Demonstrating Spatial Patterns of Crop Productivity in a Minnesota Corn Field Using Hierarchical Multiple Regression Models and Ordination

Accurate and timely assessment of within-field crop vigor heterogeneity is essential for detecting field-wide crop productivity and yield, contributing to improvements in the management of corn fields. Yet, studies designed to explore the spatial heterogeneity of crop vigor in corn over different productivity zones, where soil nutrient characteristics are known to limit crop productivity during the growing season, as yet been reported. We assessed whether changes in temporal weather conditions within a growing season, contribute to crop vigor variability. Furthermore, we evaluated whether within-season changes in precipitation and temperature contribute to variable nutrient concentrations within different productivity zones. More so, we utilized random forest regression to calculate the relative importance of predictor variables to crop vigor variability. We then employed hierarchical multiple regression (HMR) to build several regression models to determine whether the collinearity of variables (soil characteristics) showed a significant improvement in the R2 i.e., the proportion of explained variance in crop vigor response. The principal component analysis (PCA) was employed to find components that express as much of the inherent variability of the complete data set as possible as well as, to plot how variables map relative to field productivity or management zones. We inferred that, changes in precipitation and temperature during the growing season influence soil nutrient concentrations within productivity zones especially, potassium, calcium, nitrogen, phosphorus, and magnesium. We hypothesize that, significant and yet subtle crop vigor differences can be observed within field productivity zones attributed to the heterogeneity of soil macro nutrient concentrations within corn fields, using the combined utility of remote sensing and hybrid statistical approaches. Thus, aiding farmers to ascertain, early season, whether they will obtain a poor harvest or not, improve on soil nutrient use efficiency, and field management practices to ensure a bumper harvest

Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model

Bayesian inference methods are applied within a Bayesian hierarchical modelling framework to the problems of joint state and parameter estimation, and of state forecasting. We explore and demonstrate the ideas in the context of a simple nonlinear marine biogeochemical model. A novel approach is proposed to the formulation of the stochastic process model, in which ecophysiological properties of plankton communities are represented by autoregressive stochastic processes. This approach captures the effects of changes in plankton communities over time, and it allows the incorporation of literature metadata on individual species into prior distributions for process model parameters. The approach is applied to a case study at Ocean Station Papa, using Particle Markov chain Monte Carlo computational techniques. The results suggest that, by drawing on objective prior information, it is possible to extract useful information about model state and a subset of parameters, and even to make useful long-term forecasts, based on sparse and noisy observations

Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems

Metabolic exchange mediates interactions among microbes, helping explain diversity in microbial communities. As these interactions often involve a fitness cost, it is unclear how stable cooperation can emerge. Here we use genome-scale metabolic models to investigate whether the release of “costless” metabolites (i.e. those that cause no fitness cost to the producer), can be a prominent driver of intermicrobial interactions. By performing over 2 million pairwise growth simulations of 24 species in a combinatorial assortment of environments, we identify a large space of metabolites that can be secreted without cost, thus generating ample cross-feeding opportunities. In addition to providing an atlas of putative interactions, we show that anoxic conditions can promote mutualisms by providing more opportunities for exchange of costless metabolites, resulting in an overrepresentation of stable ecological network motifs. These results may help identify interaction patterns in natural communities and inform the design of synthetic microbial consortia.We thank Dr. Niels Klitgord for pioneering ideas that inspired launch of this work. We are also grateful to David Bernstein, Joshua E. Goldford, Meghan Thommes, Demetrius DiMucci, and all members of the Segre Lab for helpful discussions. A.R.P. is supported by a National Academies of Sciences, Engineering, and Medicine Ford Foundation Predoctoral Fellowship and a Howard Hughes Medical Institute Gilliam Fellowship. This work was supported by funding from the Defense Advanced Research Projects Agency (purchase request no. HR0011515303, contract no. HR0011-15-C-0091), the U.S. Department of Energy (grants DE-SC0004962 and DE-SC0012627), the NIH (grants 5R01DE024468, R01GM121950, and Sub_P30DK036836_P&F), the National Science Foundation (grants 1457695 and NSFOCE-BSF 1635070), MURI Grant W911NF-12-1-0390, the Human Frontiers Science Program (grant RGP0020/2016), and the Boston University Inter-disciplinary Biomedical Research Office. (National Academies of Sciences, Engineering, and Medicine Ford Foundation Predoctoral Fellowship; Howard Hughes Medical Institute Gilliam Fellowship; HR0011515303 - Defense Advanced Research Projects Agency; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; DE-SC0004962 - U.S. Department of Energy; DE-SC0012627 - U.S. Department of Energy; 5R01DE024468 - NIH; R01GM121950 - NIH; Sub_P30DK036836_PF - NIH; 1457695 - National Science Foundation; NSFOCE-BSF 1635070 - National Science Foundation; W911NF-12-1-0390 - MURI Grant; RGP0020/2016 - Human Frontiers Science Program; Boston University Inter-disciplinary Biomedical Research Office)Published versio

Modular System for Shelves and Coasts (MOSSCO v1.0) - a flexible and multi-component framework for coupled coastal ocean ecosystem modelling

Shelf and coastal sea processes extend from the atmosphere through the water column and into the sea bed. These processes are driven by physical, chemical, and biological interactions at local scales, and they are influenced by transport and cross strong spatial gradients. The linkages between domains and many different processes are not adequately described in current model systems. Their limited integration level in part reflects lacking modularity and flexibility; this shortcoming hinders the exchange of data and model components and has historically imposed supremacy of specific physical driver models. We here present the Modular System for Shelves and Coasts (MOSSCO, http://www.mossco.de), a novel domain and process coupling system tailored---but not limited--- to the coupling challenges of and applications in the coastal ocean. MOSSCO builds on the existing coupling technology Earth System Modeling Framework and on the Framework for Aquatic Biogeochemical Models, thereby creating a unique level of modularity in both domain and process coupling; the new framework adds rich metadata, flexible scheduling, configurations that allow several tens of models to be coupled, and tested setups for coastal coupled applications. That way, MOSSCO addresses the technology needs of a growing marine coastal Earth System community that encompasses very different disciplines, numerical tools, and research questions.Comment: 30 pages, 6 figures, submitted to Geoscientific Model Development Discussion

A datamining approach to identifying spatial patterns of phosphorus forms in the Stormwater Treatment Areas in the Everglades

The Everglades ecosystem in Florida, USA, is naturally phosphorus (P) limited, and faces threats of ecosystem change and associated losses to habitat, biodiversity, and ecosystem function if subjected to high inflows of P and other nutrients. In addition to changes in historic hydropattern, upstream agriculture (sugar cane, vegetable, citrus) and urbanization has placed the Everglades at risk due to nutrient-rich runoff. In response to this threat, the Stormwater Treatment Areas (STAs) were constructed along the northern boundary of the Everglades as engineered ecological systems designed to retain P from water flowing into the Everglades. This research investigated data collected over a period from 2002 to 2014 from the interior of the STAs using data mining and analysis techniques including (a) exploratory methods such as Principal Component Analysis to test for patterns and groupings in the data, and (b) modelling approaches to test for predictive relationships between environmental variables. The purpose of this research was to reveal and compare spatial trends and relationships between environmental variables across the various treatment cells, flow-ways, and STAs. Common spatial patterns and their drivers indicated that the flow-ways do not function along simple linear gradients; instead forming zonal patterns of P distribution that may increasingly align with the predominant flow path over time. Findings also indicate that the primary drivers of the spatial distribution of P in many of these systems relate to soil characteristics. The results suggest that coupled cycles may be a key component of these systems; i.e. the movement and transformation of P is coupled to that of nitrogen (N)

Key drivers of stream water quality along an urban-rural transition : a watershed-scale perspective

Detecting trends in stream water quality is one of the key objectives of environmental monitoring. Identifying factors controlling stream water pollutants is challenging due to the diversity of potential sources, pathways, and processes. Natural processes regulating the water quality of a watershed are often affected by anthropogenic activities, resulting in the redistribution of runoff from base flow to storm flow and the introduction of new pollutant sources. Despite the observed consequences of urbanization, a lack of understanding of the factors simultaneously controlling water quality is among the biggest gaps in our current knowledge of hydrogeography. Moreover, prevailing discussions of land-cover effects often neglect the potential contribution of other factors, such as surficial deposits, in stream water concentrations. This thesis aims to 1) examine the most influential watershed properties determining spatial variation in stream water quality; 2) identify key water quality and watershed variables controlling stream biotic responses (i.e. diatom community composition); 3) investigate the effects of multiscale temporal variation on urban runoff in cold climatic regions; and 4) evaluate whether advanced statistical methods are applicable in hydrogeographical modeling of small watersheds. To fulfill these objectives, spatial watershed-scale analyses were conducted using modern non-parametric approaches and theory-driven methods such as structural equation modeling. This thesis is based on unique data sets of both multibasin and multiyear sampling and spatial data from the Helsinki region, southern Finland. A combination of GIS-based approaches and statistical analyses revealed significant links and novel insights into complex relationships between water quality and spatial biogeophysical properties of the surrounding landscape. The importance of land cover was emphasized throughout the thesis. Under base flow conditions the significance of soil type was mainly controlled by land cover. Further, this thesis demonstrates how land cover and stream water quality strongly determine the spatial assemblages of aquatic biota, as elevated pollutant levels were linked to decreased species richness and dominance of more tolerant species of diatom taxa. From a temporal perspective, the results suggest that urban runoff pollution is a chronic phenomenon, and is controlled by both runoff volume (summer) and pollutant sources (winter). Both the divergent temporal behavior and dominant role of diffuse pollution sources indicated challenges for stream water management practices. Based on the observed substance levels, year-round runoff treatment in urban areas is highly recommended. Finally, this thesis increases our knowledge of stream water quality variation in space and time. In this thesis, key local phenomena in contemporary hydrogeography were identified with a spatial modeling framework. The inclusion of indirect effects into the models improved our understanding of these systems, thus emphasizing the importance of simultaneously studying multiple concurrent processes.Yksi ympäristötutkimuksen tärkeimmistä tavoitteista on tunnistaa virtavesien, kuten purojen ja jokien laadun vaihtelu. Tärkeimpien vedenlaatua säätelevien ympäristötekijöiden tunnistaminen on kuitenkin vaikeaa aineiden lukuisten lähteiden, kulkeutumisreittien ja prosessien takia. Ihmistoiminta vaikuttaa myös luonnollisiin vedenlaatua sääteleviin tekijöihin valuma-alueella, muuttaen virtaamaa pohjavalunnasta enemmän sateiden yhteyteen, ja tuottaen uusia haitta-ainelähteitä. Vaikka kaupungistumisen vaikutukset ympäristöön ovat kiistattomia, emme juurikaan tunne eri ympäristötekijöiden yhteisvaikutusta vaikutusta vedenlaatuun. On myös huomionarvoista, että tieteellinen keskustelu maanpeitteen vaikutuksista vedenlaatuun poissulkee usein muiden, kuten maaperätekijöiden vaikutukset virtavesien pitoisuuksiin. Tutkimuksen tavoitteena on 1) tunnistaa virtavesien laadun alueellista vaihtelua vahvimmin säätelevät tekijät 2) tunnistaa tärkeimmät elollisia vedenlaatuindikaattoreita säätelevät valuma-alue- ja vedenlaatutekijät, 3) tutkia kylmän ilmaston kaupungin virtavesien (huleveden) ajallista vaihtelua ja 4) arvioida, voiko nykyaikaisia tilastollisia menetelmiä soveltaa pienten valuma-alueiden alueellisessa mallinnuksessa. Tutkimus perustuu moderneihin ja teorialähtöisiin menetelmiin, kuten rakenneyhtälömalliin. Työssä sovelletaan sekä alueellisesti laajaa että ajallisesti kattavaa mittausaineistoa Helsingin seudulta, Etelä-Suomesta. Työssä käytetyt paikkatietopohjaiset lähestymistavat ja tilastolliset analyysit tuottivat uutta tietoa tärkeistä vedenlaadun ja ympäristötekijöiden monimutkaisista suhteista. Maanpeitteen tärkeys vedenlaatua säätelevänä tekijänä korostui läpi tutkielman. Sateettomana aikana eli pohjavaluntatilanteissa maaperän tärkeys vedenlaadulle oli vahvasti maanpeitteen säätelemä. Tulokset osoittavat, miten sekä maanpeite että vedenlaatu molemmat vaikuttavat virtavesien eliölajirunsauteen. Vesien korkeat ainepitoisuudet olivat suoraan yhteydessä pienempään lajirunsauteen ja enemmän tolerantteihin lajeihin. Ajallisesti tarkasteltuna kaupunkivesien korkeat pitoisuudet ovat jatkuva ongelma viitaten krooniseen ympäristöongelmaan. Pitoisuuksien ajallinen vaihtelu on kesäaikaan vahvasti virtaaman ja talviaikaan ainelähteiden säätelemä. Sekä virtavesien laadun voimakas ajallinen vaihtelu, että hajakuormituksen suuri merkitys ovat merkittäviä haasteita virtavesien kestävälle hallinnalle. Tulosten perusteella kaupunkialueiden hulevedet tarvitsisivat ympärivuotista käsittelyä ennen niiden purkamista muihin vesiin. Lopuksi, työ lisäsi ymmärrystämme virtavesien laadun alueellisesta ja ajallisesta vaihtelusta. Tutkimuksessa tunnistettiin tärkeimmät paikalliset hydrogeografiset ilmiöt soveltaen alueellisen mallintamisen menetelmiä. Epäsuorien vaikutusten tarkastelu lisäsi ymmärrystämme näistä monimutkaisista systeemeistä, korostaen sitä, miten tärkeää on tarkastella useita prosesseja samanaikaisesti

Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms

Patterns, Processes, And Scale: An Evaluation Of Ecological And Biogeochemical Functions Across An Arctic Stream Network

Ecosystems are highly variable in space and time. Understanding how spatial and temporal scales influence the patterns and processes occurring across watersheds presents a fundamental challenge to aquatic ecologists. The goal of this research was to elucidate the importance of spatial scale on stream structure and function within the Oksrukuyik Creek, an Arctic watershed located on the North Slope of Alaska (68°36’N, 149°12’W). The studies that comprise this dissertation address issues of scale that affect our ability to assess ecosystem function, such as: methodologies used to scale ecosystem measurements, multiple interacting scales, translation between scales, and scale-dependencies. The first methodological study examined approaches used to evaluate chlorophyll a in ethanol extracts of aquatic biofilms. Quantification of chlorophyll a is essential to the study of aquatic ecosystems, yet differences in methodology may introduce significant errors to its determination that can lead to issues of comparability between studies. A refined analytical procedure for the determination of chlorophyll a was developed under common acidification concentrations at multiple common reaction times. The refined procedure was used to develop a series of predictive equations that could be used to correct and normalize previously evaluated chlorophyll a data. The predictive equations were validated using benthic periphyton samples from northern Alaska and northwestern Vermont, U.S.A. The second study examined interaction and translation between scales by examining how normalization approaches affect measurements of metabolism and nutrient uptake in stream sediment biofilms. The effect of particle size and heterogeneity on rates of biofilm metabolism and nutrient uptake was evaluated in colonized and native sediments normalized using two different scaling approaches. Functional rates were normalized by projected surface area and sediment surface area scaling approaches, which account for the surface area in plan view (looking top-down) and the total surface area of all sediment particles, respectively. Findings from this study indicated that rates of biogeochemical function in heterogeneous habitats were directly related to the total sediment surface area available for biofilm colonization. The significant interactions between sediment surface area and rates of respiration and nutrient uptake suggest that information about the size and distribution of sediment particles could substantially improve our ability to predict and scale measurements of important biogeochemical functions in streams. The final study examined how stream nutrient dynamics are influenced by the presence or absence of lakes across a variety of discharge conditions and how catchment characteristics can be used to predict stream nutrients. Concentrations of dissolved organic carbon (DOC) and other inorganic nutrients were significantly greater in streams without lakes than in streams in with lakes and DOC, total dissolved nitrogen (TDN), and soluble reactive phosphorus concentrations increased as a function of discharge. Catchment characteristic models explained between 20% and 76% of the variance of the nutrients measured. Organic nutrient models were driven by antecedent precipitation and watershed vegetation cover type while inorganic nutrients were driven by antecedent precipitation, landscape characteristics and reach vegetation cover types. The developed models contribute to existing and future understanding of the changing Arctic and lend new confidence to the prediction of nutrient dynamics in streams where lakes are present