Search

Abstract Timely and accurate detection and identification of species are crucial for monitoring wildlife for conservation and management. Technological advances, including connectivity of camera traps to mobile phone networks and artificial intelligence (AI) algorithms for automated species identification, can potentially improve the timeliness and accuracy of species detection and identification. Adoption of this new technology, however, is often seen as cost-prohibitive as it has been difficult to calculate the cost savings or qualitative benefits over the life of the program. We developed a decision tool to quantify potential cost savings associated with incorporating the use of mobile phone network connectivity and AI technologies into monitoring programs. Using a feral cat eradication program as a case study, we used our decision tool to quantify technology-related savings in costs and carbon emissions, and compared the accuracy of AI species identification to that of experienced human observers. Over the life of the program, AI technology yielded cost savings of $0.27 M and when coupled with mobile phone network connectivity, AI saved $2.15 M and 115,838 kg in carbon emissions, with AI algorithms outperforming human observers in both speed and accuracy. Our case study demonstrates how advanced technologies can improve accuracy and cost-effectiveness and improve monitoring program efficiencies.

This commentary reflects on the first Global Stocktake (GST) under the Paris Agreement on climate change to offer insights for advancing climate actions and informing future GST cycles. The first GST, which concluded at COP28 in 2023, demonstrates the vital importance of a comprehensive, balanced, and inclusive approach to multilateral climate action. The GST's call to transition away from fossil fuels is an important political achievement. Yet, the GST outcome also reveals gaps, shortcomings, and potential dangers ahead. Future climate negotiations, we argue, would benefit from a more integrated, holistic perspective, and more nuanced balancing of ambition and implementation. More needs to be done to protect human rights, increase loss and damage funding, go beyond technological solutions, and address gender-differentiated consequences of climate change. Moreover, a great deal of work, including by nonstate actors, will be required to ensure the first GST translates into real action on the ground.

Abstract Marine megafauna exposed to fisheries bycatch belong to some of the most threatened taxonomic groups and include apex and mesopredators that contribute to ecosystem regulation. Fisheries bycatch is a major threat to the conservation of albatrosses, large petrels and other pelagic seabirds. Using data sourced from a fisheries electronic monitoring system, we assessed the effects of the time-of-day and relative depth of fishing on seabird and target species catch rates for a Pacific Ocean pelagic longline fishery that targets albacore tuna with an apparently high albatross bycatch rate. Using a Bayesian inference workflow with a spatially-explicit generalized additive mixed model for albacore tuna and generalized linear mixed regression models both for combined albatrosses and combined seabirds, we found that time-of-day and fishing depth did not significantly affect the target species catch rate while night-time deep setting had > 99% lower albatross and total seabird catch rates compared to both deep and shallow partial day-time sets. This provides the first evidence that night-time setting in combination with fishing deep reduces seabird catch risk and may be commercially viable in this and similar albacore tuna longline fisheries. Findings support evidence-informed interventions to reduce the mortality of threatened seabird bycatch species in pelagic longline fisheries.

Highlights • Households in African secondary urban areas have food linkages with rural areas and other urban areas • Rural-urban food linkages are dominated by grains, while urban-urban food linkages comprise more higher value foods • City-level characteristics explain more of the variability in food sourcing behaviors than household-level characteristics

Tuberculosis (TB) remains a leading infectious cause of death worldwide and the coronavirus disease 2019 pandemic has negatively impacted the global TB burden of disease indicators. If the targets of TB mortality and incidence reduction set by the international community are to be met, new more effective adult and adolescent TB vaccines are urgently needed. There are several new vaccine candidates at different stages of clinical development. Given the limited funding for vaccine development, it is crucial that trial designs are as efficient as possible. Prevention of infection (POI) approaches offer an attractive opportunity to accelerate new candidate vaccines to advance into large and expensive prevention of disease (POD) efficacy trials. However, POI approaches are limited by imperfect current tools to measure Mycobacterium tuberculosis infection end-points. POD trials need to carefully consider the type and number of microbiological tests that define TB disease and, if efficacy against subclinical (asymptomatic) TB disease is to be tested, POD trials need to explore how best to define and measure this form of TB. Prevention of recurrence trials are an alternative approach to generate proof of concept for efficacy, but optimal timing of vaccination relative to treatment must still be explored. Novel and efficient approaches to efficacy trial design, in addition to an increasing number of candidates entering phase 2–3 trials, would accelerate the long-standing quest for a new TB vaccine.

Accurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision. We investigate whether antecedent agro-meteorological variables and household-level attributes can be used to predict planting dates of small-scale maize producers in central Kenya. Using random forest models, we compare remote estimates of SOS with field-level survey data of actual planting dates. We compare three years of planting dates (2016–2018) for two rainy seasons (the October-to-December short rains, and the March-to-May long rains) gathered from weekly Short Message Service (SMS) mobile phone surveys. In situ data are compared to SOS from the Water Requirement Satisfaction Index (SOSWRSI) and other agro-meteorological variables from Earth observation (EO) datasets (rainfall, NDVI, and evaporative demand). The majority of farmers planted within 20 days of the SOSWRSI from 2016 to 2018. In the 2016 long rains season, many farmers reported planting late, which corresponds to drought conditions. We find that models relying solely on EO variables perform as well as models using both socio-economic and EO variables. The predictive accuracy of EO variables appears to be insensitive to differences in reference periods that were tested for deriving EO anomalies (1, 3, 5, or 10 years). As such, it would appear that farmers are either responding to short-term weather conditions (e.g., intra-seasonal variability), or longer trends than were included in this study (e.g., 25–30 years), when planting. The methodologies used in this study, weekly SMS surveys, provide an operational means for estimating farmer behaviors–information which is traditionally difficult and costly to collect.

Seasonal climate variability frequently undermines farm yields, reduces food availability, and lowers income. This is particularly evident among small-scale agricultural producers in both irrigated and non-irrigated agroecosystems in the Global South where maize cultivars constitute a critical component of food production. In these systems, farmers make climate-sensitive decisions that include the selection of late- and/or early-maturing seed varieties, the diversity of seed varieties sown, and when to plant. Farmers’ expectations of future rainfall would therefore seem to be critical determinants of agricultural outcomes and foreshadow climate impacts. However, few studies have quantified the linkages between on-farm decisions and farmer seasonal predictions. We report on detailed household and phone surveys of 501 smallholder farmers in central Kenya based on the 2018 growing seasons and expectations for the 2019 March-April-May growing season. We show that farmers’ expectations of the upcoming seasonal rainfall have important associations with selections of seed maturity varieties and the number of maturing varieties farmers expect to plant and less important associations with the seeds’ planting dates. Furthermore, we show that 79% of the farmers form an expectation of the future seasonal climate and about two-thirds of them formed expectations based on a heuristic that connects the past climate to future seasonal conditions. More problematically, one-third of the farmers formed their rainfall expectation based on the prior season, and we show that no such correlation exists in observational data nor is correlation of seasonal rainfall supported by current understanding of climate variability. These results highlight the challenges farmers face in anticipating seasonal rainfall, which has implications for crop diversification and choices to adopt drought tolerant cultivars. The results suggest that farmers’ expectations of upcoming seasonal climate are important measures of farm decision-making.

Summary: There are a limited number of adjuvants that elicit effective cell-based immunity required for protection against intracellular bacterial pathogens. Here, we report that STING-activating cyclic dinucleotides (CDNs) formulated in a protein subunit vaccine elicit long-lasting protective immunity to Mycobacterium tuberculosis in the mouse model. Subcutaneous administration of this vaccine provides equivalent protection to that of the live attenuated vaccine strain Bacille Calmette-Guérin (BCG). Protection is STING dependent but type I IFN independent and correlates with an increased frequency of a recently described subset of CXCR3-expressing T cells that localize to the lung parenchyma. Intranasal delivery results in superior protection compared with BCG, significantly boosts BCG-based immunity, and elicits both Th1 and Th17 immune responses, the latter of which correlates with enhanced protection. Thus, a CDN-adjuvanted protein subunit vaccine has the capability of eliciting a multi-faceted immune response that results in protection from infection by an intracellular pathogen. : Van Dis et al. demonstrate that STING-activating cyclic dinucleotides provide significant protection when used as adjuvants in a protein subunit vaccine against Mycobacterium tuberculosis and show that mucosal administration of this vaccine elicits a Th17 immune response that correlates with enhanced protection. Keywords: Mycobacterium tuberculosis, vaccine adjuvant, cyclic dinucleotides, Th17

Chronic hepatitis B virus (HBV) infection affects 257 million people globally. Current therapies suppress HBV but viral rebound occurs on cessation of therapy; novel therapeutic strategies are urgently required. To develop a therapeutic HBV vaccine that can induce high magnitude T cells to all major HBV antigens, we have developed a novel HBV vaccine using chimpanzee adenovirus (ChAd) and modified vaccinia Ankara (MVA) viral vectors encoding multiple HBV antigens. ChAd vaccine alone generated very high magnitude HBV specific T cell responses to all HBV major antigens. The inclusion of a shark Invariant (SIi) chain genetic adjuvant significantly enhanced the magnitude of T-cells against HBV antigens. Compared to ChAd alone vaccination, ChAd-prime followed by MVA-boost vaccination further enhanced the magnitude and breadth of the vaccine induced T cell response. Intra-cellular cytokine staining study showed that HBV specific CD8+ and CD4+ T cells were polyfunctional, producing combinations of IFNγ, TNF-α, and IL-2. In summary, we have generated genetically adjuvanted ChAd and MVA vectored HBV vaccines with the potential to induce high-magnitude T cell responses through a prime-boost therapeutic vaccination approach. These pre-clinical studies pave the way for new studies of HBV therapeutic vaccination in humans with chronic hepatitis B infection.

Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region’s food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. We present summary results for this methodology applied to a land cover classification of Zambia’s Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%.

Efforts to promote forest conservation have focused on two separate types of policy reforms. Decentralization reforms have attempted to make local forest governance more accountable to demands from voters. Meanwhile, Payment for Ecosystem Services (PES) schemes like the REDD program (Reducing Emissions from Deforestation and Forest Degradation) have sought to use economic incentives to promote conservation. These programs make different assumptions about the incentives most likely to work in forest conservation. Decentralization reforms assume that citizen pressures on politicians will encourage conservation, while PES approaches assume that an economic incentive—money—is the best approach. Which type of incentive works best in settings with weak institutions? Here, using a unique longitudinal dataset of forest policy in 100 Bolivian municipalities, we examine the relationships between citizen pressures and economic incentives on forest policy. We find that both types of incentives are positively and significantly associated with government investments in forest conservation, and that the magnitudes of these relationships are similar. Further, we find that economic incentives may be especially effective at promoting conservation where citizen pressures are weak or absent.

A number of frameworks have been used to study the water-food-energy nexus; but few of these consider the role of institutions in mediating environmental outcomes. In this paper we aim to start filling that gap by combining insights from the Institutional Analysis and Development (IAD) framework and value chain analysis. Specifically we study food, energy and water value chains as networks of action situations (NAS) where actorsʼ decisions depend not only on the institutional structure of a particular situation but also on the decisions made in related situations. Although the IAD framework has developed a solid reputation in the policy sciences, empirical applications of the related NAS concept are rare. Value-chain analysis can help drawing the empirical boundaries of NAS as embedded in production processes. In this paper we first use value-chain analysis to identify important input-output linkages among water, food and energy production processes, and then apply the IAD-NAS approach to better understand the effect of institutions within and across those processes. The resulting combined framework is then applied to four irrigation-related case studies including: the use of energy for water allocation and food production in an irrigation project in Spain; the production and allocation of treated water for food and bioenergy production in Germany; the allocation of water for food production and urban use in Kenya; and the production and allocation of energy for food production in Hyderabad, India. The case analyses reveal the value of the framework by demonstrating the importance of establishing linkages across energy, water and food-related situations and the ways in which institutions limit or facilitate synergies along the value chains.

Africa is projected to add one billion urban residents by 2050. Yet developing sustainable solutions to tackle the host of challenges posed by rapid urban population growth is stymied by a lack municipality-level population data across the continent. To fill this gap, we intersect volunteered urban settlement data from OpenStreetMap with five synthetic gridded population datasets to estimate the how Africa’s urban population is distributed among over 4750 individual urban settlements across Africa. We assess how urban settlement distributions changed from 2000 to 2015 within and between countries and across moisture zones. To this end, we construct urban settlement Lorenz curves to calculate change in Gini coefficients and test the degree to which Africa’s urban settlements distributions fit power law distributions exhibited by Zipf’s law. Our results reveal that 77%–85% of urban settlements in Africa have fewer than 100 000 people and that at least 50% of Africa’s urban population live in urban settlements with fewer than 1 million residents. Across almost all African countries, the distribution of urban population shifted towards larger cities between 2000 and 2015. However, in arid regions, our results indicate that small- and medium-sized urban settlements are absorbing a greater share of urban population growth compared to large urban settlements. While our urban population estimates vary across gridded population datasets and differ from United Nations estimates, this is the first paper to measure urban population across Africa using a consistent methodology to identify urban settlement populations. Unlike UN urban population data for Africa, our results can readily be incorporated with geolocated environmental, public health, and economic data to support efforts to monitor United Nations Sustainable Development Goals related to urban sustainability, poverty reduction, and food security across Africa’s ever-growing urban settlements.

A novel exonuclease, designated as MszExo I, was cloned from Methylocaldum szegediense, a moderately thermophilic methanotroph. It specifically digests single-stranded DNA in the 3' to 5' direction. The protein is composed of 479 amino acids, and it shares 47% sequence identity with E. coli Exo I. The crystal structure of MszExo I was determined to a resolution of 2.2 Å and it aligns well with that of E. coli Exo I. Comparative studies revealed that MszExo I and E. coli Exo I have similar metal ion binding affinity and similar activity at mesophilic temperatures (25-47°C). However, the optimum working temperature of MszExo I is 10°C higher, and the melting temperature is more than 4°C higher as evaluated by both thermal inactivation assays and DSC measurements. More importantly, two thermal transitions during unfolding of MszExo I were monitored by DSC while only one transition was found in E. coli Exo I. Further analyses showed that magnesium ions not only confer structural stability, but also affect the unfolding of MszExo I. MszExo I is the first reported enzyme in the DNA repair systems of moderately thermophilic bacteria, which are predicted to have more efficient DNA repair systems than mesophilic ones.

Prior work has demonstrated the ability of common property systems to sustain institutional arrangements governing natural resources over long periods of time. Much of this work has focused on irrigation systems where upstream users agree to management arrangements that distribute water resources across both upstream and downstream users. A series of design principles have been identified that tend to lead to long-term sustained water management in these types of irrigation systems. However, this prior work has focused on the aggregate outcomes of the water system, and there has been little work evaluating the heterogeneity of water delivery within irrigation systems in developing countries. Heterogeneity of water resources within these systems has implications for livelihood outcomes because it can be indicative of a social, technological, and/or biophysical element facilitating or detracting from water delivery. We present a multilevel analysis of households nested within 25 smallholder irrigation systems in Kenya. Specifically, we examine household-level water outcomes (i.e., average flow rate and reliability of water provisioning) and the community-level and household-level drivers that affect household water outcomes. These drivers include physical infrastructure, institutional infrastructure, and biophysical variables. Much of the common-pool resource literature addresses the rule clusters responsible for natural resource outcomes, but by considering an array of both institutional and physical features and the water delivery outcomes produced at the household level, we offer new explanations for water disparities within smallholder-operated irrigation systems. We further discuss the ability of user-group members to reshape their water delivery outcomes through information exchange.

Lectin-like bacteriocins consist of tandem monocot mannose-binding domains and display a genus-specific killing activity. Here we show that pyocin L1, a novel member of this family from Pseudomonas aeruginosa, targets susceptible strains of this species through recognition of the common polysaccharide antigen (CPA) of P. aeruginosa lipopolysaccharide that is predominantly a homopolymer of D-rhamnose. Structural and biophysical analyses show that recognition of CPA occurs through the C-terminal carbohydrate-binding domain of pyocin L1 and that this interaction is a prerequisite for bactericidal activity. Further to this, we show that the previously described lectin-like bacteriocin putidacin L1 shows a similar carbohydrate-binding specificity, indicating that oligosaccharides containing D-rhamnose and not D-mannose, as was previously thought, are the physiologically relevant ligands for this group of bacteriocins. The widespread inclusion of d-rhamnose in the lipopolysaccharide of members of the genus Pseudomonas explains the unusual genus-specific activity of the lectin-like bacteriocins.

A significant challenge in the assessment of forest management outcomes is the limited ability to compare forest conditions quantitatively across ecological zones. We propose an approach for comparing different forest types through the use of reference forests. We tested our idea by drawing a sample of 42 forests from the Midwest USA, Mexico, Guatemala, Honduras, Brazil, Bolivia, Uganda, and Nepal. We grouped these forests by shared characteristics and selected a reference forest to serve as a baseline for each forest type. We developed an index of disturbances using ratios of several forest measurements to assess differences between each study forest and its reference forest. None of the study forests was known to have been impacted by major natural disturbances during the past 50 years. Therefore, the disturbances in these forests appear to be largely related to human activities. The forests most similar to their reference forests have had limited human interventions. Our results indicate the potential of this approach to compare different forest conditions across biomes. We argue that development of this approach could facilitate analyses of forest management institutions, promote reliable indicators to compare management outcomes, and contribute to improved policies for conservation.

This paper discusses opportunities for alternative collaborative approaches for social-ecological research in general and, in this context, for modeling land-use/land-cover change. In this field, the rate of progress in academic research is steady but perhaps not as rapid or efficient as might be possible with alternative organizational frameworks. The convergence of four phenomena provides new opportunities for cross-organizational collaboration: (1) collaborative principles related to "open source" (OS) software development, (2) the emerging area of "open content" (OC) licensing, (3) the World Wide Web as a platform for scientific communication, and (4) the traditional concept of peer review. Although private individuals, government organizations, and even companies have shown interest in the OS paradigm as an alternative model for software development, it is less commonly recognized that this collaborative framework is a potential innovation of much greater proportions. In fact, it can guide the collective development of any intellectual content, not just software. This paper has two purposes. First, we describe OS and OC licensing, dispense with some myths about OS, and relate these structures to traditional scientific process. Second, we outline how these ideas can be applied in an area of collaborative research relevant to the study of social-ecological systems. It is important to recognize that the concept of OS is not new, but the idea of borrowing OS principles and using OC licensing for broader scientific collaboration is new. Over the last year, we have been trying to initiate such an OS/OC collaboration in the context of modeling land use and land cover. In doing so, we have identified some key issues that need to be considered, including project initiation, incentives of project participants, collaborative infrastructure, institutional design and governance, and project finance. OS/OC licensing is not a universal solution suitable for all projects, but the framework presented here does present tangible advantages over traditional methods of academic research.