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USGS2018

Accounting for location uncertainty in azimuthaltelemetry data improves ecological inference

Background Characterizing animal space use is critical for understanding ecological relationships. Animal telemetry technology has revolutionized the fields of ecology and conservation biology by providing high quality spatial data on animal movement. Radio-telemetry with very high frequency (VHF) radio signals continues to be a useful technology because of its low cost, miniaturization, and low battery requirements. Despite a number of statistical developments synthetically integrating animal location estimation and uncertainty with spatial process models using satellite telemetry data, we are unaware of similar developments for azimuthal telemetry data. As such, there are few statistical options to handle these unique data and no synthetic framework for modeling animal location uncertainty and accounting for it in ecological models. We developed a hierarchical modeling framework to provide robust animal location estimates from one or more intersecting or non-intersecting azimuths. We used our azimuthal telemetry model (ATM) to account for azimuthal uncertainty with covariates and propagate location uncertainty into spatial ecological models. We evaluate the ATM with commonly used estimators (Lenth (1981) maximum likelihood and M-Estimators) using simulation. We also provide illustrative empirical examples, demonstrating the impact of ignoring location uncertainty within home range and resource selection analyses. We further use simulation to better understand the relationship among location uncertainty, spatial covariate autocorrelation, and resource selection inference. Results We found the ATM to have good performance in estimating locations and the only model that has appropriate measures of coverage. Ignoring animal location uncertainty when estimating resource selection or home ranges can have pernicious effects on ecological inference. Home range estimates can be overly confident and conservative when ignoring location uncertainty and resource selection coefficients can lead to incorrect inference and over confidence in the magnitude of selection. Furthermore, our simulation study clarified that incorporating location uncertainty helps reduce bias in resource selection coefficients across all levels of covariate spatial autocorrelation. Conclusion The ATM can accommodate one or more azimuths when estimating animal locations, regardless of how they intersect; this ensures that all data collected are used for ecological inference. Our findings and model development have important implications for interpreting historical analyses using this type of data and the future design of radio-telemetry studies.

Movement Ecology↗

USGS2011

A spatial exploration of informal trail networks within Great Falls Park, VA

Informal (visitor-created) trails represent a threat to the natural resources of protected natural areas around the globe. These trails can remove vegetation, displace wildlife, alter hydrology, alter habitat, spread invasive species, and fragment landscapes. This study examines informal and formal trails within Great Falls Park, VA, a sub-unit of the George Washington Memorial Parkway, managed by the U.S. National Park Service. This study sought to answer three specific questions: 1) Are the physical characteristics and topographic alignments of informal trails significantly different from formal trails, 2) Can landscape fragmentation metrics be used to summarize the relative impacts of formal and informal trail networks on a protected natural area? and 3) What can we learn from examining the spatial distribution of the informal trails within protected natural areas? Statistical comparisons between formal and informal trails in this park indicate that informal trails have less sustainable topographic alignments than their formal counterparts. Spatial summaries of the lineal and areal extent and fragmentation associated with the trail networks by park management zones compare park management goals to the assessed attributes. Hot spot analyses highlight areas of high trail density within the park and findings provide insights regarding potential causes for development of dense informal trail networks.

Virginia↗

USGS2022

A statistical framework for modelling migration corridors

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

Idaho, Wyoming↗

USGS2019

Morphodynamic modelling of the wilderness breach, Fire Island, New York. Part I: Model set-up and validation

On October 29, 2012, storm surge and large waves produced by Hurricane 13 Sandy resulted in the formation of a breach in eastern Fire Island, NY. The goals of this study 14 are to gain a better understanding of the physical processes that govern breach behavior and 15 to assess whether process-based models can be used to forecast the evolution of future 16 breaches. The Wilderness Breach grew rapidly in size during the first winter following 17 formation. Growth of the breach was accompanied by the formation of a complex of flood 18 shoals inside Great South Bay, a primary channel that flowed through the eastern part of the 19 flood shoals, and an ebb shoal on the ocean side of the breach. From the summer of 2013 20 through late 2015, the breach continued to change and evolve, albeit at a much slower pace 21 than in the first year after formation. A hybrid combination of Delft3D and XBeach models is 22 used to hindcast the morphodynamic evolution of the Wilderness Breach over the first three 23 years after formation. The formation of the breach during Hurricane Sandy is not part of the 24 simulations. Model simulations are initiated with a post-storm topography in which the 25 breach is already present. The models are capable of hindcasting the main morphodynamic 26 changes of the Wilderness Breach. The spatial patterns, as well as the bulk statistics, such as 27 2 breach geometry and sediment volume changes, are reasonably 28 well reproduced by the model. 29 The model sheds light on previously unknown processes of breach evolution, especially 30 regarding sediment transport and flow regimes within the breach complex.

New York↗

USGS2010

Comment on RamaRao et al. [1995] and LaVenue et al. [1995]

A method for stochastic modeling of groundwater flow systems using a combination of pilot point parameterization and conditional simulation was presented by RamaRao et. al [1995] and LaVenue et. al [1995]. (We will collectively term these two papers RLMM and term the method developed in RLMM the CS method here.) RLMM (pp. 478-479) state that the CS method is intended to provide a frequency distribution of possible alternative spatial transmissivity T distributions that (1) are statistically similar to the observed T distribution, (2) are equally likely given the calibration data, and (3) closely reproduce the measured pressures. The frequency distribution of transmissivities is then used to form frequency distribution of derived functions, such as travel times, which are summarized in the form of uncertainty measures, such as (RLMM, p. 512) "confidence (or tolerance) intervals," on the actual values, which in the case of travels times, are values that could occur sometime in the future (RLMM, p.513). Cooley [2000] analyzes the RLMM method using linearization and bootstrap theory and concludes that that the method can yield accurate uncertainty estimates but only under some limited circumstances. In this comment we use Cooley's analysis to critique the method. We also identify and discuss some statements made my by RLMM about model calibration and their methodology that appear to be misleading. It is unusual to comment on a paper so long after publication. Subsequent work has expanded the method of RLMM, and the method was used advantageously in the testing documented by Zimmerman et. al [1998], so it is clear the method has significant strengths. We go back to the 1995 papers for this comment, however, because they display most clearly the methodological difficulties with which we are concerned.

Water Resources Research↗

USGS2025

The influence of scale in modeling social vulnerability and disaster assistance

Understanding how social vulnerability relates to disaster impacts is critical for addressing social equity, yet the role of spatial scale in this relationship is often overlooked. Most studies use aggregated data, risking ecological fallacy—misinterpreting individual outcomes from group-level data. This study examines how spatial scale influences the relationship between social vulnerability and federal disaster assistance after Hurricane Harvey. Using spatial econometric models at both household and census tract levels, we assessed the strength of key vulnerability indicators in explaining disaster assistance. Results show that disability, housing tenure, household size, and income predict assistance at the household level, but their influence shifts across scales. Income and disability remain strong predictors at the tract level, whereas housing factors weaken or reverse. These findings suggest that using aggregated data to model household-level relationships between social vulnerability and disaster assistance can distort understanding of vulnerability processes, potentially leading to misinformed disaster policies and inequitable outcomes. Our findings have implications for disaster management, but the primary contribution of this study is methodological, in providing a critical evaluation of how spatial scale and data aggregation shape the statistical interpretation of social vulnerability and aid distribution.

Texas↗

USGS2019

Cohesive framework for modeling plant cover class data

The study of plant distribution and abundance is a fundamental pursuit in ecology and conservation biology. Measuring plant abundance by visually assessing percent cover and recording a cover class is a common field method that yields ordinal data. Statistical models for ordinal data exist but entail cumbersome interpretations and sometimes restrictive assumptions. We propose a Bayesian hierarchical framework for analysing cover class data that allows for linking ordinal observations to a latent beta distribution and accounts for zero inflation. Harnessing a latent beta distribution supports interpreting changes in abundance in terms of mean percent cover rather than odds ratios of cumulative cover classes as for cumulative link models. The zero augmentation allows for simultaneous inferences on both occurrence (distribution) and abundance. We show how our model can account for true and false zeros, misclassification of cover classes, multiple species and hierarchical sampling designs, using empirical examples and simulations. Simulated observation errors, when ignored, led to models overestimating abundance and underestimating occurrence. Based on simulations, we found no substantial difference between mean percent cover estimates when analyzing ordinal cover classes versus continuous percent cover as the response. Our empirical datasets displayed high probability of detection (>0.85 on average for all species), likely due to the sampling design used and training of observers. Probability of occurrence was slightly underestimated for bare ground, Artemisia tridentata , Elycap medusae , and Poa secunda using a model that ignored imperfect detection. Estimated mean percent cover was not substantially impacted by ignoring measurement error for five plant species and bare ground. Our modelling framework for cover class data allows for an explicit separation of distribution from abundance and, importantly, allows for interpreting species–environment relationships in terms of variation in mean percent cover as compared to cumulative odds ratios. The beta distribution inherently accommodates heteroscedasticity and skewness, statistical properties that are a consequence of spatially aggregated patterns common to plant survey data. Recording cover classes provides a reliable, efficient way to measure plants and our simulations suggest little loss of information compared to assuming continuous percent cover. We provide JAGS and Stan model code for implementation.

Methods in Ecology and Evolution↗

USGS2011

Mapping rice areas of South Asia using MODIS multitemporal data

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R 2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.

South Asia↗

USGS2005

Submersed Aquatic Vegetation Modeling Output Online

Introduction The ability to predict the distribution of submersed aquatic vegetation in the Upper Mississippi River on the basis of physical or chemical variables is useful to resource managers. Wildlife managers have a keen interest in advanced estimates of food quantity such as American wildcelery (Vallisneria americana) population status to give out more informed advisories to hunters before the fall hunting season. Predictions for distribution of submerged aquatic vegetation beds can potentially increase hunter observance of voluntary avoidance zones where foraging birds are left alone to feed undisturbed. In years when submersed aquatic vegetation is predicted to be scarce in important wildlife habitats, managers can get the message out to hunters well before the hunting season (Jim Nissen, Upper Mississippi River National Wildlife and Fish Refuge, La Crosse District Manager, La Crosse, Wisconsin, personal communication). We developed a statistical model to predict the probability of occurrence of submersed aquatic vegetation in Pool 8 of the Upper Mississippi River on the basis of a few hydrological, physical, and geomorphic variables. Our model takes into consideration flow velocity, wind fetch, bathymetry, growing-season daily water level, and light extinction coefficient in the river (fig. 1) and calculates the probability of submersed aquatic vegetation existence in Pool 8 in individual 5- x 5-m grid cells. The model was calibrated using the data collected in 1998 (516 sites), 1999 (595 sites), and 2000 (649 sites) using a stratified random sampling protocol (Yin and others, 2000b). To validate the model, we chose the data from the Long Term Resource Monitoring Program (LTRMP) transect sampling in backwater areas (Rogers and Owens 1995; Yin and others, 2000a) and ran the model for each 5- x 5-m grid cell in every growing season from 1991 to 2001. We tallied all the cells and came up with an annual average percent frequency of submersed aquatic vegetation occurrence and compared the results with actual LTRMP survey data (fig. 2). Both a paired Student's test (P = 0.4620) and a Wilcoxon's two-sample test (P = 0.4738) did not contradict our null hypothesis that the model prediction and the sampling data are statistically the same. We have not found an effective statistical test to compare model-predicted spatial pattern with aerial photography geographic information, but we are satisfied with the model's outcome on the basis of visual inspection (fig. 3). A unique feature about this model is that a prediction can be made by the end of June each year; therefore, providing wildlife managers an assessment of current year vegetation growth condition 3 to 4 months ahead of the arrival of migrating waterfowl that feed on submersed aquatic vegetation. We are working with the LTRMP partnership to create a mechanism so that model predictions (fig. 4) can be updated annually and the results posted on the LTRMP Web site. Our model underestimated the prevalence of vegetation from 2001 to 2004. We speculate that the summer water level reduction conducted in 2001 and 2002 triggered vegetation responses that are outside the model's domain. Future enhancement of the model will incorporate the summer water level drawdown effects as well as the effects of growth conditions in previous years.

Open-File Report↗

USGS1998

Sampling benthic macroinvertebrates in a large flood-plain river: Considerations of study design, sample size, and cost

Estimation of benthic macroinvertebrate populations over large spatial scales is difficult due to the high variability in abundance and the cost of sample processing and taxonomic analysis. To determine a cost-effective, statistically powerful sample design, we conducted an exploratory study of the spatial variation of benthic macroinvertebrates in a 37 km reach of the Upper Mississippi River. We sampled benthos at 36 sites within each of two strata, contiguous backwater and channel border. Three standard ponar (525 cm 2 ) grab samples were obtained at each site ('Original Design'). Analysis of variance and sampling cost of strata-wide estimates for abundance of Oligochaeta, Chironomidae, and total invertebrates showed that only one ponar sample per site ('Reduced Design') yielded essentially the same abundance estimates as the Original Design, while reducing the overall cost by 63%. A posteriori statistical power analysis (α = 0.05, β = 0.20) on the Reduced Design estimated that at least 18 sites per stratum were needed to detect differences in mean abundance between contiguous backwater and channel border areas for Oligochaeta, Chironomidae, and total invertebrates. Statistical power was nearly identical for the three taxonomic groups. The abundances of several taxa of concern (e.g., Hexagenia mayflies and Musculium fingernail clams) were too spatially variable to estimate power with our method. Resampling simulations indicated that to achieve adequate sampling precision for Oligochaeta, at least 36 sample sites per stratum would be required, whereas a sampling precision of 0.2 would not be attained with any sample size for Hexagenia in channel border areas, or Chironomidae and Musculium in both strata given the variance structure of the original samples. Community-wide diversity indices (Brillouin and 1-Simpsons) increased as sample area per site increased. The backwater area had higher diversity than the channel border area. The number of sampling sites required to sample benthic macroinvertebrates during our sampling period depended on the study objective and ranged from 18 to more than 40 sites per stratum. No single sampling regime would efficiently and adequately sample all components of the macroinvertebrate community.

Environmental Monitoring and Assessment↗

USGS2002

A statistical evaluation of non-ergodic variogram estimators

Geostatistics is a set of statistical techniques that is increasingly used to characterize spatial dependence in spatially referenced ecological data. A common feature of geostatistics is predicting values at unsampled locations from nearby samples using the kriging algorithm. Modeling spatial dependence in sampled data is necessary before kriging and is usually accomplished with the variogram and its traditional estimator. Other types of estimators, known as non-ergodic estimators, have been used in ecological applications. Non-ergodic estimators were originally suggested as a method of choice when sampled data are preferentially located and exhibit a skewed frequency distribution. Preferentially located samples can occur, for example, when areas with high values are sampled more intensely than other areas. In earlier studies the visual appearance of variograms from traditional and non-ergodic estimators were compared. Here we evaluate the estimators' relative performance in prediction. We also show algebraically that a non-ergodic version of the variogram is equivalent to the traditional variogram estimator. Simulations, designed to investigate the effects of data skewness and preferential sampling on variogram estimation and kriging, showed the traditional variogram estimator outperforms the non-ergodic estimators under these conditions. We also analyzed data on carabid beetle abundance, which exhibited large-scale spatial variability (trend) and a skewed frequency distribution. Detrending data followed by robust estimation of the residual variogram is demonstrated to be a successful alternative to the non-ergodic approach.

Environmental and Ecological Statistics↗

USGS1970

Using radar imagery for crop discrimination: a statistical and conditional probability study

A number of the constraints with which remote sensing must contend in crop studies are outlined. They include sensor, identification accuracy, and congruencing constraints; the nature of the answers demanded of the sensor system; and the complex temporal variances of crops in large areas. Attention is then focused on several methods which may be used in the statistical analysis of multidimensional remote sensing data. Crop discrimination for radar K-band imagery is investigated by three methods. The first one uses a Bayes decision rule, the second a nearest-neighbor spatial conditional probability approach, and the third the standard statistical techniques of cluster analysis and principal axes representation. Results indicate that crop type and percent of cover significantly affect the strength of the radar return signal. Sugar beets, corn, and very bare ground are easily distinguishable, sorghum, alfalfa, and young wheat are harder to distinguish. Distinguishability will be improved if the imagery is examined in time sequence so that changes between times of planning, maturation, and harvest provide additional discriminant tools. A comparison between radar and photography indicates that radar performed surprisingly well in crop discrimination in western Kansas and warrants further study.

Remote Sensing of Environment↗

USGS2007

Ground-water quality data in the Monterey Bay and Salinas Valley Basins, California, 2005— Results from the California GAMA program

Ground-water quality in the approximately 1,000-square-mile Monterey Bay and Salinas Valley study unit was investigated from July through October 2005 as part of the California Ground-Water Ambient Monitoring and Assessment (GAMA) program. The study was designed to provide a spatially unbiased assessment of raw ground-water quality, as well as a statistically consistent basis for comparing water quality throughout California. Samples were collected from 94 public-supply wells and 3 monitoring wells in Monterey, Santa Cruz, and San Luis Obispo Counties. Ninety-one of the public-supply wells sampled were selected to provide a spatially distributed, randomized monitoring network for statistical representation of the study area. Six wells were sampled to evaluate changes in water chemistry: three wells along a ground-water flow path were sampled to evaluate lateral changes, and three wells at discrete depths from land surface were sampled to evaluate changes in water chemistry with depth from land surface. The ground-water samples were analyzed for volatile organic compounds (VOCs), pesticides, pesticide degradates, nutrients, major and minor ions, trace elements, radioactivity, microbial indicators, and dissolved noble gases (the last in collaboration with Lawrence Livermore National Laboratory). Naturally occurring isotopes (tritium, carbon-14, helium-4, and the isotopic composition of oxygen and hydrogen) also were measured to help identify the source and age of the sampled ground water. In total, 270 constituents and water-quality indicators were investigated for this study. This study did not attempt to evaluate the quality of water delivered to consumers; after withdrawal from the ground, water typically is treated, disinfected, and (or) blended with other waters to maintain water quality. In addition, regulatory thresholds apply to treated water that is served to the consumer, not to raw ground water. In this study, only six constituents, alpha radioactivity, N-nitrosodimethylamine, 1,2,3-trichloropropane, nitrate, radon-222, and coliform bacteria were detected at concentrations higher than health-based regulatory thresholds. Six constituents, including total dissolved solids, hexavalent chromium, iron, manganese, molybdenum, and sulfate were detected at concentrations above levels set for aesthetic concerns. One-third of the randomized wells sampled for the Monterey Bay and Salinas Valley GAMA study had at least a single detection of a VOC or gasoline additive. Twenty-eight of the 88 VOCs and gasoline additives investigated were found in ground-water samples; however, detected concentrations were one-third to one-sixty-thousandth of their respective regulatory thresholds. Compounds detected in 10 percent or more of the wells sampled include chloroform, a compound resulting from the chlorination of water, and tetrachloroethylene (PCE), a common solvent. Pesticides and pesticide degradates also were detected in one-third of the ground-water samples collected; however, detected concentrations were one-thirtieth to one-fourteen-thousandth of their respective regulatory thresholds. Ten of the 122 pesticides and pesticide degradates investigated were found in ground-water samples. Compounds detected in 10 percent or more of the wells sampled include the herbicide simazine, and the pesticide degradate deethylatrazine. Ground-water samples had a median total dissolved solids (TDS) concentration of 467 milligrams per liter (mg/L), and 16 of the 34 samples had TDS concentrations above the recommended secondary maximum contaminant level (SMCL-a threshold established for aesthetic qualities: taste, odor, and color) of 500 mg/L, while four samples had concentrations above the upper SMCL of 1,000 mg/L. Concentrations of nitrate plus nitrite ranged from 0.04 to 37.8 mg/L (as nitrogen), and two samples had concentrations above the health-based threshold for nitrate of 10 mg/L (as nitrogen). The median sulfate concentration

California↗

USGS2008

Development of a Monitoring Protocol to Detect Ecological Change in the Intertidal Zone of Sitka National Historical Park, Alaska

A pilot study to develop and test a probability-based intertidal monitoring protocol for Sitka National Historical Park was conducted from 1999 to 2003. In 1999, the basic design, with a focus on sampling the whole of the designated intertidal was created, and sampling was conducted for sessile species and large mobile invertebrates by point-intercept sampling of vertical transects and band surveys along transects, respectively. In 2002 and 2003, the same types of sampling were conducted, but quadrat sampling for small mobile invertebrates was added and then modified. This project has produced basic data on the presence, abundance, and spatial distribution of substrates and intertidal biota. Additionally, statistical power analyses conducted on the biological data have allowed assessment of the ability of the sampling to detect trends in the abundance of the predominant species. Current sampling has an 80 percent probability to detect +10 percent annual changes in abundance of all targeted species with an a = 0.05; the ability to detect -10 percent trends is not as uniformly high. Various options are discussed for decreasing the spatial variance of the data. The information presented provides a basis for discussion of the major questions being asked, how the sampling design might be reconfigured to be consistent in approach, and how the intertidal monitoring should interface with other potential intertidal monitoring.

Scientific Investigations Report↗

USGS2009

Incorporating uncertainty into the ranking of SPARROW model nutrient yields from Mississippi/Atchafalaya River basin watersheds

Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient-reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight-digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from watersheds in the Central Mississippi, Ohio, and Lower Mississippi River basins. With 90% confidence, only a few watersheds can be reliably placed into the highest 150 category; however, many more watersheds can be removed from consideration as not belonging to the highest 150 category. Results from this ranking procedure provide robust information on watershed nutrient yields that can benefit management efforts to reduce nutrient loadings to downstream coastal waters, such as the Gulf of Mexico, or to local receiving streams and reservoirs.

Atchafalaya River;Gulf Of Mexico;Mississippi River↗

USGS2019

Size distributions of Arctic waterbodies reveal consistent relations in their statistical moments in space and time

Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km 2 (100 m 2 ) to 1 km 2 . We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size ( R 2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic.

Alaska↗

USGS2014

An application of Social Values for Ecosystem Services (SolVES) to three national forests in Colorado and Wyoming

Despite widespread recognition that social-value information is needed to inform stakeholders and decision makers regarding trade-offs in environmental management, it too often remains absent from ecosystem service assessments. Although quantitative indicators of social values need to be explicitly accounted for in the decision-making process, they need not be monetary. Ongoing efforts to map such values demonstrate how they can also be made spatially explicit and relatable to underlying ecological information. We originally developed Social Values for Ecosystem Services (SolVES) as a tool to assess, map, and quantify nonmarket values perceived by various groups of ecosystem stakeholders. With SolVES 2.0 we have extended the functionality by integrating SolVES with Maxent maximum entropy modeling software to generate more complete social-value maps from available value and preference survey data and to produce more robust models describing the relationship between social values and ecosystems. The current study has two objectives: (1) evaluate how effectively the value index, a quantitative, nonmonetary social-value indicator calculated by SolVES, reproduces results from more common statistical methods of social-survey data analysis and (2) examine how the spatial results produced by SolVES provide additional information that could be used by managers and stakeholders to better understand more complex relationships among stakeholder values, attitudes, and preferences. To achieve these objectives, we applied SolVES to value and preference survey data collected for three national forests, the Pike and San Isabel in Colorado and the Bridger–Teton and the Shoshone in Wyoming. Value index results were generally consistent with results found through more common statistical analyses of the survey data such as frequency, discriminant function, and correlation analyses. In addition, spatial analysis of the social-value maps produced by SolVES provided information that was useful for explaining relationships between stakeholder values and forest uses. Our results suggest that SolVES can effectively reproduce information derived from traditional statistical analyses while adding spatially explicit, social-value information that can contribute to integrated resource assessment, planning, and management of forests and other ecosystems.

Colorado;Wyoming↗

USGS2004

A hierarchical spatial model of avian abundance with application to Cerulean Warblers

Surveys collecting count data are the primary means by which abundance is indexed for birds. These counts are confounded, however, by nuisance effects including observer effects and spatial correlation between counts. Current methods poorly accommodate both observer and spatial effects because modeling these spatially autocorrelated counts within a hierarchical framework is not practical using standard statistical approaches. We propose a Bayesian approach to this problem and provide as an example of its implementation a spatial model of predicted abundance for the Cerulean Warbler (Dendroica cerulea) in the Prairie-Hardwood Transition of the upper midwestern United States. We used an overdispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods. We used 21 years of North American Breeding Bird Survey counts as the response in a loglinear function of explanatory variables describing habitat, spatial relatedness, year effects, and observer effects. The model included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land cover composition and configuration, climate, terrain heterogeneity, and human influence. The inherent hierarchy in the model was from counts occurring, in part, as a function of observers within survey routes within years. We found that the percentage of forested wetlands, an index of wetness potential, and an interaction between mean annual precipitation and deciduous forest patch size best described Cerulean Warbler abundance. Based on a map of relative abundance derived from the posterior parameter estimates, we estimated that only 15% of the species' population occurred on federal land, necessitating active engagement of public landowners and state agencies in the conservation of the breeding habitat for this species. Models of this type can be applied to any data in which the response is counts, such as animal counts, activity (e.g.,nest) counts, or species richness. The most noteworthy practical application of this spatial modeling approach is the ability to map relative species abundance. The functional relationships that we elucidated for the Cerulean Warbler provide a basis for the development of management programs and may serve to focus management and monitoring on areas and habitat variables important to Cerulean Warblers.

Ecological Applications↗