The North Central Climate Science Center Paleoenvironmental Database serves as an archive of Pleistocene proxy records, metadata and derivative products (e.g., chronologies, vegetation and climate reconstructions), and provides a resource for environmental research, facilitating data viewing, synthesis and joint analysis of multiproxy datasets.  As of March 2014, the database consists of 1270 paleoenvironmental records, including proxies of climate (i.e., tree-rings, borehole temperatures, isotopes, diatoms, electrical conductivity, ice cores, loess accumulation), streamflow (i.e., tree rings), fauna (i.e., fossils), vegetation (i.e., pollen, plant macrofossils) and fire (i.e., tree-scars, charcoal). 

ABSTRACT: U.S. National Park Service land managers face a variety of challenges to preserving the biodiversity in their parks. A principle challenge is to minimize the impacts of surrounding land use on park condition and biodiversity. In the absence of ideal sets of data and models, the present study develops methods and results that demonstrate a coarse-filter approach to understanding the effects of land use change on habitat types for four pilot study-areas. The area of analysis for each park is defined by a protected-area-centered-ecosystem. Habitat types were defined by biophysical factors assumed to represent the distribution of vegetation communities as they may have existed prior to European settlement. Present-day land use was overlaid on historical habitat and change in area and pattern was quantified for private and public lands separately. Results suggest that patterns of development are affecting study-areas differently. Therefore, the conservation challenges faced by each study-area are distinct to their landscape contexts. For some parks, the primary challenge is to work towards maintaining ecosystem condition in its present or near-present state while paying particular attention to habitats that are underrepresented on public lands. For other parks, the challenge is to address spatially aggregated land use that is affecting only a few habitat types. For still other parks, the challenge is to maintain connectivity with a regional network of protected lands and to undertake restoration projects where feasible. The present methods and results help to focus conservation attention on habitats that have been most impacted by land use change. Remote sensing for inventory and monitoring of the U.S. national parks - ResearchGate. Available from: http://www.researchgate.net/publication/230720086_Remote_sensing_for_inventory_and_monitoring_of_the_U.S._national_parks [accessed Apr 23, 2015].

Locating meadow study sitesMeadow centers as recorded in the ‘Copy of sitecords_areaelev from Caruthers thesis.xls’ file delivered by Debinski in November 2012 were matched to polygons as recorded in files ‘teton97map_area.shp’ and ‘gallatin97map_area.shp’ both also delivered by Debinski in November 2012.In cases where the meadow center did not fall within a meadow polygon, if there was a meadow polygon of the same meadow TYPE nearby (judgment was used here), the meadow center was matched with the meadow polygon of same meadow TYPE. In total, 29 of 30 Gallatin meadow sites and 21 of 25 Teton meadow sites were positively located.Identifying meadow pixels for analysisThe native MODIS 250-meter grid was reprojected to match meadow data and added to the GIS project window along with the meadow polygons. For context, aerial photography from ESRI’s basemap streaming services were also added to the ArcMap project. MODIS pixels that were at least half-covered by meadow polygon area were used in further ndvi analysis. Meadows that did not cover at least half of one MODIS pixel were eliminated from the analysis. In total, 17 Gallatin meadow sites (M1= 0; M2= 0; M3= 4; M4= 4; M5= 4; M6=5), covering at least half of 39 MODIS pixels (M1= 0; M2= 0; M3= 12; M4= 4; M5= 6; M6= 17), were used in further analysis and 16 Teton meadow sites (M1=3; M2=1; M3=4; M5=5; M6=3) covering at least half of 1252 MODIS pixels (M1= 105; M2= 1; M3= 25; M4=0 ; M5= 19; M6=1102), were used in further analysis.List of site names that were located, but not used in the NDVI analysis b/c they were too small: Gallatin – Porcupine Exclosure; Twin Cabin Willows; Figure 8; Taylor Fork; Teepee Sage; Daly North; Wapiti (Taylor Fork); Specimen Creek; Bacon Rind M1; Bacon Rind M4, Teepee wet; Daly SouthTeton – Cygnet Pond; Christian Pond; Willow Flats North; Willow Flats South; Sound of MusicMODIS preprocessing methods: MODIS MOD13Q1 representing observations of normalized difference vegetation index (NDVI) from March 2000 through December 2012 were downloaded from the USGS Land Processes Distributed Area Archive Center (LPDAAC) during the spring of 2013. Also downloaded at the same time were grids that described the estimated reliability of NDVI observations and the actual day of the year for each NDVI observation used in maximum compositing routines by the MODIS program. All MODIS data layers were reprojected to match meadow data layers.All negative NDVI values which are thought to correspond to standing water, partial snow-cover or wet bare soil were set to ‘NA’values (Huete, Justice and van Leeuwen 1999)The following steps were used to remove any conifer/evergreen signal from NDVI data and are based on an understanding that each pixel has a different “background”(i.e. no-growth) greenness against which any seasonal change must be compared (Beever et al. 2013; Piekielek and Hansen 2013). These methods also help to eliminate long gaps in data that can allow smoothing algorithms to interpolate beyond the valid range of data (in the case of NDVI from 0 –1):Annual minimum NDVI values that were labeled as high-quality were identified in the 13 year time-series.The bottom first percentileof a distribution of minimum values was used as the “background”value to fill-in missing values when the target was identified as being under snow cover.All NDVI values identified by pixel-reliability grids as being of high or marginal quality werepreserved and snow-covered pixels and dates were filled in with each pixels “background”value.Composite day of year grids were used to identify the actual date from which the 16-day maximum composite NDVI value came.Each pixel’s entire time-series (2000 –2012) was smoothed in a weighted regression framework against time using smoothing splines (Chambers and Hastie 1992). NDVI data of marginal quality and snow-covered background values contributed half the weight to final smoothed values as did high-quality values. The final smoothed values were used to interpolate the time-series to a daily time-step and to record annual NDVI amplitudes. Land surface phenology metrics were calculated as follows:Start of season (SOS) –the first annual day of year when smoothed NDVI crosses half of its annual amplitude (White et al. 2009).End of Season (EOS) –the last day of year when smoothed NDVI crosses half of its annual amplitude.Maximum annual NDVI (MAX) –the highest annual smoothed NDVITiming of annual maximum (DOYMAX) –the smoothed day of year when NDVI reaches its maximum valueEstimated annual productivity (INDVI) –the integrated area under the growing season (SOS to EOS) NDVI curve (Goward et al 1985).

The dynamic global vegetation model MC1 simulates plant growth and biogeochemical cycles, vegetation type, wildfire, and their interactions. The model simulates competition between trees and grasses (including other herbaceous species), as affected by differential access to light and water, and fire-caused tree mortality (Bachelet et al., 2000; 2001). MC1 projects the dynamics of lifeforms, including evergreen and deciduous needleleaf and broadleaf trees, as well as C3 and C4 grasses. However, the model can also be parameterized for a particular dominant species of the associated lifeform. For this project we used two versions of MC1, both of which modified the standard code to improve the simulation of potential evapotranspiration (PET).For the western northern Great Plains (NGP) the model was calibrated to project the observed ecotone between ponderosa pine and grasslands at Wind Cave National Park in the Black Hills of South Dakota; full documentation of this version of the code is described by King et al. (2013a). In this case the evergreen needleleaf life form corresponds to ponderosa pine (Pinus ponderosa). For the eastern NGP we recalibrated MC1 so that the evergreen needleleaf lifeform corresponds to juniper; principally eastern redcedar (Juniperus virginiana), but also to Rocky Mountain juniper (Juniperus scopularum), which is present in the western and central NGP.

Assessments of vegetation response to climate change have generally been made only by equilibrium vegetation models that predict vegetation composition under steady-state conditions. These models do not simulate either ecosystem biogeochemical processes or changes in ecosystem structure that may, in turn, act as feedbacks in determining the dynamics of vegetation change. MC1 is a new dynamic global vegetation model created to assess potential impacts of global climate change on ecosystem structure and function at a wide range of spatial scales from landscape to global. This new tool allows us to incorporate transient dynamics and make real time predictions about the patterns of ecological change. MC1 was created by combining physiologically based biogeographic rules defined in the MAPSS model with a modified version of the biogeochemical model, CENTURY. MC1 also includes a fire module, MCFIRE, that mechanistically simulates the occurrence and impacts of fire events.

Abstract (from http://www.srmjournals.org/doi/abs/10.2111/REM-D-13-00079.1):  Big sagebrush,  Artemisia tridentata  Nuttall (Asteraceae), is the dominant plant species of large portions of semiarid western North America. However, much of historical big sagebrush vegetation has been removed or modified. Thus, regeneration is recognized as an important component for land management. Limited knowledge about key regeneration processes, however, represents an obstacle to identifying successful management practices and to gaining greater insight into the consequences of increasing disturbance frequency and global change. Therefore, our objective is to synthesize knowledge about natural big sagebrush regeneration. We identified and characterized the controls of big sagebrush seed production, germination, and establishment. The largest knowledge gaps and associated research needs include quiescence and dormancy of embryos and seedlings; variation in seed production and germination percentages; wet-thermal time model of germination; responses to frost events (including freezing/thawing of soils), CO2  concentration, and nutrients in combination with water availability; suitability of microsite vs. site conditions; competitive ability as well as seedling growth responses; and differences among subspecies and ecoregions. Potential impacts of climate change on big sagebrush regeneration could include that temperature increases may not have a large direct influence on regeneration due to the broad temperature optimum for regeneration, whereas indirect effects could include selection for populations with less stringent seed dormancy. Drier conditions will have direct negative effects on germination and seedling survival and could also lead to lighter seeds, which lowers germination success further. The short seed dispersal distance of big sagebrush may limit its tracking of suitable climate; whereas, the low competitive ability of big sagebrush seedlings may limit successful competition with species that track climate. An improved understanding of the ecology of big sagebrush regeneration should benefit resource management activities and increase the ability of land managers to anticipate global change impacts.

VisTrails is an open-source management and scientific workflow system designed to integrate the best of both scientific workflow and scientific visualization systems. Developers can extend the functionality of the VisTrails system by creating custom modules for bundled VisTrails packages. The Invasive Species Science Branch of the U.S. Geological Survey (USGS) Fort Collins Science Center (FORT) and the U.S. Department of the Interior’s North Central Climate Science Center have teamed up to develop and implement such a module—the Software for Assisted Habitat Modeling (SAHM). SAHM expedites habitat modeling and helps maintain a record of the various input data, the steps before and after processing, and the modeling options incorporated in the construction of an ecological response model. There are four main advantages to using the SAHM:VisTrails combined package for species distribution modeling: (1) formalization and tractable recording of the entire modeling process; (2) easier collaboration through a common modeling framework; (3) a user-friendly graphical interface to manage file input, model runs, and output; and (4) extensibility to incorporate future and additional modeling routines and tools. In order to meet increased interest in the SAHM:VisTrails package, the FORT offers a training course twice a year. The course includes a combination of lecture, hands-on work, and discussion. Please join us and other ecological modelers to learn the capabilities of the SAHM:VisTrails package.

Abstract (from http://onlinelibrary.wiley.com/doi/10.1002/joc.4127/abstract):  Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56 °C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets.

In the North Central U.S., drought is a dominant driver of ecological, economic, and social stress. Drought conditions have occurred in the region due to lower precipitation, extended periods of high temperatures and evaporative demand, or a combination of these factors. This project aimed to improve our understanding of drought in the North Central region and determine what future droughts might look like over the 21st century, as climate conditions change. Researchers evaluated, with the intent to improve, available and emerging data on climate conditions that influence drought (such as changes in temperature, precipitation, evaporative demand, snow and soil moisture), as well as datasets related to the surface water balance (such as evapotranspiration and streamflow). Researchers sought to use these data to identify a range of plausible future climate conditions for the region, known as “scenarios”, to help land managers better understand the threat posed by drought and to plan for its potential impacts. Researchers aimed to make relevant climate datasets available to ecologists and land managers for modeling ecosystem response under different future climate scenarios. This project team is part of the North Central Climate Science Center’s Foundational Science Area Team, which supports foundational research and advice, guidance, and technical assistance to other NC CSC projects as they address climate science challenges that are important for land managers and ecologists in the region.  

In the North Central U.S., temperatures are rising and precipitation patterns are changing, with consequences ranging from more frequent and severe wildfires to prolonged drought to widespread forest pest outbreaks. As a result, land managers are becoming increasingly concerned about how climate change is affecting natural resources and the essential services they provide communities.   The rates and ecological impacts of changing conditions vary across this diverse region, which stretches from the Great Plains to the High Rockies. The goal of this project was to understand how native grasslands, shrublands, and forests will respond to changing conditions. Researchers first modeled how these vegetation types have changed over the past 50 years, then projected how they might change over the next century under different possible future conditions.   Understanding how these native ecosystems may change is critical, particularly in light of the wildlife and communities that depend on them. Species such as the greater sage-grouse, elk, deer, and grizzly bears could lose important habitat if conditions change. Humans could also be impacted – subalpine forests, for example, control snow accumulation and melt, which in turn affect the water supply. The results of this research are meant to be used to support local stakeholders in developing strategies for coping with and adapting to projected changes in vegetation across the North Central region.   This project team is part of the North Central Climate Science Center’s Foundational Science Area Team, which supports foundational research and advice, guidance, and technical assistance to other NC CSC projects as they address climate science challenges that are important for land managers and ecologists in the region.