As our world changes and communities are faced with uncertain future climate conditions, decision making and resource planning efforts can often no longer rely on historic scientific data alone. Scientific projections of what might be expected in the future are increasingly needed across the country and around the world. Scientists and researchers can develop these projections by using computer models to simulate complex elements of our climate and their interactions with ecosystems, wildlife, and biodiversity. While an extensive array of general circulation models (GCMs, climate models of the general circulation of the atmosphere and ocean) exist, there is currently a lack of global biodiversity models. This project aims to bring together climate, ecosystem, and biodiversity modeling experts through a series of in-person workshops and virtual discussions to promote development of integrated approaches in modeling global biodiversity. The main goals of these workshops and discussions are to 1) identify lessons learned (both qualitative and quantitative) from climate models to then be applied to large-scale terrestrial biodiversity models, 2) to explore NASA and other remote sensing products to assist in global biodiversity and ecosystem models, and 3) to address and build on gaps and data needs (e.g., finer scale ecological and evolutionary processes) previously identified by the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) as necessary to inform the IPBES global biodiversity assessment.
Time and money for conservation are limited, so there is a need for responsible investments that embrace the realities of climate change. Droughts, floods, wildfires, hotter temperatures, declining snowpack, and changing streamflow are already significantly affecting wildlife and their habitats. In some cases, managers may decide to make strategic adjustments in how their actions are designed, where those actions are located, and when actions are needed most, in order to achieve management goals. A key part of making these forward-looking decisions is having access to climate information that can be integrated into an agency’s decision-making process. When science is conducted without an understanding of how that research might be incorporated into a management decision, the information produced may not be useful to decision makers. We addressed these concerns by creating an opportunity for wildlife and habitat managers and climate experts to work hand-in-hand to discuss how changing landscapes might affect management decisions, identify available climate science that can inform those decisions, and identify gaps in available knowledge that need to be filled in order to make better, climate-informed decisions. Our multi-year project had three parts: 1) Asking state wildlife managers in the North Central region what species, habitats, or issues are high priorities for their agencies and constituencies, and vulnerable to the effects of a changing climate, 2) Working with one of those state wildlife agencies—the Wyoming Game and Fish Department--to develop and apply a process for integrating best-available climate science and expert opinion into the Wyoming Statewide Habitat Plan, and 3) Identifying management-relevant information gaps that could drive climate research investments by the North Central CASC and others, to better inform future management decisions. The climate-informed Wyoming Statewide Habitat Plan and other project products offer useful models for making climate science actionable and relevant for managers’ decisions.
As the National Climate Adaptation Science Center (CASC) develops a strategic effort around fire science, there is a critical need to develop a national-scale synthesis effort that identifies key regional CASC activities previously conducted, as well as major science gaps that may be addressed by a coordinated CASC network approach. The North Central CASC postdoctoral fellow will play a leadership role in the National CASC Climate Adaptation Postdoctoral (CAP) Fellows Future of Fire cohort to help identify the common efforts and leveraging points to shape the national-scale synthesis. Currently there is limited North Central CASC supported fire science available for the North Central region. To meet this need, the North Central CASC postdoctoral fellow will develop region-specific fire information relevant to resource managers that are challenged with making decisions to adapt to changing fire risk and ecosystem responses. This project aims to determine the future size and number of fires, total burn area, and rates of change among years and across space in the contiguous United States. The goal is to explain changes in these fire variables in relation to climate change and changing housing density, which drives human ignitions and fire suppression efforts. To predict the future size and number of fires, statiscal models that look at fire-climate relationships will be applied to climate data output from several global climate models under two future climate scenarios. The results will help improve future fire projections based on climate modeling and data at spatial- and temporal-scales relevant for resource managers, with a focus on: i) identifying regions where fire has historically been infrequent or absent; ii) changes to fire extremes and other important aspects of fire behavior that have an impact on fire operations/management (i.e., timing, intensity, seasonal length); and iii) changes that will exceed the capacity of current institutional management approaches. Additionally, the postdoctoral fellow will help coordinate a team of regional partners, scientists and managers to determine what information is most useful for decision-making. This engagement with practitioners will be beneficial in informing the national-scale synthesis and identification of key metrics.
The design of this survey protocol is based on the indicator framework presented in Wall et. al (2017 https://doi.org/10.1175/WCAS-D-16-0008.1) and is intended to evaluate projects funded by Climate Adaptation Science Centers. The intended respondents are stakeholders who were engaged in the creation of scientific knowledge and tools during these projects. The questions cover three topical areas: process (engagement in the process of knowledge production), outputs/outcomes (use of information), and impacts (building of relationships and trust).
These data were compiled for the study: Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients. The objectives of our study were to (1) describe how climate change will alter the biomass and composition of key plant functional types; (2) quantify the impacts of climate change on future functional type biomass and composition along climatic gradients; (3) identify if and which geographic locations will be relatively unaffected by climate change while others experience large effects; and (4) determine if there is consistency in climate change impacts on plant communities among a representative set of climate scenarios. These data represent geographic patterns in simulated plant functional biomass of big sagebrush plant communities (cheatgrass, perennial forbs, C3 perennial grasses, C4 perennial grasses, perennial grasses, big sagebrush) as across-year averages of differences ("change") between projected future climates (years 2030-2060 and 2070-2100) derived from STEPWAT2 simulations run with each of 13 Global Climate Models (GCMs; CanESM2, CESM1-CAM5, CSIRO-Mk3-6-0, FGOALS-g2, FGOALS-s2, GISS-E2-R, HadGEM2-CC, HadGEM2-ES, inmcm4, IPSL-CM5A-MR, MIROC5, MIROC-ESM, and MRI-CGCM3; Maurer et al. 2007) that participated in CMIP5 for representative concentration pathways RCP4.5 and RCP8.5 and historical (years 1980-2010) values. Data of across-year averages of simulations under historical ("current"; years 1980-2010) climate and median differences ("change") between projected future climates (years 2030-2060 and 2070-2100) derived as medians across 13 Global Climate Models are available from the data release by Renne et al. (2021). These data were created in 2020 and 2021 for the area of the sagebrush region in the western U.S.A. These data were created by a collaborative research project between the U.S. Geological Survey, Marshall University, U.S. Fish and Wildlife Service, Yale University, and University of Wyoming, using a new multivariate matching algorithm (Renne et al., 202X.) which transfers simulated plant functional biomass of big sagebrush plant communities from 200 sites to a gridded product with 30-arcsecond spatial resolution. These data can be used with high resolution matching of projected decreases of big sagebrush, perennial C3 grass and perennial forb biomass in warm, dry sites; no projected change or increases in functional type biomass in cold, moist sites; and widespread projected increases in perennial C4 grasses across big sagebrush plant communities in the sagebrush region of the western U.S.A. (Palmquist et al. 2021) and within a scope as defined by the study. These data may also be used to evaluate the potential impact of changing climate conditions on geographic patterns in simulated plant functional biomass of big sagebrush plant communities within the scope defined by the study. In particular, these results will be useful for informing the design of long-term landscape conservation efforts to maintain and expand wildlife habitat across the sagebrush biome.
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.
How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for biological impact assessments and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative assessments. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate–ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop a more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.
The National Park Service is responsible for managing livestock grazing on 94 locations across the country and several grazing management planning efforts for this work are underway. However, there is a recognized need to update grazing management plans to address potential future effects of climate change on related resources and practices. This is the second phase of a project that is using scenario planning (a strategic planning technique used to inform decision-making in the face of uncertain future conditions) to support grazing management at Dinosaur National Monument. In the first phase of the project (Integrating Climate Considerations into Grazing Management Programs in National Parks), the research team convened a participatory climate change scenario planning workshop to qualitatively assess how grazing resources and management at Dinosaur National Monument may be affected under climate change. Now in phase two, this project will develop an ecological modeling approach to provide quantitative information about potential future scenarios to grazing management planning, continuing with Dinosaur National Monument as a case study. It will leverage recent advances in modeling to estimate the combined effects of climate change scenarios and alternative management actions (e.g., stocking rates, prescribed fire, and invasive plant management) on rangeland vegetation. The results of this project will add to the development of a transferable process to help parks ensure that grazing management practices are responsive and adaptive to future climate change.
Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides a powerful proxy for gross primary productivity (GPP). It is particularly promising in boreal ecosystems where seasonal downregulation of photosynthesis occurs without significant changes in canopy structure or chlorophyll content. The use of SIF as a proxy for GPP is complicated by inherent non-linearities due to both physical (illumination effects) and ecophysiological (light use efficiencies) controls at fine spatial (tower/leaf) and temporal (half-hourly) scales. To study the SIF-GPP relationship, we investigated the diurnal and seasonal dynamics of continuous tower-based measurements of SIF, GPP, and common vegetation indices at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA over the course of two years. We find that SIF outperforms other vegetation indices as a proxy for GPP at all temporal scales but shows a non-linear relationship with GPP at a half-hourly resolution. At small temporal scales, SIF and GPP are predominantly driven by light and non-linearity between SIF and GPP is due to the light saturation of GPP. Averaged over daily and monthly scales, the relationship between SIF and GPP is linear due to a reduction in the observed PAR range. Seasonal changes in the light responses of SIF and GPP are driven by changes in light use-efficiency which co-vary with changes in temperature, while illumination and canopy structure partially linearize the SIF-GPP relationship. Additionally, we find that the SIF-GPP relationship has a seasonal dependency. Our results help to clarify the utility of SIF for estimating carbon assimilation in boreal forests.
Even when faced with uncertainty about future climate conditions, resource managers are tasked with making planning and adaptation decisions that impact important natural and cultural resources. Species distribution models are widely used by both researchers and managers to estimate species responses to climate change. These models combine data on environmental variables (e.g., temperature, precipitation) with field samples of a species’ presence, absence, and/or abundance to project and visualize potential habitat of the species across space and time. However, species distribution modeling software previously developed and supported by USGS (the Software for Assisted Habitat Modeling [SAHM] package for VisTrails) is no longer under active development. Furthermore, species distribution models alone are not able to represent all of the complex ecological dynamics that dictate actual species’ distributions; thus, species distribution models are most powerful when coupled to other types of modeling approaches. There is a need to develop a new system for generating, running, and visualizing species distribution models and for connecting them to other modeling tools. The goal of this project is to design and develop a prototype package for running species distribution models in the software platform, SyncroSim. This prototype package will improve the functionality of species distribution models for researchers and resource managers by: 1. allowing end users to customize existing species distribution models written in the R programming language, visualize and store data for different scenarios of species distribution model inputs and outputs, and run species distribution model workflows from SyncroSim; and 2. laying the foundation for more seamless integration of species distribution models with other modeling approaches.