Control of glyphosate-resistant (GR) junglerice is a challenging task in eastern Australia. There is limited information on the efficacy and reliability of alternate herbicides for GR populations of junglerice, especially when targeting large plants and when temperatures are high. A series of experiments were conducted to confirm the level of glyphosate resistance in three populations of junglerice and to evaluate the efficacy of alternate herbicides for the control of GR junglerice populations. The LD50 of glyphosate of B17/7, B17/34, and B17/35 populations was found to be 298, 2,260, and 1,715 g ae ha–1, respectively, suggesting that populations B17/34 and B17/35 were highly resistant to glyphosate. Glyphosate efficacy was reduced at high-temperature (35 C day/25 C night) compared with low-temperature conditions (25 C day/15 C night), suggesting that control of susceptible populations may also be reduced if glyphosate is sprayed under hot conditions. Preemergence herbicides dimethenamid-P (1,000 g ai ha–1) and pendimethalin (1,500 g ai ha–1) provided 100% control of GR populations (B17/34 and 17/35). Postemergence herbicides, such as clethodim (60 or 90 g ai ha–1), glufosinate (750 g ai ha–1), haloxyfop (52 or 78 g ai ha–1), and paraquat (400 or 600 g ai ha–1), applied at the four-leaf stage provided 100% control of GR populations. For larger junglerice plants (eight-leaf stage), postemergence applications of paraquat (400 or 600 g ai ha–1) provided greater weed control than clethodim, glufosinate, and haloxyfop. A mixture of either glufosinate or haloxyfop with glyphosate provided poor control of GR junglerice populations compared with application of glufosinate or haloxyfop applied alone. Efficacy of glufosinate and haloxyfop for the control of GR populations decreased when applied in the sequential spray after glyphosate application. This study identified alternative herbicide options for GR junglerice populations that can be used in herbicide rotation programs for sustainable weed management.

Homelessness is a growing problem, with perhaps greater than a 150 million homeless people globally. The global community has prioritized the problem, as eradicating homelessness is one of the United Nation’s sustainability goals of 2030. Homelessness is a variable entity with individual, population, cultural, and regional characteristics complicating emergency preparedness. Overall, there are many factors that make homeless individuals and populations more vulnerable to disasters. These include, but are not limited to: shelter concerns, transportation, acute and chronic financial and material resource constraints, mental and physical health concerns, violence, and substance abuse. As such, homeless population classification as a special or vulnerable population with regard to disaster planning is well-accepted. Much work has been done regarding best practices of accounting for and accommodating special populations in all aspects of disaster management. Utilizing what is understood of homeless populations and emergency management for special populations, a review of disaster planning with recommendations for communities was conducted. Much of the literature on this subject generates from urban homeless in the United States, but it is assumed that some lessons learned and guidance will be translatable to other communities and settings.

Seeds offer a unique perspective from which to view biology. An individual seed is an autonomous biological entity that must rely on its own resources (and resourcefulness) to persist after dispersal and to time its transition to germination and seedling growth to coincide with environmental opportunities for survival. At the same time, seed biology in agriculture and ecology is determined largely by the behaviours of populations of individual seeds. The percentage of seeds in a population that is in a particular state (e.g. dormant, germinated, dead) at a given time is a fundamental metric of seed biology. This duality of individual diversity underlying consistent population-wide behaviour patterns can be described quantitatively using population-based threshold (PBT) models. While conceptually simple, these models are highly flexible and can describe the wide diversity of responses of seed populations to temperature, water potential, hormones, oxygen, light, ageing and combinations of these factors. This seed behaviour is linked to respiratory rates of individual seeds, indicating that basic metabolic processes within seeds vary among individuals in accordance with PBT principles. Looking more broadly across microbial, plant and animal biology, examples of cellular diversity in hormonal sensitivity, gene expression, developmental responses and signalling abound. This variation often is termed ‘noise’, and analysis efforts are focused on extracting mean signals from this variation to understand regulatory pathways. However, extension of the PBT approach to the cellular and molecular levels suggests that population sensitivity distributions and recruitment phenomena may underlie many fundamental biological processes. Thus, concepts and quantitative approaches developed for the analysis of seed populations can be applied across biological scales from molecules to ecosystems to interpret inherent biological variation and provide mechanistic insights into the nature of biological regulatory systems.

The first step in appearance of herbicide-resistant weed populations is the appearance of resistant plants or mutants. While efforts are underway to study and predict the spread of resistant plants within weed populations, knowledge of the conditions prevailing at the time of appearance of the first resistant plants is misunderstood. We try to shed some light on this phenomenon using the example of atrazine-resistant common lambsquarters. The population structure and variability, the presence of unusual genotypes that have a high mutation rate, the occurrence of a low-dose resistant phenotype that is the precursor of a high-dose resistant phenotype, and the potential for multiplication and spread are shown to be of major importance in the behavior of herbicide resistance.

Triazine-resistant (TR) common waterhemp was reported and confirmed in Fillmore County, NE in 1990. A survey of 81 fields was conducted to characterize the occurrence of common waterhemp and grower practices. Sampled fields fell into three categories: suspected by growers to contain resistant plants (26 fields), randomly selected (28 fields), and adjacent to fields containing TR common waterhemp (27 fields). Resistant plants were found in 64% of all fields. Resistance was confirmed in 92% of the fields suspected by growers to contain resistant plants. Adjacent fields were no more likely to contain resistant plants than randomly chosen fields. Crop rotation did not significantly affect occurrence of resistance. Resistance was associated with the grower, indicating movement of resistant seed between fields via equipment. Atrazine, bromoxynil plus atrazine, and bentazon plus atrazine provided less than 75% postemergence control of TR common waterhemp, while primisulfuron, dicamba plus atrazine, primisulfuron plus dicamba, dicamba, 2,4-D ester, and metribuzin plus bentazon gave over 85% control.

Crop rotation promotes productivity, nutrient cycling, and effective pestmanagement. However, in row-crop systems, rotation is frequently limited totwo crops. Adding a third crop, especially a perennial crop, might increasecrop-rotation benefits, but concerns about disruption of agricultural andecological processes preclude grower adoption of a three-crop rotation. Theobjective of the present research was to determine whether weed seed banksdiffer between a sod-based rotation (bahiagrass–bahiagrass–peanut–cotton)and a conventional peanut–cotton rotation (peanut–cotton–cotton) and theimportance of crop phase in weed seed-bank dynamics in a long-termexperiment initiated in 1999 in Florida. Extractable (ESB) and germinable(GSB) seed banks were evaluated at the end of each crop phase in 2012 and2013, and total weed seed or seedling number, Shannon-Weiner's diversity (H′), richness, and evenness were determined. ESBincreased in H′ (36%), richness (29%), and total number ofweed seeds (40%) for sod-based compared with conventional rotation, whereasGSB increased 32% in H′, 27% in richness, and 177% in totalnumber of weed seedlings. Crop phase was a determinant factor in thedifferences between crop rotations. The first year of bahiagrass (B1)exhibited increases in weed seed and seedling number, H′,and richness and had the highest values observed in the sod-based rotation.These increases were transient, and in the second year of bahiagrass (B2),weed numbers and H′ decreased and reached levels equivalentto those in the conventional peanut–cotton rotation. The B1 phase increasedthe germinable fraction of the seed bank, compared with the other cropphases, but not the total number of weed seeds as determined by ESB. Theincreases in H′ and richness in bahiagrass phases weremainly due to grass weed species. However, these grass weed species were notassociated with peanut and cotton phases of the sod-based rotation. Theresults of the present study demonstrated that including bahiagrass as athird crop in a peanut–cotton rotation could increase weed communitydiversity, mainly by favoring increases in richness and diversity, but thestructure and characteristics of the rotation would prevent continuousincreases in the weed seed bank that could affect the peanut and cottonphases.

Simulation models of nutrient utilisation ignore that variation in pig system components can influence the predicted mean and variance of the performance of a group of pigs. The objective of this study was to develop a methodology to investigate how variation in feed composition would (a) affect the outputs of a nutrient utilisation model and (b) interact with variation that arises from the traits of individual pigs. We used a P intake and utilisation model to address these characteristics. Introduction of stochasticity gave rise to a number of methodological challenges – for example, how to generate variation in both feed composition and pigs and account for correlations between ingredients when modelling variation associated with feed mixing efficiency. Introducing variation in feed composition and pig phenotype resulted in moderate decreases in mean digested, retained and excreted P predicted for a population of pigs and an increase in their associated CV. A lower percentage of pigs in the population were predicted to meet their requirements during the feeding period considered, by comparison with the no-variation scenario. Variation in feed ingredient composition contributed more to performance variation than variation due to mixing efficiency. When variations in both feed composition and pig traits were considered, it was the former rather than the latter that had the dominant influence on variability in pig performance. The developed framework emphasises the consequences of random variability on the predictions of nutrient utilisation models. Such consequences will have a significant impact on decisions about management strategies such as feeding that are subject to variation.

Toxoplasma gondii is a globally distributed parasite infecting humans and warm-blooded animals. Although many surveys have been conducted for T. gondii infection in mammals, little is known about the detailed distribution in localized natural populations. In this study, host genotype and spatial location were investigated in relation to T. gondii infection. Wood mice (Apodemus sylvaticus) were collected from 4 sampling sites within a localized peri-aquatic woodland ecosystem. Mice were genotyped using standard A. sylvaticus microsatellite markers and T. gondii was detected using 4 specific PCR-based markers: SAG1, SAG2, SAG3 and GRA6 directly from infected tissue. Of 126 wood mice collected, 44 samples were positive giving an infection rate of 34·92% (95% CI: 27·14–43·59%). Juvenile, young adults and adults were infected at a similar prevalence, respectively, 7/17 (41·18%), 27/65 (41·54%) and 10/44 (22·72%) with no significant age-prevalence effect (P = 0·23). Results of genetic analysis of the mice showed that the collection consists of 4 genetically distinct populations. There was a significant difference in T. gondii prevalence in the different genotypically derived mouse populations (P = 0·035) but not between geographically defined populations (P = 0·29). These data point to either a host genetic/family influence on parasite infection or to parasite vertical transmission.