the Carnegie Airborne Observatory to assess giant sequoia foliage status during 2015 and 2016 of the 2012–2016 droughts. A series of statistical models were generated to classify giant sequoias and to map their location in Sequoia and Kings Canyon National Parks (SEKI) and vicinity. We explored the environmental correlates and the spatial patterns of CWC change at the landscape scale. The mapped CWC was highly variable throughout the landscape during the two observation years, and proved to be most closely related to geological substrates, topography, and site-specific water balance. While there was an overall net gain in sequoia CWC between 2015 and 2016, certain locations (lower elevations, steeper slopes, areas more distant from surface water sources, and areas with greater climate water deficit) showed CWC losses. In addition, we found greater CWC loss in shorter sequoias and those growing in areas with lower sequoia stem densities. Our results suggest that CWC change indicates sequoia response to droughts across landscapes. Long-term monitoring of giant sequoia CWC will likely be useful for modeling and predicting their population-level response to future climate change.

California↗

USGS2021

Stable oxygen isotopes in shallow marine ostracodes from the northern Bering and Chukchi Seas

Stable oxygen isotope measurements on calcitic valves of benthic ostracodes (δ18Oost) from the northern Bering and Chukchi Seas were used to examine ecological and hydrographic processes governing ostracode and associated seawater δ18O values. Five cryophilic taxa were analyzed for δ18Oost values: Sarsicytheridea bradii; Paracyprideis pseudopunctillata; Heterocyprideis sorbyana; Heterocyprideis fascis; and the subarctic species Normanicythere leioderma. Controls on the stable oxygen isotope composition of ostracode calcite were investigated by first establishing species' vital effects and then comparing δ18Oost to seawater δ18O values (that ranged from −2.7 to −0.5‰), CTD temperature (−1.7 to 8.7 °C) and salinity (30–34) measured at sampling stations in the Bering and Chukchi Seas during the six summers of 2013–2018. Results from 297 δ18Oost measurements from 53 sites on the Bering and Chukchi Sea continental shelves are consistent with the temporal and spatial variation in δ18O values of continental shelf bottom water, as impacted by seasonality, regional hydrography, and physical processes (i.e., sea-ice melt and extent, vertical mixing, precipitation/evaporation). Regression statistics for δ18Oost values of two species, N. leioderma and P. pseudopunctillata, showed correlations to temperature and salinity that may facilitate prediction of water-mass characteristics when applied to sediment core records. Specifically, a significant linear regression relationship was found between δ18Oost values of N. leioderma and P. pseudopunctillata and temperature (R2 = 0.67 and 0.52, respectively). A principal component analysis confirmed temperature as the main controlling factor in the δ18Oost values of all species except S. bradii, with samples of distinct water masses grouping together. The δ18Oost values of S. bradii exhibited a narrow range of values (~3 to 4.5‰) across a temperature range of 10 °C. Due to strong vital effects and possibly other undetermined factors, the incorporation of δ18Oost in S. bradii was not driven by any obvious predominant environmental factors.

Marine Micropaleontology↗