Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long-term vegetation trajectories were better explained by initial post-fire weather conditions or by general five-year antecedent weather, and if weather effects helped predict post-fire vegetation abundances at a new site. We parameterized models using a 30-yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well-sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post-fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5-yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post-fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post-fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse-scale gridded weather data are considered.
Background: The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L. species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots. Results: Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods. Conclusions: While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations. Keywords: Change point detection, National Land Cover Database, Post-fire monitoring, Sagebrush
This data release provides inputs needed to run the LANDIS-II landscape change model, NECN and Base Fire extensions for the Greater Yellowstone Ecosystem (GYE), USA, and simulation results that underlie figures and analysis in the accompanying publication. We ran LANDIS-II simulations for 112 years, from 1988-2100, using interpolated weather station data for 1988-2015 and downscaled output from 5 general circulation models (GCMs) for 2016-2100. We also included a control future scenario with years drawn from interpolated weather station data from 1980-2015. Model inputs include raster maps (250 × 250 m grid cells) of climate regions and tables of monthly temperature and precipitation for each climate region. We provide initial conditions in 1987 as rasters and tables (i.e., species-age cohorts, aboveground biomass, soil carbon and nitrogen in surface litter 3 soil layers, soil percent sand, soil percent clay, soil wilting point, soil field capacity, soil drainage, soil storm flow and base flow fractions, and soil depth), historical fire data for model calibration, climate-inferred lognormal fire size distributions for each simulation year, and LANDIS-II control files including parameters for species and functional groups. Outputs from 10 replicates for each of 5 GCMs and the control scenario are provided as rasters and tables. Tables include spatially-weighted mean annual temperature and precipitation of the GYE for each GCM and the control scenario, summarize annual area burned by scenario, summarize biomass pools, and summarize changes in mean stand age. Rasters include annual simulated fire severity for 2015-2100, simulated total aboveground biomass in 4-year timesteps, aboveground biomass of all species in 4-year timesteps, stand age in 4-year timesteps, maximum and minimum cohort age for three dominant species (Pinus contorta, Picea engelmannii, and Pseudotsuga menziesii) in 4-year timesteps, forest type in 1988 and 2100, net ecosystem exchange in 2040 and 2100, and total ecosystem carbon in 4-year timesteps.
Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.
The NC CASC supports co-produced actionable science, data-intensive discovery, and open science to support tribal, federal, state, and local natural resource managers and decision-makers in the North Central region, which serves Colorado, Wyoming, Montana, North Dakota, South Dakota, Kansas and Nebraska. NC CASC is hosted by the University of Colorado Boulder (CU Boulder) within the Cooperative Institute for Research in Environmental Sciences , and is a partnership between CU Boulder, the U.S. Geological Survey, and five consortium partners: University of Montana; South Dakota State University; Conservation Science Partners; Wildlife Conservation Society; and Great Plains Tribal Water Alliance. During the period of 2018 - 2023, the NC CASC consortium will strive to i) collaborate with resource managers to deliver usable climate science; ii) capitalize on the wealth of remote sensing and diverse big data to inform resource management decisions at relevant scales in the region; and iii) leverage open science work within and across the CASC-network to synthesize information on climate-sensitive wildlife, critical habitats, and cultural resources. These focal areas will help address key climate-sensitive management priorities in the region, including water availability and drought; habitat loss, connectivity and transformation; wildlife disease; invasives and encroachment; wildfire; and wildlife phenology. NC CASC activities align with the center’s core goals: partnerships; science; capacity building; and communication/outreach. Trusted partnerships are at the foundation of all NC CASC activities, and include the U.S. Fish and Wildlife Service, National Park Service’s Climate Change Response Program, and Tribal Colleges and Universities. Tribal Nations are unique and distinct partners. To better support and facilitate climate resilience in Tribal communities, the NC CASC partners with the Great Plains Tribal Water Alliance to host a regional Bureau of Indian Affairs Tribal Liaison. NC CASC science is use-inspired. Ongoing engagement between researchers and natural resource managers fosters a culture of collaboration and engagement. NC CASC Projects and Tools & Data are accessible online. Capacity building activities include leveraging the training capacity of CU Boulder’s Earth Lab, supporting a cohort of CASC-network Climate Adaptation Postdoctoral Fellows, and the launching of the NC CASC Tribal Climate Leaders Program that currently supports five Native American graduate fellows pursuing a graduate degree at CU Boulder in the area of climate adaptation science. Communication/Outreach activities include an NC CASC website, social media (Facebook and Twitter), a bi-monthly newsletter and a monthly webinar series. Each CASC is a formal collaboration between the USGS, a regional host university, and a multi-institution partner consortium. Through this agreement, the host and consortium institutions undertake a number of activities, including conducting research science projects, supporting fellows and engaging with resource management partners. To learn more about the work of the North Central CASC, visit: https://nccasc.colorado.edu/.
Tribal resource managers in the southwest U.S. are facing a host of challenges related to environmental change, including increasing temperatures, longer periods of drought, and invasive species. These threats are exacerbating the existing challenges of managing complex ecosystems. In a rapidly changing environment, resource managers need powerful tools and the most complete information to make the most effective decisions possible. Traditional Ecological Knowledge has enabled Indigenous peoples to adaptively manage and thrive in diverse environments for thousands of years, yet it is generally underutilized and undervalued, particularly in the context of western scientific approaches. Traditional Ecological Knowledge and western science offer complementary insights and, together, can facilitate climate change adaptation. This project will use both methods of understanding the environment to provide tribal resource managers cutting edge information about what their environment looked like in the past to better understand it in the present and make more informed decisions for the future. In particular, this project will work directly with Ute Mountain Ute decision-makers in using a combination of Traditional Ecological Knowledge and paleo-ecological records to explore past vegetation changes relevant to the stakeholder community. This work will then inform a forward-looking assessment of climate change impacts and adaptation options. Tribal youth will be involved in collecting information, and in developing and distributing outreach materials that summarize the work. By utilizing both Traditional Ecological Knowledge and western science techniques, this project will: 1) show how two different methods of understanding the environment can be utilized in a resource management context to assist with decision making, 2) establish how useful these methods are in tandem, and 3) provide southwest resource managers with better historic and holistic information to use in resource management decision making.
The Prairie Pothole Region is recognized as one of the most critical breeding habitats for waterfowl in North America and is used by an estimated 50–80 % of the continent’s breeding duck population. The ongoing acquisition program of the U.S. Fish and Wildlife Service National Wildlife Refuge System has conserved approximately 1.3 million hectares of critical breeding-waterfowl habitat. This current conservation approach assumes that past distributions of waterfowl habitat and populations are relatively representative of future distributions, however, due to changes in the area’s hydrology this may not be the case. Understanding how climate change may impact these wetland and grassland ecosystems is key for management agencies to set priorities for future conservation actions. The goal of this project is to co-produce novel information for land-management agencies to better plan for future impacts of climate change on the wetland habitat for breeding waterfowl pairs in the U.S. Prairie Pothole Region. The researchers will use a mechanistic hydrology model with U.S. Fish and Wildlife Service datasets that span multiple decades and predictive breeding waterfowl pair statistical models to simulate wetland-waterfowl responses under different climate futures. By working directly with scientists and decision makers at the U.S. Fish & Wildlife Service, the team will ensure delivery of actionable science that can readily inform the agency about potential climate-driven impacts to breeding waterfowl pairs in currently monitored wetlands. This project will generate new, more robust predictions of the future status of the wetland ecosystem and waterfowl habitat of the Prairie Pothole Region.
Postfire shifts in vegetation composition will have broad ecological impacts. However, information characterizing postfire recovery patterns and their drivers are lacking over large spatial extents. In this analysis, we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of postfire, dual‐season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field‐measured patterns of re‐vegetation, and (3) identify seasonally specific drivers of postfire rates of NDVI recovery. Rates of postfire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n = 230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in postfire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track postfire forest recovery across the western United States. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.