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At least 1,621 records · Page 90Linked to original sources
USGS2014

Exposure pathways and biological receptors: baseline data for the canyon uranium mine, Coconino County, Arizona

Recent restrictions on uranium mining within the Grand Canyon watershed have drawn attention to scientific data gaps in evaluating the possible effects of ore extraction to human populations as well as wildlife communities in the area. Tissue contaminant concentrations, one of the most basic data requirements to determine exposure, are not available for biota from any historical or active uranium mines in the region. The Canyon Uranium Mine is under development, providing a unique opportunity to characterize concentrations of uranium and other trace elements, as well as radiation levels in biota, found in the vicinity of the mine before ore extraction begins. Our study objectives were to identify contaminants of potential concern and critical contaminant exposure pathways for ecological receptors; conduct biological surveys to understand the local food web and refine the list of target species (ecological receptors) for contaminant analysis; and collect target species for contaminant analysis prior to the initiation of active mining. Contaminants of potential concern were identified as arsenic, cadmium, chromium, copper, lead, mercury, nickel, selenium, thallium, uranium, and zinc for chemical toxicity and uranium and associated radionuclides for radiation. The conceptual exposure model identified ingestion, inhalation, absorption, and dietary transfer (bioaccumulation or bioconcentration) as critical contaminant exposure pathways. The biological survey of plants, invertebrates, amphibians, reptiles, birds, and small mammals is the first to document and provide ecological information on .200 species in and around the mine site; this study also provides critical baseline information about the local food web. Most of the species documented at the mine are common to ponderosa pine Pinus ponderosa and pinyon–juniper Pinus–Juniperus spp. forests in northern Arizona and are not considered to have special conservation status by state or federal agencies; exceptions are the locally endemic Tusayan flameflower Phemeranthus validulus, the long-legged bat Myotis volans, and the Arizona bat Myotis occultus. The most common vertebrate species identified at the mine site included the Mexican spadefoot toad Spea multiplicata, plateau fence lizard Sceloporus tristichus, violetgreen swallow Tachycineta thalassina, pygmy nuthatch Sitta pygmaea, purple martin Progne subis, western bluebird Sialia mexicana, deermouse Peromyscus maniculatus, valley pocket gopher Thomomys bottae, cliff chipmunk Tamias dorsalis, black-tailed jackrabbit Lepus californicus, mule deer Odocoileus hemionus, and elk Cervus canadensis. A limited number of the most common species were collected for contaminant analysis to establish baseline contaminant and radiological concentrations prior to ore extraction. These empirical baseline data will help validate contaminant exposure pathways and potential threats from contaminant exposures to ecological receptors. Resource managers will also be able to use these data to determine the extent to which local species are exposed to chemical and radiation contamination once the mine is operational and producing ore. More broadly, these data could inform resource management decisions on mitigating chemical and radiation exposure of biota at high-grade uranium breccia pipes throughout the Grand Canyon watershed.

Arizona↗
USGS2021

The spatial-temporal relationship of blue-winged teal to domestic poultry: Movement state modeling of a highly mobile avian influenza host

1. Migratory waterfowl facilitate long distance dispersal of zoonotic pathogens and are increasingly recognized as contributing to the geographic spread of avian influenza viruses (AIV). AIV are globally distributed and have the potential to produce highly contagious poultry disease, economically impact both large-scale and backyard poultry producers, and raise the specter of epidemics and pandemics in human populations. 2. Because migratory waterfowl behavior varies across multiple spatial and temporal scales, the timing and distribution of wild bird AIV introductions to poultry are also heterogeneous in time and space. To help reduce economic impacts to the poultry industry and enable poultry producers to better anticipate when and where poultry outbreaks may occur, it is critically important to consider the movement ecology of the waterfowl species transporting and transmitting AIV. 3. We used telemetry for a geographically widespread and common AIV host, blue-winged teal ( Spatula discors ; BWTE), to model reservoir host movement states with respect to backyard and commercial poultry facilities in the United States. Our modeling framework enabled us to estimate wild bird proximity to poultry facilities while concurrently assessing the influence of poultry facilities on BWTE movement state transition. Our primary objective was to estimate the likelihood of duck and poultry overlap by estimating when and where BWTE were geographically closest to poultry. 4. Synthesis and applications . Migratory waterfowl facilitate dispersal of the avian influenza viruses that cause highly contagious poultry disease. Movement analysis of blue-winged teal indicates that spatio-temporal overlap between wild birds and poultry facilities varies by season, the poultry type produced (e.g., turkey, chicken), and if the facility is a commercial or backyard operation. These findings are broadly applicable to disease ecology research and can be applied by poultry producers to improve bio-security, enhance poultry management, and prioritize disease surveillance efforts.

Journal of Applied Ecology↗
USGS2014

Scaling up watershed model parameters--Flow and load simulations of the Edisto River Basin

The Edisto River is the longest and largest river system completely contained in South Carolina and is one of the longest free flowing blackwater rivers in the United States. The Edisto River basin also has fish-tissue mercury concentrations that are some of the highest recorded in the United States. As part of an effort by the U.S. Geological Survey to expand the understanding of relations among hydrologic, geochemical, and ecological processes that affect fish-tissue mercury concentrations within the Edisto River basin, analyses and simulations of the hydrology of the Edisto River basin were made with the topography-based hydrological model (TOPMODEL). The potential for scaling up a previous application of TOPMODEL for the McTier Creek watershed, which is a small headwater catchment to the Edisto River basin, was assessed. Scaling up was done in a step-wise process beginning with applying the calibration parameters, meteorological data, and topographic wetness index data from the McTier Creek TOPMODEL to the Edisto River TOPMODEL. Additional changes were made with subsequent simulations culminating in the best simulation, which included meteorological and topographic wetness index data from the Edisto River basin and updated calibration parameters for some of the TOPMODEL calibration parameters. Comparison of goodness-of-fit statistics between measured and simulated daily mean streamflow for the two models showed that with calibration, the Edisto River TOPMODEL produced slightly better results than the McTier Creek model, despite the significant difference in the drainage-area size at the outlet locations for the two models (30.7 and 2,725 square miles, respectively). Along with the TOPMODEL hydrologic simulations, a visualization tool (the Edisto River Data Viewer) was developed to help assess trends and influencing variables in the stream ecosystem. Incorporated into the visualization tool were the water-quality load models TOPLOAD, TOPLOAD-H, and LOADEST. Because the focus of this investigation was on scaling up the models from McTier Creek, water-quality concentrations that were previously collected in the McTier Creek basin were used in the water-quality load models.

South Carolina↗
USGS2005

Designing occupancy studies: General advice and allocating survey effort

1. The fraction of sampling units in a landscape where a target species is present (occu pancy) is an extensively used concept in ecology. Yet in many applications the species will not always be detected in a sampling unit even when present, resulting in biased estimates of occupancy. Given that sampling units are surveyed repeatedly within a r elatively short timeframe, a number of similar methods have now been developed to provide unbiased occupancy estimates. However, practical guidance on the efficient design of occupancy studies has been lacking. 2. In this paper we comment on a number of general issues related to designing occu pancy studies, including the need for clear objectives that are explicitly linked to science or management, selection of sampling units, timing of repeat surveys and allocation of survey effort. Advice on the number of repeat surveys per sampling unit is considered in terms of the variance of the occupancy estimator, for three possible study designs. 3. We r ecommend that sampling units should be surveyed a minimum of three times w hen detection probability is high (> 0·5 survey − 1 ), unless a removal design is used. 4. We f ound that an optimal removal design will generally be the most efficient, but we suggest it may be less robust to assumption violations than a standard design. 5. Our results suggest that for a rare species it is more efficient to survey more sampling units less intensively, while for a common species fewer sampling units should be surveyed more intensively. 6. Synthesis and applications . Reliable inferences can only result from quality data. To ma ke the best use of logistical resources, study objectives must be clearly defined; sampling units must be selected, and repeated surveys timed appropriately; and a sufficient n umber of repeated surveys must be conducted. Failure to do so may compromise the integrity of the study. The guidance given here on study design issues is particularly a pplicable to studies of species occurrence and distribution, habitat selection and modelling, metapopulation studies and monitoring programmes.

Journal of Applied Ecology↗
USGS2013

Movements of wild ruddy shelducks in the Central Asian Flyway and their spatial relationship to outbreaks of highly pathogenic avian influenza H5N1

Highly pathogenic avian influenza H5N1 remains a serious concern for both poultry and human health. Wild waterfowl are considered to be the reservoir for low pathogenic avian influenza viruses; however, relatively little is known about their movement ecology in regions where HPAI H5N1 outbreaks regularly occur. We studied movements of the ruddy shelduck ( Tadorna ferruginea ), a wild migratory waterfowl species that was infected in the 2005 Qinghai Lake outbreak. We defined their migration with Brownian Bridge utilization distribution models and their breeding and wintering grounds with fixed kernel home ranges. We correlated their movements with HPAI H5N1 outbreaks, poultry density, land cover, and latitude in the Central Asian Flyway. Our Akaike Information Criterion analysis indicated that outbreaks were correlated with land cover, latitude, and poultry density. Although shelduck movements were included in the top two models, they were not a top parameter selected in AICc stepwise regression results. However, timing of outbreaks suggested that outbreaks in the flyway began during the winter in poultry with spillover to wild birds during the spring migration. Thus, studies of the movement ecology of wild birds in areas with persistent HPAI H5N1 outbreaks may contribute to understanding their role in transmission of this disease.

Viruses↗
USGS2025

Predicting secretive species distribution using Bayesian networks with and without expert elicitation: A case study incorporating double-blind peer review

1. Species that are secretive, imperilled and consequently data deficient often re-quire conservation action despite limited available information. In such scenarios, Bayesian networks (BNs) offer a versatile and intuitive approach for utilizing various information sources, including literature reviews, community science data sets and expert knowledge. Although it has been suggested that peer review be incorporated during expert elicitations in a BN modelling context, little information exists about how to implement this approach or about how models constructed using this approach perform. 2. We documented a double-blind peer review approach for expert elicitation in a BN modelling context. Further, we compared BN models that were generated by experts who engaged in this peer-review process (PRBNs) to those that were generated by a single expert whose knowledge was supplemented only by a literature review (LRBNs). These comparisons were based on the ability to predict the occurrence (via community science and satellite telemetry data) of a secretive and data deficient species, the King Rail ( Rallus elegans ), throughout a large region. 3. We found that the LRBNs tended to predict King Rail occurrence as well as, or better than, the PRBNs. The LRBNs that we evaluated provided more consistent predictions across our study area. However, preliminary data suggest that the PRBNs may better distinguish between locations of focal and non-focal species within smaller regions. 4. Practical implication. Our framework for utilizing double-blind peer review could serve as a useful guide and have practical implications for incorporating expert knowledge in BN models. Further, our model comparison case study suggests that, in some contexts, a single expert who uses a literature review to inform the creation of BN models may be able to accurately predict the occurrence of a secretive and data-deficient focal species. Taken together, this information could help ecologists decide when a double-blind peer review approach to expert elicitation is necessary and how to implement this approach in a BN modelling context.

Arkansas, Louisiana, Mississippi, Missouri, Tennes↗
USGS2018

Variation in the vital rates of an Antarctic marine predator: the role of individual heterogeneity

Variation in life‐history traits such as lifespan and lifetime reproductive output is thought to arise, in part, due to among‐individual differences in the underlying probabilities of survival and reproduction. However, the stochastic nature of demographic processes can also generate considerable variation in fitness‐related traits among otherwise‐identical individuals. An improved understanding of life‐history evolution and population dynamics therefore depends on evaluating the relative role of each of these processes. Here, we used a 33‐yr data set with reproductive histories for 1,274 female Weddell seals from Erebus Bay, Antarctica, to assess the strength of evidence for among‐individual heterogeneity in the probabilities of survival and reproduction, while accounting for multiple other sources of variation in vital rates. Our analysis used recent advances in Bayesian model selection techniques and diagnostics to directly compare model fit and predictive power between models that included individual effects on survival and reproduction to those that did not. We found strong evidence for costs of reproduction to both survival and future reproduction, with breeders having rates of survival and subsequent reproduction that were 3% and 6% lower than rates for non‐breeders. We detected age‐related changes in the rates of survival and reproduction, but the patterns differed for the two rates. Survival rates steadily declined from 0.92 at age 7 to 0.56 at the maximal age of 31 yr. In contrast, reproductive rates increased from 0.68 at age 7 to 0.79 at age 16 and then steadily declined to 0.37 for the oldest females. Models that included individual effects explained more variation in observed life histories and had better estimated predictive power than those that did not, indicating their importance in understanding sources of variation among individuals in life‐history traits. We found that among‐individual heterogeneity in survival was small relative to that for reproduction. Our study, which found patterns of variation in vital rates that are consistent with a series of predictions from life‐history theory, is the first to provide a thorough assessment of variation in important vital rates for a long‐lived, high‐latitude marine mammal while taking full advantage of recent developments in model evaluation.

Ecology↗
USGS2015

Modeling apple snail population dynamics on the Everglades landscape

Context The Florida Everglades has diminished in size and its existing wetland hydrology has been altered. The endangered snail kite ( Rostrhamus sociabilis ) has nearly abandoned the Everglades, and its prey, the apple snail ( Pomacea paludosa ), has declined. Objective We developed a population model (EverSnail) to understand apple snail response to inter- and intra-annual fluctuations in water depths over the Everglades landscape. EverSnail was developed as a tool to understand how apple snails respond to different hydrologic scenarios. Methods EverSnail is an age- and size-structured, spatially-explicit landscape model of P. paludosa in the Everglades. Landscape-level inputs are water depth and air temperature. We conducted sensitivity analyses by running EverSnail with ± 20 % the baseline value of eight parameters. Results EverSnail was sensitive to changes in survival and water depth associated with reproduction. The EverSnail population varied with changes and/or differences in depth generally consistent with empirical data; site-specific comparisons to field data proved less reliable. A simulated 3-year wet period resulted in a shift in apple snail distribution, but little change in total abundance over the landscape. In contrast, a simulated 3-year succession of relatively dry years resulted in overall lower snail abundances. Conclusions Comparisons of model output to empirical data indicate the need for more data to better understand, and eventually parameterize, several aspects of snail ecology in support of EverSnail. A primary value of EverSnail is its capacity to describe the relative response of snail abundance to alternative hydrologic scenarios considered for Everglades water management and restoration.

Florida↗
USGS2017

A Bayesian method for assessing multiscalespecies-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

Nebraska↗
USGS2003

Estimating rates of local extinction and colonization in colonial species and an extension to the metapopulation and community levels

Coloniality has mainly been studied from an evolutionary perspective, but relatively few studies have developed methods for modelling colony dynamics. Changes in number of colonies over time provide a useful tool for predicting and evaluating the responses of colonial species to management and to environmental disturbance. Probabilistic Markov process models have been recently used to estimate colony site dynamics using presence–absence data when all colonies are detected in sampling efforts. Here, we define and develop two general approaches for the modelling and analysis of colony dynamics for sampling situations in which all colonies are, and are not, detected. For both approaches, we develop a general probabilistic model for the data and then constrain model parameters based on various hypotheses about colony dynamics. We use Akaike's Information Criterion (AIC) to assess the adequacy of the constrained models. The models are parameterised with conditional probabilities of local colony site extinction and colonization. Presence–absence data arising from Pollock's robust capture–recapture design provide the basis for obtaining unbiased estimates of extinction, colonization, and detection probabilities when not all colonies are detected. This second approach should be particularly useful in situations where detection probabilities are heterogeneous among colony sites. The general methodology is illustrated using presence–absence data on two species of herons. Estimates of the extinction and colonization rates showed interspecific differences and strong temporal and spatial variations. We were also able to test specific predictions about colony dynamics based on ideas about habitat change and metapopulation dynamics. We recommend estimators based on probabilistic modelling for future work on colony dynamics. We also believe that this methodological framework has wide application to problems in animal ecology concerning metapopulation and community dynamics.

Oikos↗
USGS2022

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↗
USGS2017

A new mechanistic approach for the further development of a population with established size bimodality

Usually, the origin of a within-cohort bimodal size distribution is assumed to be caused by initial size differences or by one discrete period of accelerated growth for one part of the population. The aim of this study was to determine if more continuous pathways exist allowing shifts from the small to the large fraction within a bimodal age-cohort. Therefore, a Eurasian perch population, which had already developed a bimodal size-distribution and had differential resource use of the two size-cohorts, was examined. Results revealed that formation of a bimodal size-distribution can be a continuous process. Perch from the small size-cohort were able to grow into the large size-cohort by feeding on macroinvertebrates not used by their conspecifics. The diet shifts were accompanied by morphological shape changes. Intra-specific competition seemed to trigger the development towards an increasing number of large individuals. A stage-structured matrix model confirmed these assumptions. The fact that bimodality can be a continuous process is important to consider for the understanding of ecological processes and links within ecosystems.

PLoS ONE↗
USGS2004

Linking surface- and ground-water levels to riparian grassland species along the Platte River in central Nebraska

Nearly all the techniques used to quantify how plants are linked to environmental gradients produce results in general terms, such as low to high elevation, xeric to mesic, and low to high concentration. While ecologists comprehend these imprecise scales, managers responsible for making decisions affecting these gradients need more precise information. For our study, we preserved the measurement scale and units of a dominant environmental gradient by using non-linear models to fit plant frequency to a water-level gradient ranging from shallow ground water to standing water along the Platte River in central Nebraska, USA. Non-linear models, unlike polynomials, have coefficients that can be interpreted with a biological meaning such as population peak, optimum gradient position, and ecological amplitude. Sixty-three riparian grassland species had sufficient information to link their plant frequency to the water-level gradient. From among 10 water-level summary statistics evaluated for a subset of 22 species, the best plant-frequency response curves were obtained by using the growing season 10% cumulative frequency water level, followed closely by the growing season 7-day moving average high water level and two other high water-level statistics. This suggests that for Platte River riparian grasslands, high water levels are more influential than mean, median, or low water levels. Land-management practices (i.e., grazing, haying, and extended rest) affected six species by a change in frequency or a shift in position along the water-level gradient. Four general plant communities composed of species responding individually to the water-level gradient and other factors were identified for Platte River riparian grasslands: emergent, sedge meadow, mesic prairie, and dry ridge. Plant response curves are the first step toward predicting how plants responding to riparian-grassland water levels might also respond to river management.

Nebraska↗
USGS2012

Near-bed turbulence and sediment flux measurements in tidal channels

Understanding the hydrodynamics and sediment transport dynamics in tidal channels is important for studies of estuary geomorphology, sediment supply to tidal wetlands, aquatic ecology and fish habitat, and dredging and navigation. Hydrodynamic and sediment transport data are essential for calibration and testing of numerical models that may be used to address management questions related to these topics. Herein we report preliminary analyses of near-bed turbulence and sediment flux measurements in the Sacramento-San Joaquin Delta, a large network of tidal channels and wetlands located at the confluence of the Sacramento and San Joaquin Rivers, California, USA (Figure 1). Measurements were made in 6 channels spanning a wide range of size and tidal conditions, from small channels that are primarily fluvial to large channels that are tidally dominated. The results of these measurements are summarized herein and the hydrodynamic and sediment transport characteristics of the channels are compared across this range of size and conditions.

California↗
USGS2012

Physical modeling of river spanning rock structures: Evaluating interstitial flow, local hydraulics, downstream scour development, and structure stability

Rock weir and ramp structures uniquely serve a necessary role in river management: to meet water deliveries in an ecologically sound manner. Uses include functioning as low head diversion dams, permitting fish passage, creating habitat diversity, and stabilizing stream banks and profiles. Existing information on design and performance of in-stream rock structures does not provide the guidance necessary to implement repeatable and sustainable construction and retrofit techniques. As widespread use of rock structures increases, the need for reliable design methods with a broad range of applicability at individual sites grows as well. Rigorous laboratory testing programs were implemented at the U.S. Bureau of Reclamation (Reclamation) and at Colorado State University (CSU) as part of a multifaceted research project focused on expanding the current knowledge base and developing design methods to improve the success rate of river spanning rock structures in meeting project goals. Physical modeling at Reclamation is being used to measure, predict, and reduce interstitial flow through rock ramps. CSU is using physical testing to quantify and predict scour development downstream of rock weirs and its impact on the stability of rock structures. ?? 2009 ASCE.

Conference Paper↗
USGS2026

Restoration based on cost-benefit optimization: A grasslands pilot study

Ecological restoration is essential to meeting global biodiversity conservation goals. Given limited conservation budgets, deciding where to restore habitat is a key challenge for the coming decade. We developed a spatially explicit framework to optimize ecological restoration site selection by integrating land use history, species distributions, and economic costs. The framework includes the following steps: identify potential restoration area based on relevant environmental measures like land use; identify species of interest; calculate restoration benefits by modeling habitat and climate suitability and estimating reduced extinction risk associated with restoring a particular land parcel based on a modified species–area relationship; aggregate benefits across species; and compare to parcel-level land acquisition costs. We applied linear programming to maximize conservation benefit/restoration cost ratios to identify optimized restoration sites. We illustrate this approach using a case study for highly threatened grassland ecosystems in the Great Plains region of Kansas, USA. We selected five grassland animal species (greater prairie chickens [ Tympanuchus cupido ], lesser prairie chickens [ Tympanuchus pallidicinctus ], swift fox [ Vulpes velox ], pronghorn [ Antilocapra americana ], and regal fritillary [ Speyeria idalia ]) as indicators of restoration benefit across taxa. For the indicator species that we chose, shortgrass and mixed-grass prairies had the highest conservation benefit to cost ratio. Setting a minimum restoration threshold for each habitat type allowed us to identify high-priority tallgrass prairie sites. Despite increasing interest in ecological restoration, optimizing restoration site selection is challenging because one must consider habitat features that do not currently exist. The modeling approach described here is flexible and can be updated for different ecosystems, species, and conservation priorities. We outline potential alterations that can be made in future analyses, depending on desired restoration goals.

Kansas↗
USGS2024

Detecting stochasticity in population time series using a non-parametric test of intrinsic predictability

1. Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise. 2. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie ( Pygoscelis adeliae ) and emperor penguin ( Aptenodytes forsteri ) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species. 3. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%). 4. rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance.

Methods in Ecology and Evolution↗
USGS2013

A causal examination of the effects of confounding factors on multimetric indices

The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach.

Ecological Indicators↗