The Department of the Interior Bison Conservation Initiative calls for its bureaus to plan and implement collaborative American bison conservation and to ensure involvement by tribal, state, and local governments and the public in that conservation. Four independently managed and geographically separated National Park Service (NPS) units in Interior Region 5 (IR5) preserve bison and other components of a formerly contiguous Great Plains landscape. Management of bison in IR5 parks has historically been specific to each park, and livestock and range management science informed much of the decision making. In the past two decades, NPS has shifted away from managing bison from this livestock-based perspective towards a wildlife stewardship approach, including ensuring their long-term adaptive potential and considering them as just one part of a complex ecosystem. This shift requires a more holistic and cooperative approach to stewardship that is challenging not only because of limitations in funding and fluctuations in leadership priorities, but also because of the constraints imposed by the parks’ relatively small, fenced areas. The IR5 NPS Bison Stewardship Strategy (“Strategy”) will help the NPS to meet its responsibilities in cooperative stewardship of bison. The Strategy will serve to organize and consolidate the NPS’s legal and policy responsibilities within a framework of collectively defined values and objectives to support the careful and transparent decision-making processes that both guide and transcend parkspecific planning. This report describes a preliminary decision framework for the Strategy, including the context, the fundamental objectives, and a range of alternative strategies developed and considered through two workshops and a series of conference calls with NPS personnel, stakeholders, and outside experts with an interest in IR5 NPS bison stewardship. Although not the Strategy itself, this framework serves as the Strategy’s starting point and identifies 14 fundamental objectives, falling in four major themes: Persistence of Wild and Healthy Bison 1. Maximize the long-term persistence of bison in IR5 parks 2. Maximize the long-term adaptive capacity of bison in North America 3. Maximize the wildness of the bison herds 4. Maximize humane treatment of bison, while allowing natural processes to occur

Abstract From: ScienceDirect (Pinyon-juniper (PJ) plant communities cover a large area across North America and provide critical habitat for wildlife, biodiversity and ecosystem functions, and rich cultural resources. These communities occur across a variety of environmental gradients, disturbance regimes, structural conditions and species compositions, including three species of juniper and two species of pinyon. PJ communities have experienced substantial changes in recent decades and identifying appropriate management strategies for these diverse communities is a growing challenge. Here, we surveyed the literature and compiled 441 studies to characterize patterns in research on PJ communities through time, across geographic space and climatic conditions, and among focal species. We evaluate the state of knowledge for three focal topics: 1) historical stand dynamics and responses to disturbance, 2) land management actions and their effects, and 3) potential future responses to changing climate. We identified large and potentially important gaps in our understanding of pinyon-juniper communities both geographically and topically. The effect of drought on Pinus edulis, the pinyon pine species in eastern PJ communities was frequently addressed, while few studies focused on drought effects on Pinus monophylla, which occurs in western PJ communities. The largest proportion of studies that examined land management actions only measured their effects for one year. Grazing was a common land-use across the geographic range of PJ communities yet was rarely studied. We found only 39 studies that had information on the impacts of anthropogenic climate change and most were concentrated on Pinus edulis. These results provide a synthetic perspective on PJ communities that can help natural resource managers identify relevant knowledge needed for decision-making and researchers design new studies to fill important knowledge gaps.)

Abstract: (From: Wiley Online Library) Relative agricultural productivity shocks emerging from climate change will alter regional cropland use. Land allocations are sensitive to crop profits that in turn depend on yield effects induced by changes in climate and technology. We develop and apply an integrated framework to assess the impact of climate change on agricultural productivity and land use for the U.S. Northern Great Plains. Crop‐specific yield‐weather models reveal crop comparative advantage due to differential yield impacts of weather across the region's major crops, i.e., alfalfa, wheat, soybeans and maize. We define crop profits as a function of the weather‐driven yields, which are then used to model land use allocation decisions. This ultimately allows us to simulate the impact of climate change under the RCP4.5 emissions scenario on land allocated to the region's major crops as well as to grass/pasture. Upon removing the trends effects in yields, climate change is projected to lower yields by 33%‐64% over 2031‐’55 relative to 1981–2005, with soybean being the least and alfalfa the most affected crops. Yield projections applied to the land use model at present‐day input costs and output prices reveals that Dakotas’ grass acreage will increase by up to 23%, displacing croplands. Wheat acreage is expected to increase by up to 54% in select south‐eastern counties of North Dakota and South Dakota, where maize/soy acreage had increased by up to 58% during 1995–2016. This article is protected by copyright. All rights reserved

The USA National Phenology Network (USA-NPN) and the North Central Climate Science Center (NC CSC) seek to enhance scientific understanding of how climate trends and variability are linked to phenology across spatial scales, with the ultimate goal of being able to understand and predict climate impacts on natural resources. A key step towards achieving this long-term goal is connecting local observations (individual plants or animals) of phenology with those at regional to continental scales (10 km to 10,000 km), which may ultimately be used to better understand phenology across ecosystems and landscapes and thereby inform natural resource management. The specific shorter-term goals of this effort are to process and distribute phenology camera (or “phenocam”) products, and to develop a plan for how these products can help meet longer-term goals. 

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.

Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere.

Land surface phenology (LSP) has been widely used as the “footprint” of urbanization and global climate change. Shifts of LSP have cascading effects on food production, carbon sequestration, water consumption, biodiversity, and public health. Previous studies mainly focused on investigating the effects of urbanization on the spatial patterns of LSP by comparing phenological metrics, e.g. start of season (SOS) and end of season (EOS), between urban center and the surrounding rural regions. However, it remains unclear how urbanization-induced land cover conversions and climate change jointly influence the temporal variations of SOS and EOS within the urban ecosystem. To fill this knowledge gap, we utilized daily two-band enhanced vegetation index, daily meteorological record, and annual land cover dataset to investigate the respective impacts of urbanization and climate change on temporal shifts of LSP between the post- and the pre-urbanization periods over 196 large cities in the northern mid-latitudes. We found 51% of the cities experienced an advanced SOS with an average of −6.39 ± 5.82 days after urbanization has occurred, while the remaining 49% of the cities had a delayed SOS with an average of 7.56 ± 5.63 days. We also found a later EOS at 53% of the cities and an earlier EOS at 47% of the cities with an average of 8.43 ± 7.59 and −5.57 ± 4.99 days between the post- and pre-urbanization periods, respectively. Multiple linear regression analysis indicates that climate variables (i.e. temperature, precipitation, and insolation) play dominant roles in regulating the temporal shifts of LSP. Furthermore, the earlier SOS and later EOS were significantly correlated with the amplitude of urbanization (i.e. increase of impervious surface area) in cities after controlling for effects of climate factors. These patterns were generally consistent across eight climate zones. Our findings provide critical information in modeling natural and anthropogenic effects on urban ecosystem, with important benefits for urban sustainability and biodiversity conservation.

Ground validation of satellite-based vegetation phenology has been challenging because ground phenology data are sparsely distributed and mostly observed from limited numbers of plant species at discrete phenophases. The recently developed PhenoCam network has measured continuous growth of vegetation canopy greenness that can be used to validate satellite-based vegetation phenology across a variety of plant functional types. In this study, we used PhenoCam green chromatic coordinate (GCC) in North America to evaluate grassland phenology derived from three types of MODIS vegetation indices: the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and a per-pixel GCC (GCCpp) which was computed to describe the average vegetation color at the pixel level. The start of greenness (SOG), end of greenness (EOG), and length of greenness (LOG), and the dates for detailed seasonal dynamics for each site-year were compared. Our results indicate that MODIS VIs can be used to predict phenological metrics and seasonal dynamics in grassland greenness measured from PhenoCam GCC. More importantly, we quantified the difference between SOG, EOG, and LOG and seasonality estimated from satellite and near-surface remote sensing and discovered that GCCpp may be more suitable than NDVI and EVI at estimating dynamics in grassland greenness during senescence.

Drylands account for approximately 40% of the global land surface and play a dominant role in the trend and variability of terrestrial carbon uptake and storage. Gross ecosystem photosynthesis – termed gross primary productivity (GPP) – is a critical driver of terrestrial carbon uptake and remains challenging to be observed directly. Currently, vegetation indices that largely capture changes in greenness are the most commonly used datasets in satellite-based GPP modeling. However, there remains significant uncertainty in the spatiotemporal relationship between greenness indices and GPP, especially for relatively heterogeneous dryland ecosystems. In this paper, we compared vegetation greenness indices from PhenoCam and satellite (Landsat and MODIS) observations against GPP estimates from the eddy covariance technique, across three representative ecosystem types of the southwestern United States. We systematically evaluated the changes in the relationship between vegetation greenness indices and GPP: i) across spatial scales of canopy-level, 30-meter, and 500-meter resolution; and ii) across temporal scale of daily, 8-day, 16-day, and monthly resolution. We found that greenness-GPP relationships were independent of spatial scales as long as land cover type and composition remained relatively constant. We also found that the greenness-GPP relationships became stronger as the time interval increased, with the strongest relationships observed at the monthly resolution. We posit that the greenness-GPP relationship breaks down at short timescales because greenness changes more slowly than plant physiological function, which responds rapidly to changes in key biophysical drivers. These findings provide insights into the potential for and limitations of modeling GPP using remotely sensed greenness indices across dryland ecosystem types.