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USGS2024

Wolf harvest management strategy evaluation: Annual Report, 2024

Wolf harvest season setting is complicated and controversial. State law requires Montana Fish, Wildlife and Parks (MFWP) to both reduce the wolf population and avoid federal relisting under the Endangered Species Act (Montana Fish, Wildlife and Parks, 2002). Disparate stakeholder groups each have different objectives for wolf management. For instance, big game advocates want to see improved big game populations and hunting opportunities in northwest Montana, while wolf advocates want to see regulations that minimize wolf mortality. Decision making about season setting tries to balance these objectives. Wolf hunting and trapping season decisions are made by the Montana Fish and Wildlife Commission and are informed by annual wolf abundance estimates from an integrated patch occupancy model (iPOM, Sells et al., 2022c) as well as the predictions of wolf abundance into the future under potential constant harvest levels. Parametric uncertainty (uncertainty surrounding the value of a parameter) from the iPOM estimates is propagated through to future projections, providing the Commission with plausible and worst-case outcomes of different levels of public harvest over the short term, i.e., five years into the future, on the wolf population in Montana (Parks et al., 2024). An alternative approach to inform wolf management and harvest decisions is through adaptive management. Adaptative management is appropriate for decisions that are made iteratively and when monitoring data are collected to learn about the outcomes from decisions, where monitoring data help to reduce critical uncertainties regarding ecosystem function or management outcomes (Walters, 1986; Williams, 2011). Management strategy evaluation (MSE) is one way to develop an adaptive management framework. MSE was developed by fisheries managers and scientists to more accurately and fully incorporate various forms of uncertainty, consider long-term time horizons, and add more transparency in a fisheries context (Punt et al., 2016). It has been used routinely and has become a standard approach for complicated and contentious marine fisheries management situations, yet it has been underutilized in wildlife management (but see Bunnefeld et al., 2013, 2011). MSE is a forward simulation approach for testing prospective management options or strategies over a wide range of possible states (Punt et al., 2016). A MSE framework captures the ‘truth’ or what is happening in the system (termed the ‘the operating model’) and the information available to the decision makers (termed ‘the estimation model’ or ‘management strategy’). More precisely, there are four main processes modeled. First, models are constructed based on current understanding and data to represent ‘truth’. Second, the collection of monitoring data is simulated from the ‘truth’ model. Third, the simulated monitoring data are fit to an estimation model and the next time step’s population metrics are predicted from the estimated parameters. Fourth, based on the estimation model results and the predictions, the decision-making process is simulated following a management strategy, whereby a decision is made and the implementation of this decision feeds back into the ‘truth’ model (Figure 1). This process continues through time. Additionally, each simulation through time is repeated to capture the full range of stochasticity and uncertainty.

Montana↗

USGS2020

Determinants and consequences of dispersal in vertebrates with complex life cycles: a review of pond-breeding amphibians

Dispersal is a central process in ecology and evolution. It strongly influences the dynamics of spatially structured populations, by affecting population growth rate and local colonization-extinction processes. Dispersal can also influence evolutionary processes because it determines rates and patterns of gene flow in spatially structured populations and is closely linked to local adaptation. For these reasons, dispersal has received considerable attention from ecologists and evolutionary biologists. However, although it has been studied extensively in taxa such as birds and mammals, much less is known about dispersal in vertebrates with complex life cycles such as pond-breeding amphibians. Over the past two decades, researchers have taken an interest in amphibian dispersal and initiated both fundamental and applied studies, using a broad range of experimental and observational approaches. This body of research reveals complex dispersal patterns, causations and syndromes, with dramatic consequences for the demography and genetics of amphibian populations. In this review, our goals are to (1) redefine and clarify the concept of amphibian dispersal, (2) review current knowledge about the effects of individual (i.e., condition-dependent dispersal) and environmental (i.e., context-dependent dispersal) factors during the three stages of dispersal (i.e., emigration, immigration, transience), (3) identify the demographic and genetic consequences of dispersal in spatially structured amphibian populations, and (4) propose new research avenues to extend our understanding of amphibian dispersal. In particular, we emphasize the need to (1) quantify dispersal rate and distance rigorously using suitable model systems, (2) investigate the genetic basis and dispersal evolution patterns, and (3) examine dispersal-related eco-evolutionary dynamics. These proposed research avenues tap from the recent advances in quantitative and molecular methods and have the potential to improve our understanding of dispersal in organisms with complex life cycles.

The Quarterly Review of Biology↗

USGS2024

Approaches for using CMIP projections in climate model ensembles to address the ‘hot model’ problem

Several recent generation global-climate models were found to have anomalously high climate sensitivities and may not be useful for certain applications. Four approaches for developing ensembles of climate projections for applications that address this issue are: Using an “all models” approach; Screening using equilibrium climate sensitivity and (or) transient climate response; Bayesian model averaging; and Using global warming levels. Advantages and disadvantages of each approach are described by using example applications to study the effects of climate change on an imaginary at-risk species. Choosing the right approach is dependent on the location, goals, and system focus of each application and the risk-tolerance and resource-management context.

Open-File Report↗

USGS2018

Maps showing predicted probabilities for selected dissolved oxygen and dissolved manganese threshold events in depth zones used by the domestic and public drinking water supply wells, Central Valley, California

The purpose of the prediction grids for selected redox constituents—dissolved oxygen and dissolved manganese—are intended to provide an understanding of groundwater-quality conditions at the domestic and public-supply drinking water depths. The chemical quality of groundwater and the fate of many contaminants is influenced by redox processes in all aquifers, and understanding the redox conditions horizontally and vertically is critical in evaluating groundwater quality. The redox condition of groundwater—whether oxic (oxygen present) or anoxic (oxygen absent)—strongly influences the oxidation state of a chemical in groundwater. The anoxic dissolved oxygen thresholds of <0.5 milligram per liter (mg/L), <1.0 mg/L, and <2.0 mg/L were selected to apply broadly to regional groundwater-quality investigations. Although the presence of dissolved manganese in groundwater indicates strongly reducing (anoxic) groundwater conditions, it is also considered a “nuisance” constituent in drinking water, making drinking water undesirable with respect to taste, staining, or scaling. Three dissolved manganese thresholds, <50 micrograms per liter (µg/L), <150 µg/L, and <300 µg/L, were selected to create predicted probabilities of exceedances in depth zones used by domestic and public-supply water wells. The 50 µg/L event threshold represents the secondary maximum contaminant level (SMCL) benchmark for manganese (U.S. Environmental Protection Agency, 2017; California Division of Drinking Water, 2014), whereas the 300 µg/L event threshold represents the U.S. Geological Survey (USGS) health-based screening level (HBSL) benchmark, used to put measured concentrations of drinking-water contaminants into a human-health context (Toccalino and others, 2014). The 150 µg/L event threshold represents one-half the USGS HBSL. The resultant dissolved oxygen and dissolved manganese prediction grids may be of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Prediction grids for selected redox constituents and thresholds were created by the USGS National Water-Quality Assessment (NAWQA) modeling and mapping team.

California↗

USGS2002

Weighting observations in the context of calibrating ground-water models

This paper investigates four issues related to weighting observations in the context of ground-water models calibrated with nonlinear regression: (1) terminology, (2) determining values for the weighting, (3) measurement and model errors, and (4) the effect weighting can have on the accuracy of calibrated models and measures of uncertainty. It is shown that the confusing aspects of weighting can be managed, and are not a practical barrier to using regression methods.

Acta Universitatis Carolinae, Geologica↗

USGS2017

Image simulation and assessment of the colour and spatial capabilities of the Colour and Stereo Surface Imaging System (CaSSIS) on the ExoMars Trace Gas Orbiter

This study aims to assess the spatial and visible/near-infrared (VNIR) colour/spectral capabilities of the 4-band Colour and Stereo Surface Imaging System (CaSSIS) aboard the ExoMars 2016 Trace Grace Orbiter (TGO). The instrument response functions for the CaSSIS imager was used to resample spectral libraries, modelled spectra and to construct spectrally ( i.e. , in I/F space) and spatially consistent simulated CaSSIS image cubes of various key sites of interest and for ongoing scientific investigations on Mars. Coordinated datasets from Mars Reconnaissance Orbiter (MRO) are ideal, and specifically used for simulating CaSSIS. The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) provides colour information, while the Context Imager (CTX), and in a few cases the High-Resolution Imaging Science Experiment (HiRISE), provides the complementary spatial information at the resampled CaSSIS unbinned/unsummed pixel resolution (4.6 m/pixel from a 400-km altitude). The methodology used herein employs a Gram-Schmidt spectral sharpening algorithm to combine the ∼18–36 m/pixel CRISM-derived CaSSIS colours with I/F images primarily derived from oversampled CTX images. One hundred and eighty-one simulated CaSSIS 4-colour image cubes (at 18–36 m/pixel) were generated (including one of Phobos) based on CRISM data. From these, thirty-three “fully”-simulated image cubes of thirty unique locations on Mars ( i.e. , with 4 colour bands at 4.6 m/pixel) were made. All simulated image cubes were used to test both the colour capabilities of CaSSIS by producing standard colour RGB images, colour band ratio composites (CBRCs) and spectral parameters. Simulated CaSSIS CBRCs demonstrated that CaSSIS will be able to readily isolate signatures related to ferrous (Fe 2+ ) iron- and ferric (Fe 3+ ) iron-bearing deposits on the surface of Mars, ices and atmospheric phenomena. Despite the lower spatial resolution of CaSSIS when compared to HiRISE, the results of this work demonstrate that CaSSIS will not only compliment HiRISE-scale studies of various geological and seasonal phenomena, it will also enhance them by providing additional colour and geologic context through its wider and longer full-colour coverage ( ∼ 9.4 × 50 "> ∼ 9.4 × 50 ∼9.4×50 km), and its increased sensitivity to iron-bearing materials from its two IR bands (RED and NIR). In a few examples, subtle surface changes that were not easily detected by HiRISE were identified in the simulated CaSSIS images. This study also demonstrates the utility of the Gram-Schmidt spectral pan-sharpening technique to extend VNIR colour/spectral capabilities from a lower spatial resolution colour/spectral dataset to a single-band or panchromatic image greyscale image with higher resolution. These higher resolution colour products (simulated CaSSIS or otherwise) are useful as means to extend both geologic context and mapping of datasets with coarser spatial resolutions. The results of this study indicate that the TGO mission objectives, as well as the instrument-specific mission objectives, will be achievable with CaSSIS.

Space Science Reviews↗

USGS2017

Sediment unmixing using detrital geochronology

Sediment mixing within sediment routing systems can exert a strong influence on the preservation of provenance signals that yield insight into the influence of environmental forcings (e.g., tectonism, climate) on the earth’s surface. Here we discuss two approaches to unmixing detrital geochronologic data in an effort to characterize complex changes in the sedimentary record. First we summarize ‘top-down’ mixing, which has been successfully employed in the past to characterize the different fractions of prescribed source distributions (‘parents’) that characterize a derived sample or set of samples (‘daughters’). Second we propose the use of ‘bottom-up’ methods, previously used primarily for grain size distributions, to model parent distributions and the abundances of these parents within a set of daughters. We demonstrate the utility of both top-down and bottom-up approaches to unmixing detrital geochronologic data within a well-constrained sediment routing system in central California. Use of a variety of goodness-of-fit metrics in top-down modeling reveals the importance of considering the range of allowable mixtures over any single best-fit mixture calculation. Bottom-up modeling of 12 daughter samples from beaches and submarine canyons yields modeled parent distributions that are remarkably similar to those expected from the geologic context of the sediment-routing system. In general, mixture modeling has potential to supplement more widely applied approaches in comparing detrital geochronologic data by casting differences between samples as differing proportions of geologically meaningful end-member provenance categories.

Earth and Planetary Science Letters↗

USGS2026

Basal area loss from fire using field-calibrated remote sensing refines western US fire severity measurements

The spatial patterns of fire effects and tree mortality have profound consequences for forest resilience. Cost-effective, medium-resolution, and spatiotemporally extensive fire severity measurements are essential for informing post-fire restoration and improving our understanding of wildfires—from forest stands to continents and from days to decades. Remote sensing advancements have improved burn severity mapping, but methods vary in interpretability, scalability, generalizability, and alignment with field measurements. One meaningful metric of fire effects on forests is proportion basal area loss, but existing methods are limited by a lack of region-specific field reference data and a scalable mapping framework. To address these issues, we compiled 3280 field reference plots from 123 fires in forests across the Western US to calculate the proportion of fire-induced basal area loss. We then used spatially cross-validated machine learning models with concurrent hyperparameter tuning to select a skillful, parsimonious model from a large candidate set of remotely-sensed, climatic, and topographic predictors. Spectral-only measures of severity over- or underestimated basal area loss in dry versus wet years and across aspects, demonstrating the value of incorporating climatic and topographic context. We also tested model performance on a separate holdout dataset in the Southwest US as a demonstration of reproducibility and transparency. We provide a Google Earth Engine tool for estimating proportional basal area loss for any fire perimeter in the Western US, enabling rapid map creation for land management and ecological modeling. All code, model parameters, and training data are released to support reproducibility, community adoption, regional refinement, and adaptation to new regions.

western United States↗

USGS2003

Estimation by capture-recapture of recruitment and dispersal over several sites

Dispersal in animal populations is intimately linked with accession to reproduction, i.e. recruitment, and population regulation. Dispersal processes are thus a key component of population dynamics to the same extent as reproduction or mortality processes. Despite the growing interest in spatial aspects of population dynamics, the methodology for estimating dispersal, in particular in relation with recruitment, is limited. In many animal populations, in particular vertebrates, the impossibility of following individuals over space and time in an exhaustive way leads to the need to frame the estimation of dispersal in the context of capture-recapture methodology. We present here a class of age-dependent multistate capture-recapture models for the simultaneous estimation of natal dispersal, breeding dispersal, and age-dependent recruitment. These models are suitable for populations in which individuals are marked at birth and then recaptured over several sites. Under simple constraints, they can be used in populations where non-breeders are not observed, as is often the case with colonial waterbirds monitored on their breeding grounds. Biological questions can be addressed by comparing models differing in structure, according to the generalized linear model philosophy broadly used in capture-recapture methodology. We illustrate the potential of this approach by an analysis of recruitment and dispersal in the roseate tern Sterna dougallii .

Oikos↗

USGS2014

Causal networks clarify productivity-richness interrelations, bivariate plots do not

Perhaps no other pair of variables in ecology has generated as much discussion as species richness and ecosystem productivity, as illustrated by the reactions by Pierce (2013) and others to Adler et al.'s (2011) report that empirical patterns are weak and inconsistent. Adler et al. (2011) argued we need to move beyond a focus on simplistic bivariate relationships and test mechanistic, multivariate causal hypotheses. We feel the continuing debate over productivity–richness relationships (PRRs) provides a focused context for illustrating the fundamental difficulties of using bivariate relationships to gain scientific understanding. Pierce (2013) disputes Adler et al.'s (2011) conclusion that bivariate productivity–richness relationships (PRRs) are ‘weak and variable’. He argues, instead, that relationships in the Adler et al. data are actually strong and, further, that failure to adhere to the humped-back model (HBM; sensu Grime 1979) threatens scientists' ability to advise conservationists. Here, we show that Pierce's reanalyses are invalid, that statistically significant boundary relations in the Adler et al. data are difficult to detect when proper methods are used and that his advice neither advances scientific understanding nor provides the quantitative forecasts actually needed by decision makers. We begin by examining Grimes' HBM through the lens of causal networks. We first translate the ideas contained in the HBM into a causal diagram, which shows explicitly how multiple processes are hypothesized to control biomass production and richness and their interrelationship. We then evaluate the causal diagram using structural equation modelling and example data from a published study of meadows in Finland. Formal analysis rejects the literal translation of the HBM and reveals additional processes at work. This exercise shows how the practice of abstracting systems as causal networks (i) clarifies possible hypotheses, (ii) permits explicit testing and (iii) provides more powerful and useful predictions. Building on the Finnish meadow example, we contrast the utility of bivariate plots compared with structural equation models for investigating underlying processes. Simulations illustrate the fallibility of bivariate analysis as a means of supporting one theory over another, while models based on causal networks can quantify the sensitivity of diversity patterns to both management and natural constraints. A key piece of Pierce's critique of Adler et al.'s conclusions relies on upper boundary regression which he claims to reveal strong relationships between production and richness in Adler et al.'s original data. We demonstrate that this technique shows strong associations in purely random data and is invalid for Adler et al.'s data because it depends on a uniform data distribution. We instead perform quantile regression on both the site-level summaries of the data and the plot-level data (using mixed-model quantile regression). Using a variety of nonlinear curve-fitting approaches, we were unable to detect a significant humped-shape boundary in the Adler et al. data. We reiterate that the bivariate productivity–richness relationships in Adler et al.'s data are weak and variable. We urge ecologists to consider productivity–richness relationships through the lens of causal networks to advance our understanding beyond bivariate analysis. Further, we emphasize that models based on a causal network conceptualization can also provide more meaningful guidance for conservation management than can a bivariate perspective. Measuring only two variables does not permit the evaluation of complex ideas nor resolve debates about underlying mechanisms.

Functional Ecology↗

USGS2021

Informed breeding dispersal following stochastic changes to patch quality in a pond-breeding amphibian

The unidirectional movement of animals between breeding patches (i.e. breeding dispersal) has profound implications for the ecological and evolutionary dynamics of spatially structured populations. In spatiotemporally variable environments, individuals are expected to adjust their dispersal decisions according to information gathered on the environmental and/or social cues that reflect the fitness prospects in a given breeding patch (i.e. informed dispersal). A paucity of empirical work limited our understanding of the ability of animals to depart from low-quality breeding patches and settle in high-quality breeding patches. We examined the capacity of individuals to respond to stochastic changes in habitat quality via informed breeding dispersal in a pond-breeding amphibian. We conducted a 5-year (2015–2019) capture–recapture study of boreal toads Anaxyrus boreas boreas ( n = 1,100) that breed in beaver ponds in western Wyoming, USA. During early spring of 2017, an extreme flooding event destroyed several beaver dams and resulted in the loss of breeding habitat. We used multi-state models to investigate how temporal changes in pond characteristics influenced breeding dispersal, and determine whether movement decisions were in accordance with prospects for reproductive fitness. Boreal toads more often departed from low-quality breeding ponds (without successful metamorphosis) and settled in high-quality breeding ponds (with successful metamorphosis). Movement decisions were context-dependent and associated with pond characteristics altered by beaver dam destruction. Individuals were more likely to depart from shallow ponds with high vegetation cover and settle in deep ponds with low vegetation cover. The probability of metamorphosis was related to the same environmental cues, suggesting that boreal toads assess the fitness prospects of a breeding patch and adjust movement decisions accordingly (i.e. informed breeding dispersal). We demonstrated that stochastic variability in environmental conditions and habitat quality can underpin dispersal behaviour in amphibians. Our study highlighted the mechanistic linkages between habitat change, movement behaviour and prospects for reproductive performance, which is critical for understanding how wild animals respond to rapid environmental change.

Wyoming↗

USGS2026

Action in uncertainty: Data-driven decisions that acknowledge emotional responses and transcendental connections

The increasing uncertainty with global change often stifles action and results in calls for more data before moving beyond status quo environmental decisions (Mahapatra & Ratha 2017; Ripple et al. 2017; Montefalcone et al. 2025). Advancing science and collecting more data is crucial; however, science alone (i.e., “western” or “positivist” science, as described in Fuller, 2001; Reid et al. 2020) may be insufficient to reduce uncertainty to a comfortable level for decision making. Therefore, increasing personal and collective capacity to make proactive decisions may require decision makers to recognize that their own understanding of the world, and therefore interpretation of scientific data, is influenced by all Four Realms of human perception: Physical, Mental, Emotional, and Transcendental (Wolf 2017; Dukes et al. 2021; Clifford et al. 2022). In the ESA Special Session, Action in Uncertainty, we introduced four questions to help participants increase cognitive awareness of how all Four Realms may affect their understanding in uncertain environmental decision contexts: 1. Physical: How do I observe uncertainty through the five senses (feel, see, hear, taste, smell)? The physical realm is what people observe, including ecological data observations and experimentation. 2. Mental: How do I think about uncertainty using logic, reason, and language-based understanding? The mental realm is how people think about the world, including scientific theory, modeling, and decision frameworks. 3. Emotional: How do I feel in uncertainty? The emotional realm is a person’s subjective emotional state, such as fear, curiosity, defensiveness, and awe. 4. Transcendental: How do I connect to something greater than myself in uncertainty? The transcendental realm includes people’s sense of purpose, responsibility for others, or moral code.

ESA Bulletin↗

USGS2024

Leveraging extensive soil, vegetation, fire, and land treatment data to inform restoration across the sagebrush biome

Context Widespread ecological degradation has prompted calls for massive global investments in ecological restoration, yet limited resources necessitate efficient application of restoration efforts. In western North America, altered fire regimes are increasing the scale of restoration needed to preserve the sagebrush ( Artemisia species) biome but prioritizing and implementing effective restoration is complicated by the vast and heterogeneous sagebrush landscape, which includes gradients in climate, disturbance, and species composition. Objectives To develop spatially explicit and context-dependent estimates of treatment efficacy and sagebrush recovery rates. Methods We leveraged a suite of spatio-temporally extensive datasets to evaluate the influence of restoration treatments and environmental conditions on trends in post-disturbance sagebrush cover, with an emphasis on understanding differences between sites recovering naturally and sites receiving restoration treatments. We used estimates from these models to develop spatially explicit projections for sagebrush recovery, conditional on disturbance, restoration practice, and environmental conditions. Results We found seeding Artemisia spp. increased sagebrush cover over time relative to natural recovery, but this relationship depended on spring soil moisture availability and treatment methods. Natural recovery was positively influenced by soil moisture and sagebrush cover and negatively influenced by cumulative burns and annual herbaceous cover, while the influence of perennial herbaceous cover varied with soil moisture. Conclusions Our results provide biome-wide insights and spatially explicit tools that can inform economic cost-effectiveness analyses, restoration prioritization tools, and other scientific endeavors to ensure managers have the tools and information needed to effectively steward the sagebrush biome in a rapidly changing world.

Landscape Ecology↗

USGS2016

Integrating local pastoral knowledge, participatory mapping, and species distribution modeling for risk assessment of invasive rubber vine ( Cryptostegia grandiflora ) in Ethiopia’s Afar region

The threats posed by invasive plants span ecosystems and economies worldwide. Local knowledge of biological invasions has proven beneficial for invasive species research, but to date no work has integrated this knowledge with species distribution modeling for invasion risk assessments. In this study, we integrated pastoral knowledge with Maxent modeling to assess the suitable habitat and potential impacts of invasive Cryptostegia grandiflora Robx. Ex R.Br. (rubber vine) in Ethiopia’s Afar region. We conducted focus groups with seven villages across the Amibara and Awash-Fentale districts. Pastoral knowledge revealed the growing threat of rubber vine, which to date has received limited attention in Ethiopia, and whose presence in Afar was previously unknown to our team. Rubber vine occurrence points were collected in the field with pastoralists and processed in Maxent with MODIS-derived vegetation indices, topographic data, and anthropogenic variables. We tested model fit using a jackknife procedure and validated the final model with an independent occurrence data set collected through participatory mapping activities with pastoralists. A Multivariate Environmental Similarity Surface analysis revealed areas with novel environmental conditions for future targeted surveys. Model performance was evaluated using area under the receiver-operating characteristic curve (AUC) and showed good fit across the jackknife models (average AUC = 0.80) and the final model (test AUC = 0.96). Our results reveal the growing threat rubber vine poses to Afar, with suitable habitat extending downstream of its current known location in the middle Awash River basin. Local pastoral knowledge provided important context for its rapid expansion due to acute changes in seasonality and habitat alteration, in addition to threats posed to numerous endemic tree species that provide critical provisioning ecosystem services. This work demonstrates the utility of integrating local ecological knowledge with species distribution modeling for early detection and targeted surveying of recently established invasive species.

Ecology and Society↗

USGS1999

Chapter 6 Geochemistry, isotopic composition and petrogenetic modelling of the Thera pyroclastics

This chapter examines the major element, trace element, and isotope geochemistry of the Thera pyroclastics, and places it in the context of the geochemical evolution of the whole volcanic field. Geochemical studies to date have been undertaken predominantly on the lavas, and the pyroclastic succession has been relatively neglected. The data on the pyroclastics are taken from Druitt (1983), Mellors (1988), Pyle (19906), and Edwards (1994). Wholerock analyses and sample descriptions are given in Appendix 2. Most analyses are of a single pumice or scoria lump, although some are combinations of several small clasts and a few are compositionally banded. The rock classification scheme used throughout the memoir is shown in Fig. 6.1. Previous isotopic studies on Santorini are also largely confined to the lavas, and no detailed studies had been carried out on the pyroclastic rocks. In this chapter we present new 87 Sr/ 86 Sr, 143 Nd/ 144 Nd, 206 Pb/ 204 Pb, 207 Pb/ 204 Pb, 208 Pb/ 204 Pb and δ 18 О isotopic data on 40 samples of the Thera pyroclastics. We also include some measurements of hydrogen isotopes. Methods of geochemical and isotopic analysis are described in Appendix 1. This chapter also presents some quantitative models of fractional crystallization and assimilation of crustal rocks. The geochemical data and models point towards the importance of crystal fractionation, magma mixing and assimilation of continental crust in magma genesis at Santorini. The chapter also documents temporal changes in geochemical features of Santorini magmas.

Santorini↗

USGS2025

A model for regional-scale oak savanna management: The roles of fire, canopy, and soils for understory plant diversity

Predicting the outcomes of land management on biodiversity is difficult without a mechanistic understanding of how management approaches, ecosystem structure, environmental conditions, and biodiversity interact. Management effects may be direct or indirect, context- or scale-dependent, or obscured by local environmental conditions. Resolving these relationships at the regional scale may be difficult, given heterogeneity in local environmental conditions, yet understanding broad-scale patterns can elucidate context dependencies and improve restoration outcomes. We confronted these challenges within globally rare oak savannas in the midwestern United States, which have been altered by fire exclusion and resulting woody encroachment. By modeling direct and indirect pathways by which management influences diversity, we test a general framework for savanna restoration. Across 100 oak savannas spanning five US states, management by prescribed fire and mechanical thinning of woody vegetation affected groundlayer plant species richness through changes to ecosystem structure (canopy openness and litter depth), and these effects were both context- and scale-dependent. Frequent prescribed fires and canopy thinning promoted greater canopy openness, which in turn increased richness at small (1 m 2 ), but not larger (1000 m 2 ) scales. Frequent fire additionally increased richness at small and larger scales through effects independent of ecosystem structure. While management effects were large relative to the influence of local edaphic conditions, soil productivity had two largely offsetting effects on small-scale richness, increasing richness directly but decreasing richness indirectly by promoting closed canopy structure. These results suggest using a combination of fire and canopy thinning to reverse the effects of decades of fire exclusion. However, management effects were also context-dependent, emphasizing that management outcomes vary regionally. Here, 1-m 2 plant species richness increased with both fire frequency and canopy thinning under low, but not high, productivity soil conditions. By demonstrating how specific management practices influence savanna structure and biodiversity by manipulating ecological processes across broad geographic and edaphic gradients, our findings provide a framework for understanding management outcomes at short and medium intervals (e.g., within and between decades, respectively), in the form of a model that can be refined by testing additional hypotheses to better predict savanna restoration outcomes.

Illinois, Indiana, Michigan, Ohio, Wisconsin↗

USGS2022

Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region

Context Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments. Objectives This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog ( Rana pipiens ) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation. Methods Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity. Results STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation. Conclusion While the R. pipiens populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.

North Dakota↗

USGS2014

Environmental and physiological influences to isotopic ratios of N and protein status in a montane ungulate in winter

Winter severity can influence large herbivore populations through a reduction in maternal proteins available for reproduction. Nitrogen (N) isotopes in blood fractions can be used to track the use of body proteins in northern and montane ungulates. We studied 113 adult female caribou for 13 years throughout a series of severe winters that reduced population size and offspring mass. After these severe winters, offspring mass increased but the size of the population remained low. We devised a conceptual model for routing of isotopic N in blood in the context of the severe environmental conditions experienced by this population. We measured δ 15 N in three blood fractions and predicted the relative mobilization of dietary and body proteins. The δ 15 N of the body protein pool varied by 4‰ and 46% of the variance was associated with year. Annual variation in δ 15 N of body protein likely reflected the fall/early winter diet and winter locations, yet 15% of the isotopic variation in amino acid N was due to body proteins. Consistent isotopic differences among blood N pools indicated that animals tolerated fluxes in diet and body stores. Conservation of body protein in caribou is the result of active exchange among diet and body N pools. Adult females were robust to historically severe winter conditions and prioritized body condition and survival over early investment in offspring. For a vagile ungulate residing at low densities in a predator-rich environment, protein restrictions in winter may not be the primary limiting factor for reproduction.

Alaska↗