Geology topics

B. C. Lubow

Publications and source records attributed to B. C. Lubow.

4 recordsLinked to original sources

USGS2021

Elk monitoring in Mount Rainier and Olympic National Parks: 2008-2017 synthesis report

In 2008, the U.S. Geological Survey (USGS) began collaborating with the National Park Service (NPS)-North Coast and Cascades Network (NCCN), the Muckleshoot Indian Tribe (MIT), Puyallup Tribe of Indians (PTOI), and Washington Department of Fish and Wildlife (WDFW) to develop a standard survey protocol for monitoring long-term changes in the abundance, distribution, and population composition of elk on key summer ranges within Mount Rainier National Park (MORA) and Olympic National Park (OLYM). In MORA, surveys were conducted in two trend count areas (TCAs) that correspond with primary summer ranges used by the North Rainier Herd, which winters outside the park to the North, and the South Rainier Herd, which winters outside the park primarily to the South. In OLYM, we defined five TCAs including an Olympic Core TCA (hereafter, Core TCA) that encompasses summer ranges on the flanks of Mount Olympus, and four TCAs that encompass other primary summer ranges throughout the park. The standard protocol allows for estimating aerial survey detection biases and adjusting raw survey counts to account for elk that were likely present but not seen during surveys. Previously, we developed a suite of aerial-bias-correction models for use in estimating aerial detection biases and adjusting raw counts of elk in MORA based on sighting conditions related to elk group size, vegetation density, lighting conditions, elk movement, as well as combinations of these and other factors. The models were based on independent sighting records of elk groups by front-seat and back-seat observer pairs in a helicopter, including detection records of some radio-collared elk groups. Here, we analyze results of the first 10 years of elk monitoring in MORA (2008-2017) and 8 years in OLYM (2008-2015). In a previous report covering surveys conducted from 2008-2011, data were not sufficient to model detection biases of aerial surveys conducted in OLYM; hence, analyses of elk population trends were based on counts adjusted for detection biases in MORA, whereas trends in OLYM were based on raw, unadjusted counts (Griffin et al. 2013, Jenkins et al. 2015). Our objectives for the current summary were to: (1) incorporate additional data to update aerial-bias-correction models previously developed for use in MORA to include corrections for aerial detection bias in both MORA and OLYM, (2) examine trends in elk abundance, distribution, and population composition estimates for subalpine summer ranges within MORA and OLYM, and (3) estimate effects of seasonal variation and weather on elk abundance and population composition estimates for subalpine summer ranges in both parks.

Washington↗

USGS2011

Structured decision making as a proactive approach to dealing with sea level rise in Florida

Sea level rise (SLR) projections along the coast of Florida present an enormous challenge for management and conservation over the long term. Decision makers need to recognize and adopt strategies to adapt to the potentially detrimental effects of SLR. Structured decision making (SDM) provides a rigorous framework for the management of natural resources. The aim of SDM is to identify decisions that are optimal with respect to management objectives and knowledge of the system. Most applications of SDM have assumed that the managed systems are governed by stationary processes. However, in the context of SLR it may be necessary to acknowledge that the processes underlying managed systems may be non-stationary, such that systems will be continuously changing. Therefore, SLR brings some unique considerations to the application of decision theory for natural resource management. In particular, SLR is expected to affect each of the components of SDM. For instance, management objectives may have to be reconsidered more frequently than under more stable conditions. The set of potential actions may also have to be adapted over time as conditions change. Models have to account for the non-stationarity of the modeled system processes. Each of the important sources of uncertainty in decision processes is expected to be exacerbated by SLR. We illustrate our ideas about adaptation of natural resource management to SLR by modeling a non-stationary system using a numerical example. We provide additional examples of an SDM approach for managing species that may be affected by SLR, with a focus on the endangered Florida manatee.

Florida↗

USGS2009

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

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

Ecological Applications↗

USGS2003

Small-mammal density estimation: A field comparison of grid-based vs. web-based density estimators

Statistical models for estimating absolute densities of field populations of animals have been widely used over the last century in both scientific studies and wildlife management programs. To date, two general classes of density estimation models have been developed: models that use data sets from capture–recapture or removal sampling techniques (often derived from trapping grids) from which separate estimates of population size ( NÌ‚ ) and effective sampling area ( AÌ‚ ) are used to calculate density ( DÌ‚ = NÌ‚ / AÌ‚ ); and models applicable to sampling regimes using distance-sampling theory (typically transect lines or trapping webs) to estimate detection functions and densities directly from the distance data. However, few studies have evaluated these respective models for accuracy, precision, and bias on known field populations, and no studies have been conducted that compare the two approaches under controlled field conditions. In this study, we evaluated both classes of density estimators on known densities of enclosed rodent populations. Test data sets ( n = 11) were developed using nine rodent species from capture–recapture live-trapping on both trapping grids and trapping webs in four replicate 4.2-ha enclosures on the Sevilleta National Wildlife Refuge in central New Mexico, USA. Additional “saturation” trapping efforts resulted in an enumeration of the rodent populations in each enclosure, allowing the computation of true densities. Density estimates ( DÌ‚ ) were calculated using program CAPTURE for the grid data sets and program DISTANCE for the web data sets, and these results were compared to the known true densities ( D ) to evaluate each model's relative mean square error, accuracy, precision, and bias. In addition, we evaluated a variety of approaches to each data set's analysis by having a group of independent expert analysts calculate their best density estimates without a priori knowledge of the true densities; this “blind” test allowed us to evaluate the influence of expertise and experience in calculating density estimates in comparison to simply using default values in programs CAPTURE and DISTANCE. While the rodent sample sizes were considerably smaller than the recommended minimum for good model results, we found that several models performed well empirically, including the web-based uniform and half-normal models in program DISTANCE, and the grid-based models M b and M bh in program CAPTURE (with AÌ‚ adjusted by species-specific full mean maximum distance moved (MMDM) values). These models produced accurate DÌ‚ values (with 95% confidence intervals that included the true D values) and exhibited acceptable bias but poor precision. However, in linear regression analyses comparing each model's DÌ‚ values to the true D values over the range of observed test densities, only the web-based uniform model exhibited a regression slope near 1.0; all other models showed substantial slope deviations, indicating biased estimates at higher or lower density values. In addition, the grid-based DÌ‚ analyses using full MMDM values for WÌ‚ area adjustments required a number of theoretical assumptions of uncertain validity, and we therefore viewed their empirical successes with caution. Finally, density estimates from the independent analysts were highly variable, but estimates from web-based approaches had smaller mean square errors and better achieved confidence-interval coverage of D than did grid-based approaches. Our results support the contention that web-based approaches for density estimation of small-mammal populations are both theoretically and empirically superior to grid-based approaches, even when sample size is far less than often recommended. In view of the increasing need for standardized environmental measures for comparisons among ecosystems and through time, analytical models based on distance sampling appear to offer accurate density estimation approaches for research studies involving small-mammal abundances.

Ecological Monographs↗