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Here, we investigate how stochasticity and age-dependence in energy dynamics influence maternal allocation in iteroparous females. We develop a state-dependent model to calculate the optimal maternal allocation strategy with respect to maternal age and energy reserves, focusing on allocation in a single offspring at a time. We introduce stochasticity in energetic costs– in terms of the amount of energy required to forage successfully and individual differences in metabolism – and in feeding success. We systematically assess how allocation is influenced by age-dependence in energetic costs, feeding success, energy intake per successful feeding attempt, and environmentally-driven mortality. First, using stochastic dynamic programming, we calculate the optimal amount of reserves M that mothers allocate to each offspring depending on their own reserves R and age A. The optimal life history strategy is then the set of allocation decisions M(R, A) over the whole lifespan which maximizes the total reproductive success of distant descendants. Second, we simulated the life histories of 1000 mothers following the optimisation strategy and the reserves at the start of adulthood R1, the distribution of which was determined, the distribution of which was determined using an iterative procedure as described . For each individual, we calculated maternal allocation Mt, maternal reserves Rt, and relative allocation Mt⁄Rt at each time period t. The relative allocation helps us to understand how resources are partitioned between mother and offspring. Third, we consider how the optimal strategy varies when there is age-dependence in resource acquisition, energetic costs and survival. Specifically, we include varying scenarios with an age-dependent increase or a decrease with age in energetic costs (c_t), feeding success (q_t), energy intake per successful feeding attempt (y_t), and environmentally-driven extrinsic mortality rate (d_t) (Table 2). We consider the age-dependence of parameters one at a time or in pairs, altering the slope, intercept, or asymptote of the age-dependence (linear or asymptotic function). Our aim is to identify whether the observed reproductive senescence can arise from optimal maternal allocation. As such, we do not impose a decline in selection in later life as all offspring are equally valuable at all ages (for a given maternal allocation), and there are no mutations. For each scenario, we run the backward iteration process with these age-dependent functions, obtain the allocation strategy, and simulate the life history of 1000 individuals based on the novel strategy. We then fit quadratic and linear models to the reproduction of these 1000 individuals using the lme function, nlme package in R. For these models, the response variable is the maternal allocation Mt and explanatory variables are the time period t and t2 (for the quadratic fit only), with individual identity as a random term. We use likelihood ratio tests to compare linear and quadratic models using the anova function (package nlme) with the maximum-likelihood method. If the comparison is significant (p-value <0.05), we considered the quadratic model to have a better fit, otherwise the linear model is considered more parsimonious. We were particularly interested in identifying scenarios where the fit was quadratic with a negative quadratic term. For each scenario, the pseudo R2 conditional value (proportion of variance explained by the fixed and random terms, accounting for individual identity) is calculated to assess the goodness-of-fit of the lme model, on a scale from 0 to 1, using the “r.squared” function, package gabtool. All calculations and coding are done in R. Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.

The ecological impacts of multiple stressors are hard to predict but important to understand. When multiple stressors influence foundation species, the effects can cascade throughout the ecosystem. Gulf of Mexico seagrass ecosystems are currently experiencing a suite of novel stressors, including warmer water temperatures and increased herbivory due to tropicalization and conservation efforts. We investigated the impact of warming temperatures and grazing history on plant performance, morphology, and palatability by integrating a mesocosm study using the seagrass Thalassia testudinum with feeding trials using the sea urchin Lytechinus variegatus. Warming temperatures negatively impacted T. testudinum tolerance traits, reducing belowground biomass by 34%, productivity by 74%, shoot density by 10%, and the number of leaves per plant by 24%, and negatively impacted resistance traits through 13% lower toughness of young leaves and a trend for reduced leaf carbon:nitrogen. Lytechinus variegatus individuals preferred to consume plants grown under heated conditions, which supports findings of enhanced palatability. Simulated turtle grazing impacted more plant traits than grazing by other herbivores, potentially diminishing plant resilience to future disturbances through reduced rhizome non-structural carbohydrate concentrations and increasing palatability through reduced fiber content and 23% lower leaf carbon:phosphorus. Simulated turtle, simulated parrotfish, and urchin grazing reduced leaf carbon:nitrogen by 11%, also potentially increasing nutritive value. Interactions between warming temperatures and grazers on plant traits were additive for 16 out of 19 response variables. However, the stressors non-additively impacted the number of leaves per plant, fiber content, and epiphyte load. We suggest that the impacts of grazers on leaf turnover rate and leaf age may vary based on water temperature, potentially driving these interactions. Overall, increased temperatures and grazing pressure will likely reduce seagrass resilience, structure, and biomass, potentially impacting feedback systems and producing negative consequences for seagrass cover, associated species, and ecosystem services.

Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.

Agricultural adaptation to climate change is critical for ensuring future food security. Social capital is important for climate change adaptation, but institutions and social networks at multiple scales (e.g., household, community, and institution) have been overlooked in studying agricultural climate change adaptation. We combine data from 13 sites in 11 low-income countries in East Africa, West Africa, and South Asia to explore how multiple scales of social capital relate to household food security outcomes among smallholder farmers. Using social network theory, we define three community organizational social network types (fragmented defined by lack of coordination, brokered defined as having a strong central actor, or shared defined by high coordination) and examine household social capital through group memberships. We find community and household social capital are positively related, with higher household group membership more likely in brokered and shared networks. Household group membership is associated with more than a 10% reduction in average months of food insecurity, an effect moderated by community social network type. In communities with fragmented and shared organizational networks, additional household group memberships is associated with consistent decreases in food insecurity, in some cases up to two months; whereas in brokered networks, reductions in food insecurity are only associated with membership in credit groups. These effects are confirmed by hierarchical random effects models, which control for demographic factors. This suggests that multiple scales of social capital—both within and outside the household—are correlated with household food security. This social capital may both be bridging (across groups) and bonding (within groups) with different implications for how social capital structure affects food security. Efforts to improve food security could recognize the potential for both household and community level social networks and collaboration, which further research can capture by analyzing multiple scales of social capital data.

Microalgae offer a great potential to contribute significantly as renewable fuels and documented as a promising platform for algae-based bio refineries. They provide solutions to mitigate the environmental concerns posed by conventional fuel sources; however, the production of microalgal biofuels in large scale production system encounters few technical challenges. High quantity of nutrients requirements and water cost constrain the scaling up microalgal biomass to large scale commercial production. Crop protection against biomass losses due to grazers or pathogens is another stumbling block in microalgal field cultivation. With our existing technologies, unless coupled with high-value or mid-value products, algal biofuel cannot reach the economic target. Many microalgal industries that started targeting biofuel in the last decade had now adopted parallel business plans focusing on algae by-products application as cosmetic supplements, nutraceuticals, oils, natural color, and animal feed. This review provides the current status and proposes a framework for key supply demand, challenges for cost-effective and sustainable use of water and nutrient. Emphasis is placed on the future industrial market status of value added by products of microalgal biomass. The cost factor for biorefinery process development needs to be addressed before its potential to be exploited for various value-added products with algal biofuel.

Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain

Data was either measured in situ in the field (N2O flux, soil moisture, rainfall and air temperature) or samples were taken, processed, and analysed in the laboratory (soil pH, electrical conductivity (EC), ammonium, nitrate, microbial community composition and crop yield). N2O flux data was measured on a mobile gas chromatograph (GC) system and integrated to obtain peak areas on Peak490Win10Canabis programme. The times, peak areas and sample ID were then exported into a .CHR file and imported into Flux.NET.3.3 which calculated N2O flux as an output in Excel which was exported as .csv file for deposit in EIDC. N2O flux was used to calculate cumulative N2O flux using trapezoidal integration in Excel and saved in a separate .csv file for deposit in EIDC. Soil moisture was measured on Accilmas with data stored as a .csv on a DataSnap that was downloaded and sorted by treatment and saved as a .csv file. Rainfall and air temperature were downloaded from the weather station as .csv file. Soil pH and EC were recorded manually into a notebook and input into an Excel spreadsheet and exported as a .csv file. Soil ammonium and nitrate content was measured using the microplate method using a programme called Gen5. Date was exported into an Excel spreadsheet and absorbance units used to calculate ammonium/nitrate content in milligrams per kilogram using a calibration curve from a set of standards in an Excel spreadsheet. This was exported as a .csv file. Crop growth data was recorded in the field in a notebook and input into an Excel spreadsheet and exported as a .csv file. Crop yield was recorded in a notebook and input into an Excel spreadsheet and exported as a .csv file. Microbial community composition was measured using 16S gene sequencing on an Illumina MiSeq. This generated raw sequencing reads which were processed using Python and filtered using QIIME v1.3.1. creating asv.count.table.csv of counts of each Amplicon Sequence Variants (ASVs) per sample and taxa.table.csv of the taxonomic lineage for each ASVs. This dataset contains field data on nitrous oxide (N2O) emissions, microbial community composition, crop yield and growth and soil biochemical properties. The field trial consisted of three different treatments of control, conventional microplastic addition and biodegradable microplastic addition where winter barley was grown. The data presented are from field and laboratory measurements. Data was collected by the data authors. The field trial was carried out from September 2020 to July 2021 at Henfaes Field Centre, UK. Research was funded through NERC Grant NE/V005871/1. Do agricultural microplastics undermine food security and sustainable development in developing countries?

Household Surveys performed in four villages selected from Oromia, Amhara and Southern Nations, Nationalities, and Peoples’ Region (SNNPR) following from the ‘Ethiopian Rural Household Survey’ (ERHS) conducted in 2004.It contains detailed data on household consumption and expenditures, assets, income, agricultural activities, land allocation, demographic characteristics, and other variables. From September 2011 to January 2012 another survey of 221 households was conducted in three major regions of central and southern Ethiopia. At the time of this latest survey effort the most recent ERHS survey data available was from 2004. The selection of respondents, determination of sample size, and apportionment of the sample were based on a proportional sampling technique.In addition to addressing important questions from the ERHS survey data, the field survey was designed to generate detailed information on household biomass energy production and consumption practices; as well as farming activities; labour and land allocation; economic and demographic characteristics; and expenditures on food, non-food items, and energy. The 2011 survey effort collected detailed household biomass energy use data. The measurement of household biomass energy use was obtained in traditional units and later converted into kilograms. The conversion factors for each of the biomass were collected from the closest urban centre of each of the study areas. Information obtained on household biomass energy use was collected for a time period of one week before the survey was conducted. It was then aggregated into annual figures, although household biomass energy use may vary seasonally. Quality/Lineage: The data was collected by qualified enumerators who had participated in previous ERHS survey. In addition to myself I recruited assistant supervisor to check the accuracy and quality of data on daily basis and followup interview process closely. Before the survey commenced a pilot survey was conducted in each of the study areas to identify the different types of energy households are using and other critical variables of interest for the research. This information was used to revise and improve questionnaire. Moreover, a one day in-depth training was given to enumerators and assistant supervisor to enrich their deeper understanding of each the question in the survey and to further improve questionnaire from their earlier experiences in those villages. Purpose: Over 90% of Ethiopian rural population rely on biomass energy. However, biomass energy utilization is linked to household livelihood as in rural households produce and consume biomass energy simultaneously with other (on and off-farm)activities. With the rampant rate of deforestation that Ethiopia is facing it is important to investigate the effect of deforestation or fuelwood scarcity which is assumed affect household welfare through influence on wage and price. In light of this, the survey effort collected information on household use of biomass energy sources, expenditure and labour allocation choices and amount of labour time used for each activities.This helped me to investigate the effect of fuelwood scarcity on household welfare from three aspects: labour allocation decision, energy expenditure and fuel choice and biomass energy consumption behavior to better understand the related linkage of household production and utilization of biomass with livelihoods or food security. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c08e08aa-3055-4651-801b-0383610c1987}.

We analyze past and anticipated future trends in crop yields, per capita consumption, and population to estimate agricultural land requirements globally by 2050 and 2100. Assuming “business as usual,” higher-income countries are expected to show little or no net growth in cropland by the end of the century, even in the face of moderate climate change. In contrast, in lower-income countries, we project that land requirements will grow dramatically, and climate change will likely double this expansion. Although economic growth is often considered to work in opposition to conservation, accelerating economic development in lower-income countries, which would help alleviate poverty and increase standards of living, would also greatly reduce potential cropland expansion in lower-income countries, even with climate change, owing to slower population growth and improved crop yields that more than offset increased per capita consumption. Combining economic development in low-income countries with reduced consumption in high-income countries could dramatically shrink global cropland requirements by the year 2100 even with moderate climate change. Such a remarkable reduction in cropland area would have enormous benefits for both biodiversity and global climate change.  All of the data files are analyzed using R.