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USGS2002

Aerial survey methodology for bison population estimation in Yellowstone National Park

I developed aerial survey methods for statistically rigorous bison population estimation in Yellowstone National Park to support sound resource management decisions and to understand bison ecology. Survey protocols, data recording procedures, a geographic framework, and seasonal stratifications were based on field observations from February 1998-September 2000. The reliability of this framework and strata were tested with long-term data from 1970-1997. I simulated different sample survey designs and compared them to high-effort censuses of well-defined large areas to evaluate effort, precision, and bias. Sample survey designs require much effort and extensive information on the current spatial distribution of bison and therefore do not offer any substantial reduction in time and effort over censuses. I conducted concurrent ground surveys, or 'double sampling' to estimate detection probability during aerial surveys. Group size distribution and habitat strongly affected detection probability. In winter, 75% of the groups and 92% of individual bison were detected on average from aircraft, while in summer, 79% of groups and 97% of individual bison were detected. I also used photography to quantify the bias due to counting large groups of bison accurately and found that undercounting increased with group size and could reach 15%. I compared survey conditions between seasons and identified optimal time windows for conducting surveys in both winter and summer. These windows account for the habitats and total area bison occupy, and group size distribution. Bison became increasingly scattered over the Yellowstone region in smaller groups and more occupied unfavorable habitats as winter progressed. Therefore, the best conditions for winter surveys occur early in the season (Dec-Jan). In summer, bison were most spatially aggregated and occurred in the largest groups by early August. Low variability between surveys and high detection probability provide population estimates with an overall coefficient of variation of approximately 8% and have high power for detecting trends in population change. I demonstrated how population estimates from winter and summer can be integrated into a comprehensive monitoring program to estimate annual growth rates, overall winter mortality, and an index of calf production, requiring about 30 hours of flight per year.

Wyoming↗

USGS2007

Assessment of exploration bias in data-driven predictive models and the estimation of undiscovered resources

The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known occurrences well, but poorly predicts undiscovered resources. Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is difficult to quantify the "degree of exploration." However, if such a degree-of-exploration map can be produced, the benefits can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered, instead of discovered, resources. Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the "explored fraction" only. Differences in the weights between the two models provide a correlation measure between the evidence and the degree of exploration. The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless, the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the evidence patterns. In this circumstance, however, the weights for the "explored" WofE model minimize this bias. Using these revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits. ?? International Association for Mathematical Geology 2007.

Natural Resources Research↗

USGS2007

Relation between fish communities and riparian zone conditions at two spatial scales

The relation offish community composition to riparian cover at two spatial scales was compared at 18 streams in the agricultural Minnesota River Basin. The two spatial scales were: (1) local riparian zone (a 200 meter wide buffer extending 2 to 3 kilometers upstream of the sampling reach); and (2) the upstream riparian zone (a 200 m wide buffer on the mainstem and all perennial tributaries upstream of the sampling reach). Analysis of variance indicated that streams with wooded-local riparian zones had greater fish species richness (means = 20 and 15, respectively) and Index of Biotic Integrity (IBI) scores (means = 40 and 26, respectively) than streams with open-local riparian zones. Streams with wooded-upstream riparian zones tended (were not statistically significant) to have greater numbers of species (means = 19 and 15, respectively) and IBI scores (means = 33 and 28, respectively) than streams with open-upstream riparian zones. There was no significant interaction between the riparian zone conditions at the two scales. This study suggests that maintenance of wooded riparian cover along streams could be effective in maintaining or improving fish community composition in streams draining heavily agricultural areas.

Journal of the American Water Resources Associatio↗

USGS2001

Assessing large-scale surveyor variability in the historic forest data of the original U.S. Public Land Survey

The U.S. General Land Office Public Land Survey (PLS) records are a valuable resource for studying pre-European settlement vegetation. However, these data were taken for legal, not ecological, purposes. In turn, the instructions the surveyors followed affected the data collected. For this reason, it has been suggested that the PLS data may not truly represent the surveyed landscapes. This study examined the PLS data of northern Wisconsin, U.S.A., to determine the extent of variability among surveyors. We statistically tested for differences among surveyors in recorded tree species, size, location, and distance from the survey point. While we cannot rule out effects from other influences (e.g., environmental factors), we found evidence suggesting some level of surveyor bias for four of five variables, including tree species and size. The PLS data remain one of the best records of pre-European settlement vegetation available. However, based on our findings, we recommend that projects using PLS records examine these data carefully. This assessment should include not only the choice of variables to be studied but also the spatial extent at which the data will be examined.

Canadian Journal of Forest Research↗

USGS2009

A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy

1. Assessing the probability that a given site is occupied by a species of interest is important to resource managers, as well as metapopulation or landscape ecologists. Managers require accurate estimates of the state of the system, in order to make informed decisions. Models that yield estimates of occupancy, while accounting for imperfect detection, have proven useful by removing a potentially important source of bias. To account for detection probability, multiple independent searches per site for the species are required, under the assumption that the species is available for detection during each search of an occupied site. 2. We demonstrate that when multiple samples per site are defined by searching different locations within a site, absence of the species from a subset of these spatial subunits induces estimation bias when locations are exhaustively assessed or sampled without replacement. 3. We further demonstrate that this bias can be removed by choosing sampling locations with replacement, or if the species is highly mobile over a short period of time. 4. Resampling an existing data set does not mitigate bias due to exhaustive assessment of locations or sampling without replacement. 5. Synthesis and applications. Selecting sampling locations for presence/absence surveys with replacement is practical in most cases. Such an adjustment to field methods will prevent one source of bias, and therefore produce more robust statistical inferences about species occupancy. This will in turn permit managers to make resource decisions based on better knowledge of the state of the system.

Journal of Applied Ecology↗

USGS2015

Statistical guidelines for assessing marine avian hotspots and coldspots: A case study on wind energy development in the U.S. Atlantic Ocean

Estimating patterns of habitat use is challenging for marine avian species because seabirds tend to aggregate in large groups and it can be difficult to locate both individuals and groups in vast marine environments. We developed an approach to estimate the statistical power of discrete survey events to identify species-specific hotspots and coldspots of long-term seabird abundance in marine environments. We illustrate our approach using historical seabird data from survey transects in the U.S. Atlantic Ocean Outer Continental Shelf (OCS), an area that has been divided into “lease blocks” for proposed offshore wind energy development. For our power analysis, we examined whether discrete lease blocks within the region could be defined as hotspots (3 × mean abundance in the OCS) or coldspots (1/3 ×) for individual species within a given season. For each of 74 species/season combinations, we determined which of eight candidate statistical distributions (ranging in their degree of skewedness) best fit the count data. We then used the selected distribution and estimates of regional prevalence to calculate and map statistical power to detect hotspots and coldspots, and estimate the p -value from Monte Carlo significance tests that specific lease blocks are in fact hotspots or coldspots relative to regional average abundance. The power to detect species-specific hotspots was higher than that of coldspots for most species because species-specific prevalence was relatively low (mean: 0.111; SD: 0.110). The number of surveys required for adequate power (> 0.6) was large for most species (tens to hundreds) using this hotspot definition. Regulators may need to accept higher proportional effect sizes, combine species into groups, and/or broaden the spatial scale by combining lease blocks in order to determine optimal placement of wind farms. Our power analysis approach provides a general framework for both retrospective analyses and future avian survey design and is applicable to a broad range of research and conservation problems.

U.S. Atlantic Ocean Outer Continental Shelf↗

USGS2024

Assessing the added value of antecedent streamflow alteration information in modeling stream biological condition

In stream systems, disentangling relationships between biology and flow and subsequent prediction of these relationships to unsampled streams is a common objective of large-scale ecological modeling. Often, streamflow metrics are derived from aggregating continuous streamflow records available at a subset of stream gages into long-term flow regime descriptors. Despite demonstrated value, shortcomings of these long-term approaches include spatial restriction to locations with long-term continuous flow records (commonly, biased toward larger systems) and omission of potentially ecologically important short-term (i.e., ≤1 year) antecedent streamflow information. We used long-term flow regime and short-term antecedent streamflow alteration information to evaluate relative performance in modeling stream fish biological condition. We compared results to understand whether short-term antecedent streamflow information improved models of fish biological condition. Results indicated that models incorporating short-term antecedent data performed better than those relying solely on long-term flow regime data (kappa statistic = 0.29 and 0.23, respectively) and improved prediction accuracy among stream sizes and in six of nine ecoregions. Additionally, models relying solely on short-term streamflow information performed similarly to those with only long-term streamflow information (kappa = 0.23). Incorporating short-term antecedent streamflow metrics may provide added ecological information not fully captured by long-term flow regime summaries in macroscale modeling efforts or perform similarly to long-term streamflow data when long-term data are not available.

Science of the Total Environment↗

USGS2006

A spatial modeling approach to identify potential butternut restoration sites in Mammoth Cave National Park

Incorporation of disease resistance is nearly complete for several important North American hardwood species threatened by exotic fungal diseases. The next important step toward species restoration would be to develop reliable tools to delineate ideal restoration sites on a landscape scale. We integrated spatial modeling and remote sensing techniques to delineate potential restoration sites for Butternut (Juglans cinerea L.) trees, a hardwood species being decimated by an exotic fungus, in Mammoth Cave National Park (MCNP), Kentucky. We first developed a multivariate habitat model to determine optimum Butternut habitats within MCNP. Habitat characteristics of 54 known Butternut locations were used in combination with eight topographic and land use data layers to calculate an index of habitat suitability based on Mahalanobis distance (D2). We used a bootstrapping technique to test the reliability of model predictions. Based on a threshold value for the D2 statistic, 75.9% of the Butternut locations were correctly classified, indicating that the habitat model performed well. Because Butternut seedlings require extensive amounts of sunlight to become established, we used canopy cover data to refine our delineation of favorable areas for Butternut restoration. Areas with the most favorable conditions to establish Butternut seedlings were limited to 291.6 ha. Our study provides a useful reference on the amount and location of favorable Butternut habitat in MCNP and can be used to identify priority areas for future Butternut restoration. Given the availability of relevant habitat layers and accurate location records, our approach can be applied to other tree species and areas. ?? 2006 Society for Ecological Restoration International.

Restoration Ecology↗

USGS2003

Spread of hybridization between native westslope cutthroat trout, Oncorhynchus clarki lewisi, and nonnative rainbow trout, Oncorhynchus mykiss

We examined spatial and temporal patterns of hybridization between native westslope cutthroat trout, Oncorhynchus clarki lewisi , and nonnative rainbow trout, O. mykiss , in streams of the Flathead River system in Montana, U.S.A. We detected hybridization in 24 of 42 sites sampled from 1998 to 2001. We found new Oncorhynchus mykiss introgression in seven of 11 sample populations that were determined to be nonhybridized in 1984. Patterns of spatial autocorrelation and linkage disequilibrium indicated that hybridization is spreading among sites and is advancing primarily via post-F 1 hybrids. Although hybridized populations were distributed widely throughout the study area, the genetic contribution from O. mykiss decreased with increasing upstream distance from the Flathead River mainstem, suggesting that O. mykiss introgression is spreading in an upstream direction. The spread of hybridization may be constrained more by demographic than by environmental factors, given that ( i ) hybridized populations generally encompassed the range of environmental variability in nonhybridized populations, and ( ii ) hybridization status was more strongly associated with neighborhood statistics than measured environmental gradients.

Montana↗

USGS2010

Models for inference in dynamic metacommunity systems

A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species‐ and location‐specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species‐specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.

Ecology↗

USGS2020

Critical evaluation of stable isotope mixing end-members for estimating groundwater recharge sources: Case study from the South Rim of the Grand Canyon, Arizona, USA

Springs and groundwater seeps along the South Rim of the Grand Canyon serve an important function for the region’s ecosystems, residents (both human and wild animal), and economy. However, these springs and seeps are potentially vulnerable to contamination, increased groundwater extraction, or reduced recharge due to climate change. Protection of South Rim groundwater resources requires improved understanding of the regional groundwater system. In this study, statistical methods are used to investigate δ2H and δ18O in precipitation, surface water, and groundwater. A mixing model for δ18O is developed using statistically distinct seasonal end-members represented by modeled winter (Nov-Apr.) precipitation and summer (May-Oct.) surface water run-off. The calculated fraction of winter recharge (Fwin) indicates that South Rim groundwater is primarily sourced from snow-melt and winter rains with an average Fwin of 0.97 ± 0.09. Groundwater sourced from the highest elevations of the study area are more depleted than the winter end-member suggesting values of Fwin are overestimated or a meaningful portion of recharge occurs at lower elevations. Lower elevation recharge from the Coconino Plateau is supported by consistent spatial trends in δ2H and δ18O with respect to longitude, Fwin values less than 0.9 for 9 of the 50 samples, and age tracer data indicating young groundwater discharging from springs which is distinct from old groundwater observed in the regional flow system. These results suggest a new conceptual model is needed to account for recharge sources from low elevation and summer precipitation. Results imply resource managers need to reconsider current land-use and water management practices on the South Rim to protect future water quantity and quality.

Arizona↗

USGS2008

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

Book↗

USGS2012

Airborne LiDAR analysis and geochronology of faulted glacial moraines in the Tahoe-Sierra frontal fault zone reveal substantial seismic hazards in the Lake Tahoe region, California-Nevada USA

We integrated high-resolution bare-earth airborne light detection and ranging (LiDAR) imagery with field observations and modern geochronology to characterize the Tahoe-Sierra frontal fault zone, which forms the neotectonic boundary between the Sierra Nevada and the Basin and Range Province west of Lake Tahoe. The LiDAR imagery clearly delineates active normal faults that have displaced late Pleistocene glacial moraines and Holocene alluvium along 30 km of linear, right-stepping range front of the Tahoe-Sierra frontal fault zone. Herein, we illustrate and describe the tectonic geomorphology of faulted lateral moraines. We have developed new, three-dimensional modeling techniques that utilize the high-resolution LiDAR data to determine tectonic displacements of moraine crests and alluvium. The statistically robust displacement models combined with new ages of the displaced Tioga (20.8 ± 1.4 ka) and Tahoe (69.2 ± 4.8 ka; 73.2 ± 8.7 ka) moraines are used to estimate the minimum vertical separation rate at 17 sites along the Tahoe-Sierra frontal fault zone. Near the northern end of the study area, the minimum vertical separation rate is 1.5 ± 0.4 mm/yr, which represents a two- to threefold increase in estimates of seismic moment for the Lake Tahoe basin. From this study, we conclude that potential earthquake moment magnitudes (M w ) range from 6.3 ± 0.25 to 6.9 ± 0.25. A close spatial association of landslides and active faults suggests that landslides have been seismically triggered. Our study underscores that the Tahoe-Sierra frontal fault zone poses substantial seismic and landslide hazards.

California;Nevada↗

USGS2023

Groundwater quality in abandoned underground coal mine aquifers across West Virginia

Abandoned underground coal mine aquifers cover a large part of West Virginia and could supply substantial quantities of water for agricultural, industrial, residential, and public use. Several Federal, State, and academic institutions have studied the availability and quality of water stored in abandoned underground coal mine aquifers for a variety of applications, such as economic development, geothermal energy, aquaculture, and wastewater disposal. However, the spatial and stratigraphic controls on water quality produced from abandoned underground coal mine aquifers are still poorly constrained on a state-wide basis. In response to these knowledge gaps, the U.S. Geological Survey initiated a study, in cooperation with the West Virginia Department of Environmental Protection, to understand the applicability of using existing secondary source data for understanding water quality in abandoned underground coal mine aquifers across the State. Results from the calculation of net alkalinity indicated that Upper Pennsylvanian coal beds primarily produce net acidic waters and Lower Pennsylvanian coal beds primarily produce net alkaline waters. Multivariate statistical analysis of elemental data supports the conclusion that abandoned underground coal mine aquifers in the northern part of the State generally produce poor water quality and abandoned underground coal mine aquifers in southern West Virginia primarily produce good water quality. These results substantiate the potential benefits of leveraging abandoned underground coal mine aquifers as a multifaceted resource in West Virginia and can be used as a reconnaissance tool for water managers to characterize abandoned underground coal mine aquifers on a local scale.

West Virginia↗

USGS2023

Assessing arthropod diversity metrics derived from stream environmental DNA: Spatiotemporal variation and paired comparisons with manual sampling

Background Benthic invertebrate (BI) surveys have been widely used to characterize freshwater environmental quality but can be challenging to implement at desired spatial scales and frequency. Environmental DNA (eDNA) allows an alternative BI survey approach, one that can potentially be implemented more rapidly and cheaply than traditional methods. Methods We evaluated eDNA analogs of BI metrics in the Potomac River watershed of the eastern United States. We first compared arthropod diversity detected with primers targeting mitochondrial 16S (mt16S) and cytochrome c oxidase 1 (cox1 or COI) loci to that detected by manual surveys conducted in parallel. We then evaluated spatial and temporal variation in arthropod diversity metrics with repeated sampling in three focal parks. We also investigated technical factors such as filter type used to capture eDNA and PCR inhibition treatment. Results Our results indicate that genus-level assessment of eDNA compositions is achievable at both loci with modest technical noise, although database gaps remain substantial at mt16S for regional taxa. While the specific taxa identified by eDNA did not strongly overlap with paired manual surveys, some metrics derived from eDNA compositions were rank-correlated with previously derived biological indices of environmental quality. Repeated sampling revealed statistical differences between high- and low-quality sites based on taxonomic diversity, functional diversity, and tolerance scores weighted by taxon proportions in transformed counts. We conclude that eDNA compositions are efficient and informative of stream condition. Further development and validation of scoring schemes analogous to commonly used biological indices should allow increased application of the approach to management needs.

PeerJ↗

USGS2000

Identifying fracture‐zone geometry using simulated annealing and hydraulic‐connection data

A new approach is presented to condition geostatistical simulation of high‐permeability zones in fractured rock to hydraulic‐connection data. A simulated‐annealing algorithm generates three‐dimensional (3‐D) realizations conditioned to borehole data, inferred hydraulic connections between packer‐isolated borehole intervals, and an indicator (fracture zone or background‐ K bedrock) variogram model of spatial variability. We apply the method to data from the U.S. Geological Survey Mirror Lake Site in New Hampshire, where connected high‐permeability fracture zones exert a strong control on fluid flow at the hundred‐meter scale. Single‐well hydraulic‐packer tests indicate where permeable fracture zones intersect boreholes, and multiple‐well pumping tests indicate the degree of hydraulic connection between boreholes. Borehole intervals connected by a fracture zone exhibit similar hydraulic responses, whereas intervals not connected by a fracture zone exhibit different responses. Our approach yields valuable insights into the 3‐D geometry of fracture zones at Mirror Lake. Statistical analysis of the realizations yields maps of the probabilities of intersecting specific fracture zones with additional wells. Inverse flow modeling based on the assumption of equivalent porous media is used to estimate hydraulic conductivity and specific storage and to identify those fracture‐zone geometries that are consistent with hydraulic test data.

Water Resources Research↗

USGS2022

New projections of 21st century climate and hydrology for Alaska and Hawaiʻi

In the United States, high-resolution, century-long, hydroclimate projection datasets have been developed for water resources planning, focusing on the contiguous United States (CONUS) domain. However, there are few statewide hydroclimate projection datasets available for Alaska and Hawaiʻi. The limited information on hydroclimatic change motivates developing hydrologic scenarios from 1950 to 2099 using climate-hydrology impact modeling chains consisting of multiple statistically downscaled climate projections as input to hydrologic model simulations for both states. We adopt an approach similar to the previous CONUS hydrologic assessments where: 1) we select the outputs from ten global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5 with Representative Concentration Pathways 4.5 and 8.5; 2) we perform statistical downscaling to generate climate input data for hydrologic models (12-km grid-spacing for Alaska and 1-km for Hawaiʻi); and 3) we perform process-based hydrologic model simulations. For Alaska, we have advanced the hydrologic model configuration from CONUS by using the full water-energy balance computation, frozen soils and a simple glacier model. The simulations show that robust warming and increases in precipitation produce runoff increases for most of Alaska, with runoff reductions in the currently glacierized areas in Southeast Alaska. For Hawaiʻi, we produce the projections at high resolution (1 km) which highlight high spatial variability of climate variables across the state, and a large spread of runoff across the GCMs is driven by a large precipitation spread across the GCMs. Our new ensemble datasets assist with state-wide climate adaptation and other water planning.

Alaska, Hawaii↗

USGS2010

A random spatial network model based on elementary postulates

A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

Water Resources Research↗