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USGS2020

Recent planform changes in the Upper Mississippi River

Geomorphic changes in the Upper Mississippi River (UMR) have long been a concern of river agencies charged with maintaining and restoring river habitat (GREAT 1980; Jackson et al. 1981; USFWS 1992). Large meandering alluvial rivers like the UMR are expected to constantly change and adjust their fluvial landforms within their riparian corridors as a result of the natural interaction of hydrologic processes, sediment movement, and vegetation over time. However, present geomorphic changes in the UMR reflect altered hydrologic, hydraulic, and sediment conditions caused by regulated flows, constructed agricultural levees and navigation dams, altered land use in the watershed, and climate change. Levees reduce lateral hydrologic and sediment connectivity between channels and floodplains on many tributaries and on the Mississippi River downstream of Pool 13. Between each of the dams are a repeating series of landforms associated with tailwater, intermediate, and impounded conditions. The dams maintain a minimum water level, thus creating many off-channel areas that act as sediment traps. Whereas high-head dams cut off sedimentological connectivity longitudinally through the river corridor (Skalak et al., 2013), low head dams on the UMR only slightly altered transport longitudinally. Deltaic-like sedimentation can be common in the impounded sections of dammed rivers. Erosion of relict land surfaces that remained above the raised impounded water levels has been the dominant change in UMR impounded sections due to increased wind fetch leading to increased wave action. Even though upland sources of sediment from tributaries have decreased over the middle to late 20th century, increased annual precipitation, the interplay of increased variability in flood magnitudes from year to year, and more fall and winter flooding have likely changed erosion and sedimentation patterns in the UMR (Belby, et al., 2019). Paradoxically, monitoring and research indicates that the concentration of some water column constituents like total suspended solids and phosphorous has decreased during the 1991 to 2014 time period (Kreiling and Houser, 2016). In areas prone to increased sedimentation, bed elevations rise and thereby water depths are reduced at a given discharge, resulting in loss of fish habitat. Sediment deposition or erosion further influences water exchange rates between main channel and off-channel areas in the river by increasing resistance in connecting channels or enlarging existing connecting channels. Water depth and water exchange rates are the most prominent features describing habitat quality in the UMR (De Jager et al. 2018), and in some cases, the trajectory of planform change from heightened deposition promises to threaten deep backwater habitats particularly important for overwintering fish. Although information on the rate of vertical change in bed elevation is needed for a complete assessment of geomorphic change associated with the loss of deep backwater habitats, mapping planform changes over time (i.e., lateral changes between the land-water boundary) provide needed information on the location, potential cause, and progressive direction of deposition, especially in the mid sections between dams where deltaic processes are the most pronounced. Several types of planform changes have been observed and identified as concerns. For example, island loss in the large impounded areas of the upper part of the UMR was one of the concerns identified by river managers in the 1980s and 90s, and subsequently island construction became a common form of restoration implemented by the Upper Mississippi River Restoration (UMRR) Program (USACE 2012). Other subtler planform changes, such as channel bank erosion and delta formation in backwaters, are perceived to be important, but have largely gone unquantified. A systemwide reconnaissance of the UMR and IWW conducted in 1998 concluded that 14-percent of the river banks were eroding (Nakato and Anderson 1998). However, stabilization of existing river banks has never been widely pursued as a restoration measure, due to the high cost and uncertain benefits. Delta formation reduces the amount of backwater habitat; however, the deltas maintain and create a mix of riparian and aquatic habitats, and that is generally considered to be beneficial for wildlife and fish. If recent hydrologic trends of more frequent and longer duration flood events continue, a better understanding of planform changes can help in describing past changes, and then be used to forecast potential future trajectories of change. If UMR resource managers determine that past and forecasted conditions are undesirable, then UMRR projects could be identified and prioritized to address those concerns. Vegetative cover associations with landform changes have been used to detect and quantify planform changes in many rivers (Johnson 1985; Hiatt 2015; Volte et al. 2015). Freyer and Jefferson (2013) completed such a study in Pool 6 of the UMR using the landcover data from 12 dates over a 115-yr period, including the 1989, 2000, and 2010/2011 landcover/use (LCU) data from the UMRR Program. Planform change detected over the last 20 years represented by the UMRR Program data best reflect present-day geomorphic patterns, rates and processes. Changes occurring prior to dam construction and changes occurring soon after dam construction are likely not the same as those happening now, 50-70 years after dam construction and creation of the impoundments (McHenry et al., 1984; Bhowmik and Adams, 1986; WEST Consultants, 2000). The LCU data from each of the 1989, 2000, and 2010/2011 imagery was developed using similar methods and is available in a Geographical Information System (GIS) for the entire UMR and therefore provides the opportunity for a more comprehensive planform change analysis. This study used GIS overlays of LCU classes to map and quantify changes in planform features over two periods, looking specifically for depositional areas where terrestrial and wetland vegetation expanded at the expense of open water. The land expansion was grouped into four possible process-based types common in large floodplain rivers, some following that used by Lewin et al. (2017). The four types include: crevasse deltas emanating from a breach from a main channel through a natural levee or narrow floodplain into backwaters (crevasse deltas), tributary deltas expanding into backwaters (tributary deltas), deltaic bars at the upstream end of impoundments (impounded deltas), and linear-like bars extending from the downstream ends of narrow levees and remnant floodplains (bar-tail limbs). The methods deployed for change detection addressed possible errors from a variety of sources.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗
USGS2009

Movement and habitat use of sika and white-tailed deer on Assateague Island National Seashore, Maryland

This research project was conducted to describe habitat use of sika deer ( Cervus nippon ) and white-tailed deer ( Odocoileus virginianus ) and possibly attribute the effects of ungulate herbivory to specific deer species, if spatial separation in habitat use could be identified. Sturm (2007) conducted an exclosure study to document the effect of feral horse ( Equus caballus ) herbivory, deer herbivory, and horse and deer herbivory combined on plant communities. Sturm (2007) found that ungulate herbivory reduced plant species richness, evenness, and diversity in the maritime forest and affected species composition in all habitats studied. Sturm (2007) also found that herbivory on some species could be directly attributable to either horse or deer. However, the effects of sika and white-tailed deer herbivory could not be separated via an exclosure study design because of the difficulty of passively excluding one deer species but not the other. We captured white-tailed deer and sika deer in January–March of 2006 and 2007 throughout the Maryland portion of Assateague Island. Deer were fitted with radio-collars and their survival and locations monitored via ground telemetry. Up to four locations were acquired per deer each week during early (May–June) and late (August–September) growth periods for vegetation on the island. Also, we estimated deer locations during a dormant vegetation period (November– December 2006). We used these data to estimate survival and harvest rates, document movements, and model habitat use. We captured and fitted 50 deer with radio-collars over the course of the study. Of these 50 deer, 36 were sika and 14 were white-tailed deer. Of the 36 sika deer, 10 were harvested, three were likely killed by hunters but not recovered, and one died of natural causes while giving birth. Of the 14 white-tailed deer, three were harvested, one was illegally killed, and two were censored because of study-related mortality. Annual survival was 0.48 (95% CI = 0.16–0.82) for male white-tailed deer, 0.74 (95% CI = 0.44–0.91) for female white-tailed deer, 0.56 (95% CI = 0.35–0.75) for male sika deer, and 0.86 (95% CI = 0.70–0.94) for female sika deer. The harvest rate was 0.12 (95% CI = 0.04–0.27) for female sika deer, 0.44 (95% CI = 0.25–0.65) for male sika deer, 0.18 (95% CI = 0.05–0.51) for female white-tailed deer, and 0.38 (95% CI = 0.10–0.78) for male white-tailed deer. Annual survival rates for both species were similar to what has been observed in other populations. Unfortunately, small sample sizes for male white-tailed deer limited inferences about harvest and survival rates, but harvest rates of females for both species were similar to other published studies. Hunting was the primary cause of mortality, and outside the hunting season survival was 0.98–1.00 for all species and sexes. We found that the home range area of sika deer was much greater than the home range area of white-tailed deer, but failed to detect any difference between sexes or among seasons. Sika deer also made long-distance movements and left the Maryland portion of Assateague Island. No sika deer left Assateague island during our study, but we did document the dispersal of a male whitetailed deer to the mainland. In their native range, sika deer have been able to readily expand populations and occupy vacant habitat (Kaji et al. 2000; Kaji et al. 2004). The long distance movements we observed on Assateague Island, especially relative to white-tailed deer, may reflect the ability of this species to exploit food resources that may be limited in quality or quantity, or both. However, we did not collect data to assess use of food resources by sika deer and whether this may have influenced long distance movements. We found both species of deer were less likely to use a habitat the further it was located from cover, which was defined as tall shrub or forest vegetation. For every 10 m (32 ft) from cover each species of deer was 1.23–1.38 times less likely to use any given habitat. Patterns in use of vegetation classes were similar across species and seasons. Relative to forest habitat, both species avoided dune herbaceous, disturbed lands, sand, and water categories. Both species neither avoided nor preferred developed herbaceous, low shrub, marsh herbaceous, and tall shrub categories compared to the forest category. However, there were consistent differences between the two species. During spring, white-tailed deer were more likely than sika deer to use forested, tall shrub, disturbed herbaceous, and sand areas, but were less likely to use all other habitats. During summer, habitat use was similar between the two species except that white-tailed deer tended to use forested habitat more. During winter, white-tailed deer were less likely to use dune herbaceous, low shrub, and forested habitats than sika deer. Sturm (2007) identified differential browsing on plant species between horses and deer, but his experimental design did not permit detection of differential browsing between sika and whitetailed deer. Our study of habitat use did not provide information to identify plant species that may be differentially consumed by sika and white-tailed deer based on differences in habitat use. We envision two approaches to addressing the effects of deer browsing. One approach would be further research that identifies the food habits of both deer species at the plant species level. This would be similar to food habits research conducted by Keiper (1985) and others or could involve direct observation of food consumption by both species. However, both fecal analysis and direct observation would be time-consuming and not guaranteed to identify differences. If the goal of ungulate population management is to protect the island ecosystem, another approach involving manipulation of deer abundance and monitoring the response of plant species known to be preferentially consumed by deer would be a more direct method of assessing effects of deer herbivory (Sturm 2007). Moreover, such an approach is not predicated on detecting differences between deer species. Direct manipulation of deer abundance could be incorporated into an adaptive management program (Williams et al. 2007) and may provide greater benefits to the management of ASIS in the long term. Harvest management decisions for white-tailed deer and sika deer are made on an ongoing basis and by coupling these decisions with a vegetative monitoring program it may be possible to reduce or minimize adverse effects of ungulate herbivory. Furthermore, management of feral horses could be incorporated into the decision process.

Maryland, Virginia↗
USGS2011

Monitoring habitat restoration projects: U.S. Fish and Wildlife Service Pacific Region Partners for Fish and Wildlife Program and Coastal Program Protocol

The U.S. Fish and Wildlife Service's (FWS) Pacific Region (Region 1) includes more than 158 million acres (almost 247,000 square miles) of land base in Idaho, Oregon, Washington, Hawai`i, the Commonwealth of the Northern Mariana Islands, American Samoa, Guam, the Republic of Palau, the Federated States of Micronesia, and the Republic of the Marshall Islands. Region 1 is ecologically diverse with landscapes that range from coral reefs, broadleaf tropical forests, and tropical savannahs in the Pacific Islands, to glacial streams and lakes, lush old-growth rainforests, inland fjords, and coastal shoreline in the Pacific Northwest, to the forested mountains, shrub-steppe desert, and native grasslands in the Inland Northwest. Similarly, the people of the different landscapes perceive, value, and manage their natural resources in ways unique to their respective regions and cultures. The Partners for Fish and Wildlife Program (Partners Program) and Coastal Program work with a variety of partners in Region 1 including individual landowners, watershed councils, land trusts, Soil and Water Conservation Districts, non-governmental organizations, Tribal governments, Native Hawaiian organizations, and local, State, and Federal agencies. The Partners Program is the FWS's vanguard for working with private landowners to voluntarily restore and conserve fish and wildlife habitat. Using non-regulatory incentives, the Partners Program engages willing partners to conserve and protect valuable fish and wildlife habitat on their property and in their communities. This is accomplished by providing the funding support and technical and planning tools needed to make on-the-ground conservation affordable, feasible, and effective. The primary goals of the Pacific Region Partners Program are to: Promote citizen and community-based stewardship efforts for fish and wildlife conservation Contribute to the recovery of at-risk species, Protect the environmental integrity of the National Wildlife Refuges, Contribute to the implementation of the State Comprehensive Wildlife Conservation Strategies, and Help achieve the objectives of the National Fish Habitat Partnerships and regionally based bird conservation plans (for example, North American Waterfowl Management Plan, U.S. Pacific Island Shorebird Conservation Plans, Intermountain West Regional Shorebird Plan, etc.). The Partners Program accomplishes these priorities by: Developing and maintaining strong partnerships, and delivering on-the-ground habitat restoration projects designed to reestablish habitat function and restore natural processes; Addressing key habitat limiting factors for declining species; Providing corridors for wildlife and decrease impediments to native fish and wildlife migration; and Enhancing native plant communities by reducing invasive species and improving native species composition. The Coastal Program is a voluntary fish and wildlife conservation program that focuses on watershed-scale, long-term collaborative resource planning and on-the-ground restoration projects in high-priority coastal areas. The Coastal Program conducts planning and restoration work on private, State, and Federal lands, and partnerships with other agencies-Native American Tribes, citizens, and organizations are emphasized. Coastal Program goals include restoring and protecting coastal habitat, providing technical and cost-sharing assistance where appropriate, supporting community-based restoration, collecting and developing information on the status of and threats to fish and wildlife, and using outreach to promote stewardship of coastal resources. The diversity of habitats and partners in Region 1 present many opportunities for conducting restoration projects. Faced with this abundance of opportunity, the Partners Program and Coastal Program must ensure that limited staffing and project dollars are allocated to benefit the highest priority resources and achieve the highest quality results for Federal trust species. In 2007, the Partners Program and Coastal Program developed a Strategic Plan to guide program operations and more efficiently conserve habitat by focusing partnership building and habitat improvement actions within 35 Partners Program Focus Areas and 9 Coastal Program Focus Areas (U.S. Fish and Wildlife Service, 2010). The Strategic Plan also contains four other goals: broaden and strengthen partnerships; improve information sharing and communications; enhance workforce; and increase accountability to ensure that program resources are used efficiently and effectively. This protocol will help achieve all goals of the Strategic Plan.

Techniques and Methods↗
USGS2021

Evaluation of a “trace” plant density score in LTRM vegetation monitoring

The Long Term Resource Monitoring (LTRM) element of the Upper Mississippi River Restoration program employs a harvest method for sampling submersed aquatic vegetation (SAV) whereby a rake is dragged ~1.5 m over the substrate and plant materials are retrieved. “Plant density” (PD) scores indicate SAV abundance and are based on the amount of plant material collected on the teeth of the rake. Standard PD scores are ordered, whole numbers from 0 (no SAV on the rake) to 5 (80-100% of rake teeth full) and are assigned at each subsite for all species combined and for each individual species. In LTRM monitoring between 1998 and 2018, ~73% of non-zero, all-species-combined PD scores were 1s, and ~89% of individual SAV species were 1s. The preponderance of PD = 1 scores along with the wide range of fresh mass represented by PD = 1 (quantified in Drake and Lund 2020) limits inference about SAV abundance from LTRM monitoring data. Field personnel noted that small plant fragments comprised a substantial fraction of PD = 1 observations and proposed a modification of the existing LTRM methods where PD = 1 was subdivided to include “trace” scores to represent such small fragments. Trace was defined as PD = 0.08, indicating a maximum of 1 of 13 gaps in the sampling rake filled to the level of an original PD = 1. Amounts of plant material greater than PD = 0.08 and up to the original score of 1 were defined PD = +1. This study used field data collected in 2018 (scoring and fresh weights of scored plant materials) from 136 vegetated sites in Pools 4, 8 and 13 to evaluate the proposed subdivision and to examine among-pool differences in PD data. In the study data, 33% of all-species-combined observations and 69% of species (grouped by morphology) that would previously have received a score of 1 were classified as PD = 0.08. PD scores of 0.08, +1, and 2-3 represented statistically distinct amounts of fresh mass in rake samples. There were systematic differences in the mass of SAV reflected by PD score based on plant morphology and species composition. The mean fresh mass of plant materials assigned a given PD score varied among the three pools, suggesting bias attributable to personnel. To reduce this bias in future data collection efforts, the field crews incorporated a calibration of plant density scores in annual field training. The results presented here describe how including a trace PD score in LTRM data collection improves the description of SAV abundance and consequently estimates of biomass from those PD scores. LTRM vegetation crews have recorded trace scores in annual sampling since 2019 as extra information (i.e. which does not change the LTRM data stream as 0.08 and +1 scores can still be combined for PD=1). Trace data are not currently available to outside users through the LTRM data browser but are available from vegetation component personnel upon request.

Long Term Resource Monitoring Technical Report↗
USGS2020

Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support

Executive Summary There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers. Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following: •The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials. •The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools. •Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community. • A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders. • Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions. • Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s interactive mapping tools. • The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities. • Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.

Open-File Report↗
USGS2014

Status of whitebarkpine in the Greater Yellowstone Ecosystem: A step-trend analysis comparing 2004-2007 to 2008-2011

Whitebark pine (Pinus albicaulis) is a foundation and keystone species in upper subalpine environments of the northern Rocky Mountains that strongly influences the biodiversity and productivity of high-elevation ecosystems (Tomback et al. 2001, Ellison et al. 2005). Throughout its historic range, whitebark pine has decreased significantly as a major component of high-elevation forests. As a result, it is critical to understand the challenges to whitebark pine—not only at the tree and stand level, but also as these factors influence the distribution of whitebark pine across the Greater Yellowstone Ecosystem (GYE). In 2003, the National Park Service (NPS) Greater Yellowstone Inventory & Monitoring Network identified whitebark pine as one of twelve significant natural resource indicators or vital signs to monitor (Jean et al. 2005, Fancy et al. 2009) and initiated a long-term, collaborative monitoring program. Partners in this effort include the U.S. Geological Survey, U.S. Forest Service, and Montana State University with representatives from each comprising the Greater Yellowstone Whitebark Pine Monitoring Working Group. The objectives of the monitoring program are to assess trends in (1) the proportion of live, whitebark pine trees (>1.4-m tall) infected with white pine blister rust (blister rust); (2) to document blister rust infection severity by the occurrence and location of persisting and new infections; (3) to determine mortality of whitebark pine trees and describe potential factors contributing to the death of trees; and (4) to assess the multiple components of the recruitment of understory whitebark pine into the reproductive population. In this report we summarize the past eight years (2004-2011) of whitebark pine status and trend monitoring in the GYE. Our study area encompasses six national forests (NF), two national parks (NP), as well as state and private lands in portions of Wyoming, Montana, and Idaho; this area is collectively described as the GYE here and in other studies. The sampling design is a probabilistic, twostage cluster design with stands of whitebark pine as the primary units and 10x50 m belt transects as the secondary units. Primary sampling units (stands) were selected randomly from a sample frame of approximately 10,770 mapped pure and mixed whitebark pine stands ≥2.0 hectares in the GYE (Dixon 1997, Landenburger 2012). From 2004 through 2007 (monitoring transect establishment or initial time-step), we established 176 permanent belt transects (secondary sampling units=176) in 150 whitebark pine stands and permanently marked approximately 4,740 individual trees >1.4 m tall to monitor long-term changes in blister rust infection and survival rates. Between 2008 and 2011 (revisit time-step), these same 176 transects were surveyed and again all previously tagged trees were observed for changes in blister rust infection and survival status. Objective 1. Using a combined ratio estimator, we estimated the proportion of live trees infected in the GYE in the initial time-step (2004-2007) to be 0.22 (0.031 SE). Following the completion of all surveys in the revisit time-step (2008-2011), we estimated the proportion of live trees infected with white pine blister rust as 0.23 (0.028 SE; Table 2). We detected no significant change in the proportion of trees infected in the GYE between the two time-steps. Objective 2. We documented blister rust canker locations as occurring in the canopy or bole. We compared changes in canker position between the initial time-step (2004-2007) and the revisit time-step (2008-2011) in order to assess changes in infection severity. This analysis included the 3,795 trees tagged during the initial time-step that were located and documented as alive at the end of the revisit time-step. At the end of the revisit time-step, we found 1,217 trees infected with blister rust. This includes the 287 newly tagged trees in the revisit time step of which 14 had documented infections. Of these 1,217 trees, 780 trees were infected with blister rust in both time steps. Trees with only canopy cankers made up approximately 43% (519 trees) of the total number of trees infected with blister rust at the end of the revisit time-step, while trees with only bole cankers comprised 20% (252 trees), and those with both canopy and bole cankers included 37% (446 trees) of the infected sample. A bole infection is considered to be more consequential than a canopy canker, as it compromises not only the overall longevity of the tree, but its functional capacity for reproductive output as well (Kendall and Arno 1990, Campbell and Antos 2000, McDonald and Hoff 2001, Schwandt and Kegley 2004). In addition to infection location, we also documented infection transition between the canopy and bole. Of the 780 live trees that were infected with blister rust in both time-steps, approximately 31% (242) maintained canopy cankers and 36% (281) retained bole infections at the end of the revisit time-step. Infection transition from canopy to bole occurred in 30% (234) of the revisit time-step trees while 3% (23) transitioned from bole to canopy infections during this period. Objective 3. To determine whitebark pine mortality, we resurveyed all belt transects to reassess the life status of permanently tagged trees >1.4 m tall. We compared the total number of live tagged trees recorded during monitoring transect establishment to the total number of resurveyed dead tagged trees recorded during the revisit time-step and identified all potential mortality-influencing conditions (blister rust, mountain pine beetle, fire and other). By the end of the revisit time-step, we observed a total of 975 dead tagged whitebark pine trees; using a ratio estimator, this represents a loss of approximately 20% (SE=4.35%) of the original live tagged tree population (GYWPMWG 2012). Objective 4. To investigate the proportion of live, reproducing tagged trees, we divided the total number of positively identified cone-bearing trees by the total number of live trees in the tagged tree sample at the end of the revisit time-step. To approximate the average density of recruitment trees per stand, trees ≤1.4 m tall were summed by stand (within the 500 m² transect area) and divided by the total number of stands. Reproducing trees made up approximately 24% (996 trees) of the total live tagged population at the end of the revisit time-step. Differentiating between whitebark pine and limber pine seedlings or saplings is problematic given the absence of cones or cone scars. Therefore, understory summaries as presented in this report may include individuals of both species when they are sympatric in a stand. The average density of small trees ≤1.4 m tall was 53 understory trees per 500 m². Raw counts of these understory individuals ranged from 0-635 small trees per belt transect. In addition, a total of 287 trees were added to the tagged tree population by the end of 2011. These newly tagged trees were individuals that upon subsequent revisits had reached a height of >1.4 m tall and subsequently added to the sample. Throughout the past decade in the GYE, monitoring has helped document shifts in whitebark pine forests; whitebark pine stands have been impacted by insect, pathogen, wildland fire, and other disturbance events. Blister rust infection is ubiquitous throughout the ecosystem and infection proportions are variable across the region. And while we have documented mortality of whitebark pine, we have also recorded considerable recruitment. We provide this first step-trend report as a quantifiable baseline for understanding the state of whitebark pine in the GYE. Many aspects of whitebark pine health are highly variable across the range of its distribution in the GYE. Through sustained implementation of the monitoring program, we will continue efforts to document and quantify whitebark pine forest dynamics as they arise under periodic upsurges in insect, pathogen, fire episodes, and climatic events in the GYE. Since its inception, this monitoring program perseveres as one of the only sustained longterm efforts conducted in the GYE with a singular purpose to track the health and status of this prominent keystone species.

Natural Resource Technical Report↗
USGS2016

Accuracy assessment/validation methodology and results of 2010–11 land-cover/land-use data for Pools 13, 26, La Grange, and Open River South, Upper Mississippi River System

Introduction/Background The U.S. Geological Survey (USGS)-Upper Midwest Environmental Sciences Center (UMESC) was responsible for development of several land cover/land use (LCU) systemic datasets of the Upper Mississippi River System (UMRS). These efforts (1989 and 2000) were funded by the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration Program (UMRR) Long Term Resource Monitoring (LTRM) element. Development of systemic datasets includes the acquisition, processing, and serving of high-resolution aerial photography and land cover/land use spatial datasets ( http://www.umesc.usgs.gov/data_library/land_cover_use/land_cover_use_data.html ). In 2008, the UMRR reached a collaborative agreement with the U.S. Fish and Wildlife Service-Region 3 to collect high-resolution digital imagery of the entire UMRS floodplain during 2010–11 for LTRM. The UMESC helped acquire, process, and serve this imagery, as well as produce and serve the 2010–11 LCU systemic dataset of the UMRS floodplain. Digital imagery for Pools 13, 26, La Grange, and Open River South was collected using an Applanix DSS 439 digital sensor system with a 40 millimeter lens and Color Infrared (CIR) filter. The imagery was collected at a resolution of 20 centimeters/pixel (8 inches/pixel) for Pool 13 and 40 centimeters/pixel (16 inches/pixel) for Pools 26, Open River South, and La Grange. All imagery was projected to Universal Transverse Mercator (UTM) Zone 15, North American Datum of 1983 (NAD 83). The General Wetland Vegetation Classification (GWVC) system used for mapping is hierarchical, and its 31 classes can be collapsed into broader categories using either a 15- or 7-class level. While the 1989 and 2000 LCU systemic datasets have not gone through a traditional thematic accuracy assessment (AA) in the past, nor have they undergone a validation analysis, the end products are of high quality. For each systemic dataset produced (1989, 2000, 2010–11), extensive field reconnaissance is performed before photointerpretation. The intent of this field reconnaissance is to learn, test, and verify image signatures as they relate to vegetation types. Questionable areas on the imagery are visited, and the plants or land features observed in the area are recorded for reference. This procedure verifies vegetation signatures on the imagery with those on the ground. In addition, once the photointerpretation is complete, the final LCU dataset undergoes extensive quality assurance/quality control to ensure the imagery is mapped correctly. Since the 2000 LCU systemic dataset was developed, there has been a growing interest in completing thematic AAs for the LTRM LCU spatial datasets. The objective of an AA is to measure the probability that a particular location has been assigned its correct vegetation class. An AA estimates thematic (map class) errors in the data, giving users information needed to determine data suitability for a particular application. At the same time, data producers are able to learn more about the nature of errors in the data. Thus, the two attributes of an AA are “producers’ accuracy,” which is the probability that an AA point has been mapped correctly (also referred to as an error of omission); and “users’ accuracy,” which is the probability that the map actually represents what was found on the ground (also referred to as error of commission). Producers’ and users’ accuracies can be obtained from the same set of data by using different analyses. Accuracy assessment is an extensive effort that requires seasonal field personnel and equipment, data entry, analyses, and post processing—tasks that are costly and time consuming. The geospatial team at the UMESC has suggested a validation process for understanding the accuracy of the spatial datasets, which will be tested on at least some areas of the UMRS. Validation is not a true verification of map-class type in the field; however, it can provide the user of the map with useful information that is similar to a field AA. Similar to an AA, validation involves generating random points based on the total area for each map class. However, instead of collecting field data, two or three individuals not involved with the photo-interpretative mapping separately review each of the points onscreen and record a best-fit vegetation type(s) for each site. Once the individual analyses are complete, results are joined together and a comparative analysis is performed. The objective of this initial analysis is to identify areas where the validation results were in agreement (matches) and areas where validation results were in disagreement (mismatches). The two or three individuals then perform an analysis, looking at each mismatched site, and agree upon a final validation class. (If two vegetation types at a specific site appear to be equally prevalent, the validation team is permitted to assign the site two best-fit vegetation types.) Following the validation team’s comparative analysis of vegetation assignments, the data are entered into a database and compared to the mappers’ vegetation assignments. Agreements and disagreements between the map and validation classes are identified, and a contingency table is produced. This document presents the AA processes/results for Pools 13 and La Grange, as well as the validation process/results for Pools 13 and 26 and Open River South.

Iowa, Illinois, Minnesota, Missouri, Wisconsin↗
USGS2014

Elk monitoring in Lewis and Clark National Historical Park: 2008-2012 synthesis report

Maintaining elk (Cervus elaphus roosevelti) herds that frequent Lewis and Clark National Historical Park (NHP) is central to the park’s purpose of preserving the historic, cultural, scenic, and natural resources associated with the winter encampment of the Lewis and Clark expedition. Elk were critically important to the Lewis and Clark expedition in providing food and hides that sustained the expedition during the winter of 1805-06 and supplied them for their return east during 1806. Today, elk remain a key component of interpreting the Lewis and Clark story to over 200,000 park visitors each year at the Fort Clatsop visitor center. In 2008, the US Geological Survey (USGS) began collaborating with Lewis and Clark NHP and the NPS North Coast and Cascades Network to develop a protocol for monitoring long-term changes in the magnitude and spatial patterns of elk use within and adjacent to Lewis and Clark NHP (Griffin et al. 2011). Specific objectives of the monitoring program were to measure trends in (1) relative use of the Fort Clatsop unit by elk during winter; (2) the proportion of areas where elk sign is present in the Fort Clatsop unit in winter; and (3) the frequency of elk sightings from roads in and around the Fort Clatsop unit. This report synthesizes the results of the first four years of monitoring elk distribution and use in Lewis and Clark NHP from 2008-2012. We also present data from FY2012 (Appendix 1), in lieu of an annual report for that year. We used fecal pellet group surveys as the cornerstone for monitoring trends in both relative use of the Fort Clatsop Unit by elk and the proportion of areas where elk sign was present at the end of winter. We estimated pellet group density based on data collected from a network of fecal pellet plots distributed systematically throughout the unit. We developed a double observer sampling scheme that enabled us to estimate detection biases and improve the accuracy of pellet group density estimates. We computed the estimated detection probability for any pellet group observed; this probability was a function of the pellet group size and stage of decay, as well as lighting and vegetation conditions, and the number of observers (one or two) searching for pellets in that subplot. We then used these estimated detection probabilities to adjust the raw counts of the detected pellet groups to account for groups that likely went undetected under similar pellet and environmental conditions (each observed pellet group was weighted by the inverse of its estimated detection probability). We also used results from the late winter fecal pellet surveys to quantify the proportion of areas where elk pellets occurred (PAO), which was based on the presence of fecal pellet groups and estimation of detection biases (i.e., accounting for pellet groups that likely went undetected by both observers). In this synthesis, we report temporal trends in both pellet group density and PAO from 2008-2012, based on weighted linear regression analyses as well as spatial variation of pellet group densities over time. We completed late winter fecal pellet surveys at 61-66 plots annually, depending on yearly variation in access. We cleared fecal pellets at survey points in late October / early November each year and returned in late February / early March to count pellet groups left by elk over the winter. The estimated probability that a pellet group was detected by any one observer during late winter was affected most by the pellet group size and was less affected by decay class and lighting conditions. Per-observer detection probabilities ranged from as low as ~10-15% for single pellets to ~85-90% for pellet groups with 50 pellets. Average pellet group density in the Fort Clatsop unit ranged annually from 0.58 (+/- 1.43 standard error [SE]) to 0.93 (+/- 2.25 SE) pellet groups per 3-m radius subplot. Pellet group density declined over time, at approximately 8.8% per year (+/- 2.5% SE), but that slope was not statistically distinguishable from zero (2- tailed P=0.16). Following correction for detection biases, the proportion of surveyed points used by elk (i.e., PAO) ranged from 0.44 (+/- 0.07 SE) to 0.53 (+/- 0.07 SE) during the 4 winters. The estimated proportion of areas where elk pellets occurred (PAO) declined at a rate of 2.6% per year (+/- 1.2% per year SE), but that trend also was not statistically distinguishable from zero (2- tailed P=0.17). Statistical significance of a measure’s trend depends on both the magnitude (i.e., slope) of the observed trend and the number of years the trend continues in the same increasing or decreasing direction. Through simulation modeling we determined how many additional years of surveys would be required to reveal a statistically significant trend, based on the same trends in pellet group density and PAO, and associated variation, observed from 2009-2012. Assuming the same trends persist in the future, simulations indicated that there is a 70% probability that a statistically significant trend would be detected after two more years of conducting pellet group surveys. Relative use by elk during winter, as indexed by elk pellet group density, was generally greatest in the southeast region of the Fort Clatsop unit in or near the large freshwater marsh at the mouth of Colewort Creek and adjacent upland areas. Pellet group density was also higher than average in the north-central forested area, not far from a privately-owned pasture north of the park boundary. This spatial pattern in pellet group densities across the Fort Clatsop unit was consistent across all four years, although specific pellet group densities varied from year to year. Pellet group density declined significantly over time at two points in the southeast of the Fort Clatsop unit, even though pellet group density at those points remained higher than the unit average. Pellet group density increased significantly over time at one point in the north-central region, and at one point in the south-central region of the unit, indicating a slight shift in the distribution of elk use within the Fort Clatsop Unit over the four years. As an index of visitors’ opportunities to see elk in and around the Fort Clatsop Unit, we conducted replicated roadside elk surveys 3-5 times monthly during February, April, June, August, October and December 2008-2012. During each morning of survey, we searched for elk along four routes that totaled 32 km. We examined bimonthly trends in the numbers of elk groups seen, the total number of elk seen, and the observed composition ratios for those six months of the year. The average number of elk groups seen per survey ranged from 0.75 (+/- 0.32 SE) during February to a peak of 1.95 (+/- 0.36 SE) during June. Despite this seasonal variation in numbers of elk groups seen, the average total number of elk seen per morning was less variable. The average ratios of antlered elk to antlerless adult elk (i.e., bulls:cows) and calves to antlerless adult elk (i.e. calves:cows) varied seasonally, with the highest of both average ratios observed in August. We detected no significant trends in the average number of elk groups and total numbers of elk seen per survey from 2008-2012. Similarly, ratios of calves and antlered elk per antlerless elk did not differ over time. Elk groups were frequently seen from January to August in the southeast region of the Fort Clatsop unit, in the vicinity of Colewort Creek. Outside of NPS lands, we observed elk most frequently in open areas near the Astoria regional airport, in the pastures and forests immediately north of the Fort Clatsop unit and, prior to the construction of a residential development, in a pasture northwest of the Fort Clatsop unit. Elk monitoring at Lewis and Clark NHP is still in its initial years and additional monitoring will be required to verify trends that appear to be emerging. For example, the initial monitoring suggested incipient declining trends in both pellet group density and proportion of plots with pellets present, as well as, potentially, a small shift in elk distribution away from a new trail that was recently constructed in the southeast portion of the Fort Clatsop unit. Continued monitoring will aid in determining whether this local change in distribution persists (or, alternatively, resulted from short-term random variation), and whether there will be any positive or negative effect in the northern portion of the unit where a new trail has been constructed. High variability in road counts prevented our ability to find any clear trend in numbers or composition of elk observed in and near Fort Clatsop, but changes in the patterns of observations of elk from roadways suggest that residential development outside the park has reduced the available habitat for elk in some of the areas surveyed, and may have affected spatial use patterns of elk adjacent to some areas of the park. In addition to monitoring future effects of land use changes outside the park, continued monitoring may also prove useful for assessing elk responses to natural succession in forests disturbed by windthrow in December 2007 and to NPS vegetation management activities such as variable density thinning in the forest, trail development, and restoration at Otter Point tidal area and Colewort Creek Slough.

Oregon;Washington↗