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USGS2003

A comparison of nested quadrat and point-line intercept sampling methods for fire effects monitoring in shortgrass prairie

Within the National Park Service (NPS) and other federal land-managing agencies, there has been widespread application of the use of standardized fire-effects monitoring protocols. While standardization is often desirable, researchers and managers have come to recognize that 1 method does not work in all habitats with regard to application and efficiency. In 1999, in response to a wildfire that burned over 2428 ha of prairie habitat within Alibates Flint Quarries National Monument (ALFL) and Lake Meredith National Recreation Area (LAMR), Texas, long-term monitoring using a newer nested quadrat frequency/importance score method was implemented. In 2001, a 2-y study was initiated to compare the time and information-gathering efficacy of the nested quadrat method with the current NPS protocol used for monitoring fire effects within grassland systems. Both sampling methods were performed within burned and unburned mesa-top prairie habitats. No statistically significant differences were detected for total species richness between the 2 methods. However, the point-line intercept transects required significantly more time to sample compared to the nested quadrats. Within shortgrass prairie habitats the nested quadrat method appears to be a more efficient and effective sampling strategy than traditional point-line intercept methods.

Conference Paper↗

USGS1977

Some Bayesian statistical techniques useful in estimating frequency and density

This paper presents some elementary applications of Bayesian statistics to problems faced by wildlife biologists. Bayesian confidence limits for frequency of occurrence are shown to be generally superior to classical confidence limits. Population density can be estimated from frequency data if the species is sparsely distributed relative to the size of the sample plot. For other situations, limits are developed based on the normal distribution and prior knowledge that the density is non-negative, which insures that the lower confidence limit is non-negative. Conditions are described under which Bayesian confidence limits are superior to those calculated with classical methods; examples are also given on how prior knowledge of the density can be used to sharpen inferences drawn from a new sample.

Special Scientific Report - Wildlife↗

USGS1998

Assessing groundwater vulnerability using logistic regression

Determining the likelihood that groundwater contains elevated concentrations of contaminants can help water resource managers protect drinking water supplies. For example, this information is useful for selecting new sites for drinking water sources and designing more cost-effective monitoring strategies for existing sources. Groundwater vulnerability has typically been assessed using largely qualitative methods and expressed as relative measures of risk. In this study, a statistical approach was used to quantify the likelihood that a well contains an elevated concentration of nitrate or a detectable concentration of atrazine. The occurrence of elevated nitrate concentrations or detectable concentrations of atrazine in groundwater was related to both natural and anthropogenic variables using logistic regression. The variables that best explain the occurrence of elevated nitrate concentrations were well depth, surficial geology, and the percentages of urban and agricultural land within a radius of 3.2 kilometers of a well. Well depth and roadside application of atrazine best explained the occurrence of detectable concentrations of atrazine. From these relations, multiple logistic regression models were developed which predict the probability that a well has an elevated nitrate concentration or a detectable concentration of atrazine.

Conference Paper↗

USGS2012

Applications of fluorescence spectroscopy for predicting percent wastewater in an urban stream

Dissolved organic carbon (DOC) is a significant organic carbon reservoir in many ecosystems, and its characteristics and sources determine many aspects of ecosystem health and water quality. Fluorescence spectroscopy methods can quantify and characterize the subset of the DOC pool that can absorb and re-emit electromagnetic energy as fluorescence and thus provide a rapid technique for environmental monitoring of DOC in lakes and rivers. Using high resolution fluorescence techniques, we characterized DOC in the Tualatin River watershed near Portland, Oregon, and identified fluorescence parameters associated with effluent from two wastewater treatment plants and samples from sites within and outside the urban region. Using a variety of statistical approaches, we developed and validated a multivariate linear regression model to predict the amount of wastewater in the river as a function of the relative abundance of specific fluorescence excitation/emission pairs. The model was tested with independent data and predicts the percentage of wastewater in a sample within 80% confidence. Model results can be used to develop in situ instrumentation, inform monitoring programs, and develop additional water quality indicators for aquatic systems.

Oregon↗

USGS2020

An interactive decision-making tool for evaluating biological and statistical standards of migrating fish survival past hydroelectric dams

Quantifying the downstream survival of migrating fish past dams is critical for conservation efforts. Regulators require assessments of survival as a condition of operation. Failure to meet an established survival standard may result in required operational or costly structural changes at a facility. Establishing the survival standard, as well as the rules of assessment, is a point of contention between regulators and operators. Management goals are based on biological criteria, but there are inherent statistical and probabilistic trade-offs when choosing a standard value and the method for assessment. We make a distinction between a “biological” goal (the conservation goal) and a “statistical” standard (a function of the biological goal, sample size, assessment method, and years of consecutive evaluation). An effective statistical standard maximizes true positives (passing the standard when the biological goal is being met) and true negatives (failing the standard when the goal is not being met), while minimizing false negatives and false positives. We explored the effects of sample size, true survival, and assessment methods on the probability of passing different statistical standards by simulating survival studies (simulating mark-recapture experiments). We observed a strong influence of assessment methods on the probability of making the right decision (true positive or true negative), especially when sample size, and recapture probability was low. As a support tool, we developed an interactive user interface to explore specific scenarios, and to aid communication among decision-makers.

River Research and Applications↗

USGS2025

Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA

Lidar and structure from motion-derived digital elevation and surface models have widespread application. Consideration of a topographic model's vertical root mean squared error (RMSEz) and systematic directional bias is important for many of these applications, particularly landscape change detection and measurement. Due to logistic, resource, and time constraints, wide area remotely sensed topographic surveys are not always accompanied by an in situ checkpoint network for validating and characterizing survey error. Here we describe and test a method for automatically generating synthetic elevation checkpoints in bulk across hundreds of kilometers using a publicly available lidar-derived DEM time-series, road vector network, and landcover classification map. Our method produced 6000–10,000 synthetic checkpoints across the developed barrier island coastline of North Carolina. These checkpoints characterized vertical error metrics in a statistically similar way as in situ checkpoints when assessing the vertical accuracy of a contemporary lidar-derived DEM and produced RMSEz metrics an average of 0.018 m from the RMSEz of historical lidar DEMs published with tested accuracy metrics. This new method has the potential to A) lower the cost and time required to validate new remotely sensed topographic surveys by reducing or eliminating the field work associated with in situ checkpoint surveys, B) provide a means of retroactively assessing the absolute vertical accuracy and systematic bias of historical topographic datasets that were not published with tested accuracy metrics, and C) generate reference networks to assess and correct spatially variable patterns of vertical bias in topographic datasets.

North Carolina↗

USGS2018

Real-time nowcasting of microbiological water quality at recreational beaches: A wavelet and artificial neural network-based hybrid modeling approach

The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate short-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input–output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R 2 values between data and the NARX models ( R 2 values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management.

Environmental Science & Technology↗

USGS2000

A decision support system for prioritizing forested wetland restoration in the Yazoo Backwater Area, Mississippi

A working prototype decision support system (DSS) was developed for the Yazoo Backwater Area, Mississippi, to help planners and managers prioritize, plan, conduct, and optimize forested wetland restoration activities. The DSS comprises geographic information system (GIS) spatial data themes, application programs that provide a cumulative analysis of the relative ability of sites to function as wetlands, and output data that are specific to a given restoration analysis scenario. The DSS input includes GIS data themes such as geomorphology, soils, land use, elevation, farmed wetlands, flood frequency, topographic depressions, streams, public lands, roads, and permanent water bodies, which can be used as spatial templates to define areal hydrologic settings. These GIS data themes can then be ranked and combined to estimate the relative suitability of a potential wetland restoration site, thereby, determining relative wetland equivalence on the landscape. The GIS applications used in this DSS perform the following three functions: assess the ecology (the Eco-Assessor); reclassify land-use in areas selected for restoration (the Tree-Translator); and generate output data to compare restoration scenarios (the Parameter-Generator). Areas selected for reforestation are translated (in the GIS) into ?forested? land use, and the tree species that are ?planted? on the landscape (in the DSS) either compose an ecologically optimal or an economically optimal community of tree species. Output from the DSS can be compared and analyzed by using economic, statistical, graphical, and tabular methods. Output data for seven selected scenarios were generated for the Yazoo Backwater Area and are presented as examples to illustrate the flexibility of the DSS to identify areas that meet restoration objectives.

Water-Resources Investigations Report↗

USGS2006

Application of municipal biosolids to dry-land wheat fields - A monitoring program near Deer Trail, Colorado (USA). A presentation for an international conference: "The Future of Agriculture: Science, Stewardship, and Sustainability", August 7-9, 2006, Sacramento, CA

Since late 1993, Metro Wastewater Reclamation District of Denver (Metro District), a large wastewater treatment plant in Denver, Colorado, has applied Grade I, Class B biosolids to about 52,000 acres of non-irrigated farmland and rangeland near Deer Trail, Colorado. In cooperation with the Metro District in 1993, the U.S. Geological Survey (USGS) began monitoring ground water at part of this site. In 1999, the USGS began a more comprehensive study of the entire site to address stakeholder concerns about the chemical effects of biosolids applications. This more comprehensive monitoring program has recently been extended through 2010. Monitoring components of the more comprehensive study included biosolids collected at the wastewater treatment plant, soil, crops, dust, alluvial and bedrock ground water, and stream bed sediment. Streams at the site are dry most of the year, so samples of stream bed sediment deposited after rain were used to indicate surface-water effects. This presentation will only address biosolids, soil, and crops. More information about these and the other monitoring components are presented in the literature (e.g., Yager and others, 2004a, b, c, d) and at the USGS Web site for the Deer Trail area studies at http://co.water.usgs.gov/projects/CO406/CO406.html. Priority parameters identified by the stakeholders for all monitoring components, included the total concentrations of nine trace elements (arsenic, cadmium, copper, lead, mercury, molybdenum, nickel, selenium, and zinc), plutonium isotopes, and gross alpha and beta activity, regulated by Colorado for biosolids to be used as an agricultural soil amendment. Nitrogen and chromium also were priority parameters for ground water and sediment components. In general, the objective of each component of the study was to determine whether concentrations of priority parameters (1) were higher than regulatory limits, (2) were increasing with time, or (3) were significantly higher in biosolids-applied areas than in a similar farmed area where biosolids were not applied. Where sufficient samples could be collected, statistical methods were used to evaluate effects. Rigorous quality assurance was included in all aspects of the study. The roles of hydrology and geology also were considered in the design, data collection, and interpretation phases of the study. Study results indicate that the chemistry of the biosolids from the Denver plant was consistent during 1999-2005, and total concentrations of regulated trace elements were consistently lower than the regulatory limits. Plutonium isotopes were not detected in the biosolids. Leach tests using deionized water to simulate natural precipitation indicate arsenic, molybdenum, and nickel were the most soluble priority parameters in the biosolids. Study results show no significant difference in concentrations of priority parameters between biosolids-applied soils and unamended soils where no biosolids were applied. However, biosolids were applied only twice during 1999-2003. The next soil sampling is not scheduled until 2010. To date concentrations of most of the priority parameters were not much greater in the biosolids than in natural soil from the sites. Therefore, many more biosolids applications would need to occur before biosolids effects on the soil priority constituents can be quantified. Leach tests using deionized water to simulate precipitation indicate that molybdenum and selenium were the priority parameters that were most soluble in both biosolids-applied soil and natural or unamended soil. Study results do not indicate significant differences in concentrations of priority parameters between crops grown in biosolids-applied areas and crops grown where no biosolids were applied. However, crops were grown only twice during 1999-2003, so only two crop samples could be collected. The wheat-grain elemental data collected during 1999-2003 for both biosolids-applied areas and unamended areas are similar

Open-File Report↗

USGS2023

Stochastic watershed model ensembles for long-range planning: Verification and validation

Deterministic watershed models (DWMs) are used in nearly all hydrologic planning, design, and management activities, yet they cannot generate streamflow ensembles needed for hydrologic risk management (HRM). The stochastic component of DWMs is often ignored in practice, leading to a systematic bias in extreme events. Since traditional stochastic streamflow models used in HRM struggle to account for anthropogenic change, there is a need to convert DWMs into stochastic watershed models (SWMs) to generate ensembles for use in HRM. A DWM can be converted to an SWM using a post-processing (pp) approach to add error to the DWM predictions. Many pp methods advanced in the area of flood forecasting are useful in HRM and for correcting extreme event biases. Selecting a suitable error model for pp is challenging due to nonnormality, skewness, heteroscedasticity, and autocorrelation. We develop a parsimonious pp method based on an autoregressive (AR) model of the logarithm of the ratio of the observations and simulations, which leads to AR model residuals that are approximately symmetric and independent. We document the value of pp for improving flood and low flow frequency analysis and we reintroduce the concepts of verification and validation of stochastic streamflow ensembles to ensure that the SWM can reproduce both statistics it was and was not designed to reproduce, respectively. These concepts are illustrated on a Massachusetts basin using the USGS Precipitation Runoff Modeling System, with an additional analysis indicating the approach may be applicable to 1,225 other sites across the United States.

Massachusetts, New Hampshire↗

USGS2004

Load estimator (LOADEST): a FORTRAN program for estimating constituent loads in streams and rivers

LOAD ESTimator (LOADEST) is a FORTRAN program for estimating constituent loads in streams and rivers. Given a time series of streamflow, additional data variables, and constituent concentration, LOADEST assists the user in developing a regression model for the estimation of constituent load (calibration). Explanatory variables within the regression model include various functions of streamflow, decimal time, and additional user-specified data variables. The formulated regression model then is used to estimate loads over a user-specified time interval (estimation). Mean load estimates, standard errors, and 95 percent confidence intervals are developed on a monthly and(or) seasonal basis. The calibration and estimation procedures within LOADEST are based on three statistical estimation methods. The first two methods, Adjusted Maximum Likelihood Estimation (AMLE) and Maximum Likelihood Estimation (MLE), are appropriate when the calibration model errors (residuals) are normally distributed. Of the two, AMLE is the method of choice when the calibration data set (time series of streamflow, additional data variables, and concentration) contains censored data. The third method, Least Absolute Deviation (LAD), is an alternative to maximum likelihood estimation when the residuals are not normally distributed. LOADEST output includes diagnostic tests and warnings to assist the user in determining the appropriate estimation method and in interpreting the estimated loads. This report describes the development and application of LOADEST. Sections of the report describe estimation theory, input/output specifications, sample applications, and installation instructions.

Techniques and Methods↗

USGS2002

Yellowstone grizzly bear investigations: Annual report of the Interagency Grizzly Bear Study Team, 2001

The contents of this Annual Report summarize results of monitoring and research from the 2001 field season. The report also contains a summary of nuisance grizzly bear ( Ursus arctos horribilis ) management actions. In addition to our normal monitoring, we completed an array of studies addressing the potential impacts of winter recreation on denning grizzly bears. This research was in response to a lawsuit filed against the Gallatin National Forest and subsequent need to develop a biological assessment addressing effects of snowmobile use on grizzly bears (Chemy 2001). Research results were also used by the National Park Service for a biological assessment and winter use plan (U.S. Department of the Interior 2001). The Interagency Grizzly Bear Study Team (IGBST) was able to use existing data collected from collared bears to address several issues and data needs for both agencies. Denning chronology (Haroldson et al. 2002), denning areas (Podruzny et al. 2002), and grizzly distribution (Schwartz et al. 2002) were all addressed. Information from these studies was presented at the International Association for Bear Research and Management (IBA) in Jackson Hole, Wyoming in 2001, and all 3 manuscripts have been officially accepted for publication in the journal Ursus . Abstracts are attached to this report (Appendices A, B. and C). Additionally, members of the study team participated in a workshop held by the National Park Service to develop monitoring protocols addressing the impacts of snowmobiles on wildlife (Graves and Reams 2001). The study team has also been working on issues associated with counts of unduplicated females with cubs-of-the-year (COY). These counts are used to establisha minimum population size, which is then used to establish mortality thresholds for the Recovery Plan (U.S. Fish and Wildlife Service [USFWS) 1993). Efforts by the Study Team to calculate more statistically sound estimates of population size have been underway for sometime. Eberhardt and Knight (1996) applied a Peterson-type capturemark-recapture estimator to unduplicated counts, and Boyce et al. (1999) recommended a maximum likelihood method. These methods assumed equal sightability of families, which was unrealistic for the Yellowstone population. Consequently, Boyce et al. (2001) recommended using a negative binomial distribution but found that they obtained reasonable results only when the coefficient of variation among sightings was assumed to be constant overtime. This assumption is also difficult to justify. Recent work by the study team (Keating et al. 2002) evaluated the application of 7 nonparametric estimators to assess their performance in determining the number of females with COY in a given year. This work identified 2 estimators that performed well using Monte Carlo simulations over a range of sampling conditions deemed plausible for the Yellowstone population: Chao's estimator (Chao 1984) and the sample coverage estimator (Chao and Lee 1992, Lee and Chao 1994). This work was presented at the IBA meeting in Jackson and the manuscript has been accepted in the journal Ursus . An abstract of this work is attached to this annual report (Appendix D). We are currently refining the application of these techniques to expand the predicted number of females with COY into a total population estimate. We anticipate completion of that work in 2002 or 2003.

Idaho, Montana, Wyoming↗

USGS2026

Accounting for biases in the analysis of building damage data for the 2023 M7.8 Türkiye/Syria earthquake sequence

In the aftermath of a large earthquake, several practical constraints affect the systematic collection of data for assessing building damage. The available data are therefore typically incomplete, frequently missing key information such as the type of construction, age and geographic location. Moreover, the observations often favour certain locations and damage grades. In the absence of robust methods, using such data directly for characterising the building stock or conducting fragility assessments can introduce systematic bias. Using the 2023 M7.8 Türkiye/Syria earthquake sequence as a case study, we propose a statistical model that enables joint inference over the inventory and fragility of the building stock while explicitly accounting for sources of data error and missing information. The results show higher-than-expected vulnerability amongst several building types, particularly mid- and high-rise reinforced concrete buildings, which performed extremely poorly during the 2023 earthquake sequence. The proposed Bayesian framework provides rigorous uncertainty quantification that can be propagated to future applications.

Earthquake Spectra↗

USGS1959

Hydrologic budget of the Beaverdam Creek basin, Maryland

A hydrologic budget is a statement accounting for the water gains and losses for selected periods in an area. Weekly measurements of precipitation streamflow, surface-water storage, ground-water stage, and soil resistivity were made during a 2year period, April 1, 1950, to March 28, 1952, in the Beaverdam Creek basin, Wicomico County, Md. The hydrologic measurements are summarized in two budgets, a total budget and a ground-water budget, and in supporting tables and graphs. The results of the investigation have some potentially significant applications because they describe a method for determining the annual replenishment of the water supply of a basin and the ways of water disposal under natural conditions. The information helps to determine the 'safe' yield of water in diversion from natural to artificial discharge. The drainage basin of Beaverdam Creek was selected because it appeared to have fewer hydrologic variables than are generally found. However, the methods may prove applicable in many places under a variety of conditions. The measurements are expressed in inches of water over the area of the basin. The equation of the hydrologic cycle is the budget balance: P= R+E+ASW+ delta SW + delta SM + delta GW where P is precipitation; R is runoff; ET is evapotranspiration; delta SW is change in surface-water storage; delta SM is change in soil moisture; and delta GW is change in ground-water storage. In this report 'change' is the final quantity minus the initial quantity and thus is synonymous with 'increase.' Further, ,delta GW= delta H .x Yg, in which delta H is the change in ground-water stage and Yg is the gravity yield, or the specific yield of the sediments as measured during the short periods of declining ground-water levels characteristic of the area. The complex sum of the revised equation P ? R - delta SW ? ET - delta SM, which is equal to delta H. x Yg, has been named the 'infiltration residual'; it is equivalent to ground-water recharge. Two unmeasured, but not entirely unknown, quantities, evapotranspiration, (ET) and gravity yield, (Yg), are included in the equation. They are derived statistically by a method of convergent approximations, one of the contributions of this investigation. On the basis of laboratory analysis, well-field tests, and general information on rates of drainage from saturated sediments, a gravity yield of 14 percent was assumed as a first approximation. The equation was then solved, by weeks, for evapotranspiration, ET. The evapotranspiration losses were plotted against the calendar week. Using the time of year as a control, a smooth curve was fitted to the evapotranspiration data, and modified values of ET were read from the curve. These were used to compute weekly values of the infiltration residual which were plotted against ground-water stage. The slope of the line of best fit gave a closer approximation of gravity yield, Yg. The process was repeated. The approximations converged, so that a fourth and final approximation resulted in a close grouping of all the points along a line whose slope indicated a Yg of 11.0 percent, and a slightly asymmetric bell-shaped curve of total evapotranspiration by weeks was obtained that is considered representative of this area. Check calculations of gravity yield were made during periods of low evapotranspiration and high infiltration, which substantiate the computed average of 11.0 percent. Refinements in the method of deriving the ground-water budget were introduced to supplement the techniques developed by Meinzer and Stearns in the study of the Pomperaug River basin in Connecticut in 1913 and 1916. The hydrologic equation for the ground-water cycle may be written Gr=D + delta H. x Yg + ETg, in which Gr is ground-water recharge (infiltration); D is ground-water drainage; delta H is the change in mean ground-water stage (final stage minus initial stage); Yg is gravity yield (taken as 11.0 percent in computations here); an

Water Supply Paper↗

USGS2024

Multi-criteria decision approach for climate adaptation of cultural resources along the Atlantic coast of the southeastern United States: Application of AHP method

Prioritizing climate adaptation actions is often made difficult by stakeholders and decision-makers having multiple objectives, some of which may be competing. Transparent, transferable, and objective methods are needed to assess and weight different objectives for complex decisions with multiple interests. In this study, the Analytic Hierarchy Process (AHP) was used to examine priorities in managing cultural resources in the face of climate change at Cape Lookout National Seashore on the Atlantic coast of the southeastern United States. In this process, we conducted facilitated discussion sessions with the selected stakeholder representatives to elicit a comprehensive list of management objectives. Objectives were then merged into three categories: 1) Maximize retention of historic character and condition (HCC); 2) Foster heritage awareness (HA); and 3) Maximize financial benefits (FB). We facilitated two AHP exercise sessions, both individually and in groups, to seek consensus on the relative importance of the objectives. The AHP process created a space for stakeholders (government agencies and local citizens) to consider and present arguments that we used to contextualize their trade-offs between the objectives. The stakeholders' top priority was to maximize the HCC. This objective was prioritized more than HA and FB in the individual trade-off choices, while HA was given nearly equal priority to FB. The consensus priority vectors of two management objectives (HCC and HA) differ significantly from FB, but the difference between HCC and HA is slight and not statistically different. FB and HA had larger changes in consensus priority vectors among the three objectives relative to individual priority vectors. For HCC, the difference between individual and consensus priority vectors was the smallest and nearly equal. Moreover, very high levels of consistency were found in consensus priority trade-off discussions and AHP application. Our research highlights the advantage of using a two-step AHP process in climate adaptation planning of vulnerable resources to enhance robustness in decision making. Coupling this approach with future efforts to develop management priorities would help estimate indices to determine the order in which adaptation treatments are applied to vulnerable cultural resources.

Climate Risk Management↗

USGS2025

Spatially explicit power analyses to inform occupancy‐based multi‐species wildlife monitoring programmes

1. Current and accurate information on wildlife populations is integral to successful biodiversity management and conservation globally. Nevertheless, many monitoring programs fail in their attempts to accurately monitor populations of interest due to interlinked issues including insufficient sample sizes, inappropriate duration, lack of reproducibility, and lack of clearly stated objectives. These common pitfalls could be avoided through the elicitation of explicit monitoring objectives and the a priori use of simulations to inform minimum sampling design requirements to meet said objectives. 2. Here, we provide a blueprint for using spatially explicit power analyses to inform the design and implementation of multi-species monitoring programs on landscape-scales. As a demonstration, we used spatially explicit simulations to devise a suitable sampling regime to meet clearly specified monitoring objectives in New York State: to use annual occupancy-based monitoring to be able to detect 25% and 50% changes in abundance of populations over five- and ten- year periods for all species of management interest in New York State, USA. We focused our simulation efforts on three challenging focal species (black bear, Ursus americanus, bobcat, Lynx rufus, and American marten, Martes americana) that differ notably in their morphology, life histories, space use, detection probability, habitat suitability, and population sizes/trajectories, and thus provide extremes in the challenges presented when it comes to sampling appropriately to detect changes in abundance. 3. Our simulations demonstrate variable context dependent trade-offs in sampling designs (i.e. number of sites [J] and number of sampling occasions [K]), and identify necessary minimum detection probabilities that must be attained to achieve statistical power to detect changes of varying magnitudes in populations of varying sizes in the three focal species. The simulations also highlight that monitoring population increases is likely beyond the reach of occupancy-based monitoring programs for wide-ranging or locally abundant species. 4. Synthesis and applications : We combine the results from the single-species simulations to produce a multi-species sampling design that meets the specified objectives for all three species. While the case study is centered on developing a multi-species sampling regime for New York State, it provides a reproducible step-by-step framework using established methods for wildlife managers and other practitioners to inform their own context- and objective- specific multi-species occupancy-based monitoring programs.

New York↗

USGS2015

User guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R packages for hydrologic data

Evaluating long-term changes in river conditions (water quality and discharge) is an important use of hydrologic data. To carry out such evaluations, the hydrologist needs tools to facilitate several key steps in the process: acquiring the data records from a variety of sources, structuring it in ways that facilitate the analysis, processing the data with routines that extract information about changes that may be happening, and displaying findings with graphical techniques. A pair of tightly linked R packages, called dataRetrieval and EGRET (Exploration and Graphics for RivEr Trends), have been developed for carrying out each of these steps in an integrated manner. They are designed to easily accept data from three sources: U.S. Geological Survey hydrologic data, U.S. Environmental Protection Agency (EPA) STORET data, and user-supplied flat files. The dataRetrieval package not only serves as a “front end” to the EGRET package, it can also be used to easily download many types of hydrologic data and organize it in ways that facilitate many other hydrologic applications. The EGRET package has components oriented towards the description of long-term changes in streamflow statistics (high flow, average flow, and low flow) as well as changes in water quality. For the water-quality analysis, it uses Weighted Regressions on Time, Discharge and Season (WRTDS) to describe long-term trends in both concentration and flux. EGRET also creates a wide range of graphical presentations of the water-quality data and of the WRTDS results. This report serves as a user guide to these two R packages, providing detailed guidance on installation and use of the software, documentation of the analysis methods used, as well as guidance on some of the kinds of questions and approaches that the software can facilitate.

Techniques and Methods↗

USGS2019

Monitoring boreal avian populations: How can we estimate trends and trajectories from noisy data?

Substantial effort has been dedicated to developing reliable monitoring schemes for North American bird populations, but our ability to monitor bird populations in the boreal forest remains limited because of the sparsity of long-term data sets, particularly in northerly regions. Given the importance of the boreal forest for many migratory birds, we set out to (1) summarize the main challenges associated with monitoring avian populations, (2) describe the available statistical tools for population monitoring and their applications, and (3) identify future directions to overcome current challenges in monitoring bird populations in the boreal forest. Defining and delineating populations of interest and identifying the drivers that affect those populations present the greatest current challenges. This is because migratory birds may be affected by many population-limiting processes at different stages of their annual life cycles. These factors are often hierarchically structured and can influence populations at the local, regional, or continental scales. Some of the challenges associated with delineating populations and identifying population drivers can be addressed via the plethora of sampling and analytic methods available to examine population change over time. Choosing the proper analytic methods depends on the goals of the study and the nature of the data such as single or multiple populations, repeated occurrence or count-based surveys, or demographic rates. Recent advances in hierarchical and integrated population models make these analytic approaches some of the most promising avenues for the development of future methods. However, these tools require large data sets, and acquiring sufficient data on bird populations and potential explanatory variables is difficult in the boreal forest. If the current challenges to monitoring birds in the boreal forest are to be overcome, serious effort should be dedicated to integrating existing data and making them accessible. Enhancing survey effort through multispecies surveys will also play an important role. Implementing spatially balanced sampling plans with a rotating panel design could balance the trade-offs between spatial versus temporal replication at an affordable cost. Improving the accessibility of environmental covariates that are spatially and temporally explicit would also enable development of mechanistic population models that improve our understanding of migratory bird population dynamics. Finally, given that long-term monitoring programs can take many decades before delivering reliable population trends and that organizational priorities often change over time, we suggest that collaborative efforts will help ensure the long-term survival of new monitoring programs.

Avian Conservation and Ecology↗