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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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

Designing occupancy studies when false-positive detections occur

1.Recently, estimators have been developed to estimate occupancy probabilities when false-positive detections occur during presence-absence surveys. Some of these estimators combine different types of survey data to improve estimates of occupancy. With these estimators, there is a tradeoff between the number of sample units surveyed, and the number and type of surveys at each sample unit. Guidance on efficient design of studies when false positives occur is unavailable. 2.For a range of scenarios, I identified survey designs that minimized the mean square error of the estimate of occupancy. I considered an approach that uses one survey method and two observation states and an approach that uses two survey methods. For each approach, I used numerical methods to identify optimal survey designs when model assumptions were met and parameter values were correctly anticipated, when parameter values were not correctly anticipated, and when the assumption of no unmodelled detection heterogeneity was violated. 3.Under the approach with two observation states, false positive detections increased the number of recommended surveys, relative to standard occupancy models. If parameter values could not be anticipated, pessimism about detection probabilities avoided poor designs. Detection heterogeneity could require more or fewer repeat surveys, depending on parameter values. If model assumptions were met, the approach with two survey methods was inefficient. However, with poor anticipation of parameter values, with detection heterogeneity, or with removal sampling schemes, combining two survey methods could improve estimates of occupancy. 4.Ignoring false positives can yield biased parameter estimates, yet false positives greatly complicate the design of occupancy studies. Specific guidance for major types of false-positive occupancy models, and for two assumption violations common in field data, can conserve survey resources. This guidance can be used to design efficient monitoring programs and studies of species occurrence, species distribution, or habitat selection, when false positives occur during surveys.

Methods in Ecology and Evolution↗

Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity

1. Autonomous Recording Units (ARUs) are now widely used to survey communities of species. These surveys generate spatially and temporally replicated counts of unmarked animals, but such data typically include false negatives and misclassified detections, both of which may vary across sites in proportion to abundance. These data challenges can bias estimates of occupancy, and the typical approach of verifying individual detections is expensive. 2. We developed a Bayesian implementation of a two-species, false-positive N-mixture model for estimating occupancy from ARU data or other counts of unmarked animals that does not require manual verification. The model accounts for species misclassification and abundance-induced detection heterogeneity, as well as false negatives. To evaluate this model, we simulated 200 data sets for each of 29 scenarios, including scenarios in which misclassifications outnumbered correct classifications for rare species. We also applied the model to acoustic surveys of bats conducted on Fort Carson Army Post and Piñon Canyon Maneuver Site, Colorado, USA. 3. In the simulation study, bias, coverage, and root mean square error for occupancy estimates obtained from the two-species false-positive N-mixture model were superior to metrics obtained from two competing two-species false-positive occupancy models. Across 29 scenarios, absolute bias was consistently low (range: -0.03–0.07), while coverage averaged 93% (range: 74%–98%). For alternative occupancy models, absolute bias was often high (range: -0.36–0.39), and coverage averaged from 47%–65%. Although our model included an abundance parameter, abundance estimates were not reliable. For two species of Myotis bats, we estimated that 1%–5% of field-recorded detections were misclassified. Estimated occupancy (0.91 and 0.76) was lower than naïve estimates (1.00 and 0.94). Competing occupancy models implausibly estimated local occupancy of 0.00 at sites with numerous detections. 4. Our two-species, false-positive N-mixture model is significant because it accounts for detection heterogeneity and improves occupancy estimates without expensive manual verification of detections. Our field application indicated that misclassifications were not common, yet affected occupancy inferences. Given that ARUs are increasingly used to survey a broad range of taxa, such an occupancy model could be widely useful.

Methods in Ecology and Evolution↗

Shallow groundwater quality and geochemistry in the Fayetteville Shale gas-production area, north-central Arkansas, 2011

The Mississippian Fayetteville Shale serves as an unconventional gas reservoir across north-central Arkansas, ranging in thickness from approximately 50 to 550 feet and varying in depth from approximately 1,500 to 6,500 feet below the ground surface. Primary permeability in the Fayetteville Shale is severely limited, and successful extraction of the gas reservoir is the result of advances in horizontal drilling techniques and hydraulic fracturing to enhance and develop secondary fracture porosity and permeability. Drilling and production of gas wells began in 2004, with a steady increase in production thereafter. As of April 2012, approximately 4,000 producing wells had been completed in the Fayetteville Shale. In Van Buren and Faulkner Counties, 127 domestic water wells were sampled and analyzed for major ions and trace metals, with a subset of the samples analyzed for methane and carbon isotopes to describe general water quality and geochemistry and to investigate the potential effects of gas-production activities on shallow groundwater in the study area. Water-quality analyses from this study were compared to historical (pregas development) shallow groundwater quality collected in the gas-production area. An additional comparison was made using analyses from this study of groundwater quality in similar geologic and topographic areas for well sites less than and greater than 2 miles from active gas-production wells. Chloride concentrations for the 127 groundwater samples collected for this study ranged from approximately 1.0 milligram per liter (mg/L) to 70 mg/L, with a median concentration of 3.7 mg/L, as compared to maximum and median concentrations for the historical data of 378 mg/L and 20 mg/L, respectively. Statistical analysis of the data sets revealed statistically larger chloride concentrations (p-value <0.001) in the historical data compared to data collected for this study. Chloride serves as an important indicator parameter based on its conservative transport characteristics and relatively elevated concentrations in production waters associated with gas extraction activities. Major ions and trace metals additionally had lower concentrations in data gathered for this study than in the historical analyses. Additionally, no statistical difference existed between chloride concentrations from water-quality data collected for this study from 94 wells located less than 2 miles from a gas-production well and 33 wells located 2 miles or more from a gas-production well; a Wilcoxon rank-sum test showed a p-value of 0.71. Major ion chemistry was investigated to understand the effects of geochemical and reduction-oxidation (redox) processes on the shallow groundwater in the study area along a continuum of increased rock-water interaction represented by increases in dissolved solids concentration. Groundwater in sandstone formations is represented by a low dissolved solids concentration (less than 30 mg/L) and slightly acidic water type. Shallow shale aquifers were represented by dissolved solids concentrations ranging upward to 686 mg/L, and water types evolving from a dominantly mixed-bicarbonate and calcium-bicarbonate to a strongly sodium-bicarbonate water type. Methane concentration and carbon isotopic composition were analyzed in 51 of the 127 samples collected for this study. Methane occurred above a detection limit of 0.0002 mg/L in 32 of the 51 samples, with concentrations ranging upward to 28.5 mg/L. Seven samples had methane concentrations greater than or equal to 0.5 mg/L. The carbon isotopic composition of these higher concentration samples, including the highest concentration of 28.5 mg/L, shows the methane was likely biogenic in origin with carbon isotope ratio values ranging from -57.6 to -74.7 per mil. Methane concentrations increased with increases in dissolved solids concentrations, indicating more strongly reducing conditions with increasing rock-water interaction in the aquifer. As such, groundwater-quality data collected for this study indicate that groundwater chemistry in the shallow aquifer system in the study area is a result of natural processes, beginning with recharge of dilute atmospheric precipitation and evolution of observed groundwater chemistry through rock-water interaction and redox processes.

Arkansas↗

A statistical framework for modelling migration corridors

1. Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. GPS tracking data allows for mapping of corridors from directly observed movements, providing important insights, but tracking data is absent for many populations. 2. We developed a novel statistical corridor modeling approach that predicts movement corridors from cost-distance movement models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad-hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer (Odocoileus hemionus) herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green-up, snow-off date, and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds. 3. The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude, and confidence levels of the effects of environmental variables on corridors. The effects environmental variables had on corridors differed depending on the herd. Within-herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross-herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds. 4. In addition to the practical benefit of mapping corridors for management, our statistical corridor modeling framework sets the stage for evaluating fundamental questions about the fitness tradeoffs, navigation, learning, fidelity, and movement constraints that influence migratory and other corridor-generating behavior. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modeling approach is also readily applied to non-migratory animal movements.

Idaho, Wyoming↗

Preliminary geologic map of the Simi 7.5' quadrangle, southern California: A digital database

The Simi Quadrangle covers an area of about 62 square miles in southern Ventura County. The Santa Clara River Valley occupies the northwestern corner of the quadrangle. Mountainous terrain of South Mountain and Oak Ridge characterizes the northern and central area. Elevation within the quadrangle ranges from about 250 feet along the arroyo bottoms to over 2200 feet. Steep, highly dissected slopes form much of the boundary of the area. In the southeast, Little Simi Valley, drained by Arroyo Simi/Arroyo Las Posas, separates the southern flank of Oak Ridge from the Las Posas Hills. The Las Posas upland area, a broad elevated region that slopes gently to the south, separates the South Mountain-Oak Ridge highlands from the Las Posas-Camarillo Hills between Little Simi Valley on the east and the Oxnard Plain on the west. This relatively low-lying area is also referred to as the Las Posas Valley. Numerous north-south-trending drainages cut South Mountain and Oak Ridge creating steep narrow canyons on north-facing slopes and wide flat-bottomed canyons with incised streams on south-facing slopes. A network of residential streets and ranch and oilfield roads that traverse the area from U.S. Highway 101 and State Highways 118, 23, and 126 provides access to the area. Current land use includes citrus and avocado orchards, oil well drilling and production, sand and gravel quarries, decorative-rock quarries, cattle grazing, suburban residential development, and golf courses. The oldest geologic unit mapped in the Simi Quadrangle is the upper Eocene to lower Miocene Sespe Formation. The Sespe Formation consists of alluvial fan and floodplain deposits of interbedded pebble-cobble conglomerate, massive to thick-bedded sandstone, and thin-bedded siltstone and claystone. In the northern part of the map area, Sespe Formation is overlain by and interfingers with the upper Oligocene to lower Miocene Vaqueros Formation that is composed of transitional and marine sandstone, siltstone, and claystone with local sandy coquina beds. In the Las Posas Hills, Sespe Formation is unconformably overlain by marine sandstones of the middle Miocene Topanga Group that are interlayered with and intruded by basalt flows, breccia, and diabase dikes of the Conejo Volcanics. Deep-marine strata of the upper Miocene Modelo Formation cover the Vaqueros Formation and Topanga Group along the crests and southern flanks of South Mountain and Oak Ridge. They also occur as isolated outcrops in the Las Posas Hills. Locally, Modelo Formation consists of interbedded diatomaceous shale, claystone, mudstone, and siltstone with minor sandstone, limestone, chert, and tuff beds. The most widely exposed rock units in the area are the Plio-Pleistocene marine and non-marine Pico and Saugus Formations that crop out on the southern flank of South Mountain-Oak Ridge. Locally, the Pico Formation consists of marine siltstone and silty shale with minor sandstone and pebbly sandstone. The Saugus Formation overlies and interfingers with the Pico Formation and is composed of interbedded shallow-marine to brackish water sandstone, siltstone, pebble-to-cobble conglomerate, and coquina beds that grade laterally and vertically into non-marine sandstone, siltstone, and conglomerate. A local member of the Saugus Formation is exposed in the southwest corner of the map area. It is predominantly a volcanic breccia conglomerate that resembles the Conejo Volcanics breccia, but is believed to represent remnants of landslide debris shed from the Conejo Volcanics into a local trough during Saugus time. Quaternary surficial deposits cover the floor and margins of the Little Simi Valley, Santa Clara River Valley in the north, and Arroyo Las Posas in the south, and extend up into the larger canyons that drain South Mountain and Oak Ridge. Extensive surficial deposits are also present in the Las Posas upland area in the southwest. These upper Pleistocene to Holocene sediments consist of older and younger alluvial fan and valley deposits, colluvium, active alluvial fans, and active stream deposits. Pleistocene- to Holocene-age landslide deposits are widespread throughout the Simi Quadrangle, especially in the finer grained Tertiary sedimentary units where bedding planes are dip slopes. In addition, massive slumps are present in the Sespe and Vaqueros Formations on anti-dip slopes. Seismic and well data from the San Fernando Valley (SFV) document evolution of that region from mid-Miocene rifting to north-south contraction. Formations in the western SFV subsurface (Cretaceous to Paleogene strata, and Miocene Topanga and Modelo Formations) trace southward to outcrops in the Santa Monica Mountains that constrain faulting along the valley's south basin edge. Cretaceous strata in the Simi Uplift to the west are over 2 km higher than equivalent strata beneath the western SFV across a boundary marked by the Chatsworth Reservoir fault, and Neogene thinning and offlap. The Simi fault, located at the eastern end of the Simi-Santa Rosa fault system, bounds the northern margins of the Simi and Tierra Rejada Valleys. West of Simi Valley, the Simi fault has placed Miocene Conejo Volcanics over Plio-Pleistocene Saugus Formation rocks. The 15.5 ± 0.8 m.y.a. base of the Conejo Volcanics, identified in oil well logs, is inferred to have a dip-slip separation of about 425 to 550 m, suggesting a low long-term slip rate of about 0.03 mm/yr. However, substantial late Quaternary offset is suggested by the presence of more than 150 m of Pleistocene and younger alluvium that fills the east-west trending, down-dropped bedrock trough beneath western Simi Valley. In addition, trenching within faulted colluvial deposits in Tierra Rejada Valley has revealed evidence of multiple shears within Holocene (?) deposits.

California↗

Nocturnal incubation recess and flushing behavior by duck hens

Incubating birds must balance the needs of their developing embryos with their own physiological needs, and many birds accomplish this by taking periodic breaks from incubation. Mallard ( Anas platyrhynchos ) and gadwall ( Mareca strepera ) hens typically take incubation recesses in the early morning and late afternoon, but recesses can also take place at night. We examined nocturnal incubation recess behavior for mallard and gadwall hens nesting in Suisun Marsh, California, USA, using iButton temperature dataloggers and continuous video monitoring at nests. Fourteen percent of all detected incubation recesses ( N = 13,708) were nocturnal and took place on 20% of nest‐days ( N = 8,668). Video monitoring showed that hens covered their eggs with down feathers when they initiated a nocturnal recess themselves as they would a diurnal recess, but they left the eggs uncovered in 94% of the nocturnal recesses in which predators appeared at nests. Thus, determining whether or not eggs were left uncovered during a recess can provide strong indication whether the recess was initiated by the hen (eggs covered) or a predator (eggs uncovered). Because nest temperature decreased more rapidly when eggs were left uncovered versus covered, we were able to characterize eggs during nocturnal incubation recesses as covered or uncovered using nest temperature data. Overall, we predicted that 75% of nocturnal recesses were hen‐initiated recesses (eggs covered) whereas 25% of nocturnal recesses were predator‐initiated recesses (eggs uncovered). Of the predator‐initiated nocturnal recesses, 56% were accompanied by evidence of depredation at the nest during the subsequent nest monitoring visit. Hen‐initiated nocturnal recesses began later in the night (closer to morning) and were shorter than predator‐initiated nocturnal recesses. Our results indicate that nocturnal incubation recesses occur regularly (14% of all recesses) and, similar to diurnal recesses, most nocturnal recesses (75%) are initiated by the hen rather than an approaching predator.

Ecology and Evolution↗

Geographic principles applied to population dynamics: A spatially interpolated integrated population model

A major impediment to wildlife conservation and management, from a quantitative perspective, is dealing with high degrees of uncertainty associated with population estimates. Integrated population models (IPMs) can help alleviate that challenge, but they are often limited to narrow spatial or temporal windows owing to the financial and logistical burdens of acquiring requisite datasets. To expand the spatiotemporal scope of practical IPM implementation, we developed a novel method that expresses demographic relatedness among sampled and unsampled locations using geographic principles of spatial autocorrelation. We interpolated demographic parameters at unsampled locations using parameter estimates from data-informed locations. Errors attributable to the interpolative process were corrected using a joint likelihood and locally recorded count data (‘cheaper’ and broadly distributed). We evaluated the spatially interpolated IPM (SIIPM) for precision and accuracy under variable levels of spatial autocorrelation using simulated data and a Leave-One-Out Cross-Validation (LOOCV) technique. Conventional IPMs and state-space models (SSM) were fit to the same simulated datasets to provide a comparative assessment of the novel method. In a final, empirical demonstration we fit the SIIPM to data collected from Greater Sage-Grouse ( Centrocercus urophasianus ; sage-grouse) populations located in Nevada, U.S.A. during 2013–2021. SIIPMs outperformed conventional IPMs when fit to data possessing moderate-to-high levels of spatial autocorrelation. Under moderate levels of autocorrelation, the average improvement in parameter estimation was 13.6% for survival, 65.3% for recruitment and 23.7% for rate of population change (𝜆). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (<67 km) to sampling locations. Under low autocorrelation-near distance scenarios, we observed SIIPM parameters that were 30.8% (recruitment), 32.5% (𝜆; IPM comparison) and 54.0% (𝜆; SSM comparison) more precise than contemporary models. Spatial autocorrelation is often assumed but rarely tested when comparing population dynamics across regions of large geographic extent. We demonstrated that SIIPMs can improve precision of species' vital rate estimation when extrapolating model inference beyond populations for which long-term monitoring data exists. Specific to sage-grouse, these results support previous conclusions of broad-scale spatial autocorrelation in population dynamics and a reproductive-survival trade-off previously documented at smaller scales.

Methods in Ecology and Evolution↗