Brecken Cherice Robb

Longleaf pine forests are economically important habitats that stabilize and enrich the soil and store carbon over long periods. When mixed with oaks, these forests provide an abundance of lichen habitats. The tree canopy lichens promote greater moisture capture and retention and encourage canopy insects. Ground lichens limit some vascular plant germination and growth, promoting a more open and healthy pine community. There is a longstanding mutualistic relationship between longleaf pine habitat and lichens. Longleaf pine habitat has a long history of natural summer burning, which promotes a diverse understory and limits tree densities. Lichen diversity exceeds vascular plant diversity in many mature longleaf pine habitats, yet information on the impacts of prescribed fire on lichen species in these habitats is limited. We assessed lichen diversity and abundance before and after a prescribed ground fire in a longleaf pine/wiregrass habitat near Ocala, Florida. Pre-burn, we found greater lichen abundance and diversity on hardwoods, primarily oak species, than on pines. Post-burn, lichen abundance on hardwoods dropped overall by 28%. Lichen abundance on conifers dropped overall by 94%. Ground lichen species were basically eliminated, with a 99.5% loss. Our study provides insights into retaining lichen diversity after a prescribed burn. Hardwood trees, whether alive or standing dead, help retain lichen biodiversity after burning, whereas conifer trees do not support as many species. Landscapes may need to be actively managed by raking pine needle litter away from ground lichen beds, moistening the ground, or removing some lichen material before the burn and returning it to the site post-fire. Based on these results, we suggest retaining some oaks and conducting burns in a mosaic pattern that retains unburned areas. This will allow for lichens to recover between burns, significantly enhancing biodiversity and the ecological health of these longleaf pine communities.

This report illustrates use of scenario planning as a climate change adaptation tool supporting Wrangell-St. Elias National Park and Preserve?s Resource Stewardship Strategy. The primary objective of scenario planning is to help resource managers and scientists make management and planning decisions informed by assessments of critical future uncertainties. This report outlines a process that synthesized future climate projections into three distinct but plausible and relevant climate summaries for the focal area and used them to develop climate-resource scenarios through participatory scenario planning. Initial steps identified the priority resource management topics and the corresponding related climate uncertainties. Next, local climate summaries were used to develop divergent climate futures: those that describe the broadest possible range of plausible conditions while capturing relevant uncertainty. The final phase further developed the climate futures and their resource implications. These participatory scenario planning exercises occurred virtually in fall (August?November) 2021. The climate-resource scenarios informed adaptation strategies in conjunction with the park?s Resource Stewardship Strategy development. The scope and complexity of this effort is unique but elements from the scenarios and resource implications have broad applicability to other large, protected areas in Alaska and Northwest Canada.

This report presents climate change-informed resource stewardship strategies for diverse Wrangell-St. Elias National Park and Preserve natural and cultural resources. The strategies were developed in early 2022 by park staff and other subject-matter experts in a scenario-based climate change adaptation planning process. Strategy development was facilitated by National Park Service (NPS) climate change adaptation specialists. Strategies address critical climate change implications for park resources identified in an immediately preceding (fall-2021) climate-resource scenario development process. The overall, nearly-year-long scenario- and strategy-development process was entirely virtual due to the COVID-19 pandemic. Although this circumstance was constraining in some ways, it also provided many benefits, including lower barriers to participant entry, smoother pacing, more opportunities to incorporate personal reflection and individual work, and more points in the process where facilitators could intervene to support participants or make changes. Absence of travel costs, plus organization of the process into a larger number of smaller steps than in the past, allowed the 11 participating park resource management staff to include a large pool of several dozen federal, tribal, state, academic, and private-sector partners. Climate change adaptation strategies are most likely to foster climate change-informed resource stewardship when they support a specific planning process. In this case, that process was the 2021?2023 development of the Wrangell-St. Elias National Park and Preserve Resource Stewardship Strategy. A Resource Stewardship Strategy is a dynamic planning tool that parks use to set goals for natural and cultural resource stewardship, identify activities to achieve and maintain desired conditions, and track progress.

The impacts of climate change (CC) on natural and cultural resources are far‐reaching and complex. A major challenge facing resource managers is not knowing the exact timing and nature of those impacts. To confront this problem, scientists, adaptation specialists, and resource managers have begun to use scenario planning (SP). This structured process identifies a small set of scenarios—descriptions of potential future conditions that encompass the range of critical uncertainties—and uses them to inform planning. We reflect on a series of five recent participatory CC SP projects at four US National Park Service units and derive guidelines for using CC SP to support natural and cultural resource conservation. Specifically, we describe how these engagements affected management, present a generalized CC SP approach grounded in management priorities, and share key insights and innovations that (1) fostered participant confidence and deep engagement in the participatory CC SP process, (2) shared technical information in a way that encouraged informed, effective participation, (3) contextualized CC SP in the broader picture of relevant longstanding or emerging nonclimate stressors, (4) incorporated quantitative approaches to expand analytical capacity and assess qualitative findings, and (5) translated scenarios and all their complexity into strategic action.

Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g. translocations and reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near‐infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We use sagebrush (Artemisia spp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75–96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (>99%), and seasonal phenology within individual plants (>97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of nonlocal seed sources from past restoration, or complex gene‐by‐environment interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long‐term changes in vegetation.

Sagebrush ecosystems (Artemisia spp.) face many threats including large wildfires and conversion to invasive annuals, and thus are the focus of intense restoration efforts across the western United States. Specific attention has been given to restoration of sagebrush systems for threatened herbivores, such as Greater Sage-Grouse (Centrocercus urophasianus) and pygmy rabbits (Brachylagus idahoensis), reliant on sagebrush as forage. Despite this, plant chemistry (e.g., crude protein, monoterpenes and phenolics) is rarely considered during reseeding efforts or when deciding which areas to conserve. Near-infrared spectroscopy (NIRS) has proven effective in predicting plant chemistry under laboratory conditions in a variety of ecosystems, including the sagebrush steppe. Our objectives were to demonstrate the scalability of these models from the laboratory to the field, and in the air with a hyperspectral sensor on an unoccupied aerial system (UAS). Sagebrush leaf samples were collected at a study site in eastern Idaho, USA. Plants were scanned with an ASD FieldSpec 4 spectroradiometer in the field and laboratory, and a subset of the same plants were imaged with a SteadiDrone Hexacopter UAS equipped with a Rikola hyperspectral sensor (HSI). All three sensors generated spectral patterns that were distinct among species and morphotypes of sagebrush at specific wavelengths. Lab-based NIRS was accurate for predicting crude protein and total monoterpenes (R2 = 0.7–0.8), but the same NIRS sensor in the field was unable to predict either crude protein or total monoterpenes (R2 < 0.1). The hyperspectral sensor on the UAS was unable to predict most chemicals (R2 < 0.2), likely due to a combination of too few bands in the Rikola HSI camera (16 bands), the range of wavelengths (500–900 nm), and small sample size of overlapping plants (n = 28–60). These results show both the potential for scaling NIRS from the lab to the field and the challenges in predicting complex plant chemistry with hyperspectral UAS. We conclude with recommendations for next steps in applying UAS to sagebrush ecosystems with a variety of new sensors.

Sagebrush identification can be improved by using a relatively easy ultraviolet (UV) light test on specimens. Sagebrush produces a variety of water-soluble polyphenols called coumarins, which fluoresce a blue color under UV light and can help differentiate species, subspecies, and hybrids. We tested 16 different sagebrush taxa (including species and subspecies) from herbarium specimens and found 3 taxa (low sagebrush, Artemisia arbuscula; Wyoming sagebrush, A. tridentata wyomingensis; and basin sagebrush, A. t. tridentata) that were often misidentified. We show that the UV light test can greatly improve identification of these species. Moreover, given that the UV+ chemicals that discriminate taxa are also considered an indirect biomarker of sagebrush palatability for some herbivores, the UV light test can be used to predict forage quality for threatened species like sage-grouse (Centrocercus spp.) and pygmy rabbits (Brachylagus idahoensis). Collecting voucher specimens of sagebrush at wildlife study sites and comparing their UV intensity to historical herbarium specimens could help identify both current and changing availability of palatable sagebrush for wildlife. We found that even herbarium specimens >80 years old still fluoresce under UV light.

Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline‐specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross‐disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape‐level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross‐scale studies that promote a holistic approach to detect, monitor and manage biodiversity.

Landscapes are rapidly changing. To understand these changes and how they may influence coexisting herbivores, it is critical that we improve the ways in which we monitor changes in plant species, populations, and functional phenotypic traits over space and time. Near infrared spectroscopy (NIRS) is proving to be a valuable tool when it comes to this goal. NIRS is noninvasive and can provide high-resolution temporal information, including structural and chemical characteristics, on objects that are otherwise expansive, inaccessible, or imperceptible. We used the threatened sagebrush-steppe ecosystem, which spans over 43 million hectares of the Western United States, as a case study to test the accuracy in which NIRS can measure and classify functional phenotypic traits of sagebrush (Artemisia spp.) populations. Sagebrush habitats are known to have extreme levels of genetic and chemical heterogeneity and plasticity. Yet, our results showed that NIRS can classify species of sagebrush within a site, populations of sagebrush within a species across sites, and phenology (both seasonally and annually) of sagebrush within a population. These taxonomic, geographic, and phenological phenotypes are functionally important in many ways, including determining species composition and distribution, identifying developmental stages of individual plants, potentially detecting past and present anthropogenic and environmental stressors, and predicting interactions with herbivores. Even so, habitat use by coexisting herbivores is not always explained by these relatively crude phenotypes. Specifically, herbivores make foraging decisions based on specific concentrations of chemical phenotypes that have functional consequences for herbivores. Our research further demonstrated that NIRS can predict concentrations of individual chemical compounds and classes of compounds, in the forms of both nutrients and toxins, in sagebrush plants across species and populations. As such, we further tested if NIRS could directly predict browsing by coexisting sagebrush herbivores, in the form of bite marks on plants. Although NIRS was not able to predict herbivore foraging behavior, it shows promise for predicting foraging behavior indirectly through predicted concentrations of phytochemicals and directly with finer tuned field validation and model calibration. To monitor the threats of climate and anthropogenic disturbances on ecosystems, it is essential we find better ways to quantify the functional phenotypes that mediate interactions among plants, herbivores, and the environment. We show that NIRS can be a powerful tool in achieving this aim.