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At least 1,045 records · Page 58Linked to original sources
USGS2016

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species-level Poisson processes and estimate patch-level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early-successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.

Ecology and Evolution↗
USGS2018

Infrared heater system for warming tropical forest understory plants and soils

The response of tropical forests to global warming is one of the largest uncertainties in predicting the future carbon balance of Earth. To determine the likely effects of elevated temperatures on tropical forest understory plants and soils, as well as other ecosystems, an infrared (IR) heater system was developed to provide in situ warming for the Tropical Responses to Altered Climate Experiment (TRACE) in the Luquillo Experimental Forest in Puerto Rico. Three replicate heated 4-m- diameter plots were warmed to maintain a 4°C increase in understory vegetation compared to three unheated control plots, as sensed by IR thermometers. The equipment was larger than any used previously and was subjected to challenges different from those of many temperate ecosystem warming systems, including frequent power surges and outages, high humidity, heavy rains, hurricanes, saturated clayey soils, and steep slopes. The system was able to maintain the target 4.0°C increase in hourly average vegetation temperatures to within ± 0.1°C. The vegetation was heterogeneous and on a 21° slope, which decreased uniformity of the warming treatment on the plots; yet, the green leaves were fairly uniformly warmed, and there was little difference among 0–10 cm depth soil temperatures at the plot centers, edges, and midway between. Soil temperatures at the 40–50 cm depth increased about 3°C compared to the controls after a month of warming. As expected, the soil in the heated plots dried faster than that of the control plots, but the average soil moisture remained adequate for the plants. The TRACE heating system produced an adequately uniform warming precisely controlled down to at least 50-cm soil depth, thereby creating a treatment that allows for assessing mechanistic responses of tropical plants and soil to warming, with applicability to other ecosystems. No physical obstacles to scaling the approach to taller vegetation (i.e., trees) and larger plots were observed.

Puerto Rico↗
USGS2015

Scale dependence of disease impacts on quaking aspen (Populus tremuloides) mortality in the southwestern United States

Depending on how disease impacts tree exposure to risk, both the prevalence of disease and disease effects on survival may contribute to patterns of mortality risk across a species' range. Disease may accelerate tree species' declines in response to global change factors, such as drought, biotic interactions, such as competition, or functional traits, such as allometry. To assess the role of disease in mediating mortality risk in quaking aspen ( Populus tremuloides ), we developed hierarchical Bayesian models for both disease prevalence in live aspen stems and the resulting survival rates of healthy and diseased aspen near the species' southern range limit using 5088 individual trees on 281 United States Forest Service Forest Inventory and Analysis plots in the southwestern United States. We found that disease prevalence depended primarily on tree size, tree allometry, and spatial variation in precipitation, while mortality depended on tree size, allometry, competition, spatial variation in summer temperature, and both temporal and spatial variation in summer precipitation. Disease prevalence was highest in large trees with low slenderness found on dry sites. For healthy trees, mortality decreased with diameter, slenderness, and temporal variation in summer precipitation, but increased with competition and spatial variation in summer temperature. Mortality of diseased trees decreased with diameter and aspen relative basal area and increased with mean summer temperature and precipitation. Disease infection increased aspen mortality, especially in trees of intermediate size and trees on plots at climatic extremes (i.e., cool, wet and warm, dry climates). By examining variation in disease prevalence, mortality of healthy trees, and mortality of diseased trees, we showed that the role of disease in aspen tree mortality depended on the scale of inference. For variation among individuals in diameter, disease tended to expose intermediate-size trees experiencing moderate risk to greater risk. For spatial variation in summer temperature, disease exposed lower risk populations to greater mortality probabilities, but the magnitude of this exposure depended on summer precipitation. Furthermore, the importance of diameter and slenderness in mediating responses to climate supports the increasing emphasis on trait variation in studies of ecological responses to global change.

Ecology, Evolution, and Systematics↗
USGS2012

Distribution of late Cenozoic volcanic vents in the Cascade Range: Volcanic arc segmentation and regional tectonic considerations

Spatial, temporal, and compositional distributions of approximately 4000 volcanic vents formed since 16 Ma in Washington, Oregon, northern California, and northwestern Nevada illustrate the evolution of volcanism related to subduction of the Juan de Fuca plate system and extension of the Basin and Range province. Vent data were obtained from published map compilations and include monogenetic and small polygenetic volcanoes in addition to major composite centers. On the basis of the distribution of 2821 vents formed since 5 Ma, the Cascade Range is divided into five segments, with vents of the High Lava Plains along the northern margin of the Basin and Range province in Oregon forming a sixth segment. Some aspects of the Cascade Range segmentation can be related to gross structural features of the subducting Juan de Fuca plate. The orientation of the volcanic front of segments one and two changes from NW in northern Washington to NE in southern Washington, paralleling the strike of the subducting Juan de Fuca plate. Segments one and two are separated by a 90-km volcanic gap between Mount Rainier and Glacier Peak that is landward of the portion of the subducting plate having the least average dip to a depth of 60 km. A narrow, N-S trending belt of predominantly andesitic vents in Oregon constitutes a third segment, which is landward of the seismically quiet portion of the subduction zone. The narrowness of this segment may indicate steep dip of the subducting plate beneath the Cascade arc in Oregon. Vents are sparse between segment four (containing the Mount Shasta and Medicine Lake centers) and segment five (containing Lassen Peak), where the Juan de Fuca and Gorda North plates are characterized by differing age, amounts of subcrustal seismicity, and probably geometry. From the relation between seismicity at depth of 60 km and the position of the volcanic front of vents formed since 5 Ma, transitions between subducting-plate segments of varying geometry likely occur near boundaries between independently defined volcanic segments in northern Oregon and northern California. In the Basin and Range province east of the Cascade arc, volcanism migrated into the region adjacent to the Cascade Range during the interval 5–10 Ma. Since 5 Ma, the impingement of the two provinces is characterized by cessation of basin-range volcanism in southern Oregon, continuation of basaltic volcanism in northeastern California where the impingement process may not yet be complete, and contraction of the area of mafic volcanism around Mount Shasta, Medicine Lake, and Lassen Peak. In central Oregon where the northern margin of basin-range volcanism (the High Lava Plains) intersects the Cascade arc, impingement of basin-range extensional volcanism approximately coincides in time and space with the development of the High Cascade graben between Three Sisters and Mount Jefferson.

Journal of Geophysical Research Solid Earth↗
USGS2021

Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies

The Great Dismal Swamp is a peat wetland in the Coastal Plain of southeastern Virginia and northeastern North Carolina. Timber harvesting and the construction of ditches to drain the swamp and facilitate the harvesting are collectively implicated in changes that altered the wetland forests, caused subsidence and decomposition of the peat, and increased the risk of fire. In response to these changes, managers have implemented strategies to control water levels and rewet the swamp using a network of 64 adjustable-height, water-control structures on the ditches. Rewetting the swamp is intended to re-establish the original wetland-forest types, reduce the risk of fire, reduce subsidence and decomposition of the peat, enhance peat accretion, and reduce the risk of fire. Knowledge of responses of the swamp to hydrologic controls, however, is critical to developing and implementing effective management goals and strategies. Because the 2008 South One fire reemphasized the need for this knowledge, the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service began studies in 2009 to identify critical hydrologic controls and responses to these controls. These studies identified water sources, topography, the two-layered hydraulic characteristics of the peat, the absence of peat in some areas, the ditch and road network, water-control structures on the ditches, the Dismal Swamp Canal and associated infrastructure, and wetland forests as the primary hydrologic controls. Precipitation is the only water source across much of the swamp. The eastward flow of streams and groundwater from the Isle of Wight Plain, across the Suffolk scarp, and into the swamp are additional water sources to the western part of the swamp. Vertical differences in the hydraulic characteristics of the peat reflect an upper peat having a high hydraulic conductivity and specific yield overlying a lower peat and sand having lower hydraulic conductivity and specific yield. The upper peat forms the main aquifer for the storage, flow, and release of water from the swamp. Maintaining water in the upper peat is critical to water availability to the wetland forests because of these properties. Groundwater flows from the swamp into the ditches and the Dismal Swamp Canal where it discharges into nearby streams. Discharge typically is to the closest ditch except where a spoil-pile road that impedes flow intervenes between the swamp and the ditch. When groundwater levels in a ditch are about 2 feet lower than levels in the other three ditches surrounding a part of the swamp, however, most groundwater typically discharges to the ditch having the lower level. This occurs even if a spoil-pile road intervenes between the swamp and the ditch having the lower level. Flow to a single ditch shifts watershed boundaries and groundwater divides toward the ditches having higher water levels and demonstrates how flow and discharge are controlled by ditch water levels. Consequently, managing water levels based on these and other hydrologic controls and responses is critical to achieving management objectives. The chemistry of water across the swamp shows the effects of the peat. Dissolved organic carbon concentrations in the groundwater are among the highest reported globally, ranging from 55 to 195 milligrams per liter. The pH of groundwater and ditch water is commonly less than 4.0 standard units because of organic acids. A relation between the pH and specific conductance of groundwater and ditch water reflects water sources, flow paths, and the chemical evolution, as waters from the different sources mix and flow along the paths.

North Carolina, Virginia↗
USGS2023

Unprecedented distribution data for Joshua trees (Yucca brevifolia and Y. jaegeriana) reveal contemporary climate associations of a Mojave Desert icon

Introduction: Forecasting range shifts in response to climate change requires accurate species distribution models (SDMs), particularly at the margins of species' ranges. However, most studies producing SDMs rely on sparse species occurrence datasets from herbarium records and public databases, along with random pseudoabsences. While environmental covariates used to fit SDMS are increasingly precise due to satellite data, the availability of species occurrence records is still a large source of bias in model predictions. We developed distribution models for hybridizing sister species of western and eastern Joshua trees ( Yucca brevifolia and Y. jaegeriana , respectively), iconic Mojave Desert species that are threatened by climate change and habitat loss. Methods: We conducted an intensive visual grid search of online satellite imagery for 672,043 0.25 km 2 grid cells to identify the two species' presences and absences on the landscape with exceptional resolution, and field validated 29,050 cells in 15,001 km of driving. We used the resulting presence/absence data to train SDMs for each Joshua tree species, revealing the contemporary environmental gradients (during the past 40 years) with greatest influence on the current distribution of adult trees. Results: While the environments occupied by Y. brevifolia and Y. jaegeriana were similar in total aridity, they differed with respect to seasonal precipitation and temperature ranges, suggesting the two species may have differing responses to climate change. Moreover, the species showed differing potential to occupy each other's geographic ranges: modeled potential habitat for Y. jaegeriana extends throughout the range of Y. brevifolia , while potential habitat for Y. brevifolia is not well represented within the range of Y. jaegeriana . Discussion: By reproducing the current range of the Joshua trees with high fidelity, our dataset can serve as a baseline for future research, monitoring, and management of this species, including an increased understanding of dynamics at the trailing and leading margins of the species' ranges and potential for climate refugia.

Frontiers Ecology and Evolution↗
USGS2022

Defining biologically relevant and hierarchically nested population units to inform wildlife management

Wildlife populations are increasingly affected by natural and anthropogenic changes that negatively alter biotic and abiotic processes at multiple spatiotemporal scales and therefore require increased wildlife management and conservation efforts. However, wildlife management boundaries frequently lack biological context and mechanisms to assess demographic data across the multiple spatiotemporal scales influencing populations. To address these limitations, we developed a novel approach to define biologically relevant subpopulations of hierarchically nested population levels that could facilitate managing and conserving wildlife populations and habitats. Our approach relied on the Spatial “K”luster Analysis by Tree Edge Removal clustering algorithm, which we applied in an agglomerative manner (bottom-to-top). We modified the clustering algorithm using a workflow and population structure tiers from least-cost paths, which captured biological inferences of habitat conditions (functional connectivity), dispersal capabilities (potential connectivity), genetic information, and functional processes affecting movements. The approach uniquely included context of habitat resources (biotic and abiotic) summarized at multiple spatial scales surrounding locations with breeding site fidelity and constraint-based rules (number of sites grouped and population structure tiers). We applied our approach to greater sage-grouse ( Centrocercus urophasianus ), a species of conservation concern, across their range within the western United States. This case study produced 13 hierarchically nested population levels (akin to cluster levels, each representing a collection of subpopulations of an increasing number of breeding sites). These closely approximated population closure at finer ecological scales (smaller subpopulation extents with fewer breeding sites; cluster levels ≥2), where >92% of individual sage-grouse's time occurred within their home cluster. With available population monitoring data, our approaches can support the investigation of factors affecting population dynamics at multiple scales and assist managers with making informed, targeted, and cost-effective decisions within an adaptive management framework. Importantly, our approach provides the flexibility of including species-relevant context, thereby supporting other wildlife characterized by site fidelity.

Ecology and Evolution↗
USGS2025

Challenges and opportunities for data integration to improve estimation of migratory connectivity

Understanding migratory connectivity, or the linkage of populations between seasons, is critical for effective conservation and management of migratory wildlife. A growing number of tools are available for understanding where migratory individuals and populations occur throughout the annual cycle. Integration of the diverse measures of migratory movements can help elucidate migratory connectivity patterns with methodology that accounts for differences in sampling design, directionality, effort, precision and bias inherent to each data type. The R package MigConnectivity was developed to estimate population-specific connectivity and the range-wide strength of those connections. New functions allow users to integrate intrinsic markers, tracking and long-distance reencounter data, collected from the same or different individuals, to estimate population-specific transition probabilities (estTransition) and the range-wide strength of those transition probabilities (estStrength). We used simulation and real-world case studies to explore the challenges and limitations of data integration based on data from three migratory bird species, Painted Bunting ( Passerina ciris ), Yellow Warbler ( Setophaga petechia ) and Bald Eagle ( Haliaeetus leucocephalus ), two of which had bidirectional data. We found data integration is useful for quantifying migratory connectivity, as single data sources are less likely to be available across the species range. Furthermore, accurate strength estimates can be obtained from either breeding-to-nonbreeding or nonbreeding-to-breeding data. For bidirectional data, integration can lead to more accurate estimates when data are available from all regions in at least one season. The ability to conduct combined analyses that account for the unique limitations and biases of each data type is a promising possibility for overcoming the challenge of range-wide coverage that has been hard to achieve using single data types. The best-case scenario for data integration is to have data from all regions, especially if the question is range-wide or data are bidirectional. Multiple data types on animal movements are becoming increasingly available and integration of these growing datasets will lead to a better understanding of the full annual cycle of migratory animals.

Methods in Ecology and Evolution↗
USGS2022

Open removal models with temporary emigration and population dynamics to inform invasive animal management

Removal sampling data are the primary source of monitoring information for many populations (e.g., invasive species, fisheries). Population dynamics, temporary emigration, and imperfect detection are common sources of variation in monitoring data and are key parameters for informing management. We developed two open robust-design removal models for simultaneously modeling population dynamics, temporary emigration, and imperfect detection: a random walk linear trend model (estimable without ancillary information), and a 2-age class informed population model (InfoPM, closely related to integrated population models) that incorporated prior information for age-structured vital rates and relative juvenile availability. We applied both models to multiyear, removal trapping time-series of a large invasive lizard (Argentine black and white tegu, Salvator merianae ) in three management areas of South Florida to evaluate the effectiveness of management programs. Although estimates of the two models were similar, the InfoPMs generally returned more precise estimates, partitioned dynamics into births, deaths, net migration, and provided a decision support tool to predict population dynamics under different effort scenarios while accounting for uncertainty. Trends in tegu superpopulation abundance estimates were increasing in two management areas despite generally high removal rates. However, tegu abundance appeared to decline in the Core management area, where trapping density was the highest and immigration the lowest. Finally, comparing abundance predictions of no-removal scenarios to those estimated in each management area suggested significant population reductions due to management. These results suggest that local tegu population control via systematic trapping may be feasible with high enough trap density and limited immigration; and highlights the value of these trapping programs. We provided the first estimates of tegu abundance, capture probabilities, and population dynamics, which is critical for effective management. Furthermore, our models are applicable to a wide range of monitoring programs (e.g., carcass recovery or removal point-counts).

Ecology and Evolution↗
USGS2017

Statistical design and analysis for plant cover studies with multiple sources of observation errors

Effective wildlife habitat management and conservation requires understanding the factors influencing distribution and abundance of plant species. Field studies, however, have documented observation errors in visually estimated plant cover including measurements which differ from the true value (measurement error) and not observing a species that is present within a plot (detection error). Unlike the rapid expansion of occupancy and N-mixture models for analysing wildlife surveys, development of statistical models accounting for observation error in plants has not progressed quickly. Our work informs development of a monitoring protocol for managed wetlands within the National Wildlife Refuge System. Zero-augmented beta (ZAB) regression is the most suitable method for analysing areal plant cover recorded as a continuous proportion but assumes no observation errors. We present a model extension that explicitly includes the observation process thereby accounting for both measurement and detection errors. Using simulations, we compare our approach to a ZAB regression that ignores observation errors (naïve model) and an “ad hoc” approach using a composite of multiple observations per plot within the naïve model. We explore how sample size and within-season revisit design affect the ability to detect a change in mean plant cover between 2 years using our model. Explicitly modelling the observation process within our framework produced unbiased estimates and nominal coverage of model parameters. The naïve and “ad hoc” approaches resulted in underestimation of occurrence and overestimation of mean cover. The degree of bias was primarily driven by imperfect detection and its relationship with cover within a plot. Conversely, measurement error had minimal impacts on inferences. We found >30 plots with at least three within-season revisits achieved reasonable posterior probabilities for assessing change in mean plant cover. For rapid adoption and application, code for Bayesian estimation of our single-species ZAB with errors model is included. Practitioners utilizing our R-based simulation code can explore trade-offs among different survey efforts and parameter values, as we did, but tuned to their own investigation. Less abundant plant species of high ecological interest may warrant the additional cost of gathering multiple independent observations in order to guard against erroneous conclusions.

Methods in Ecology and Evolution↗
USGS2013

Estimating true instead of apparent survival using spatial Cormack-Jolly-Seber models

Survival is often estimated from capture–recapture data using Cormack–Jolly–Seber (CJS) models, where mortality and emigration cannot be distinguished, and the estimated apparent survival probability is the product of the probabilities of true survival and of study area fidelity. Consequently, apparent survival is lower than true survival unless study area fidelity equals one. Underestimation of true survival from capture–recapture data is a main limitation of the method. We develop a spatial version of the CJS model that allows estimation of true survival. Besides the information about whether a specific individual was encountered at a given occasion, it is often recorded where the encounter occurred. Thus, information is available about the fraction of dispersal that occurs within the study area, and we use it to model dispersal and estimate true survival. Our model is formulated hierarchically and consists of survival, dispersal and observation submodels, assuming that encounters are possible anywhere within a study area. In a simulation study, our new spatial CJS model produced accurate estimates of true survival and dispersal behaviour for various sizes and shapes of the study area, even if emigration is substantial. However, when the information about dispersal is scarce due to low survival, low recapture probabilities and high emigration, the estimators are positively biased. Moreover, survival estimates are sensitive to the assumed dispersal kernel. We applied the spatial CJS model to a data set of adult red-backed shrikes ( Lanius collurio ). Apparent survival of males ( c . 0·5) estimated with the CJS model was larger than in females ( c . 0·4), but the application of the spatial CJS model revealed that both sexes had similar survival probabilities ( c . 0·6). The mean breeding dispersal distance in females was c . 700 m, while males dispersed only c . 250 m between years. Spatial CJS models enable study of dispersal and survival independent of study design constraints such as imperfect detection and size of the study area provided that some of the dispersing individuals remain in the study area. We discuss possible extensions of our model: alternative dispersal models and the inclusion of covariates and of a habitat suitability map.

Methods in Ecology and Evolution↗
USGS2020

Post-fire vegetation response in a repeatedly burned low-elevation sagebrush steppe protected area provides insights about resilience and invasion resistance

Sagebrush steppe ecosystems are threatened by human land-use legacies, biological invasions, and altered fire and climate dynamics. Steppe protected areas are therefore of heightened conservation importance but are few and vulnerable to the same impacts broadly affecting sagebrush steppe. To address this problem, sagebrush steppe conservation science is increasingly emphasizing a focus on resilience to fire and resistance to non-native annual grass invasion as a decision framework. It is well-established that the positive feedback loop between fire and annual grass invasion is the driving process of most contemporary steppe degradation. We use a newly developed ordinal zero-augmented beta regression model fit to large-sample vegetation monitoring data from John Day Fossil Beds National Monument, USA, spanning 7 years to evaluate fire responses of two native perennial foundation bunchgrasses and two non-native invasive annual grasses in a repeatedly burned, historically grazed, and inherently low-resilient protected area. We structured our model hierarchically to support inferences about variation among ecological site types and over time after also accounting for growing-season water deficit, fine-scale topographic variation, and burn severity. We use a state-and-transition conceptual diagram and abundances of plants listed in ecological site reference conditions to formalize our hypothesis of fire-accelerated transition to ecologically novel annual grassland. Notably, big sagebrush ( Artemisia tridentata ) and other woody species were entirely removed by fire. The two perennial grasses, bluebunch wheatgrass ( Pseudoroegneria spicata ) and Thurber's needlegrass ( Achnatherum thurberianum ) exhibited fire resiliency, with no apparent trend after fire. The two annual grasses, cheatgrass ( Bromus tectorum ) and medusahead ( Taeniatherum caput-medusae ), increased in response to burn severity, most notably medusahead. Surprisingly, we found no variation in grass cover among ecological sites, suggesting fire-driven homogenization as shrubs were removed and annual grasses became dominant. We found contrasting responses among all four grass species along gradients of topography and water deficit, informative to protected-area conservation strategies. The fine-grained influence of topography was particularly important to variation in cover among species and provides a foothold for conservation in low-resilient, aridic steppe. Broadly, our study demonstrates how to operationalize resilience and resistance concepts for protected areas by integrating empirical data with conceptual and statistical models.

Oregon↗
USGS2023

A comprehensive multi-state conditional occupancy model for evaluating interactions of non-native and native species

A major challenge in ecology is disentangling interactions of non-native, potentially invasive species on native species. Conditional two-species occupancy models examine the effects of dominant species (e.g., non-native) on subordinate species (e.g., native) while considering the possibility that occupancy of one species may affect occupancy and/ or detection of the other. Although conditional two-species models are useful for evaluating the influence of one species on presence of another, it is possible that species interactions are density dependent. Therefore, we developed a novel two-species occupancy model that incorporates multiple abundance states (i.e., absent, present, abundant) of the native species. We showcase the utility of this model with a case study that incorporates random effects and covariates on both occupancy and detection to help disentangle species interactions given varying occupancy and detection in different abundance states. We use snorkel survey data from the Umpqua basin, Oregon, where it is hypothesized that smallmouth bass Micropterus dolomieu , a non-native piscivore, exclude Umpqua chub Oregonichthys kalawatseti , a small endemic minnow. From our two-species multi-state (2SMS) model, we concluded that average occupancy was low for both fishes, and that when non-native bass were present, overall native chub occupancy in the present (0.18 ± 0.05 SD) and abundant (0.19 ± 0.03) states was higher than when non-natives were absent (0.14 ± 0.02/ 0.08 ± 0.02), indicating the non-native was not excluding the native species. By incorporating a species interaction factor, we found a positive association (6.75 ± 5.54 SD) between native chub and non-native bass. The covariates strongly related to occupancy were elevation, algae, and land cover type (urban and shrub). Detection probability for both species (0.21–0.82) was most strongly related to the covariates day of year, water temperature, gravel substrate, and stream order/ magnitude. Incorporation of detection probability and covariates enabled interpretation of interactions between the two species that may have been missed without their inclusion in the modeling process. Our new 2SMS occupancy model can be used by scientists and managers with a broad range of survey and covariate data to disentangle species interactions problems to help them inform management decisions.

Frontiers in Ecology and Evolution↗
USGS1988

Cyclic development of igneous features and their relationship to high-temperature hydrothermal features in the Henderson porphyry molybdenum deposit, Colorado

The Henderson porphyry molybdenum deposit was formed by the superposition of coupled alteration and mineralization events, of varying intensity and size, that were associated with each of at least 11 intrusions. Deposition of molybdenite was accompanied by time-equivalent silicic and potassic alteration. High-temperature alteration and mineralization are spatially and temporally linked to the crystallization of compositionally zoned magma in the apex of stocks. Differences in hydrothermal features associated with each intrusion (e.g., mass of ore, orientation and type of veins, density of veins, and intensity of alteration) correlate with differences in primary igneous features (e.g., composition, texture, morphology, and size). The systematic relations between hydrothermal and magmatic features suggest that primary magma compositions, including volatile contents, largely control the geometry, volume, level of emplacement, and mechanisms of crystallization of stocks. These elements in turn govern the orientations and densities of fractures, which ultimately determine the distribution patterns of hydrothermal alteration and mineralization.Based on primary igneous features, intrusions are grouped into four types, 1 to 4. The first three types define a potassic series of compositions (normarive orthoclase/albite greater than 1) and form a continuum from texturally and compositionally simple intrusions (type 1) to complex intrusions (type 3). Intensity and volume of high-temperature alteration and mineralization associated with a given stock increase from type 1 (2% of total ore) to type 3 (77% of total ore). Complex and strongly mineralizing intrusions (type 3) are separated from one another in time by relatively simple and weakly mineralizing intrusions (type 1). Stocks of type 4, the youngest intrusions, define a sodic series of compositions (normative orthoclase/albite less than 1) and are associated with the waning stages of mineralization.A number of textural features indicate that highly mineralizing type 3 stocks contained high primary concentrations of volatile components, e.g., the presence of unidirectional solidification textures, aplitic rather than aphanitic groundmass, extreme local textural variability, low percentages of phenocrysts in apices, brecciation of apices, and low volumes of dikes. From apex to root zone, textural units within the Seriate stock (type 3), the most productive stock in the deposit, include: (1) a Brain Rock unit with abundant quartz + or - fluorite crenulate layers, (2) a Border unit of generally low but variable phenocryst content (0-25%), (3) a transition zone, 5 to 10 m wide, of unidirectional solidification textures, (4) an Intermediate unit of moderate phenocryst content (25%), (5) another transition zone, 5 to 10 m wide, of unidirectional solidification textures, (6) a Porphyry unit of high phenocryst contents (40%), and (7) a Granite Porphyry or Granite unit. Crystals in layers of unidirectional textures always project toward the interior of the stock and indicate progressive inward solidification of magma along the walls of a chamber. Variations in texture are accompanied by variations in primary composition: the apex of the Seriate stock (the region above the deepest transition zone of unidirectional textures) is enriched in K 2 O and SiO 2 and depleted in Na 2 O, F, Nb, and Y relative to deeper units.Mineralization in the deposit is controlled by moderately outward-dipping concentric veins of both replacement and open-space origin, and by steeply dipping radial veins of replacement origin. Both sets of veins were formed nearly simultaneously about a stock and are focused in a systematic manner on the apex. A classic stockwork of veins occurs only in regions where vein sets associated with spatially separated intrusions overlap. Fluids that filled outward-dipping concentric structures flowed downward and outward from a stock. Fluid pressures in excess of lithostatic pressures, generated during crystallization of magma, were required to open and fill concentric structures. As magma solidified progressively inward, the zone of fluid evolution also migrated downward and inward. With time, the volume of evolved fluids decreased: vein densities decrease from greater than 200/m in the Seriate Brain Rock unit to less than 0.1/m immediately below the last transition zone separating the Seriate Intermediate unit from Seriate Porphyry unit. Crosscutting relations between veins and high-level dikes associated with the Seriate stock establish that most of the ore related to the Seriate stock was deposited prior to solidification of its deepest transition zone of unidirectional solidification textures.The absence of high-temperature veins and associated hydrothermal alteration in deep cores of stocks, the distribution of ore about the high levels of stocks, the orientation of veins about the apex of stocks, and the crosscutting relations between veins and dikes of the same stock are suggestive of initially high concentrations of molybdenum and volatiles in the apex of a stock immediately prior to the onset of significant crystallization. Additional accumulation of ore components, if any, ceased after solidification of the apex. Assignment of molybdenum in the ore shell about the Seriate stock to the volume of solid occupied by the apex of the Seriate stock yields concentration levels in the apical magma of approximately 13,000 ppm Mo.

Economic Geology↗
USGS2022

Simple statistical models can be sufficient for testing hypotheses with population time series data

Time-series data offer wide-ranging opportunities to test hypotheses about the physical and biological factors that influence species abundances. Although sophisticated models have been developed and applied to analyze abundance time series, they require information about species detectability that is often unavailable. We propose that in many cases, simpler models are adequate for testing hypotheses. We consider three relatively simple regression models for time series, using simulated and empirical (fish and mammal) datasets. Model A is a conventional generalized linear model of abundance, model B adds a temporal autoregressive term, and model C uses an estimate of population growth rate as a response variable, with the option of including a term for density dependence. All models can be fit using Bayesian and non-Bayesian methods. Simulation results demonstrated that model C tended to have greater support for long-lived, lower-fecundity organisms (K life-history strategists), while model A, the simplest, tended to be supported for shorter-lived, high-fecundity organisms (r life-history strategists). Analysis of real-world fish and mammal datasets found that models A, B, and C each enjoyed support for at least some species, but sometimes yielded different insights. In particular, model C indicated effects of predictor variables that were not evident in analyses with models A and B. Bayesian and frequentist models yielded similar parameter estimates and performance. We conclude that relatively simple models are useful for testing hypotheses about the factors that influence abundance in time-series data, and can be appropriate choices for datasets that lack the information needed to fit more complicated models. When feasible, we advise fitting datasets with multiple models because they can provide complementary information.

Ecology and Evolution↗
USGS2010

Temperature and precipitation history of the Arctic

As the planet cooled from peak warmth in the early Cenozoic, extensive Northern Hemisphere ice sheets developed by 2.6 Ma ago, leading to changes in the circulation of both the atmosphere and oceans. From ∼2.6 to ∼1.0 Ma ago, ice sheets came and went about every 41 ka, in pace with cycles in the tilt of Earth’s axis, but for the past 700 ka, glacial cycles have been longer, lasting ∼100 ka, separated by brief, warm interglaciations, when sea level and ice volumes were close to present. The cause of the shift from 41 ka to 100 ka glacial cycles is still debated. During the penultimate interglaciation, ∼130 to ∼120 ka ago, solar energy in summer in the Arctic was greater than at any time subsequently. As a consequence, Arctic summers were ∼5 °C warmer than at present, and almost all glaciers melted completely except for the Greenland Ice Sheet, and even it was reduced in size substantially from its present extent. With the loss of land ice, sea level was about 5 m higher than present, with the extra melt coming from both Greenland and Antarctica as well as small glaciers. The Last Glacial Maximum (LGM) peaked ∼21 ka ago, when mean annual temperatures over parts of the Arctic were as much as 20 °C lower than at present. Ice recession was well underway 16 ka ago, and most of the Northern Hemisphere ice sheets had melted by 6 ka ago. Solar energy reached a summer maximum (9% higher than at present) ∼11 ka ago and has been decreasing since then, primarily in response to the precession of the equinoxes. The extra energy elevated early Holocene summer temperatures throughout the Arctic 1–3 °C above 20th century averages, enough to completely melt many small glaciers throughout the Arctic, although the Greenland Ice Sheet was only slightly smaller than at present. Early Holocene summer sea ice limits were substantially smaller than their 20th century average, and the flow of Atlantic water into the Arctic Ocean was substantially greater. As summer solar energy decreased in the second half of the Holocene, glaciers re-established or advanced, sea ice expanded, and the flow of warm Atlantic water into the Arctic Ocean diminished. Late Holocene cooling reached its nadir during the Little Ice Age (about 1250–1850 AD), when sun-blocking volcanic eruptions and perhaps other causes added to the orbital cooling, allowing most Arctic glaciers to reach their maximum Holocene extent. During the warming of the past century, glaciers have receded throughout the Arctic, terrestrial ecosystems have advanced northward, and perennial Arctic Ocean sea ice has diminished. Here we review the proxies that allow reconstruction of Quaternary climates and the feedbacks that amplify climate change across the Arctic. We provide an overview of the evolution of climate from the hot-house of the early Cenozoic through its transition to the ice-house of the Quaternary, with special emphasis on the anomalous warmth of the middle Pliocene, early Quaternary warm times, the Mid Pleistocene transition, warm interglaciations of marine isotope stages 11, 5e, and 1, the stage 3 interstadial, and the peak cold of the last glacial maximum.

Quaternary Science Reviews↗
USGS2017

Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question—How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km 2 ); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation—approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.

Ecology and Evolution↗
USGS2024

Monitoring animal populations with cameras using open, multistate, N-mixture models

Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages or states are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose states are known. However, this powerful class of models has been underutilized because they are computationally intensive, and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with multistate, Dail-Madsen (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated four commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, and 12; and years = 2, 5, 10) and evaluated the bias and precision of abundance, survival, and recruitment estimates. We also analyzed our empirical camera data on moose ( Alces alces ) with multistate DM models and compared inference with telemetry studies from the same time and region to assess the accuracy of camera studies to track moose populations. Most scenarios recovered the known parameters from our simulated data with higher accuracy and increased precision for scenarios with more sites, surveys, and/or years. Large amounts of missing data and fewer camera sites, especially at higher abundances, reduced accuracy, and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental states are clearly identifiable. We discuss several avenues for future research and caveats for using multistate DM models for large-scale population monitoring.

New Hampshire, Vermont↗