Integrated Analysis of Productivity and Biodiversity in a Southern Alberta Prairie

Grasslands play important roles in ecosystem production and support a large farming and grazing industry. An accurate and efficient way is needed to estimate grassland health and production for monitoring and adjusting management to get sustainable products and other ecosystem services. Previous studies of grasslands have shown varying relationships between productivity and biodiversity, with most showing either a positive or a hump-shaped relationship where productivity peaks at intermediate diversity. In this study, we used airborne imaging spectrometry combined with ground sampling and eddy covariance measurements to estimate the spatial pattern of production and biodiversity for two sites of contrasting productivity in a southern Alberta prairie ecosystem. Resulting patterns revealed that more diverse sites generally had greater productivity, supporting the hypothesis of a positive relationship between production and biodiversity for this site. We showed that the addition of evenness to richness (using the Shannon Index of dominant species instead of the number of dominant species alone) improved the correlation with optical diversity, an optically derived metric of biodiversity based on the coefficient of variation in spectral reflectance across space. Similarly, the Shannon Index was better correlated with productivity (estimated via NDVI (Normalized Difference Vegetation Index)) than the number of dominant species alone. Optical diversity provided a potent proxy for other more traditional biodiversity metrics (richness and Shannon index). Coupling field measurements and imaging spectrometry provides a method for assessing grassland productivity and biodiversity at a larger scale than can be sampled from the ground, and allows the integrated analysis of the productivity–biodiversity relationship over large areas

Prairie Grasslands: An Undervalued Resource \u3cbr\u3e Grass, Cows and Environmental Management on the Canadian Prairies

Grasslands are integral to the economic health of Canada\u27s rural prairies, making up 30% of the agricultural land base in Canada\u27s three prairie provinces. These grasslands support agriculture, through grazing of livestock, and recreation, such as hunting and ecotourism. These grasslands are also environmentally significant, providing habitat for native plants and animals. The economic and environmental significance of these grasslands should not be undervalued. Economic opportunities and environmental policies and regulations affect the management of these lands. Current issues that may affect how the prairie is used include the: potential species-at-risk legislation, other initiatives for biodiversity enhancement, greenhouse gas regulations or incentives, carbon sequestration opportunities, and economic diversification to support economic growth. Decisions on these issues will affect the way prairie grasslands contribute to the region\u27s economic and environmental sustainability

The spatial sensitivity of the spectral diversity–biodiversity relationship: an experimental test in a prairie grassland

Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm2 to 1 m2 to conventional biodiversity metrics, including species richness, Shannon’s index, Simpson’s index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon’s index and Simpson’s index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson’s index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm2 pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing a diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scaledependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods

The spatial sensitivity of the spectral diversity–biodiversity relationship: an experimental test in a prairie grassland

Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm2 to 1 m2 to conventional biodiversity metrics, including species richness, Shannon’s index, Simpson’s index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon’s index and Simpson’s index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson’s index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm2 pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing a diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scaledependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods

Interannual Variability in Dry Mixed-Grass Prairie Yield: A Comparison of MODIS, SPOT, and Field Measurements

Remote sensing is often used to assess rangeland condition and biophysical parameters across large areas. In particular, the relationship between the Normalized Difference Vegetation Index (NDVI) and above-ground biomass can be used to assess rangeland primary productivity (seasonal carbon gain or above-ground biomass “yield”). We evaluated the NDVI–yield relationship for a southern Alberta prairie rangeland, using seasonal trends in NDVI and biomass during the 2009 and 2010 growing seasons, two years with contrasting rainfall regimes. The study compared harvested biomass and NDVI from field spectrometry to NDVI from three satellite platforms: the Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT 4 and 5). Correlations between ground spectrometry and harvested biomass were also examined for each growing season. The contrasting precipitation patterns were easily captured with satellite NDVI, field NDVI and green biomass measurements. NDVI provided a proxy measure for green plant biomass, and was linearly related to the log of standing green biomass. NDVI phenology clearly detected the green biomass increase at the beginning of each growing season and the subsequent decrease in green biomass at the end of each growing season due to senescence. NDVI–biomass regressions evolved over each growing season due to end-of-season senescence and carryover of dead biomass to the following year. Consequently, mid-summer measurements yielded the strongest correlation (R2 = 0.97) between NDVI and green biomass, particularly when the data were spatially aggregated to better match the satellite sampling scale. Of the three satellite platforms (MODIS Aqua, MODIS Terra, and SPOT), Terra yielded the best agreement with ground-measured NDVI, and SPOT yielded the weakest relationship. When used properly, NDVI from satellite remote sensing can accurately estimate peak-season productivity and detect interannual variation in standing green biomass, and field spectrometry can provide useful validation for satellite data in a biomass monitoring program in this prairie ecosystem. Together, these methods can be used to identify the effects of year-to-year precipitation variability on above-ground biomass in a dry mixed-grass prairie. These findings have clear applications in monitoring yield and productivity, and could be used to support a rangeland carbon monitoring program

Seasonal Variation in the NDVI–Species Richness Relationship in a Prairie Grassland Experiment (Cedar Creek)

Species richness generally promotes ecosystem productivity, although the shape of the relationship varies and remains the subject of debate. One reason for this uncertainty lies in the multitude of methodological approaches to sampling biodiversity and productivity, some of which can be subjective. Remote sensing offers new, objective ways of assessing productivity and biodiversity. In this study, we tested the species richness–productivity relationship using a common remote sensing index, the Normalized Difference Vegetation Index (NDVI), as a measure of productivity in experimental prairie grassland plots (Cedar Creek). Our study spanned a growing season (May to October, 2014) to evaluate dynamic changes in the NDVI–species richness relationship through time and in relation to environmental variables and phenology. We show that NDVI, which is strongly associated with vegetation percent cover and biomass, is related to biodiversity for this prairie site, but it is also strongly influenced by other factors, including canopy growth stage, short-term water stress and shifting flowering patterns. Remarkably, the NDVI-biodiversity correlation peaked at mid-season, a period of warm, dry conditions and anthesis, when NDVI reached a local minimum. These findings confirm a positive, but dynamic, productivity–diversity relationship and highlight the benefit of optical remote sensing as an objective and non-invasive tool for assessing diversity–productivity relationships

Cows and Plows: Science-based Conservation for Grassland Songbirds in Agricultural Landscapes

Temperate grasslands are among earth’s most imperiled ecosystems. In North America, steep declines of endemic songbird populations indicate that grassland loss and degradation may be approaching critical levels. Grasslands are agricultural landscapes largely (~85%) under private ownership with little formal protection status. Remaining bird populations depend on grazing lands that have not been converted to cropland. We combine regional data from a hotspot for grassland bird diversity (northeast Montana, USA; 26,500-km2) with continental data spanning the northern Great Plains (1,000,000-km2) to evaluate how land use and management influence bird distribution and abundance. Regionally, habitat used by seven grassland specialists spanned a gradient of sparse to dense herbaceous cover. Livestock grazing influenced cover and birds but its effect was highly dependent on precipitation and soil productivity. Species distributions were variable across relatively broad spatial scales and only large landscapes (≥ 1,492-km2) were sufficient to capture maximum diversity and stability in community composition. At this scale, more grassland habitat and a wider range in herbaceous cover values were associated with high bird diversity. Sprague’s Pipit (Anthus spragueii), Baird’s Sparrow (Ammodramus bairdii), Chestnut-collared (Calcarius ornatus) and McCown’s (Rhynchophanes mccownii) longspurs were particularly sensitive to habitat amount and had reduced densities in grass-poor landscapes despite local conditions. Continentally, the breeding range of Sprague’s Pipit was restricted to areas with a high proportion of continuous grassland and a relatively cool, moist climate. Most of the pipit population (70%) relied on private lands and a quarter occurred in habitat at risk of future tillage. Spatially hierarchical models placing response to local habitat within its landscape context revealed that broad-scale patterns in land use and grassland productivity constrained the continental distribution of pipits and Chestnut-collared Longspur. Findings suggest that maintenance of large and intact grassland landscapes should be a top conservation priority. Remaining populations rely on private land, emphasizing the importance of voluntary approaches that incentivize good stewardship. Accounting for interactions between climate, soils and livestock within existing grassland landscapes may enable managers to maintain high bird diversity

Factors Influencing Wheat Yield and Variability: Evidence from Manitoba, Canada

Production functions to explain regional wheat yields have not been studied extensively in the Canadian prairies. The objective of this study is to employ a Just-Pope production function to examine the relationship between fertilizer inputs, soil quality, biodiversity indicators, cultivars qualifying for Plant Breeders’ Rights (PBR), and climatic conditions on the mean and variance of spring wheat yields. Using regional-level wheat data from Manitoba, Canada, model results show nitrogen fertilizer, temporal diversity, and PBR wheat cultivars are associated with increased yield variance. Mean wheat yield is reduced by the proportion of land in wheat, the interaction of growing temperature and precipitation, and spatial diversity. By contrast, higher soil quality and PBR wheat cultivars increase mean yield. The wheat yield increases attributed to PBR range from 37.2 (1.4%) to 54.5 kg/ha (2.0%). Plant Breeders’ Rights may have enhanced royalties from increased certified seed sales, but the benefits in terms of higher wheat yield or lower yield variability are limited. Future research is required to understand the interactive effects of fertilization practices, genetic diversity, and environmental conditions on regional wheat yield stability.climate, fertilizer, Manitoba, Plant Breeders’ Rights, production risk, wheat, yield, Agribusiness, Crop Production/Industries, Farm Management, Land Economics/Use, Productivity Analysis, Risk and Uncertainty, O18, Q16,

A Forest of Blue - Canada's Boreal Forest, the World's Waterkeeper

Describes how the lakes, rivers, and wetlands comprising the boreal forest preserve biodiversity, mitigate global climate change effects, and offer food and cultural benefits to rural communities. Suggests policies to limit damaging industrial activities

Vulnerability of Northern Prairie Wetlands to Climate Change

The prairie pothole region (PPR) lies in the heart of North America and contains millions of glacially formed, depressional wetlands embedded in a landscape matrix of natural grassland and agriculture. These wetlands provide valuable ecosystem services and produce 50% to 80% of the continent\u27s ducks. We explored the broad spatial and temporal patterns across the PPR between climate and wetland water levels and vegetation by applying a wetland simulation model (WETSIM) to 18 stations with 95-year weather records. Simulations suggest that the most productive habitat for breeding waterfowl would shift under a drier climate from the center of the PPR (the Dakotas and southeastern Saskatchewan) to the wetter eastern and northern fringes, areas currently less productive or where most wetlands have been drained. Unless these wetlands are protected and restored, there is little insurance for waterfowl against future climate warming. WETSIM can assist wetland managers in allocating restoration dollars in an uncertain climate future