Rangelands in the western United States provide habitat for >3,000 species of mammals, birds, reptiles, fish, and amphibians. They are also shared with most of the cattle produced in the 11 western states. Managing livestock and wildlife on public lands in the western United States is one of the most challenging issues for managers of rangelands and answers do not come easily. To complicate issues further, the influences of livestock on flora and fauna are intuitively considered to be detrimental, but these relationships are not well understood. In part, this text was developed to improve the understanding of wildlife on rangelands and their interaction with livestock. Readers will discover very rapidly that many of the answers to questions about the influence of livestock on wildlife will not be uncovered here: the controlled research simply has not been conducted. However, scientific organizations such as the Society for Range Management, The Wildlife Society, and The Society for Conservation Biology call for rangeland management that is governed by scientific inquiry and the application of those results to management problems. For example, The Wildlife Society (The Wildlife Society, Inc., 5410 Grosvenor Lane, Bethesda, MD 20814- 2197) recognizes the importance of western rangelands to wildlife. The Wildlife Society Position Statement on Livestock Grazing on Federal Rangelands in the Western United States states "that properly functioning rangeland ecosystems supporting a wide diversity of native plant species are critically important to sustaining wildlife diversity and productivity in the American west. Scientifically sound management plans and practices are key to restoring lands degraded by many years of livestock grazing that damaged soils, water, and plant diversity." The Wildlife Society supports the scientific management of rangelands as exemplified in the first of 17 specific tenants listed in the Position Statement: " .. .it shall be the policy of the Wildlife Society to support livestock grazing management on federal rangelands in the west that; (a) is based on scientific study and considers all rangeland resources, trends, and interactions as well as the broad spectrum of human values and needs; (b) provides for adaptive management and continued improvement of programs and practices as new knowledge and understanding of rangeland ecosystems become available; (c) includes provisions, support, and criteria for monitoring; and (d) involves effective coordination and cooperation among agencies and affected publics." The purpose of this book was to provide information about the major vertebrates on rangelands in the western United States and to provide some insight into their interactions with livestock. Not all vertebrates were considered. Collared peccaries were not addressed because of their limited distribution in the United States. Reptiles and amphibians should be addressed in this book but are not. Unfortunately, the author that agreed to prepare the chapter failed to do so.
Elk (Cervus elaphus) are an important herbivore on North American rangelands . Their large size, herding behavior, pioneering habits , and high mobility make them an especially conspicuous herbivore in s uch open areas. Moreover, the potential for elk to compete with livestock makes them an obvious source of controversy between stockgrowers and wildlife advocates. In this chapter, we describe the history of elk on rangelands. We discu ss the competitive interactions between elk and livestock, and identify methods of stocking allocation between both. We also provide concepts, prescriptions, and examples for managing rangelands for elk, particularly through the use of livestock grazing systems.
Occupancy-based monitoring has become an important tool in wildlife conservation and management. Nonetheless, meeting occupancy modeling assumptions and providing biologically accurate information are difficult tasks over long time periods, large areas, or whenmonitoringmultiple species. In occupancymodeling frameworks, derived grids are commonly used to divide landscapes intodiscrete units. Grid sizes thatmatch the home range size of the species of interest are considered optimal, but this practice is complicated as home range size may vary by sex, habitat quality, or among species. Additionally, studies often assume their survey methods sample an entire grid cell when the actual effective sampling area may be much smaller. The effect of reduced effective sampling area on occupancy estimation has received little attention to date, despite being flagged as a critical issue. In this study, we assessed (1) how the relationship between effective area, home range size, and grid size affects power to detect trends in occupancy; (2) how varying the sampling design factors of effective area, duration, detection probability, and resurvey interval influence monitoring efficiency; and (3) determine whether a single sampling design can simultaneously detect declines in two species with different home range sizes. We used a spatially explicit simulation framework to create biologically realistic declining populations over 10 yr and assessed statistical power to detect known declines using occupancy modeling.We found that effective area and detection probability had the greatest influence on statistical power.We could not reliably detect declines when detection probability was low or when effective sampling area was < 1/4 cell. We conclude that failing to account for effective area less than the cell size will result in overestimation of statistical power. Our simulations suggest occupancymodels can detect declines for two specieswith different home range sizes using the same grid cell size under certain conditions, for instance, surveying > 25% of the landscape, ≥ 25% effective area, and fixed sampling locations. Further, increasing resampling interval greatly increasedmonitoring efficiency. Our results show monitoring planning requires explicit consideration of effective sampling area and methodswith sufficient detectability to detect population declines.
Insects are essential components of forest ecosystems, representing most of the biological diversity and affecting virtually all ecological processes (Schowalter 1994). Most species are beneficial (Coulson and Witter 1984, Haack and Byler 1993), yet others periodically become so abundant that they threaten ecological, economic, social or aesthetic values at local to regional scales (tables 6.1 through 6.3). Insects influence forest ecosystem structure and function in complex and dynamic ways, for example, by regulating certain aspects of primary production; nutrient cycling; ecological succession; and the size, distribution and abundance of plants and other animals (Mattson 1977, Mattson and Addy 1975). Effects on forest vegetation range from being undetectable, to short-term reductions in crown cover, to modest increases in background levels of tree mortality, to extensive amounts of tree mortality observed at regional scales.
Western North American forest ecosystems are experiencing rapid changes in disturbance regimes because of climate change and land use legacies (Littell et al. 2018). In many of these forests, the accumulation of surface and ladder fuels from a century of fire suppression, coupled with a warming and drying climate, has led to increases in the number of large fires (Westerling 2016) and the proportion of areas burning at higher severity (Safford and Stevens 2017, Singleton et al. 2018). While the annual area burned by fire is still below historical levels (Taylor et al. 2016), some forest types in the west are burning at higher severities when compared to pre-European settlement periods (Mallek et al. 2013, Safford and Stevens 2017). As such, they face an increased risk of conversion to non-forest ecosystems (e.g., shrublands, non-native grasslands) following large, severe fires because of compromised seed sources, post-fire soil erosion and loss, high-severity re-burn, and climatic thresholds (Coppoletta et al. 2016, Stevens et al. 2017, Rissman et al. 2018, Shive et al. 2018, Wood and Jones 2019). Restoration methods such as mechanical thinning and prescribed and managed wildland fire that reduce accumulated surface and ladder fuels (e.g., removal of smalland medium-sized trees, especially non-fire adapted species) may reduce the spatial extent of severe fires and increase forest resilience to fire in a changing climate (Agee and Skinner 2005, Stephens et al. 2013, Hessburg et al. 2016, Tubbesing et al. 2019) and, in doing so, promote key ecosystem services (Hurteau et al. 2014, Kelsey et al. 2017, Wood and Jones 2019).
Forests provide a suite of goods and services that are vital to human health and livelihoods. Studies of ecosystem services, which frequently attempt to place a monetary value on forest processes and organisms, can help inform management decisions by providing a baseline for discussing the costs and benefits of different management options.
A recent study by Pacific Northwest Research Station researchers, Adelaide “Di” Johnson and Ryan Bellmore, along with retired Forest Service fisheries biologist Ron Medel and Alaska Department of Fish and Game fisheries biologist Stormy Haught, aimed to quantify the number and monetary value of commercially caught Pacific salmon from Alaska’s Tongass and Chugach National Forests. These two national forests contain some of the world’s largest remaining tracts of intact temperate rain forest.
Between 2007 and 2016, the Tongass and Chugach supported harvests of approximately 48 million salmon per year, valued at more than $88 million annually. This comprised approximately 25 percent of all commercially caught salmon in Alaska and 16 percent of its total monetary value. Quantitative information about the value of Alaska’s national forests for fish production can contribute to discussions about management decisions that might influence the capacity of these forests to sustain Pacific salmon in the future.
Bayesian population models can be exceedingly slow due, in part, to the choice to simulate discrete latent states. Here, we discuss an alternative approach to discrete latent states, marginalization, that forms the basis of maximum likelihood population models and is much faster. Our manuscript has two goals: (1) to introduce readers unfamiliar with marginalization to the concept and provide worked examples and (2) to address topics associated with marginalization that have not been previously synthesized and are relevant to both Bayesian and maximum likelihood models. We begin by explaining marginalization using a Cormack- Jolly-Seber model. Next, we apply marginalization to multistate capture–recapture, community occupancy, and integrated population models and briefly discuss random effects, priors, and pseudo-R2. Then, we focus on recovery of discrete latent states, defining different types of conditional probabilities and showing how quantities such as population abundance or species richness can be estimated in marginalized code. Last, we show that occupancy and site-abundance models with auto-covariates can be fit with marginalized code with minimal impact on parameter estimates. Marginalized code was anywhere from five to >1,000 times faster than discrete code and differences in inferences were minimal. Discrete latent states and fully conditional approaches provide the best estimates of conditional probabilities for a given site or individual. However, estimates for parameters and derived quantities such as species richness and abundance are minimally affected by marginalization. In the case of abundance, marginalized code is both quicker and has lower bias than an N-augmentation approach. Understanding how marginalization works shrinks the divide between Bayesian and maximum likelihood approaches to population models. Some models that have only been presented in a Bayesian framework can easily be fit in maximum likelihood. On the other hand, factors such as informative priors, random effects, or pseudo-R2 values may motivate a Bayesian approach in some applications. An understanding of marginalization allows users to minimize the speed that is sacrificed when switching from a maximum likelihood approach. Widespread application of marginalization in Bayesian population models will facilitate more thorough simulation studies, comparisons of alternative model structures, and faster learning.