A nonlinear mixed-effects modeling approach for ecological data: Using temporal dynamics of vegetation moisture as an example

Impact of Foliar Fungicide Application on the Culturable Fungal Endophyte Community of Soybean Seed in the Midwest United States

The purpose of our study was to determine whether the application of quinone outside inhibitor (QoI) and pyrazole-carboxamide fungicides as a tank mix would impact the endophyte community of soybean seed. Field trials during 2018 in Iowa, South Dakota, and Wisconsin, U.S.A., investigated the impact of a single combination fungicide spray at early pod set in soybeans. The composition of culturable endophytic fungi in mature soybean seed was assessed on three cultivars per state, with maturity groups (MGs) ranging from 1.1 to 4.7. An unusually wet 2018 season delayed harvest, which led to a high level of fungal growth in grain. The survey included 1,080 asymptomatic seeds that were disinfested and individually placed on 5-cm-diameter Petri plates of acidified water agar. The survey yielded 721 fungal isolates belonging to 24 putative species in seven genera; taxa were grouped into genera based on a combination of morphological and molecular evidence. The dominant genera encountered in the survey were Alternaria, Diaporthe, and Fusarium. The study showed that the fungicide treatment reduced the incidence of Fusarium in Wisconsin seed, increased the incidence of Diaporthe in seed from all states, and had no impact on the incidence of Alternaria. This is one of the first attempts to characterize the diversity of seed endophytes in soybean and the first to characterize the impacts of fungicide spraying on these endophyte communities across three states. Our study provides evidence that the impact of a fungicide spray on soybean seed endophyte communities may be influenced by site, weather, and cultivar maturity group.

The Utility of Mixed Effects Models in the Evaluation of Complex Genomic Traits In Vitro

In pharmacogenomic studies, the use of human liver microsomes as a model system to evaluate the impact of complex genomic traits (i.e., linkage-disequilibrium patterns, coding, and non-coding variation, etc.) on efficiency of drug metabolism is challenging. To accurately predict the true effect size of genomic traits requires large richly sampled datasets representative of the study population. Moreover, the acquisition of this data can be labor-intensive if the study design or bioanalytical methods are not high throughput, and it is potentially unfeasible if the abundance of sample needed for experiments is limited. To overcome these challenges, we developed a novel strategic approach using non-linear mixed effects models (NLME) to determine enzyme kinetic parameters for individual liver specimens using sparse data. This method can facilitate evaluation of the impact that complex genomic traits have on the metabolism of xenobiotics in vitro when tissue and other resources are limited. In addition to facilitating the accrual of data, it allows for rigorous testing of covariates as sources of kinetic parameter variability. In this in silico study, we present a practical application of such an approach using previously published in vitro cytochrome P450 (CYP) 2D6 data and explore the impact of sparse sampling, and experimental error on known kinetic parameter estimates of CYP2D6 mediated formation of 4-hydroxy-atomoxetine in human liver microsomes. SIGNIFICANCE STATEMENT: This study presents a novel non-linear mixed effects model (NLME)-based framework for evaluating the impact of complex genomic traits on saturable processes described by a Michaelis-Menten kinetics in vitro using sparse data. The utility of this approach extends beyond gene variant associations, including determination of covariate effects on in vitro kinetic parameters and reduced demand for precious experimental material.

Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation

In plant science, the suboptimal match of growing conditions hampers the transfer of knowledge from controlled environments in glasshouses or climate chambers to field environments. Here we present the PhenoSphere, a plant cultivation infrastructure designed to simulate field-like environments in a reproducible manner. To benchmark the PhenoSphere, the effects on plant growth of weather conditions of a single maize growing season and of an averaged season over three years are compared to those of a standard glasshouse and of four years of field trials. The single season simulation proves superior to the glasshouse and the averaged season in the PhenoSphere: The simulated weather regime of the single season triggers plant growth and development progression very similar to that observed in the field. Hence, the PhenoSphere enables detailed analyses of performance-related trait expression and causal biological mechanisms in plant populations exposed to weather conditions of current and anticipated future climate scenarios.

Background matching through fast and reversible melanin-based pigmentation plasticity in tadpoles comes with morphological and antioxidant changes

Facultative colour change is widespread in the animal kingdom, and has been documented in many distantly related amphibians. However, experimental data testing the extent of facultative colour change, and associated physiological and morphological implications are comparatively scarce. Background matching in the face of spatial and temporal environmental variation is thought to be an important proximate function of colour change in aquatic amphibian larvae. This is particularly relevant for species with long larval periods such as the western spadefoot toad, Pelobates cultripes, whose tadpoles spend up to six months developing in temporary waterbodies with temporally variable vegetation. By rearing tadpoles on different coloured backgrounds, we show that P. cultripes larvae can regulate pigmentation to track fine-grained differences in background brightness, but not hue or saturation. We found that colour change is rapid, reversible, and primarily achieved through changes in the quantity of eumelanin in the skin. We show that this increased eumelanin production and/or maintenance is also correlated with changes in morphology and oxidative stress, with more pigmented tadpoles growing larger tail fins and having an improved redox status.

Importance of mangrove plantations for climate change mitigation in Bangladesh

Mangroves have been identified as blue carbon ecosystems that are natural carbon sinks. In Bangladesh, the establishment of mangrove plantations for coastal protection has occurred since the 1960s, but the plantations may also be a sustainable pathway to enhance carbon sequestration, which can help Bangladesh meet its greenhouse gas (GHG) emission reduction targets, contributing to climate change mitigation. As a part of its Nationally Determined Contribution (NDC) under the Paris Agreement 2016, Bangladesh is committed to limiting the GHG emissions through the expansion of mangrove plantations, but the level of carbon removal that could be achieved through the establishment of plantations has not yet been estimated. The mean ecosystem carbon stock of 5-42 years aged (average age: 25.5 years) mangrove plantations was 190.1 (±30.3) Mg C ha^-1 , with ecosystem carbon stocks varying regionally. The biomass carbon stock was 60.3 (±5.6) Mg C ha^-1 and the soil carbon stock was 129.8 (±24.8) Mg C ha^-1 in the top 1 m of which 43.9 Mg C ha^-1 was added to the soil after plantation establishment. Plantations at age 5 to 42 years achieved 52% of the mean ecosystem carbon stock calculated for the reference site (Sundarbans natural mangroves). Since 1966, the 28,000 ha of established plantations to the east of the Sundarbans have accumulated approximately 76,607 Mg C year^-1 sequestration in biomass and 37,542 Mg C year^-1 sequestration in soils, totaling 114,149 Mg C year^-1 . Continuation of the current plantation success rate would sequester an additional 664,850 Mg C by 2030, which is 4.4% of Bangladesh's 2030 GHG reduction target from all sectors described in its NDC, however, plantations for climate change mitigation would be most effective 20 years after establishment. Higher levels of investment in mangrove plantations and higher plantation establishment success could contribute up to 2,098,093 Mg C to blue carbon sequestration and climate change mitigation in Bangladesh by 2030.

Adaptive monitoring of coral health at Scott Reef where data exhibit nonlinear and disturbed trends over time

Time series data are often observed in ecological monitoring. Frequently, such data exhibit nonlinear trends over time potentially due to complex relationships between observed and auxiliary variables, and there may also be sudden declines over time due to major disturbances. This poses substantial challenges for modeling such data and also for adaptive monitoring. To address this, we propose methods for finding adaptive designs for monitoring in such settings. This work is motivated by a monitoring program that has been established at Scott Reef; a coral reef off the Western coast of Australia. Data collected for monitoring the health of Scott Reef are considered, and semiparametric and interrupted time series modeling approaches are adopted to describe how these data vary over time. New methods are then proposed that enable adaptive monitoring designs to be found based on such modeling approaches. These methods are then applied to find future monitoring designs at Scott Reef where it was found that future information gain is expected to be similar across a variety of different sites, suggesting that no particular location needs to be prioritized at Scott Reef for the next monitoring phase. In addition, it was found that omitting some sampling sites/reef locations was possible without substantial loss in expected information gain, depending upon the disturbances that were observed. The resulting adaptive designs are used to form recommendations for future monitoring in this region, and for reefs where changes in the current monitoring practices are being sought. As the methods used and developed throughout this study are generic in nature, this research has the potential to improve ecological monitoring more broadly where complex data are being collected over time.

Genetic Parameters of Diameter Growth Dynamics in Norway Spruce Clones

The breeding of Norway spruce in northern Europe has substantially contributed to the production of high-quality wood. The vegetative propagation of robust elite clones could help to sustain the provision of high-quality timber in the face of changing climates. For the adequate evaluation of genetic gains, the altered tree growth dynamics of the clones need to be understood, yet essential information about the long-term growth dynamics of improvedboreal trees is still lacking. We examined a 50-year-old clonal plantation in Latvia to distinguish the clonal effects on diameter growth function parameters and estimate the genetic parameters. A mixed-effect modelling approach was used, in which the clones were applied as random effects on the parameters of the Chapman–Richard equation. All model parameters showed significant variance in the genotypic coefficients of variation CVg which ranged between 11.0 and 17.1%, with the highest being for the growth rate. The heritability (H2) of the diameter at breast height (DBH) reached 0.35 at the age of 40, while CVg decreased from 12.9% to 7.8% between the ages of 20 and 45. Age–age genotypic correlations were positive and were strong or very strong (>0.76). The realised genetic gain varied from −6.3 to +24.0% around the trial mean. A substantial improvement in DBH was indicated when elite clones were selected for vegetative propagation based not only on early measurements, but also considering the genetic variance in the model parameters.

Winter weight loss of different subspecies of honey bee Apis mellifera colonies (Linnaeus, 1758) in southwestern Sweden

Honey bees are currently facing mounting pressures that have resulted in population declines in many parts of the world. In northern climates winter is a bottleneck for honey bees and a thorough understanding of the colonies’ ability to withstand the winter is needed in order to protect the bees from further decline. In this study the influence of weather variables on colony weight loss was studied over one winter (2019–2020) in two apiaries (32 colonies in total) in southwestern Sweden with weather stations recording wind and temperature at 5-min intervals. Three subspecies of honey bees and one hybrid were studied: the native Apis mellifera mellifera, the Italian A. m. ligustica, the Carniolan A. m. carnica and the hybrid Buckfast. Additionally, we recorded Varroa mite infestation. To analyze factors involved in resource consumption, three modelling approaches using weather and weight data were developed: the first links daily consumption rates with environmental variables, the second modelled the cumulative weight change over time, and the third estimated weight change over time taking light intensity and temperature into account. Weight losses were in general low (0.039 ± 0.013kg/day and colony) and comparable to southern locations, likely due to an exceptionally warm winter (average temperature 3.5°C). Weight losses differed only marginally between subspecies with indications that A. m. mellifera was having a more conservative resource consumption, but more studies are needed to confirm this. We did not find any effect of Varroa mite numbers on weight loss. Increased light intensity and temperature both triggered the resource consumption in honey bees. The temperature effect on resource consumption is in accordance with the metabolic theory of ecology. The consequences of these findings on honey bee survival under predicted climate changes, is still an open question that needs further analysis.

Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean

Plant breeding is a decision-making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize the rate of genetic improvement and minimize the loss of useful genetic variance. For commercial plant breeders, competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast, public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing the loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short-term genetic gains from genomic selection are much greater than phenotypic selection, while phenotypic selection provides better long-term genetic gains because it retains useful genetic diversity during the early cycles of selection. With limited resources, must a soybean breeder choose between the two extreme responses provided by genomic selection or phenotypic selection? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs, and whether the breeding population should be organized as family islands. For breeding populations organized into islands, decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to 10 cycles using genomic selection and a hub network mating design, where the hub parents with the largest selection metric make large parental contributions. It also requires that the breeding populations be organized as fully connected family islands, where every island is connected to every other island, and migration rules allow the exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except that the mating design could be hub network, chain rule, or a multi-objective optimization method-based mating design. Weighted genomic selection applied to centralized populations also resulted in the realization of the greatest proportion of the genetic potential of the founders but required more cycles than the best compromise strategy.

Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands

High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs better than K-Nearest Neighbors (KNN) algorithm in the classification of pixels and objects and the classification based on pixel analysis is slightly better than the object-based analysis. The agreement between the classifications of objects and pixels is higher in Random Forest. This is likely due to the heterogeneity of the study areas, where pixel-based classifications are most appropriate. In addition, from an ecological perspective, as these wetlands are heterogeneous, the pixel-based classification reflects a more realistic interpretation of plant community distribution.

Species and Phenotypic Distribution Models Reveal Population Differentiation in Ethiopian Indigenous Chickens

Smallholder poultry production dominated by indigenous chickens is an important source of livelihoods for most rural households in Ethiopia. The long history of domestication and the presence of diverse agroecologies in Ethiopia create unique opportunities to study the effect of environmental selective pressures. Species distribution models (SDMs) and Phenotypic distribution models (PDMs) can be applied to investigate the relationship between environmental variation and phenotypic differentiation in wild animals and domestic populations. In the present study we used SDMs and PDMs to detect environmental variables related with habitat suitability and phenotypic differentiation among nondescript Ethiopian indigenous chicken populations. 34 environmental variables (climatic, soil, and vegetation) and 19 quantitative traits were analyzed for 513 adult chickens from 26 populations. To have high variation in the dataset for phenotypic and ecological parameters, animals were sampled from four spatial gradients (each represented by six to seven populations), located in different climatic zones and geographies. Three different ecotypes are proposed based on correlation test between habitat suitability maps and phenotypic clustering of sample populations. These specific ecotypes show phenotypic differentiation, likely in response to environmental selective pressures. Nine environmental variables with the highest contribution to habitat suitability are identified. The relationship between quantitative traits and a few of the environmental variables associated with habitat suitability is non-linear. Our results highlight the benefits of integrating species and phenotypic distribution modeling approaches in characterization of livestock populations, delineation of suitable habitats for specific breeds, and understanding of the relationship between ecological variables and quantitative traits, and underlying evolutionary processes.