Seeding Methods for Revegetation in Western Montana

Seeding is a commonly used tool to revegetate riparian soils made bare during channel reconfiguration, but its efficacy and factors that limit its success remain largely understudied. Ninemile Creek in Western Montana has been degraded by placer mining, which has filled the riparian areas with 10 m tall gravel piles. Extensive restoration efforts have been conducted by Trout Unlimited (TU) in the Ninemile Valley, and seeding has been used in each stage to attempt to revegetate the newly reestablished riparian areas of the Ninemile valley. However, the success of this approach has been limited, presenting an opportunity to study the optimization of this technique. It is known that soil fertility can influence plant growth and establishment. The rate of addition of seeds can also influence play a role, as does the timing of seed addition. In our study, we will determine the ideal conditions and techniques for revegetating the riparian area in the Ninemile by looking at the effect of fertilization, seed addition rate, and seasonal timing of seed addition. We will establish plots in which we will add fertilizer or low or high seed addition rates. These plots will all be seeded in spring of 2021 and will be compared to sites where seeds were added in the Fall of 2020. Our results will help us identify factors limiting the establishment of riparian plants from seed, and help provide insights to TU and others on how to optimize their seeding effort

Improvements to Bayesian Gene Activity State Estimation from Genome-Wide Transcriptomics Data

An important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with certainty. Second, MultiMM assumes that genes that change states in the dataset can be distinguished with certainty from those that remain in one state. Third, the model can be sensitive to extreme measures (outliers) of gene expression. In this manuscript, we propose a modified Bayesian approach, which addresses these three limitations by improving outlier handling and by explicitly modeling network and other uncertainty yielding improved gene activity state estimates when compared to MultiMM

Physiological and Morphological Responses of Cassava Genotypes to Fertilization Regimes in Chromi-Haplic Acrisols Soils

The objective of this study was to evaluate the performances of three cassava genotypes on yield, physiology and morphological traits under different fertilization regimes. A field experiment was conducted in a split-plot design for two consecutive seasons in the Mansa district of the Luapula Province of Northern Zambia in the highly weathered Chromi-haplic Acrisol soils. Four fertilization regimes, control-M3, lime-M1, NPK fertilizer-M4 and NPK fertilizer + lime-M2 were the main plots, while three varieties (Mweru-V1, Bangweulu-V2 and Katobamputa (local)-V3) were subplots. Periodic measurements of leaf area index, light interception, yield and yield components from 75 days after planting (DAP) up to 410 DAP and daily weather measurements of data were recorded. Fertilization significantly increased the radiation use efficiency (RUE) and light extinction coefficient (K) in two seasons compared to the control. Significant fertilization regimes and varietal effects were observed for seasonal LAI, stem yield, root yield, biomass, harvest index (HI), tuber number, root diameter, plant height and SPAD (chlorophyll index). A significant year’s effects on root yield, yield components and physiological performances were observed while significant fertilization × variety interaction was observed on seasonal LAI, tuber number, root diameter, plant height and SPAD. Significant fertilization × year interaction effects were observed on root yield, yield components and physiological performances. Variety × year interaction was significant for seasonal LAI, stem yield, harvest index and plant height and no three-way interactions were observed on all the traits. NPK fertilizer + lime and NPK fertilizer treatments may be adopted to increase the response of cassava varietal yield, physiology and morphological traits in low soil nutrient conditions under high rain-fed conditions

Cropping Practices and Effects on Soil Nutrient Adequacy Levels and Cassava Yield of Smallholder Farmers in Northern Zambia

Cassava is a staple food and a major source of income for many smallholder farmers. However, its yields are less than 6 t ha-1 compared to a potential yield of 20-25 t ha-1 in Zambia. Understanding cropping practices and constraints in cassava production systems is imperative for sustainable intensification. Therefore, a survey of 40 households each with three fields of cassava at 12, 24, and 36 months after planting (MAP) was conducted. Analyzed soil data, leaf area index (LAI), intercepted photosynthetically active radiation, and management practices from 120 fields were collected and subjected to descriptive statistics. To explain yield differences within the same cassava growth stage group, the data were grouped into low- and high-yield categories using the median, before applying a nonparametric test for one independent sample. Stepwise regressions were performed on each growth stage and the whole dataset to determine factors affecting tuber yield. Cassava intercropping and monocropping systems were the main cropping systems for the 12 and 24-36 MAP, respectively. Cassava yields declined by 209 and 633 kg ha-1 at 12 and 36 MAP due to soil nutrient depletion for each year of cultivation until field abandonment at 8-9 years. Fresh cassava yields ranged from 3.51-8.51, 13.52-25.84, and 16.92-30.98 t ha-1 at 12, 24, and 36 MAP, respectively. For every one unit increment in exchangeable K (cmol (+)/kg soil), cassava yield increased by 435, 268, and 406 kg ha-1 at 12, 24, and 36 MAP, respectively. One unit increment of magnesium (cmol (+)/kg soil) gave the highest yield increase of 525 kg ha-1 at 24 MAP. The low levels of soil organic carbon explained the deficient nitrogen in cassava fields, which limits the LAI growth and consequently reduced intercepted radiation and low yields. The effect of exchangeable K on growth was limited by the moderate availability of Mg and low N, thus the need for balanced fertilizer regimes. © 2021 Peter Kaluba et al

Examining Factors Limiting Post-Restoration Riparian Revegetation in Western Montana

Revegetating riparian soils made bare during restoration is important to stabilize soils and minimize the spread of non-native invasive plants. Seeding is a commonly used approach for revegetation, but its efficacy and factors that limit its success remain understudied. It is known that nutrient availability can limit plant growth and establishment, and that seed addition rates can also play a role. However, the success of these approaches has varied, presenting an opportunity to study the optimization of revegetation techniques. In this study, we examined ways to increase the efficacy of reseeding riparian areas on a reach of Ninemile Creek in Western Montana by looking for evidence of nutrient limitation and seeding limitation. Ninemile Creek was historically degraded by placer mining, which covered the riparian areas with 10 meter tall gravel piles. Extensive restoration efforts have been conducted by Trout Unlimited in the Ninemile Valley through removing the gravel piles and reconfiguring the stream channel and floodplain. We established plots in a reach that had been restored in 2020 with a combination of nutrient additions and low or high density seeding rates. Our results suggest that nutrients were more limiting to the establishment of riparian plants than seed at this site, providing new insights to Trout Unlimited and others on how to optimize their revegetation efforts

glmm: Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation (R software package)

Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information. (v. 1.0

Likelihood-Based Inference for Generalized Linear Mixed Models: Inference with the R Package glmm

The R package glmm enables likelihood‐based inference for generalized linear mixed models with a canonical link. No other publicly available software accurately conducts likelihood‐based inference for generalized linear mixed models with crossed random effects. glmm is able to do so by approximating the likelihood function and two derivatives using importance sampling. The importance sampling distribution is an essential piece of Monte Carlo likelihood approximation, and developing a good one is the main challenge in implementing it. The package glmm uses the data to tailor the importance sampling distribution and is constructed to ensure finite Monte Carlo standard errors. In the context of the generalized linear mixed model, the salamander model with crossed random effects has become a benchmark example. We use this model to illustrate the complexities of the likelihood function and to demonstrate the use of the R package glmm

Copula models of economic capital for insurance companies

The objective of this project is to construct, select, calibrate and validate a practically applicable copula internal model of economic capital for insurance companies. Copula methodology makes it possible to address multiple dependent risk factors. We identify the relevant set of asset and liability variables, and suggest a copula model for the joint distribution of these variables. Estimates of economic capital can be based on the tails of this joint distribution. Models are implemented in open source software (R and Microsoft EXCEL) and tested using simulated asset/liability data. The results are presented as a finished software product which can be utilized for customization and direct user application. The novelty of the approach consists in estimating interdependent mortality, morbidity, lapse and investment risks in one multivariate model. In particular, we address the challenges that life insurance companies face in the low interest environment. This approach requires a methodology of copula model comparison and selection and implementation of Monte Carlo simulation to the estimation of economic capital

Likelihood-based inference for generalized linear mixed models: Inference with the R package glmm

The R package glmm enables likelihood-based inference for generalized linear mixed models with a canonical link. No other publicly available software accurately conducts likelihood-based inference for generalized linear mixed models with crossed random effects. glmm is able to do so by approximating the likelihood function and two derivatives using importance sampling. The importance sampling distribution is an essential piece of Monte Carlo likelihood approximation, and developing a good one is the main challenge in implementing it. The package glmm uses the data to tailor the importance sampling distribution and is constructed to ensure finite Monte Carlo standard errors. In the context of the generalized linear mixed model, the salamander model with crossed random effects has become a benchmark example. We use this model to illustrate the complexities of the likelihood function and to demonstrate the use of the R package glmm