Mass-production of arthropods for biological weed control can increase the rate of field establishment of agents for biological control of damaging nonnative, invasive weeds of aquatic, riparian, rangeland and forest environments, with possible application to crop weeds. Mass-rearing of biological control agents (BCAs) of weeds is almost always performed on undefined diets consisting of host plant material, in greenhouses, shade houses or field gardens. Rearing protocols can, in some cases, be adapted for use by nontechnical stakeholders. Across 24 insect species, including six presented case studies spanning five countries, rearing output varied from a few thousand to billions of individuals. Production capability is dependent on knowledge of arthropod agent biology, and also on the degree of investment and success of coordination of facilities and personnel. Mass-production has increased in recent decades and is benefiting from increased ecological, biological and genetic information about the BCAs and their host weeds. Biological control will remain an important tool in improving the control of environmental weeds.
Natural mortality (M) is one of the most influential parameters in fisheries stock assessment and management. It relates directly to stock productivity and reference points used for fisheries management advice. Unfortunately, M is also very difficult to estimate, and hence very uncertain. Representing the uncertainty in M and how this influences estimates of management quantities is therefore an important component of conducting stock assessments. This paper outlines the range of methods available to estimate M for use in stock assessment. The methods include those based on maximum age, life history theory, relationships between “well-known” values for M (those found in the literature and based on data for the stock being assessed) and covariates, use of tagging data and catch curve analysis, and estimation within a single- or multi-species stock assessment model. All methods are likely subject to bias and imprecision due to incorrect assumptions and incomplete data. Furthermore, M is generally assumed to be constant over time, age, and sex - assumptions that are unlikely to be true for any stock. Based on our review, there is an obvious benefit to directly estimating M using data and within a stock assessment while assigning a prior based on empirical methods. This approach effectively uses all the available information while also representing the uncertainty. Carefully examining diagnostics and checking for model misspecification is required to ensure that the available data and stock assessment model assumptions are appropriately informative about M when it is estimated during the model fitting process. For situations where direct estimation is not possible (a condition found in data-limited to data-rich stock assessments), the use of multiple methods with robust sensitivity exploration is recommended. Even when direct data are integrated into a stock assessment, we recommend using other methods to estimate M and analysing the direct data outside the stock assessment model as diagnostic tools.
Improving resource efficiency (RE) is an important objective of the Sustainable Development Goals. In this study we find a strong exponential relationship between economic complexity index (ECI) and RE of countries. ECI measures the level of accumulated knowledge of a society enabling the products it makes. The relationship between ECI and RE is stronger for primary material importers and countries with stable institutions. Assessing a country's level of ECI also allows the outlook of future RE trends. We explain how ECI influences RE at the product level by establishing the product space for each country and by defining core products that contribute to a high product complexity index, high RE (i.e., unit price) and promising expansibility (i.e., core number), which indicates the potential to produce more advanced products in the future. Policies that improve economic complexity and invest in core products seem to be a priority to achieve sustainable development.
This paper presents an adaptive robust optimization approach to optimal operation of multi-layout energy hubs under uncertainty. In the first step, the multi-layout energy hub concept is presented and discussed comprehensively followed by its required energy management model, but in the deterministic form. In the next step, an adaptive robust optimization approach is developed for the energy management model of multi-layout energy hubs. The uncertainties of energy hub load as well as upstream energy market prices are considered through bounded intervals using polyhedral uncertainty sets. The proposed adaptive-robust multi-layout EHS optimizer (ARMEO) is developed as a tri-level min-max-min optimization problem which cannot be solved directly. To do so, column-and-constraint (C&C) technique is used to recast the tri-level model into a "min" master problem and a "max-min" sub-problem. However, the "max-min" sub-problem is still a bi-level model and cannot be solved directly. To cope, block coordinate descent (BCD) methodology is applied to the sub-problem to iteratively solve the "max-min" sub-problem. An industrial-based case study is conducted to show the effectiveness of the proposed model in 1) managing multi-layout energy hubs, and 2) provide immunized operational solutions against uncertainties. Based on the results, it is observed that the ARMEO model is subject to a higher operation cost (compared to deterministic model), however, the obtained operating solutions are immunized against the uncertainties. Moreover, it has been shown that the proposed multi-layout EHS model can provide reasonable operating solutions for all layouts of the system as a whole.
Ending poverty in all its forms everywhere is the first global goal of the United Nations 2030 Agenda for Sustainable Development. Poverty eradication is a long-term process that faces many uncertainties and complex interactions with other Sustainable Development Goals (SDGs). In order to better understand poverty and contribute to addressing poverty and sustainability, this paper aims to conduct a systematic review of model-based poverty scenario analyses in the context of the SDGs. We first review 144 studies in terms of bibliometrics (i.e., publication types, research topics for poverty, research objects, research scales and geographic locations) and the characteristics and scope of the models and analyses (i.e., model types, purposes, states, temporal and spatial range, sectors considered, poverty and other SDGs indicators). Second, we discuss the pros and cons of different types of models and identify seven representative models. We also discuss the synergies and trade-offs between poverty and other SDGs. Finally, we identify four potential research gaps in model-based poverty scenario analysis and provide suggestions for future research. The review shows that poverty scenario analysis was carried out mainly from a single perspective, such as economic, ecological, and agricultural. Few studies used effective models to analyze poverty in an integrated analysis of interactions between multiple sectors. Comprehensive multi-sector models are needed for global and regional poverty scenario analysis over the medium- and long-term to enhance the understanding of combined effects, synergies, and trade-offs between poverty and other SDGs.
Offshore wind and wave power are abundant energy sources and could provide long term contributions to our future energy supply. The combined exploration of wind and wave power has been proposed as an effective way to mitigate the non-negligible power intermittency and variability of offshore renewables. However, the assessments of wind and wave resources have been developed separately in Australia, and the potential of diversified wind and wave power has not been studied systematically. This study investigates offshore wind and wave energy sources in multiple locations around the Australian coastline and their potentials for integration in terms of energy availability, power variability, coherence and correlation, and annual and seasonal variability over the last seven years. In addition, wind and wave mixed energy farms are studied using commercial wind turbine models and various wave energy converter prototype models. The energy availability, power smoothing effect, capacity factor and downtime of these mixed energy farms are also discussed. Moreover, this paper proposes an effective matrix for assessing the potential of hybrid energy farms in multiple sites in terms of power availability, power variability and combination performance and the sensitivity of selecting various wave energy converter (WEC) models is also investigated to provide a general guideline for future work. The regional comparative results indicate that the swell wave dominated sites in Western and Southern Australia present merits for combining wind and wave power, while wind–wave dominated regions, such as Eastern Australia, are not preferable for this diversified system. It can be found that power variability and downtime can be significantly reduced for the specific wind–wave capacity mix if a lower correlation or longer lag time exists between the two renewables. The results also illustrate that the combinations with different WEC systems present varying benefits in the different locations in Australia.
This study investigates the risk plastic debris ingestion poses to coastal marine taxa in the Balearic Islands in the western Mediterranean Sea. Here, we use species observations and environmental data to model habitat maps for 42 species of fish. For each species, we then match estimates of habitat suitability against the spatial distribution of plastic debris to quantify plastic exposure, which we further combine with species-wise ingestion rates to map the risk of plastic ingestion. The results indicate that the risk of plastic ingestion is particularly high in the north-west and south-east regions and the risks varied strongly between species, with those at higher trophic levels being the most vulnerable overall. Extending this work to other coastal regions within the Mediterranean Sea and beyond will allow managers and policymakers to target the most appropriate areas and types of interventions for mitigating plastic pollution on coastal diversity in the marine environment.
Key message Stripe rust resistance gene YrAet672 from Aegilopstauschii accession CPI110672 encodes a nucleotide-binding and leucine-rich repeat domain containing protein similar to YrAS2388 and both these members were haplotypes of Yr28. Abstract New sources of host resistance are required to counter the continued emergence of new pathotypes of the wheat stripe rust pathogen Pucciniastriiformis Westend. f. sp. tritici Erikss. (Pst). Here, we show that CPI110672, an Aegilopstauschii accession from Turkmenistan, carries a single Pst resistance gene, YrAet672, that is effective against multiple Pst pathotypes, including the four predominant Pst lineages present in Australia. The YRAet672 locus was fine mapped to the short arm of chromosome 4D, and a nucleotide-binding and leucine-rich repeat gene was identified at the locus. A transgene encoding the YrAet672 genomic sequence, but lacking a copy of a duplicated sequence present in the 3′ UTR, was transformed into wheat cultivar Fielder and Avocet S. This transgene conferred a weak resistance response, suggesting that the duplicated 3′ UTR region was essential for function. Subsequent analyses demonstrated that YrAet672 is the same as two other Pst resistance genes described in Ae.tauschii, namely YrAS2388 and Yr28. They were identified as haplotypes encoding identical protein sequences but are polymorphic in non-translated regions of the gene. Suppression of resistance conferred by YrAet672 and Yr28 in synthetic hexaploid wheat lines (AABBDD) involving Langdon (AABB) as the tetraploid parent was associated with a reduction in transcript accumulation.
Coral reefs are important regional sources of biogenic sulfur to the tropical marine atmosphere, through stress-induced emissions of dimethylsulfide (DMS). Recent estimates suggest that the Great Barrier Reef (GBR), Australia emits 0.02-0.05 Tg yr ⁻¹ of DMS (equivalent to 0.010-0.026 Tg yr ⁻¹ S), with potential implications for local aerosol-cloud processes. However, the impact of ocean warming on DMS emissions from coral reefs remains uncertain, complicating efforts to improve the representation of coral reefs in DMS climatologies and climate models. We investigate the influence of predicted changes in sea surface temperature (SST), photosynthetically active radiation (PAR) and wind speed on contemporary DMS emissions from the GBR using model output from the Coupled Model Intercomparison Project Phase 6 (CMIP6). A multiple linear regression is used to calculate seawater surface DMS (DMS w ) concentration in the GBR in a contemporary (2001-2020) and end-of-century (2081-2100) scenario, as simulated by CMIP6 models under a SSP2-4.5 and SSP5-8.5 Shared Socioeconomic Pathway. By the end of this century, a 1.5-3.0°C rise in annual mean SST and a 1.1-1.7 mol m ⁻² d ⁻¹ increase in PAR could increase DMS w concentration in the GBR by 9.2-14.5%, leading to an increase in DMS flux of 9.5-14.3%. Previous model studies have suggested that the aerosol system has a low sensitivity to relatively large changes in coral reef-derived DMS. Therefore, the predicted change in contemporary DMS emissions is unlikely to influence the regional atmosphere. Further research is needed to understand the combined effects of temperature, light, pH, salinity and ecosystem structure on DMS production in coral reefs to better predict potential changes in emissions. Nevertheless, the findings provide insight into how predicted ocean warming may affect present-day DMS emissions and the source-strength of the GBR to the atmospheric sulfur budget.
Sea surface temperature (SST) variability plays a key role in the global weather and climate system, with phenomena such as El Niño-Southern Oscillation (ENSO) regarded as a major source of interannual climate variability at the global scale. The ability to make long-range forecasts of SST variations and extreme marine heatwave events have potentially significant economic and societal benefits, especially in a warming climate. We have developed a deep learning time series prediction model (Unet-LSTM), based on more than 70 years (1950–2021) of ECMWF ERA5 monthly mean SST and 2-m air temperature data, to predict global 2-dimensional SSTs up to a 24-month lead. Model prediction skills are high in the equatorial and subtropical Pacific. We have assessed the ability of the model to predict SST anomalies in the Niño3.4 region, an ENSO index in the equatorial Pacific, and the Blob marine heatwave events in the northeast Pacific in detail. An assessment of the predictions of the 2019–2020 El Niño and the 2016–2017 and 2017–2018 La Niña show that the model has skill up to 18 months in advance. The prediction of the 2015–2016 extreme El Niño is less satisfactory, which suggests that subsurface ocean information may be crucial for the evolution of this event. Note that the model makes predictions of the 2-d monthly SST field and Nino 3.4 is just one region embedded in the global field. The model also shows long lead prediction skills for the northeast Pacific marine heatwave, the Blob. However, the prediction of the marine heatwaves in the southeast Indian Ocean, the Ningaloo Niño, shows a short lead prediction. These results indicate the significant potential of data-driven methods to yield long-range predictions of SST anomalies.
Low-permeability deposits currently yield insufficient metal recovery using in-situ recovery (ISR) due to the weak interaction between lixiviant solution and the ore body as a result of low fluid flow and mineral/lixiviant contact. To improve the lixiviant/ore interaction, the use of a solution pulsing method (intermittent rather than continuous pumping) to improve the mass transfer of ions and fluid flow in such deposits could be an option. Solution pulsing alters the solution flow and mass transfer at the microscale between low- and high-permeability regions and can result in a higher overall mass transfer. This paper reports on research in which laboratory-scale solution pulsing-ISR experiments were undertaken to assess the effects of various parameters on lixiviant movement through ideal synthetic core samples. The concentration of lixiviant solution was tracked. The findings revealed that when the pump resting period was too long, the pulsed pumping was inefficient. A short pumping on-and-off time (30 min) was found to be more efficient. The results also confirmed that an increase in the hydrostatic pressure that drives the pumping increased the migration of ions. For the most effective pumping parameters, the effect of synthetic core sample permeability was also measured to confirm that a lower permeability results in a lower ion movement, as is expected from continuous pumping experiments.
The World Climate Research Programme (WCRP) envisions a world “that uses sound, relevant, and timely climate science to ensure a more resilient present and sustainable future for humankind.” This bold vision requires the climate science community to provide actionable scientific information that meets the evolving needs of societies all over the world. To realize its vision, WCRP has created five Lighthouse Activities to generate international commitment and support to tackle some of the most pressing challenges in climate science today. The overarching goal of the Lighthouse Activity on Explaining and Predicting Earth System Change is to develop an integrated capability to understand, attribute, and predict annual to decadal changes in the Earth system, including capabilities for early warning of potential high impact changes and events. This article provides an overview of both the scientific challenges that must be addressed, and the research and other activities required to achieve this goal. The work is organized in three thematic areas: (i) monitoring and modeling Earth system change; (ii) integrated attribution, prediction and projection; and (iii) assessment of current and future hazards. Also discussed are the benefits that the new capability will deliver. These include improved capabilities for early warning of impactful changes in the Earth system, more reliable assessments of meteorological hazard risks, and quantitative attribution statements to support the Global Annual to Decadal Climate Update and State of the Climate reports issued by the World Meteorological Organization.
Achieving the Sustainable Development Goals (SDGs) is contingent on managing complex interactions that create synergies and trade‐offs between different goals. It is, therefore, important to improve our understanding of them, their underlying causal drivers, future behaviors, and policy implications. Prominent methods of interaction analysis that focus on modeling or data‐driven statistical correlation are often insufficient for giving an integrated view of interaction drivers and their complexity. These methods are also usually too technically complex and heavily data‐driven to provide decision‐makers with simple practical tools and easily actionable and understandable results. Here, we introduce a flexible and practical systemic approach, termed archetype analysis, that generalizes a number of recurring interaction patterns among the SDGs with unique drivers, behaviors, and policy implications. We review eight interaction archetypes as thinking aids to analyze some of the important synergies and trade‐offs, supported by several empirical examples related to the SDGs (e.g., poverty, food, well‐being, water, energy, housing, climate, and land use) to demonstrate how they can be operationalized in practice. The interaction archetypes are aimed to help researchers and policymakers as a diagnostic tool to identify fundamental mechanisms of barriers or policy resistance to SDG progress, a comparative tool to enhance knowledge transfer between different cases with similar drivers, and a prospective tool to design synergistic policies for sustainable development.
The global consumption of nuts is steadily increasing in recent years. Tree nuts have many beneficial physiological functions because of the presence of diverse range of phytochemicals. However, nuts also contain some anti-nutritional compounds, such as birch pollen and phytic acid, negatively affecting bioavailability of nut. Total phenolic content and antioxidant ability of some tree nuts can be enhanced with thermal processing. Some novel non-thermal pasteurization applications in nuts processing industries can strengthen the bioaccessibility and bioavailability by reducing the antinutri-tional compounds and decreasing the allergenic protein solubility. The current review summarized the bioaccessibility of some typical phytochemicals in nuts and general impact of processing on them. The processing for nuts emphasis on the common means (roasting, peeling, etc.) and some novel treatments (e.g. pulsed electric field). In addition, the bioavailability of those phytochemicals mentioned in this review.
Trichomes are differentiated epidermal cells and exist on above-ground organs of nearly all land plants with important roles in resistance to a wide range of biotic and abiotic stresses. We attempted to obtain candidate gene (s) for Hairy glume ( Hg ), responsible for the trichome on wheat glume, by using bulked segregant exome capture sequencing (BSE-Seq), while Hg was only mapped in 0.52–3.26 Mb of 1AS. To further fine map this gene and identify candidate genes in this region, a near isogenic line-derived population consisting of 2,050 F 2 lines was generated in the present study. By analyzing this population, Hg was fine mapped into a 0.90 cM region covering a physical distance of ~825.03 Kb encompassing 6 high- and 23 low-confidence genes in the reference genome of Chinese Spring. A presence-absence variation was identified in the fine mapping region through analyses of sequence-tagged sites markers and genome sequences of the hairy glume parent of the near isogenic lines. The results presented here will be useful for further cloning Hg in wheat.
Mineral dust is a key source of essential micronutrients, particularly iron (Fe), for phytoplankton in the Southern Ocean. However, observations of dust deposition over the Southern Ocean are sparse, hindering assessments of its influence on marine biogeochemistry. We present a time series (2010–2019) of lithogenic particle flux estimates using sediment trap samples collected at 1,000 m depth at the subantarctic Southern Ocean Time Series (SOTS) site. Lithogenic flux was estimated using individual Fe, aluminium (Al), titanium, and thorium concentrations in sediment trap particles less than 1 mm in size. These tracers showed good agreement with one another, and their average was investigated as a proxy for mineral dust deposition. This multi‐tracer average lithogenic flux exhibited strong seasonality, peaking in late spring and summer. No significant Fe enrichment was observed compared to the average upper continental crust, indicating that lithogenic material dominates particulate Fe flux at SOTS. Similar Fe:Al ratios in our samples compared to those reported in marine aerosols off southern Australia, coupled with particle trajectory analysis, suggested Australian dust constitutes the primary lithogenic source to SOTS sinking particles. Lead enrichment in our samples also highlighted an anthropogenic contribution to sinking particles, which might represent an additional aeolian source of more bio‐available Fe to subantarctic waters. This study contributes a new long‐term estimate of lithogenic particle fluxes and aeolian deposition over the subantarctic Southern Ocean. These estimates may enhance model representation of trace metal contribution to biogeochemical processes in the Southern Ocean.
Knowledge on the distribution and abundance of organisms is fundamental to understanding their roles within ecosystems and their ecological importance for other taxa. Such knowledge is currently lacking for insects, which have long been regarded as the “little things that run the world”. Even for ubiquitous insects, such as ants, which are of tremendous ecological significance, there is currently neither a reliable estimate of their total number on Earth nor of their abundance in particular biomes or habitats. We compile data on ground-dwelling and arboreal ants to obtain an empirical estimate of global ant abundance. Our analysis is based on 489 studies, spanning all continents, major biomes, and habitats. We conservatively estimate total abundance of ground-dwelling ants at over 3 × 10 ¹⁵ and estimate the number of all ants on Earth to be almost 20 × 10 ¹⁵ individuals. The latter corresponds to a biomass of ∼12 megatons of dry carbon. This exceeds the combined biomass of wild birds and mammals and is equivalent to ∼20% of human biomass. Abundances of ground-dwelling ants are strongly concentrated in tropical and subtropical regions but vary substantially across habitats. The density of leaf-litter ants is highest in forests, while the numbers of actively ground-foraging ants are highest in arid regions. This study highlights the central role ants play in terrestrial ecosystems but also major ecological and geographic gaps in our current knowledge. Our results provide a crucial baseline for exploring environmental drivers of ant-abundance patterns and for tracking the responses of insects to environmental change.
Urbanization is rapidly transforming much of Southeast Asia, altering the structure and function of the landscape, as well as the frequency and intensity of the interactions between people, animals, and the environment. In this study, we explored the impact of urbanization on zoonotic disease risk by simultaneously characterizing changes in the ecology of animal reservoirs (rodents), ectoparasite vectors (ticks), and pathogens across a gradient of urbanization in Kuching, a city in Malaysian Borneo. We sampled 863 rodents across rural, developing, and urban locations and found that rodent species diversity decreased with increasing urbanization—from 10 species in the rural location to 4 in the rural location. Notably, two species appeared to thrive in urban areas, as follows: the invasive urban exploiter Rattus rattus ( n = 375) and the native urban adapter Sundamys muelleri ( n = 331). R. rattus was strongly associated with built infrastructure across the gradient and carried a high diversity of pathogens, including multihost zoonoses capable of environmental transmission (e.g., Leptospira spp.). In contrast, S. muelleri was restricted to green patches where it was found at high densities and was strongly associated with the presence of ticks, including the medically important genera Amblyomma , Haemaphysalis , and Ixodes . Our analyses reveal that zoonotic disease risk is elevated and heterogeneously distributed in urban environments and highlight the potential for targeted risk reduction through pest management and public health messaging.
C-reactive protein (CRP) is a member of the highly conserved pentraxin superfamily of proteins and is often used in clinical practice as a marker of infection and inflammation. There is now increasing evidence that CRP is not only a marker of inflammation, but also that destabilized isoforms of CRP possess pro-inflammatory and pro-thrombotic properties. CRP circulates as a functionally inert pentameric form (pCRP), which relaxes its conformation to pCRP* after binding to phosphocholine-enriched membranes and then dissociates to monomeric CRP (mCRP). with the latter two being destabilized isoforms possessing highly pro-inflammatory features. pCRP* and mCRP have significant biological effects in regulating many of the aspects central to pathogenesis of atherothrombosis and venous thromboembolism (VTE), by directly activating platelets and triggering the classical complement pathway. Importantly, it is now well appreciated that VTE is a consequence of thromboinflammation. Accordingly, acute VTE is known to be associated with classical inflammatory responses and elevations of CRP, and indeed VTE risk is elevated in conditions associated with inflammation, such as inflammatory bowel disease, COVID-19 and sepsis. Although the clinical data regarding the utility of CRP as a biomarker in predicting VTE remains modest, and in some cases conflicting, the clinical utility of CRP appears to be improved in subsets of the population such as in predicting VTE recurrence, in cancer-associated thrombosis and in those with COVID-19. Therefore, given the known biological function of CRP in amplifying inflammation and tissue damage, this raises the prospect that CRP may play a role in promoting VTE formation in the context of concurrent inflammation. However, further investigation is required to unravel whether CRP plays a direct role in the pathogenesis of VTE, the utility of which will be in developing novel prophylactic or therapeutic strategies to target thromboinflammation.
In recent years reported cases of Buruli ulcer, caused by Mycobacterium ulcerans, have increased substantially in Victoria, Australia, with the epidemic also expanding geographically. To develop an understanding of how M. ulcerans circulates in the environment and transmits to humans we analyzed environmental samples collected from 115 properties of recent Buruli ulcer cases and from 115 postcode-matched control properties, for the presence of M. ulcerans. Environmental factors associated with increased odds of M. ulcerans presence at a property included certain native plant species and native vegetation in general, more alkaline soil, lower altitude, the presence of common ringtail possums (Pseudocheirus peregrinus) and overhead powerlines. However, only overhead powerlines and the absence of the native plant Melaleuca lanceolata were associated with Buruli ulcer case properties. Samples positive for M. ulcerans were more likely to be found at case properties and were associated with detections of M. ulcerans in ringtail possum feces, supporting the hypothesis that M. ulcerans is zoonotic, with ringtail possums the strongest reservoir host candidate. However, the disparity in environmental risk factors associated with M. ulcerans positive properties versus case properties indicates the involvement of human behavior or the influence of other environmental factors in disease acquisition that requires further study.
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