Bayesian Small Area Models for Assessing Wildlife Conservation Risk in Patchy Populations

Abstract:  Species conservation risk assessments require accurate, probabilistic, and biologically meaningful maps of population distribution. In patchy populations, the reasons for discontinuities are not often well understood. We tested a novel approach to habitat modeling in which methods of small area estimation were used within a hierarchical Bayesian framework. Amphibian occurrence was modeled with logistic regression that included third-order drainages as hierarchical effects to account for patchy populations. Models including the random drainage effects adequately represented species occurrences in patchy populations of 4 amphibian species in the Oregon Coast Range (U.S.A.). Amphibian surveys from other locations within the same drainage were used to calibrate local drainage-scale effects. Cross-validation showed that prediction errors for calibrated models were 77% to 86% lower than comparable regionally constructed models, depending on species. When calibration data were unavailable, small area and regional models performed similarly, although poorly. Small area estimation models complement wildlife ecology and habitat studies, and can help managers develop a regional picture of the conservation status for relatively rare species.     

An assessment methodology for successional systems. I. Null models and the regulatory framework

Standard procedures for evaluating environmental impact involve comparison between before and after conditions or scenarios or between treatment and control site pairs. In many cases, however, endogenous directional change (natural succession) is expected to occur at a significant rate over the period of concern, particularly for manmade systems such as impoundments. Static evaluations do not provide an adequate approach to such problems. A new evaluation frame is proposed. Nominal system behavior over time is characterized by a stochastic envelope around a nominal trajectory. We show that both the state variance and the sampling variance can change over time. In this context, environmental regulations can be framed as constraints, targets, or conformance to ideal trajectories. Statistical tests for determining noncompliance are explored relative to process variance, sample error, and sample size. Criteria are elucidated for choosing properties to monitor, sample size, and sampling interval.     

Differences among model simulations of climate change on the scale of resource regions

The climate simulations from atmospheric general circulation models (GCMs) are often used to analyze the potential effects of climate change on environmental resources. It has been demonstrated that there are differences among the simulations from various GCMs, on spatial scales ranging from global to regional. This paper quantifies the differences in temperature and precipitation simulated by three major GCMs for four specific regions: an agricultural region (the North American winter wheat belt), a hydrologic region (the Great Basin), a demographic region (the high-density population corridor of the northeast United States), and a political region (the state of Texas). Both the current (control) climate and the climatic response to a doubling of atmospheric carbon dioxide (CO₂) are consideredIn each region, even when the data are averaged on a seasonal basis, marked differences occurred in the areal average climate simulated by the different GCMs for both the control climate and the doubled-CO₂ climate. Thus, climate impact studies based on the simulations of more than one GCM could easily yield a range of possible results     

COMPARISON OF REGRESSION AND TIME-SERIES METHODS FOR SYNTHESIZING MISSING STREAMFLOW RECORDS1

ABSTRACT: Regression and time-series techniques have been used to synthesize and predict the stream flow at the Foresta Bridge gage from information at the upstream Pohono Bridge gage on the Merced River near Yosemite National Park. Using the available data from two time periods (calendar year 1979 and water year 1986), we evaluated the two techniques in their ability to model the variation in the observed flows and in their ability to predict stream flow at the Foresta Bridge gage for the 1979 time period with data from the 1986 time period. Both techniques produced reasonably good estimates and forecasts of the flow at the downstream gage. However, the regression model was found to have a significant amount of autocorrelation in the residuals, which the time-series model was able to eliminate. The time-series technique presented can be of great assistance in arriving at reasonable estimates of flow in data sets that have large missing portions of data.     

THE SENSITWITY OF NORTHERN SIERRA NEVADA STREAMFLOW TO CLIMATE CHANGE1

ABSTRACT: The sensitivity of streamflow to climate change was investigated in the American, Carson, and Truckee River Basins, California and Nevada. Nine gaging stations were used to represent streamflow in the basins. Annual models were developed by regressing 1961–1991 streamflow data on temperature and precipitation. Climate-change scenarios were used as inputs to the models to determine streamflow sensitivities. Climate-change scenarios were generated from historical time series by modifying mean temperatures by a range of +4°C to—4°C and total precipitation by a range of +25 percent to -25 percent. Results show that streamflow on the warmer, lower west side of the Sierra Nevada generally is more sensitive to temperature and precipitation changes than is streamflow on the colder, higher east side. A 2°C rise in temperature and a 25-percent decrease in precipitation results in stream-flow decreases of 56 percent on the American River and 25 percent on the Carson River. A 2°C decline in temperature and a 25-percent increase in precipitation results in streamflow increases of 102 percent on the American River and 22 percent on the Carson River.     

ESTIMATING REGIONAL SNOW WATER EQUIVALENT WITH A SIMPLE SIMULATION MODEL1

A simple simulation model designed to monitor snow-packs of the central Sierra Nevada is described. The model estimates average snow water equivalent for rectangular subregions in the area. Static subregion characteristics, daily precipitation and mean and minimum air temperatures measured at three index stations are the only needed input values. A water balance technique simulates daily snowpack changes in each subregion. Reasonable basinwide water equivalent values are produced. The procedure should be useful for estimating snow water distribution in large mountainous watersheds.     

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS1

ABSTRACT: A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that includsion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.     

IDENTIFICATION OF SOLUTE LOADING SOURCES TO A SURFACE STREAM1

ABSTRACT: A study was performed to identify sources of solute loading to the Dirty Devil River and its major tributaries, in southeastern Utah. A primary goal was to determine the contribution of gypsum dissolution to total dissolved solids concentration, and its potential increase in the future if salinity control measures are instituted. Synoptic field data were collected during the low flow period in October 1983. Data were analyzed using the geochemistry models WATEQF and BALANCE to postulate mineral reactions leading to solute loading. Three known sources of solute loading, involving two different geochemical mechanisms, were clearly discernable. Two additional areas of possible gypsum dissolution were located.     

DERIVING THE NONLINEAR RISK-BENEFIT ALGORITHM FOR RESERVOIRS1

ABSTRACT: The Nonlinear Risk-Benefit (NRB) Algorithm includes risk as one of the objectives in a multiple-objective optimization problem. The NRB Algorithm is derived by extending the Surrogate Worth Trade-Off method to quadratic programming. This category of problem is common in water resources planning and design, especially multipurpose reservoir systems. Consequently, an example is given using the algorithm for optimally operating a multipurpose reservoir.     

PREDICTION OF CHLOROPHYLL A CONCENTRATIONS IN FLORIDA LAKES: THE IMPORTANCE OF PHOSPHORUS AND NITROGEN1

ABSTRACT: Models for the prediction of chlorophyll a concentrations were developed and tested using data on 223 Florida lakes. A statistical analysis showed that the best model was log (Chl a) =?2.49 + 0.269 log (TP) + 1.06 log (TN) or log (Chl a) =?2.49 + 1.06 log (TN/TP) + 1.33 log (TP) where Chl a is the chlorophyll a concentration (mg m^-3), TP is the total phosphorus concentration (mg m^-3) and TN is the total nitrogen concentration (mg m^-3). The model yields unbiased estimates of chlorophyll a concentrations over a wide range of lake types and has a 95 percent confidence interval of 29–319 percent of the calculated chlorophyll a concentrations. Other models, especially the published Dillon-Rigler and Jones-Bachmann phosphorus-chlorophyll models, are less precise when applied to Florida lakes. The data support the hypothesis that nitrogen is an important limiting nutrient in hypereutrophic lakes.