Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Ecological Modelling”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,207 records · Page 67Linked to original sources
USGS2023

Model-based surveillance system design under practical constraints with application to white-nose syndrome

Infectious diseases are powerful ecological forces structuring ecosystems, causing devastating economic impacts and disrupting society. Successful prevention and control of pathogens requires knowledge of the current scope and severity of disease, as well as the ability to forecast future disease dynamics. Assessment of the current situation as well as prediction of the future conditions, rely on spatially referenced information regarding the presence or absence of a pathogen, and the prevalence of the pathogen in the population. In particular, knowledge about the location of the disease front is foundational for deploying disease countermeasures to prevent further disease spread and focusing control efforts to reduce disease intensity in affected areas. In this paper, we develop a model-based approach to designing sampling strategies for wildlife disease surveillance at the disease front. Specifically, we use a mechanistic spatio-temporal model based on an underlying partial differential equation to track the disease dynamics and predict the disease prevalence in the future. We also devise an optimal surveillance system design at the disease front that takes into account the practical constraints of sampling. We evaluate the effectiveness of our proposed design via a simulation study and demonstrate the application of the proposed approach by designing a surveillance strategy for the pathogen that causes white-nose syndrome.

Environmental and Ecological Statistics↗
USGS2017

Habitat suitability models for groundfish in the Gulf of Alaska

Identifying and quantifying the major ecosystem processes that regulate recruitment strength of commercially and ecologically important fish species is a central goal of fisheries management research. In the Gulf of Alaska (GOA) five groundfish species are of particular interest: sablefish ( Anoplopoma fimbria ), Pacific cod ( Gadus macrocephalus ), walleye pollock ( Gadus chalcogrammus ), arrowtooth flounder ( Atheresthes stomias ), and Pacific ocean perch ( Sebastes alutus ). Habitat suitability models (HSM) were developed for the demersal early juvenile stage to inform survival to recruitment for these species, using catch data and seafloor habitat metrics with presence-only models. Regional-scale maps were produced that predict the probability of suitable habitat available in the GOA from settlement through residency in nursery areas. For example, the HSM for sablefish (150–399 mm) described suitable habitat as bathymetrically low-lying areas with low rocky structure within 25–300 m depth. In contrast, the HSM for Pacific ocean perch (50–200 mm) described suitable habitat as bathymetry rises with rocky structure present on north-south facing slopes within 85–270 m depth. These habitat covariates are useful to refine population estimates for North Pacific groundfish species and to inform life stage-specific definitions of Essential Fish Habitat. This application of MaxEnt models should be applicable for species with low occurrence of spatial data in other marine ecosystems globally.

Alaska↗
USGS2022

High-resolution 3D forest structure explains ecomorphological trait variation in assemblages of saproxylic beetles

Climate, topography and the 3D structure of forests are major drivers affecting local species communities. However, little is known about how the specific functional traits of saproxylic (wood-living) beetles, involved in the recycling of wood, might be affected by those environmental characteristics. Here, we combine ecological and morphological traits available for saproxylic beetles and airborne laser scanning (ALS) data in Bayesian trait-based joint species distribution models to study how traits drive the distributions of more than 230 species in temperate forests of Europe. We found that elevation (as a proxy for temperature and precipitation) and the proportion of conifers played important roles in species occurrences while variables related to habitat heterogeneity and forest complexity were less relevant. Furthermore, we showed that local communities were shaped by environmental variation primarily through their ecological traits whereas morphological traits were involved only marginally. As predicted, ecological traits influenced species' responses to forest structure, and to other environmental variation, with canopy niche, wood decay niche and host preference as the most important ecological traits. Conversely, no links between morphological traits and environmental characteristics were observed. Both models, however, revealed strong phylogenetic signal in species' response to environmental characteristics. These findings imply that alterations of climate and tree species composition have the potential to alter saproxylic beetle communities in temperate forests. Additionally, ecological traits help explain species' responses to environmental characteristics and thus should prove useful in predicting their responses to future change. It remains challenging, however, to link simple morphological traits to species' complex ecological niches.

Functional Ecology↗
USGS1996

Empirical Bayes estimation of proportions with application to cowbird parasitism rates

Bayesian models provide a structure for studying collections of parameters such are considered in the investigation of communities, ecosystems, and landscapes. This structure allows for improved estimation of individual parameters by considering them in the context of a group of related parameters. Individual estimates are differntially adjusted toward in overall mean, with the magnitude of their adjustment based on their precision. Consequently, Bayesian estimation allows for a more reliable ranking of parameters and, in particular, a more credible identification of extreme values from a collection of estimates. In Bayesian models, individual parameters are regarded as values sampled from a specified probability distribution, called a prior. The requirements that the prior be known is often regarded as an unattractive feature of Bayesian analysis and may be the reason Bayesian analyses are not frequently applied in ecological studies. Empirical Bayes methods provide an alternative approach that incorporates the structural advantages of Bayesian models while requirng a less stringent specification of prior knowledge. Empirical Bayes methods require only that the prior be in a certain family of distributions, indexed by hyperparameters that can be estimated from the available data. This structur is of interest per se, in addition to its value in allowing for improved estimation of individual parameters; for example, hypothese regarding the existence of distinct subgroups in a collection of paramet ers can be considered under the empirical Bayes framework by allowing the hyperparameters to vary among subgroups. We describe the empirical Bayes approach in application to estimation of proportions, using data obtained in a community—wide study Brown—headed Cowbird paratism rates for illustration. Empirical Bayes estimates identify those species for which there is the greatest evidence of extreme parasitism rates. Subgroup analysis of our data on cowbird parasitism rates indicates that parasitisms rates for neotropical migrants as a group are no greater than those of resident/short—distance migrant in this forest community. Our data and analyses demonstrate that the parasitism rates for certain neotropical migrant species (Wood Thrush and Rose—breasted Grosbeak) are remarkably low while those for others (Ovenbird and Red—eyed Vireo) are remarkably high.

Ecology↗
USGS2009

Structured decision making as a conceptual framework to identify thresholds for conservation and management

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of the decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (system models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision thresholds are determined by the above-listed components of a structured decision process. These components may themselves vary over time, inducing variation in the decision thresholds inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structured decision processes that include all three kinds of thresholds. ?? 2009 by the Ecological Society of America.

Ecological Applications↗
USGS2021

Increasing hydroperiod in a karst-depression wetland based on 165 years of simulated daily water levels

The hydrology of seasonally inundated depression wetlands can be highly sensitive to climatic fluctuations. Hydroperiod—the number of days per year that a wetland is inundated—is often of primary ecological importance in these systems and can vary interannually depending on climate conditions. In this study we re-examined an existing hydrologic model to simulate daily water levels in Sinking Pond, a 35-hectare seasonally inundated karst-depression wetland in Tennessee, USA. We recalibrated the model using 22 years of climate and water-level observations and used the recalibrated model to reconstruct (hindcast) daily water levels over a 165-year period from 1855 to 2019. A trend analysis of the climatic data and reconstructed water levels over the hindcasting period indicated substantial increases in pond hydroperiod over time, apparently related to increasing regional precipitation. Wetland hydroperiod increased on average by 5.9 days per decade between 1920 and 2019, with a breakpoint around the year 1970. Hydroperiod changes of this magnitude may have profound consequences for wetland ecology, such as a transition from a forested wetland to a mostly open-water pond at the Sinking Pond site. More broadly, this study illustrates the needs for robust hydrologic models of depression wetlands and for consideration of model transferability in time (i.e., hindcasting and forecasting) under non-stationary hydroclimatic conditions. As climate change is expected to influence water cycles, hydrologic processes, and wetland ecohydrology in the coming decades, hydrologic model projections may become increasingly important to detect, anticipate, and potentially mitigate ecological impacts in depression wetland ecosystems.

Tennessee↗
USGS2016

Effects of water-supply reservoirs on streamflow in Massachusetts

State and local water-resource managers need modeling tools to help them manage and protect water-supply resources for both human consumption and ecological needs. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a decision-support tool to estimate the effects of reservoirs on natural streamflow. The Massachusetts Reservoir Simulation Tool is a model that simulates the daily water balance of a reservoir. The reservoir simulation tool provides estimates of daily outflows from reservoirs and compares the frequency, duration, and magnitude of the volume of outflows from reservoirs with estimates of the unaltered streamflow that would occur if no dam were present. This tool will help environmental managers understand the complex interactions and tradeoffs between water withdrawals, reservoir operational practices, and reservoir outflows needed for aquatic habitats. A sensitivity analysis of the daily water balance equation was performed to identify physical and operational features of reservoirs that could have the greatest effect on reservoir outflows. For the purpose of this report, uncontrolled releases of water (spills or spillage) over the reservoir spillway were considered to be a proxy for reservoir outflows directly below the dam. The ratio of average withdrawals to the average inflows had the largest effect on spillage patterns, with the highest withdrawals leading to the lowest spillage. The size of the surface area relative to the drainage area of the reservoir also had an effect on spillage; reservoirs with large surface areas have high evaporation rates during the summer, which can contribute to frequent and long periods without spillage, even in the absence of water withdrawals. Other reservoir characteristics, such as variability of inflows, groundwater interactions, and seasonal demand patterns, had low to moderate effects on the frequency, duration, and magnitude of spillage. The reservoir simulation tool was used to simulate 35 single- and multiple-reservoir systems in Massachusetts over a 44-year period (water years 1961 to 2004) under two water-use scenarios. The no-pumping scenario assumes no water withdrawal pumping, and the pumping scenario incorporates average annual pumping rates from 2000 to 2004. By comparing the results of the two scenarios, the total streamflow alteration can be parsed into the portion of streamflow alteration caused by the presence of a reservoir and the additional streamflow alteration caused by the level of water use of the system. For each reservoir system, the following metrics were computed to characterize the frequency, duration, and magnitude of reservoir outflow volumes compared with unaltered streamflow conditions: (1) the median number of days per year in which the reservoir did not spill, (2) the median duration of the longest consecutive period of no-spill days per year, and (3) the lowest annual flow duration exceedance probability at which the outflows are significantly different from estimated unaltered streamflow at the 95-percent confidence level. Most reservoirs in the study do not spill during the summer months even under no-pumping conditions. The median number of days during which there was no spillage was less than 365 for all reservoirs in the study, indicating that, even under reported pumping conditions, the reservoirs refill to full volume and spill at least once during nondrought years, typically in the spring. Thirteen multiple-reservoir systems consisting of two or three hydrologically connected reservoirs were included in the study. Because operating rules used to manage multiple-reservoir systems are not available, these systems were simulated under two pumping scenarios, one in which water transfers between reservoirs are minimal and one in which reservoirs continually transferred water to intermediate or terminal reservoirs. These two scenarios provided upper and lower estimates of spillage under average pumping conditions from 2000 to 2004. For sites with insufficient data to simulate daily water balances, a proxy method to estimate the three spillage metrics was developed. A series of 4,000 Monte Carlo simulations of the reservoir water balance were run. In each simulation, streamflow, physical reservoir characteristics, and daily climate inputs were randomly varied. Tobit regression equations that quantify the relation between streamflow alteration and physical and operational characteristics of reservoirs were developed from the results of the Monte Carlo simulations and can be used to estimate each of the three spillage metrics using only the withdrawal ratio and the ratio of the surface area to the drainage area, which are available statewide for all reservoirs. A graphical user-interface for the Massachusetts Reservoir Simulation Tool was developed in a Microsoft Access environment. The simulation tool contains information for 70 reservoirs in Massachusetts and allows for simulation of additional scenarios than the ones considered in this report, including controlled releases, dam seepage and leakage, demand management plans, and alternative water withdrawal and transfer rules.

Massachusetts↗
USGS2018

Functional group, biomass, and climate change effects on ecological drought in semiarid grasslands

Water relations in plant communities are influenced both by contrasting functional groups (grasses, shrubs) and by climate change via complex effects on interception, uptake and transpiration. We modelled the effects of functional group replacement and biomass increase, both of which can be outcomes of invasion and vegetation management, and climate change on ecological drought (soil water potential below which photosynthesis stops) in 340 semiarid grassland sites over 30‐year periods. Relative to control vegetation (climate and site‐determined mixes of functional groups), the frequency and duration of drought were increased by shrubs and decreased by annual grasses. The rankings of shrubs, control vegetation, and annual grasses in terms of drought effects were generally consistent in current and future climates, suggesting that current differences among functional groups on drought effects predict future differences. Climate change accompanied by experimentally‐increased biomass (i.e. the effects of invasions that increase community biomass, or management that increases productivity through fertilization or respite from grazing) increased drought frequency and duration, and advanced drought onset. Our results suggest that the replacement of perennial temperate semiarid grasslands by shrubs, or increased biomass, can increase ecological drought both in current and future climates.

Journal of Geophysical Research G: Biogeosciences↗
USGS2026

Socio-ecological impacts of the 2025 Los Angeles urban fires on communities, neighborhoods, and homes

Human settlements are increasingly being impacted by urban fires initiated by wildfires. Metrics such as area burned and number of structures destroyed are important, but research often overlooks the socio-ecological complexity of urban fires. We study the impacts of the 2025 Los Angeles fires on two communities at the neighborhood and residential parcel scales. Geospatial analyses and econometric modeling explore the relationships between urban morphology, socio-demographic factors, and home destruction. Here we show that socio-ecological characteristics and scale are key in parsing the dynamics of urban fires. Also, new socio-demographic populations are being affected and urban morphology metrics are more important than vegetation cover. Despite parallels with 19 th and early 20 th century urban conflagrations, understanding these re-emerging urban fires requires transdisciplinary approaches and unique metrics. Investigating the socio-ecological scales and dynamics of urban fires provides a valuable next step towards understanding and adapting to the risk associated with these disasters.

California↗
USGS2018

Disentangling the effects of low pH and metal mixture toxicity on macroinvertebrate diversity

One of the primary goals of biological assessment of streams is to identify which of a suite of chemical stressors is limiting their ecological potential. Elevated metal concentrations in streams are often associated with low pH, yet the effects of these two potentially limiting factors of freshwater biodiversity are rarely considered to interact beyond the effects of pH on metal speciation. Using a dataset from two continents, a biogeochemical model of the toxicity of metal mixtures (Al, Cd, Cu, Pb, Zn) and quantile regression, we addressed the relative importance of both pH and metals as limiting factors for macroinvertebrate communities. Current environmental quality standards for metals proved to be protective of stream macroinvertebrate communities and were used as a starting point to assess metal mixture toxicity. A model of metal mixture toxicity accounting for metal interactions was a better predictor of macroinvertebrate responses than a model considering individual metal toxicity. We showed that the direct limiting effect of pH on richness was of the same magnitude as that of chronic metal toxicity, independent of its influence on the availability and toxicity of metals. By accounting for the direct effect of pH on macroinvertebrate communities, we were able to determine that acidic streams supported less diverse communities than neutral streams even when metals were below no-effect thresholds. Through a multivariate quantile model, we untangled the limiting effect of both pH and metals and predicted the maximum diversity that could be expected at other sites as a function of these variables. This model can be used to identify which of the two stressors is more limiting to the ecological potential of running waters.

Environmental Pollution↗
USGS2022

Modeling risk dynamics of contaminants of emerging concern in a temperate-region wastewater effluent-dominated stream

Wastewater effluent-dominated streams are becoming increasingly common worldwide, including in temperate regions, with potential impacts on ecological systems and drinking water sources. We recently quantified the occurrence/spatiotemporal dynamics of pharmaceutical mixtures in a representative temperate-region wastewater effluent-dominated stream (Muddy Creek, Iowa) under baseflow conditions and characterized relevant fate processes. Herein, we quantified the ecological risk quotients (RQs) of 19 effluent-derived contaminants of emerging concern (CECs; including: 14 pharmaceuticals, 2 industrial chemicals, and 3 neonicotinoid insecticides) and 1 run-off-derived compound (atrazine) in the stream under baseflow conditions, and estimated the probabilistic risks of effluent-derived CECs under all-flow conditions ( i.e. , including runoff events) using stochastic risk modeling. We determined that 11 out of 20 CECs pose medium-to-high risks to local ecological systems ( i.e. , algae, invertebrates, fish) based on literature-derived acute effects under measured baseflow conditions. Stochastic risk modeling indicated decreased, but still problematic, risk of effluent-derived CECs ( i.e. , RQ ≥ 0.1) under all-flow conditions when runoff events were included. Dilution of effluent-derived chemicals from storm flows thus only minimally decreased risk to aquatic biota in the effluent-dominated stream. We also modeled in-stream transport. Thirteen out of 14 pharmaceuticals persisted along the stream reach (median attenuation rate constant k < 0.1 h −1 ) and entered the Iowa River at elevated concentrations. Predicted and measured concentrations in the drinking water treatment plant were below the human health benchmarks. This study demonstrates the application of probabilistic risk assessments for effluent-derived CECs in a representative effluent-dominated stream under variable flow conditions (when measurements are less practical) and provides an enhanced prediction tool transferable to other effluent-dominated systems.

Iowa↗
USGS2019

Spatio-temporal variability of human-fire interactions on the Navajo Nation

Unraveling the effects of climate and land-use on historical fire regimes provides important insights into broader human-fire-climate dynamics, which are necessary for ecologically-based forest management. We developed a spatial human land-use model for Navajo Nation forests across which we sampled a network of tree-ring fire history sites to reflect contrasting historical land-use intensity: high human use, primarily in the Chuska Mountains, and low human use, primarily on the central Defiance Plateau. We tested for and compared human- and climate-driven changes in the fire regimes by applying change point detection, regression, and superposed epoch analyses. The historical fire regimes and fire-climate relationships reflect those of similar forests regionally, and are similar between the two Navajo landscapes until the early 1800s. We then determined that a previously identified, localized, early (1830s) decline in fire activity was geographically widespread across higher human use sites. In contrast, fires continued to burn uninterrupted through this period at the lower use sites. Though the 1830s included significantly wet and cold periods that could have contributed to fire regime decline, human factors pose a more spatio-temporally consistent explanation. A rise in Navajo pastoralism in the 1820s-1830s was concentrated seasonally in the heavy use sites. By the 1880s, livestock numbers more than doubled, grazing became far more spatially widespread, and frequent fire regimes of Navajo forests collapsed. The last widespread fire recorded on either landscape was in 1886. In the Chuska Mountains, livestock and fire co-existed for over 50 years between the initial 1832 fire decline and the end of frequent fires after 1886, an exceptional pattern in the western US. Though unique in its timing, character, and spatial dynamics, the collapse of historical fire regimes in Navajo forests contributed to now over a century without frequent surface fire, leaving Navajo forests at risk for large, uncharacteristic high-severity fires.

Arizona, New Mexico↗
USGS2023

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m 3 /s, most prominently in the lower quantiles of salinity (τ = 0.05–0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events. It is implemented with a simple R routine, and is less computationally-intensive than finite difference hydrodynamic modeling. The characterizations of salinity uncertainty developed in these analyses can be directly applied to future studies of fish and wildlife responses to changes in watershed management.

Florida↗
USGS2020

Investigating apparent misalignment of predator-prey dynamics: Great Lakes lake trout and sea lampreys

Interpreting ecological dynamics is challenging when observed patterns are not aligned with presumed models. Investigating possible sources of uncertainty is critical to understand the underlying system and ultimately inform management decisions. In this study, we used simulation to investigate the hypothesis that observed inconsistencies in Great Lakes lake trout ( Salvelinus namaycush ) and sea lamprey ( Petromyzon marinus ) predator-prey dynamics were caused by measurement error in the abundance and predation metrics. When lake trout abundances increase and sea lamprey abundances decrease, predation rates are expected to decline (and vice versa). Occasionally predation rates do not change as expected, leading to an inconsistency in expected predator-prey dynamics. We used a Type II functional response model to align lake trout relative abundance, adult sea lamprey abundance, and sea lamprey marking rates of lake trout in each Great Lake. Then we added measurement error to each of the simulated metrics to see how it contributed to observed inconsistencies in the marking rates. The simulated inconsistency rate was far less than the observed inconsistency rate in Lakes Superior and Erie, indicating that measurement error was not primarily responsible for the misalignment of metrics, contrary to our hypothesis. Rather than ignoring these inconsistencies as unfortunate consequences of imperfect assessments, we recommend that future inconsistencies be scrutinized for possible mechanistic explanations. We suspect that predator-prey dynamics are being influenced by spatially structured within-lake components and the presence of alternative hosts, neither of which were accounted for in the functional response model we used.

Great Lakes↗
USGS2014

Developing and testing temperature models for regulated systems: a case study on the Upper Delaware River

Water temperature is an important driver of many processes in riverine ecosystems. If reservoirs are present, their releases can greatly influence downstream water temperatures. Models are important tools in understanding the influence these releases may have on the thermal regimes of downstream rivers. In this study, we developed and tested a suite of models to predict river temperature at a location downstream of two reservoirs in the Upper Delaware River (USA), a section of river that is managed to support a world-class coldwater fishery. Three empirical models were tested, including a Generalized Least Squares Model with a cosine trend (GLScos), AutoRegressive Integrated Moving Average (ARIMA), and Artificial Neural Network (ANN). We also tested one mechanistic Heat Flux Model (HFM) that was based on energy gain and loss. Predictor variables used in model development included climate data (e.g., solar radiation, wind speed, etc.) collected from a nearby weather station and temperature and hydrologic data from upstream U.S. Geological Survey gages. Models were developed with a training dataset that consisted of data from 2008 to 2011; they were then independently validated with a test dataset from 2012. Model accuracy was evaluated using root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), and index of agreement (d) statistics. Model forecast success was evaluated using baseline-modified prime index of agreement (md) at the one, three, and five day predictions. All five models accurately predicted daily mean river temperature across the entire training dataset (RMSE = 0.58–1.311, NSE = 0.99–0.97, d = 0.98–0.99); ARIMA was most accurate (RMSE = 0.57, NSE = 0.99), but each model, other than ARIMA, showed short periods of under- or over-predicting observed warmer temperatures. For the training dataset, all models besides ARIMA had overestimation bias (PBIAS = −0.10 to −1.30). Validation analyses showed all models performed well; the HFM model was the most accurate compared other models (RMSE = 0.92, both NSE = 0.98, d = 0.99) and the ARIMA model was least accurate (RMSE = 2.06, NSE = 0.92, d = 0.98); however, all models had an overestimation bias (PBIAS = −4.1 to −10.20). Aside from the one day forecast ARIMA model (md = 0.53), all models forecasted fairly well at the one, three, and five day forecasts (md = 0.77–0.96). Overall, we were successful in developing models predicting daily mean temperature across a broad range of temperatures. These models, specifically the GLScos, ANN, and HFM, may serve as important tools for predicting conditions and managing thermal releases in regulated river systems such as the Delaware River. Further model development may be important in customizing predictions for particular biological or ecological needs, or for particular temporal or spatial scales.

Delaware, New York, Pennsylvania↗
USGS2020

Deglacial temperature controls on no-analog community establishment in the Great Lakes Region

Understanding the drivers of vegetation dynamics and no-analog communities in eastern North America is hampered by a scarcity of independent temperature indicators. We present a new branched glycerol dialkyl glycerol tetraether (brGDGT) temperature record from Bonnet Lake, Ohio (18 to 8 ka) and report uncertainty estimates based on Bayesian linear regression and bootstrapping. We also reanalyze a previously published brGDGT record from Silver Lake, Ohio, using improved chromatographic methods. All pollen- and brGDGT-based temperature reconstructions showed qualitatively similar deglacial trends but varying magnitudes. Separating 5- and 6- methyl brGDGTs resulted in substantially lower estimates of deglacial temperature variations (6.4 °C) than inferred from earlier brGDGT methods and pollen (11.8 °C, 12.0 °C respectively). Similar trends among proxies suggest good fidelity of brGDGTs to temperature, despite calibration uncertainties. At both sites, the rise and decline of no-analog communities closely track brGDGT-inferred temperatures, with a lag of 0 to 150 years. The timing of temperature and ecological events varies between Bonnet and Silver Lakes, likely due to age model uncertainties. Climate sensitivity analyses indicate a linear sensitivity of vegetation composition to temperature variations, albeit noisy and significant only with a 500-year bin. The formation of no-analog plant communities in the upper Midwest is closely linked to late-glacial warming, but other factors, such as temperature seasonality or end-Pleistocene megafaunal extinctions, remain viable.

Great Lakes Region↗
USGS2016

When environmentally persistent pathogens transform good habitat into ecological traps

Habitat quality plays an important role in the dynamics and stability of wildlife metapopulations. However, the benefits of high-quality habitat may be modulated by the presence of an environmentally persistent pathogen. In some cases, the presence of environmental pathogen reservoirs on high-quality habitat may lead to the creation of ecological traps, wherein host individuals preferentially colonize high-quality habitat, but are then exposed to increased infection risk and disease-induced mortality. We explored this possibility through the development of a stochastic patch occupancy model, where we varied the pathogen&rsquo;s virulence, transmission rate and environmental persistence as well as the distribution of habitat quality in the host metapopulation. This model suggests that for pathogens with intermediate levels of spread, high-quality habitat can serve as an ecological trap, and can be detrimental to host persistence relative to low-quality habitat. This inversion of the relative roles of high- and low-quality habitat highlights the importance of considering the interaction between spatial structure and pathogen transmission when managing wildlife populations exposed to an environmentally persistent pathogen.

Royal Society Open Science↗
USGS2019

Habitat preference modulates trans-oceanic dispersal in a terrestrial vertebrate

The importance of long-distance dispersal (LDD) in shaping geographical distributions has been debated since the nineteenth century. In terrestrial vertebrates, LDD events across large water bodies are considered highly improbable, but organismal traits affecting dispersal capacity are generally not taken into account. Here, we focus on a recent lizard radiation and combine a summary-coalescent species tree based on 1225 exons with a probabilistic model that links dispersal capacity to an evolving trait, to investigate whether ecological specialization has influenced the probability of trans-oceanic dispersal. Cryptoblepharus species that occur in coastal habitats have on average dispersed 13 to 14 times more frequently than non-coastal species and coastal specialization has, therefore, led to an extraordinarily widespread distribution that includes multiple continents and distant island archipelagoes. Furthermore, their presence across the Pacific substantially predates the age of human colonization and we can explicitly reject the possibility that these patterns are solely shaped by human-mediated dispersal. Overall, by combining new analytical methods with a comprehensive phylogenomic dataset, we use a quantitative framework to show how coastal specialization can influence dispersal capacity and eventually shape geographical distributions at a macroevolutionary scale.

Proceedings of the Royal Society B: Biological Sci↗