Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data

This paper aims to provide guidance to ecologists with limited experience in spatial analysis to help in their choice of techniques, It uses examples to compare methods of spatial analysis for ecological field data. A taxonomy of different data types is presented, including point- and area-referenced data, with and without attributes. Spatially and non-spatially explicit data are distinguished. The effects of sampling and other transformations that convert one data type to another are discussed; the possible loss of spatial information is considered. Techniques for analyzing spatial pattern, developed in plant ecology, animal ecology, landscape ecology, geostatistics and applied statistics are reviewed briefly and their overlap in methodology and philosophy noted. The techniques are categorized according to their output and the inferences that may be drawn from them, in a discursive style without formulae. Methods are compared for four case studies with field data covering a range of types. These are: 1) percentage cover of three shrubs along a line transect 2) locations and volume of a desert plant in a I ha area: 3) a remotely-sensed spectral index and elevation from 10(5) km(2) of a mountainous region; and 4) land cover from three rangeland types within 800 km2 of a coastal region. Initial approaches utilize mapping, frequency distributions and variance-mean indices. Analysis techniques we compare include: local quadrat variance, block, quadrat variance, correlograms, variograms, angular correlation, directional variograms, wavelets, SADIE, nearest neighbour methods, Ripley's L(t), and various landscape ecology metrics. Our advice to ecologists is to use simple visualization techniques for initial analysis, and subsequently to select methods that are appropriate for the data type and that answer their specific questions of interest, It is usually prudent to employ several different techniques

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

Improved leaf area index estimation by considering both temporal and spatial variations

Variations in Leaf Area Index (LAI) can greatly alter output values and patterns of various models that deal with energy flux exchange between the land surface and the atmosphere. Customarily, such models are initiated by LAI estimated from satellite-level Vegetation Indices (VIs) including routinely produced Normalized Difference Vegetation Index (NDVI) products. However, the accuracy from LAI-VI relationships greatly varies due to many factors, including temporal and spatial variations in LAI and a selected VI. In addition, NDVI products derived from various sensors have demonstrated variations in a certain degree on describing temporal and spatial variations in LAI, especially in semi-arid areas. This thesis therefore has three objectives: 1) determine a suitable VI for quantifying LAI temporal variation; 2) improve LAI estimation by considering both temporal and spatial variations in LAI; and 3) evaluate routinely produced NDVI products on monitoring temporal and spatial variations in LAI. The study site was set up in conserved semi-arid mixed grassland in St. Denis, Saskatchewan, Canada. One 600 m - long sampling transect was set up across the rolling typography, and six plots with a size of 40 × 40 m each were randomly designed and each was in a relatively homogenous area. Plant Area Index (PAI, which was validated to obtain LAI), ground hyperspectral reflectance, ground covers (grasses, forbs, standing dead, litter, and bare soil), and soil moisture data were collected over the sampling transect and plots from May through September, 2008. Satellite data used are SPOT 4/5 images and 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) 250m, 1km as well as 10-day SPOT-vegetation (SPOT-VGT) NDVI products from May to October, 2007 and 2008. The results show that NDVI is the most suitable VI for quantifying temporal variation of LAI. LAI estimation is much improved by considering both temporal and spatial variations. Based on the ground reflectance data, the r2 value is increased by 0.05, 0.31, and 0.23 and an averaged relative error is decreased by 1.57, 1.62, and 0.67 in the early, maximum, and late growing season, respectively. MODIS 250m NDVI products are the most useful datasets and MODIS 1km NDVI products are superior to SPOT-VGT 1km composites for monitoring intra-annual spatiotemporal variations in LAI. The proposed LAI estimation approach can be used in other studies to obtain more accurate LAI, and thus this research will be beneficial for grassland modeling

Relationship between Space-Based Vegetation Productivity Index and Radial Growth of Main Tree Species in the Dry Afromontane Forest Remnants of Northern Ethiopia

Investigating the relations between Normalized Difference Vegetation Index (NDVI) and tree growth is vital for quantifying ecosystem primary productivity over large spatial and long temporal scales. In this study, the relationships between forest growth (tree-ring width indices) and space-based measurement of vegetation activity (NDVI) were examined in the dry Afromontane forest remnants of northern Ethiopia. The results showed that radial growth of the main tree species (Juniperus procera, Olea europaea and Podocarpus falcatus) is positively correlated with inter-annual NDVI values. Moreover, the relationships between ring width – rainfall and rainfall – NDVI were positive and significant (p<0.05), suggesting that rainfall seasonality is an overriding growth-limiting factor in the study region. Rainfall during the wet-season largely controls cambial activities and phenological processes of the founding tree species, and hence affects overall vegetation dynamics in the region. Overall, the study showed the coupling of tree-ring growth and NDVI values with potential implications for understanding forest growth dynamics. Thus, it gives insights to the applicability of NDVI – treerings integration approach to predict landscape-level patterns of vegetation productivityKeywords: Dry Afromontane Forest, Remote-sensing, Tree-ring

Characterizing Spatiotemporal Patterns of White Mold in Soybean across South Dakota Using Remote Sensing

Soybean is among the most important crops, cultivated primarily for beans, which are used for food, feed, and biofuel. According to FAO, the United States was the biggest soybeans producer in 2016. The main soybean producing regions in the United States are the Corn Belt and the lower Mississippi Valley. Despite its importance, soybean production is reduced by several diseases, among which Sclerotinia stem rot, also known as white mold, a fungal disease that is caused by the fungus Sclerotinia sclerotiorum is among the top 10 soybean diseases. The disease may attack several plants and considerably reduce yield. According to previous reports, environmental conditions corresponding to high yield potential are most conducive for white mold development. These conditions include cool temperature (12-24 °C), continued wet and moist conditions (70-120 h) generally resulting from rain, but the disease development requires the presence of a susceptible soybean variety. To better understand white mold development in the field, there is a need to investigate its spatiotemoral characteristics and provide accurate estimates of the damages that white mold may cause. Current and accurate data about white mold are scarce, especially at county or larger scale. Studies that explored the characteristics of white mold were generally field oriented and local in scale, and when the spectral characteristics were investigated, the authors used spectroradiometers that are not accessible to farmers and to the general public and are mostly used for experimental modeling. This study employed free remote sensing Landsat 8 images to quantify white mold in South Dakota. Images acquired in May and July were used to map the land cover and extract the soybean mask, while an image acquired in August was used to map and quantify white mold using the random forest algorithm. The land cover map was produced with an overall accuracy of 95% while white mold was mapped with an overall accuracy of 99%. White mold area estimates were respectively 132 km2, 88 km2, and 190 km2, representing 31%, 22% and 29% of the total soybean area for Marshall, Codington and Day counties. This study also explored the spatial characteristics of white mold in soybean fields and its impact on yield. The yield distribution exhibited a significant positive spatial autocorrelation (Moran’s I = 0.38, p-value \u3c 0.001 for Moody field, Moran’s I = 0.45, p-value \u3c 0.001, for Marshall field) as an evidence of clustering. Significant clusters could be observed in white mold areas (low-low clusters) or in healthy soybeans (high-high clusters). The yield loss caused by the worst white mold was estimated at 36% and 56% respectively for the Moody and the Marshall fields, with the most accurate loss estimation occurring between late August and early September. Finally, this study modeled the temporal evolution of white mold using a logistic regression analysis in which the white mold was modeled as a function of the NDVI. The model was successful, but further improved by the inclusion of the Day of the Year (DOY). The respective areas under the curves (AUC) were 0.95 for NDVI and 0.99 for NDVI+DOY models. A comparison of the NDVI temporal change between different sites showed that white mold temporal development was affected by the site location, which could be influenced by many local parameters such as the soil properties, the local elevation, management practices, or weather parameters. This study showed the importance of freely available remotely sensed satellite images in the estimation of crop disease areas and in the characterization of the spatial and temporal patterns of crop disease; this could help in timely disease damage assessment

A Model For Determining Drivers of Phenology in Western United States Rangelands

Plant phenology has long been used as an indicator of climate. Recent changes in plant phenology are evidence of the influence of climate change. Modeling plant phenology has become an effective tool to understand the impacts of climate change. Using machine learning techniques I developed a modeling process for accurately predicting phenology across a diverse landscape. This model uses individual site data to set site specific climate thresholds for plant phenology. This model also identifies the limiting factors to vegetation phenology for rangelands in the western United States. NDVI remotely sensed data was used to quantify land surface phenology and DAYMET data was used to quantify climate variables. I found that random forest modeling can predict observed plant phenological dates across western rangelands to within a single day for start of season, end of season and day of max NDVI. The model can also identify the most highly correlated variables for phenological events in the study area and highlight which variables limit growth in different vegetative communities. These results confirm previous work on drivers of temperate phenology. This study’s results show that random forest modeling can accurately identify the most important climate variables for phenological events and use those variables to predict phenological events on a large spatial scale

Master of Science

thesisThe climate of urban areas is influenced by the composition and configuration of different land cover types. Urban forests increase human comfort in urban areas by cooling the environment through evapotranspiration and shade. A tradeoff of urban forests in semiarid and arid climates is that they require large quantities of irrigation water to maintain. This study aimed to quantify the relationship between urban vegetation and land surface temperature (LST). Datasets derived from high-resolution lidar and National Agriculture Imagery Program (NAIP) orthoimagery were used in a random forest algorithm to classify urban vegetation, human-made and natural surfaces at a 1-meter scale, in the Salt Lake Valley of Utah. The resulting classification accuracy was 94%. LST was retrieved from an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scene captured on a hot, summer day. Percentages of each land cover class were calculated per ASTER pixel. These composition variables were compared to LST using Pearson’s correlation analysis and were also used to create a multiple linear regression model. Percent deciduous tree cover was the variable most strongly correlated with LST, with a correlation coefficient of -0.55. Irrigated low-stature vegetation was also negatively correlated with LST (-0.33). Residuals from the multiple linear regression model varied over space, and additional dates of ASTER imagery are needed to determine whether these second-order spatial patterns are persistent

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

Vegetation- memory effects and their association with vegetation resilience in global drylands

Vegetation memory describes the effect of antecedent environmental and ecological conditions on the present ecosystem state and has been proposed as an important proxy for vegetation resilience. In particular, strong vegetation memory has been identified in dryland regions, but the factors underlying the spatial patterns of vegetation memory remain unknown. We aim to map the components and drivers of vegetation memory in dryland regions using state-of-the-art climate reanalysis data and refined approaches to identify vegetation-memory characteristics across dryland regions worldwide. Using a framework which distinguishes between intrinsic and extrinsic ecological memory, we show that (i) intrinsic memory is a much stronger component than extrinsic memory in the majority of dryland regions and (ii) climate reanalysis datasets change the detection of extrinsic vegetation memory in some global dryland regions. Synthesis. Our study offers a global picture of the vegetation response to two climate variables using satellite data, information which is potentially relevant for mapping components and properties of vegetation responses worldwide. However, the large differences in the spatial patterns in intrinsic vegetation memory in our study compared to previous analyses show the overall sensitivity of this component to the initial choice of extrinsic predictor variables. As a result, we caution against using the oversimplified link between intrinsic vegetation-memory and vegetation recovery rates at large spatial scales.publishedVersio