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Climate change mitigation benefits from the land sector are not being fully realised because of uncertainty and controversy about the role of native forest management. The dominant policy view, as stated in the IPCC’s Fifth Assessment Report, is that sustainable forest harvesting yielding wood products, generates the largest mitigation benefit. We demonstrate that changing native forest management from commercial harvesting to conservation can make an important contribution to mitigation. Conservation of native forests results in an immediate and substantial reduction in net emissions relative to a reference case of commercial harvesting. We calibrated models to simulate scenarios of native forest management for two Australian case studies: mixed-eucalypt in New South Wales and Mountain Ash in Victoria. Carbon stocks in the harvested forest included forest biomass, wood and paper products, waste in landfill, and bioenergy that substituted for fossil fuel energy. The conservation forest included forest biomass, and subtracted stocks for the foregone products that were substituted by non-wood products or plantation products. Total carbon stocks were lower in harvested forest than in conservation forest in both case studies over the 100-year simulation period. We tested a range of potential parameter values reported in the literature: none could increase the combined carbon stock in products, slash, landfill and substitution sufficiently to exceed the increase in carbon stock due to changing management of native forest to conservation. The key parameters determining carbon stock change under different forest management scenarios are those affecting accumulation of carbon in forest biomass, rather than parameters affecting transfers among wood products. This analysis helps prioritise mitigation activities to focus on maximising forest biomass. International forest-related policies, including negotiations under the UNFCCC, have failed to recognize fully the mitigation value of native forest conservation. Our analyses provide evidence for decision-making about the circumstances under which forest management provides mitigation benefits.

In this paper, we use choice architecture techniques to activate both social and personal norms, seeking to increase pro-environmental choices and to better understand the effect of such norm types on post-choice emotional responses. In four experiments, we make different social or personal norms salient by aligning choice environments with psychosocial mechanisms that activate different types of norms. We use different choice architecture techniques to change information, alter product sets, and generate the social consequences of choices. The target behavior, purchasing a recycled paper notebook, is captured through direct purchase behaviors or willingness to pay commitments. We find that choice architecture activates personal but not social norms, and that associated positive and negative emotions (guilt, shame, regret and pride) are elicited by choices but not by willingness to pay. Moreover, manipulating choice environment moderates the relationship between choice and norm-related emotions, such that positive emotional responses seem to be stronger than negative ones. The results suggest that choice architecture interventions can activate individual level beliefs about sustainability and help reduce the attitude-behavior gap.

The expansion of urban ecosystems and climate change, both outcomes of massive lifestyle changes, contribute to a series of side effects such as environmental deterioration, spread of diseases, increased greenhouse gas emissions and introduction of invasive species. In the case of the Athens metropolitan area, an invasive mosquito species—the Asian tiger mosquito (Aedes albopictus)–has spread widely in the last decade. This spread is favoured within urban environments and is also affected by changing climatic trends. The Asian tiger mosquito is accompanied by risks of mosquito-borne diseases, greater nuisance levels, and increased expenses incurring for its confrontation. The main aims of this paper are (i) to estimate the various costs associated with the control of this invasive species, as well as its health and nuisance impacts, (ii) to evaluate the level of citizens’ well-being from averting these impacts and (iii) to record citizens’ and experts’ perceptions regarding alternative control measures. Evidence shows that experts tend to place a high value on mosquito control when associated with serious health risks, while citizens are more sensitive and concerned about the environmental impacts of control methods. The synthesis of results produced by the current study could act as a preliminary guide for the estimation of societal welfare from the confrontation of similar problems in the context of a complex ecosystem., Author summary This paper is based on several years’ collaboration among researchers from various disciplines, key health policy makers and stakeholders in an attempt to evaluate the economic dimensions related to the presence of the Asian Tiger Mosquito (Aedes albopictus) and the challenges of tackling mosquito-borne disease outbreaks in Greece and Southern Europe. Similar studies have been conducted and continue to be published in Europe and the USA examining the socioeconomic benefit from the implementation of relevant control and prevention strategies. These studies conclude that there are significant benefits related both to the reduction of nuisance levels and the reduction of the health risks posed by various mosquito species. In our case, the application of an updated economic analysis on the effectiveness of relevant public control and prevention programs provides essential information for public health decision-making, bearing in mind the significant restructuring of the public sector and the fiscal crisis apparent in the European South.

There is an error in the third sentence of the "What proportion of cities will experience novel climate conditions?" subsection of the Results. The correct sentence is: As such, 22% of the world's cities are likely to exist in a climatic regime that does not currently exist on the planet today. There is an error in reference 30. The correct reference is: Cormack RM, Legendre L, Legendre P. Numerical Ecology. Biometrics. Elsevier; 2006;40: 280. There is an error in reference 33. The correct reference is: LondonMof. London Environment Strategy. 2018; https://www.london.gov.uk/sites/default/files/london_environment_ strategy.pdf. The Data Availability statement for this paper is incorrect. All relevant data are not provided in the published paper and its Supporting Information files. The authors have provided the data as Supporting Information S1-S3 Files. Additionally, the authors have provided guidelines for replicating the results of their paper. The guidelines are as follows: 1. Compute the 19 bioclimatic variables from the present (WORLDCLIM2) and future for the data specifically mentioned in the Methods section. The transformation, corrections, etc. applied on these are detailed on the CCAFS-CLIMATE website (referenced below). The authors used the downscaled model developed from CGIAR-CCAFS, scenario RCP4.5, for 2050. Following these instructions leads here: http://ccafs-climate.org/file-list.php?tile_name= &scenarios%5B%5D=8&extent=1&period%5B%5D=6&variables%5B%5D=1&resolution=1. 2. Build the City-Climate tables, one for the present, one for the future. 3. Compute a PCA of the 19 bioclimatic variables for the 1970-2000 to standardize the contribution of each variable and avoid autocorrelation issues (avoid double accounting of the same information). 4. Keep the 4 main axes. 5. Project the 19 bioclimatic variables from 2050 in these 4 axes, 4 axes built from the 1970- 2000 conditions. 6. Perform a distance matrix between the two tables (each table has 4 values for each city, related to the 4 axes of the PCA summarizing the 19 bioclimatic variables). 7. Assess which cities are similar to each other. Supporting information S1 File. (R) S2 File. (CSV) S3 File. (CSV).

Many countries have some kind of energy-system transformation either planned or ongoing for various reasons, such as to curb carbon emissions or to compensate for the phasing out of nuclear energy. One important component of these transformations is the overall reduction in energy demand. It is generally acknowledged that the domestic sector represents a large share of total energy consumption in many countries. Increased energy efficiency is one factor that reduces energy demand, but behavioral approaches (known as “sufficiency”) and their respective interventions also play important roles. In this paper, we address citizens’ heterogeneity regarding both their current behaviors and their willingness to realize their sufficiency potentials—that is, to reduce their energy consumption through behavioral change. We collaborated with three Swiss cities for this study. A survey conducted in the three cities yielded thematic sets of energy-consumption behavior that various groups of participants rated differently. Using this data, we identified four groups of participants with different patterns of both current behaviors and sufficiency potentials. The paper discusses intervention types and addresses citizens’ heterogeneity and behaviors from a city-based perspective., PLoS ONE, 12 (10), ISSN:1932-6203

One of the main manifestations of climate change will be increased rainfall variability. How to deal with this in agriculture will be a major societal challenge. In this paper we explore flexibility in land use, through deliberate seasonal adjustments in cropped area, as a specific strategy for coping with rainfall variability. Such adjustments are not incorporated in hydro-meteorological crop models commonly used for food security analyses. Our paper contributes to the literature by making a comprehensive model assessment of inter-annual variability in crop production, including both variations in crop yield and cropped area. The Ganges basin is used as a case study. First, we assessed the contribution of cropped area variability to overall variability in rice and wheat production by applying hierarchical partitioning on time-series of agricultural statistics. We then introduced cropped area as an endogenous decision variable in a hydro-economic optimization model (WaterWise), coupled to a hydrology-vegetation model (LPJmL), and analyzed to what extent its performance in the estimation of inter-annual variability in crop production improved. From the statistics, we found that in the period 1999-2009 seasonal adjustment in cropped area can explain almost 50% of variability in wheat production and 40% of variability in rice production in the Indian part of the Ganges basin. Our improved model was well capable of mimicking existing variability at different spatial aggregation levels, especially for wheat. The value of flexibility, i.e. the foregone costs of choosing not to crop in years when water is scarce, was quantified at 4% of gross margin of wheat in the Indian part of the Ganges basin and as high as 34% of gross margin of wheat in the drought-prone state of Rajasthan. We argue that flexibility in land use is an important coping strategy to rainfall variability in water stressed regions.

Over the past twenty years, government advisory bodies have placed increasing emphasis on the need for adaptive measures in response to the effects of human-induced climate change. Integrated Assessment Models (IAMs), which incorporate macroeconomic and climate variables, feature prominently in advisory content, though they rarely draw on data from outside strictly constrained hypothetical systems. This has led to assertions that they are not well-suited to approximate complex systemic human-environment processes. Modular, interdisciplinary approaches have offered a way to address this shortcoming; however, beyond climate records, prehistoric data continue to be under-utilised in developing such models. In this paper we highlight the contribution that archaeology and palaeoecology can make to the development of the next generation IAMs that are expected to enhance provision for more local and pro-active adaptations to future climate change. We present data from one of Southeast Asia’s most heavily developed river deltas: the Red River (Song Hong) Delta, in Vietnam and localised analysis from the Tràng An Landscape Complex World Heritage Site, on the delta’s southern margin. Comparison is made between Shared Socio-economic Pathways (SSP) 5–8.5 and SSP2–4.5 emission projection models and the Mid-Holocene inundation of the Red River Basin. We highlight the value to taking a scientific long view of coastal evolution through an illustrative set of eight research foci where palaeo-data can bring new and localised empirical data to bear on future risk management planning. We proceed to demonstrate the applicability of palaeoenvironmental, zooarchaeological and historical evidence to management and the development of sustainable conservation strategies using Tràng An as a case study. In so doing, we further highlight the importance of knowledge exchange between scientific, corporate, non-governmental, local, and state stakeholders to achieve tangible results on the ground.

The consequences of anthropogenic climate change are extensively debated through scientific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a forum where individuals of diverse backgrounds can share their thoughts and opinions. As consumption shifts from old media to new, Twitter has become a valuable resource for analyzing current events and headline news. In this research, we analyze tweets containing the word "climate" collected between September 2008 and July 2014. Through use of a previously developed sentiment measurement tool called the Hedonometer, we determine how collective sentiment varies in response to climate change news, events, and natural disasters. We find that natural disasters, climate bills, and oil-drilling can contribute to a decrease in happiness while climate rallies, a book release, and a green ideas contest can contribute to an increase in happiness. Words uncovered by our analysis suggest that responses to climate change news are predominately from climate change activists rather than climate change deniers, indicating that Twitter is a valuable resource for the spread of climate change awareness., 11 pages, 10 figures

Cocoa is a plant with origins in northwestern South America with high relevance in the global economy. Evidence indicates that cocoa is sensitive to a dry climate, under which crop production is reduced. Projections for future climate change scenarios suggest a warmer and drier climate in the Amazon basin. In this paper, we quantify the potential effects in cocoa production due to its edaphoclimatic suitability changes to the Brazilian Amazon biome and account for regional differences in planning occupation territories. We modeled the suitability of cocoa’s geographical distribution using an ensemble of 10 correlative models that were run in the “biomod2” library and projected to two future climate scenarios (RCPs 4.5 and 8.5) by 2050. Combining information on climate and soil suitability and installed infrastructure in the macro-regions of the Brazilian Amazon. We defined a zoning system to indicate how cocoa production may respond to climate change according to the current and future suitability model. Our results suggest that a reduction in precipitation and an increase in temperature may promote a reduction in the suitability of cocoa production in the Brazilian Amazon biome. In addition of the areas suitable for cocoa plantation, we found a 37.05% and 73.15% decrease in the areas suitable for intensification and expansion zones under RCP 4.5 and 8.5, respectively, compared with the current scenario. We conclude that there may be a need to expand land to cocoa production in the future, or else it will be necessary to plant a cocoa variety resistant to new climatic conditions. Besides, we recommend procedures to combat illegal deforestation to prevent the most critical climate change scenarios from occurring.

Using microdata from the Chinese General Social Survey (CGSS 2010), this paper investigates whether there are green returns to education in China, where educational attainment promotes pro-environmental attitudes and behaviors. We establish causality by exploiting the exogenous variation induced by the implementation of the Compulsory Schooling Law (CSL) in China. We find evidence that educational attainment is associated with higher levels of pro-environmental attitudes and behaviors, and these estimates are robust to various robustness checks. Further analysis reveals that the acquisition of environmental knowledge is the channel that drives the effect of education on pro-environmental attitudes and behaviors. Finally, the effects of education are heterogeneous across individuals.

Understanding the temporal and spatial distribution characteristics of the cultural heritage of the Yellow River Basin can effectively improve the scientific understanding of the historical changes, environmental evolution, and cultural and economic development of the Yellow River Basin and thus provide a scientific and reasonable decision-making basis for the protection and development of its cultural heritage. The research object of this paper are the national cultural relic protection units. These are examined using the GIS spatial analysis method to explore the spatial and temporal distribution characteristics and spatial structure of 2,102 national material cultural heritage sites in the Yellow River basin. The results show that the spatial distribution of cultural heritage has a significant spatial agglomeration effect. The whole basin is concentrated in stable high- and low-value areas, and the difference between the high- and low-value areas is clear. Some aspects of the spatial structure heterogeneity are strong, showing a low value dispersion distribution trend. In different periods, the distribution direction and scope of cultural heritage have low ranges of rotation, a clear direction, and a high degree of centripetal distribution. The spatial and temporal distribution of cultural heritage is the result of the combined action of natural geographical environment such as climate change, topography, river hydrology, and human environment such as administrative institutional changes, ideological evolution, and social and economic development.

A key challenge of environmental planning is to craft recommendations for future sustainable spatial development amid ubiquitous uncertainties. This paper aims to explore how different data uncertainties, usually unknown to the planner, may influence environmental planning recommendations. We apply a case study-based approach, in which we provide three illustrative examples of how data with different kinds and levels of uncertainty affect environmental assessments and, by that, the decision-support provided by environmental planning. The cases stem from different spatial levels in Germany and consider ‘Regional soil-based climate change mitigation’ in the region of Hannover, ‘State-wide habitat conservation siting’ in the federal state of Saxony-Anhalt, and ‘National renewable energy planning’. Based on the three examples, we discuss implications for planning practice and derive recommendations for further research. The three cases studies illustrate the substantial effects of data uncertainty on environmental assessments and planning recommendations derived from those results. We identify four problem constellations of dealing with data uncertainty in environmental planning that relate to the severeness of uncertainty impacts, the responsibility of the decision-maker, and the kinds of impacts that wrong decisions may have. We close with recommendations for further research, among others to develop robust and pragmatic methods for identifying the uncertainty levels in environmental data and assessment results.

23 Pags.- 8 Tabls.- 2 Figs. © 2021 Jebari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited., Temperate grassland soils store significant amounts of carbon (C). Estimating how much livestock grazing and manuring can influence grassland soil organic carbon (SOC) is key to improve greenhouse gas grassland budgets. The Rothamsted Carbon (RothC) model, although originally developed and parameterized to model the turnover of organic C in arable topsoil, has been widely used, with varied success, to estimate SOC changes in grassland under different climates, soils, and management conditions. In this paper, we hypothesise that RothC-based SOC predictions in managed grasslands under temperate moist climatic conditions can be improved by incorporating small modifications to the model based on existing field data from diverse experimental locations in Europe. For this, we described and evaluated changes at the level of: (1) the soil water function of RothC, (2) entry pools accounting for the degradability of the exogenous organic matter (EOM) applied (e.g., ruminant excreta), (3) the month-on-month change in the quality of C inputs coming from plant residues (i.e above-, below-ground plant residue and rhizodeposits), and (4) the livestock trampling effect (i.e., poaching damage) as a common problem in areas with higher annual precipitation. In order to evaluate the potential utility of these changes, we performed a simple sensitivity analysis and tested the model predictions against averaged data from four grassland experiments in Europe. Our evaluation showed that the default model’s performance was 78% and whereas some of the modifications seemed to improve RothC SOC predictions (model performance of 95% and 86% for soil water function and plant residues, respectively), others did not lead to any/or almost any improvement (model performance of 80 and 46% for the change in the C input quality and livestock trampling, respectively). We concluded that, whereas adding more complexity to the RothC model by adding the livestock trampling would actually not improve the model, adding the modified soil water function and plant residue components, and at a lesser extent residues quality, could improve predictability of the RothC in managed grasslands under temperate moist climatic conditions., We gratefully acknowledge the financial support of the Fundación Cándido de Iturriaga y Maria del Dañobeitia, Juan de la Cierva and the European Union’s Horizon 2020 Research and Innovation Action (RIA) through the project “Innovation for sustainable sheep and Goat production in Europe (iSAGE)” undergrant agreement No 679302. BC3 is supported by the Basque Government through the BERC 2018-2021 program and by Spanish Ministry of Economy and Competitiveness MINECO through BC3 María de Maeztu excellence accreditation MDM-2017-0714. Agustin del Prado is financed by the programme Ramon y Cajal from the Spanish Ministry of Economy, Industry and Competitiveness (RYC2017-22143). María Almagro was supported by the Juan de la Cierva Program (grant IJCI-2015- 23500). F

This paper explores whether efficacy beliefs can alter the relationship between worry about climate change and personal energy-saving behaviors, controlling for climate change beliefs and socio-demographics. For this purpose, we used data from 23 countries that participated in the European Social Survey Round 8 (N = 44 387). Worry about climate change, personal efficacy, personal outcome expectancy, and collective outcome expectancy were each associated with personal energy-saving behaviors concerning either energy curtailment or energy efficiency. The results further show that outcome expectancies moderate the association between worry about climate change and both types of energy behaviors. Worry was more strongly related to energy curtailment behaviors among those with high levels of personal and collective outcome expectancy. A similar pattern was found for energy efficiency behaviors, which were more strongly predicted by worry about climate change when combined with high levels of collective outcome expectancy. These findings are relevant for climate change communication, especially informational campaigns aiming to lower overall household energy use. publishedVersion

The idea that households produce and consume their own energy, that is, energy self-sufficiency at a very local level, captures the popular imagination and commands political support across parts of Europe. This paper investigates the technical and economic feasibility of household energy self-sufficiency in Switzerland, which can be seen as representative for other regions with a temperate climate, by 2050. We compare sixteen cases that vary across four dimensions: household type, building type, electricity demand reduction, and passenger vehicle use patterns. We assume that photovoltaic (PV) electricity supplies all energy, which implies a complete shift away from fossil fuel based heating and internal combustion engine vehicles. Two energy storage technologies are considered: short-term storage in lithium-ion batteries and long-term storage with hydrogen, requiring an electrolyzer, storage tank, and a fuel cell for electricity conversion. We examine technological feasibility and total system costs for self-sufficient households compared to base cases that rely on fossil fuels and the existing power grid. PV efficiency and available rooftop/facade area are most critical with respect to the overall energy balance. Single-family dwellings with profound electricity demand reduction and urban mobility patterns achieve self-sufficiency most easily. Multi-family buildings with conventional electricity demand and rural mobility patterns can only be self-sufficient if PV efficiency increases, and all of the roof plus most of the facade can be covered with PV. All self-sufficient cases are technically feasible but more expensive than fully electrified grid-connected cases. Self-sufficiency may even become cost-competitive in some cases depending on storage and fossil fuel prices. Thus, if political measures improve their financial attractiveness or individuals decide to shoulder the necessary investments, self-sufficient buildings may start to become increasingly prevalent., PLoS ONE, 15 (3), ISSN:1932-6203

Southern African cities face several challenges including management of rapid urbanization, rising populations, expanding informal settlements; adequate water and other service provision, and a host of governance challenges. Climate change and variability add a compounding effect to this complex, multi stressor context. Addressing the complexity requires an understanding of urban ecosystems functioning and interactions amongst the built and natural environment (climate) and human systems. In this paper we argue that learning is essential for cities to be resilient to current and future challenges. We profile the Future Resilience for African CiTies And Lands (FRACTAL) project which contributed towards climate resilient development by providing relevant climate information for decision-making at the city regional scale in southern Africa. Following FRACTAL’s city-to-city learning approach of sharing good practices, knowledge and experiences framed around transdisciplinary research, the study cities of Harare, Lusaka, Windhoek and Durban conducted city learning exchange visits between 2017 and 2018. We used a mixed methods approach to collect and analyze historical climate and hydrological data and current socio-economic and development data among the cities. A qualitative, in-depth, case study comparative analysis was used to identify similarities and differences as well as lessons drawn from the learning process during the city exchanges and these were complimented by desktop studies. Results showed water scarcity, large informal settlements, reliance on external water and energy sources, inadequate protection of ecologically sensitive resources and service provision as some of the common complications in the cities. Several lessons and transferable practices learnt from the cities included effective water conservation and waste management and the use of public-private partnerships in Windhoek, community engagements in Durban and Lusaka while lessons on decisive leadership in dealing with informal settlements emanated from Harare’s limited informal settlements. Lastly, Durban’s Adaptation Charter and integrated climate planning provided lessons for biodiversity protection and mainstreaming climate change at city governance level. While we recognize that cities are context-specific we consider these good practices as being broadly transferable to other southern African cities. We conclude that social, experiential and structured learning can be an innovative way of multi-stakeholder engagement and a useful approach to increase city resilience planning across southern Africa and cities that face similar developmental challenges.

Vulnerability to climate change is highly dynamic, varying between and within communities over different timescales. This paper draws upon complex adaptive systems thinking to develop an approach for capturing, understanding, and monitoring climate vulnerability in a case study from northern Canada, focusing on Inuit food systems. In the community of Ulukhaktok, Northwest Territories, we followed 10 hunters over a 2-year period, asking them to document their harvesting activities and discuss their lived experience of harvesting under changing environmental and societal conditions. GPS monitoring and participatory mapping sessions were used to document 23,996km of trails (n = 409), with conversational bi-weekly semi-structured interviews and secondary instrumental weather data used to contextualise climate change within a nexus of other socioeconomic, cultural, and political stressors that also affect harvesting. Our results demonstrate that climate change has considerable potential to affect harvesting activities, particularly when its impacts manifest as anomalous/extreme events. However, climate change impacts are not necessarily the most salient issues affecting harvesting on a day-to-day basis. Instead, factors relating to economics (particularly financial capital and the wage-based economy), social networks, and institutions are found to have a greater influence, either as standalone factors with cascading effects or when acting synchronously to augment the impacts of environmental change.

The importance of coastal upwelling systems is widely recognized. However, several aspects of the current and future behaviors of these systems remain uncertain. Fluctuations in temperature because of anthropogenic climate change are hypothesized to affect upwelling-favorable winds and coastal upwelling is expected to intensify across all Eastern Boundary Upwelling Systems. To better understand how upwelling may change in the future, it is necessary to develop a more rigorous method of quantifying this phenomenon. In this paper, we use SST data and wind data in a novel method of detecting upwelling signals and quantifying metrics of upwelling intensity, duration, and frequency at four sites within the Benguela Upwelling System. We found that indicators of upwelling are uniformly detected across five SST products for each of the four sites and that the duration of those signals is longer in SST products with higher spatial resolutions. Moreover, the high-resolution SST products are significantly more likely to display upwelling signals at 25 km away from the coast when signals were also detected at the coast. Our findings promote the viability of using SST and wind time series data to detect upwelling signals within coastal upwelling systems. We highlight the importance of high-resolution data products to improve the reliability of such estimates. This study represents an important step towards the development of an objective method for describing the behavior of coastal upwelling systems.

Agri-food systems are besieged by malnutrition, yield gaps, and climate vulnerability, but integrated, research-based responses in public policy, agricultural, value chains, and finance are constrained by short-termism and zero sum thinking. As they respond to current and emerging agri-food system challenges, decision makers need new tools that steer toward multi-sector, evidence-based collaboration. To support national agri-food system policy processes, the Integrated Agri-food System Initiative (IASI) methodology was developed and validated through case studies in Mexico and Colombia. This holistic, multi-sector methodology builds on diverse existing data resources and leverages situation analysis, modeled predictions, and scenarios to synchronize public and private action at the national level toward sustainable, equitable, and inclusive agri-food systems. Culminating in collectively agreed strategies and multi-partner tactical plans, the IASI methodology enabled a multi-level systems approach by mobilizing design thinking to foster mindset shifts and stakeholder consensus on sustainable and scalable innovations that respond to real-time dynamics in complex agri-food systems. To build capacity for these types of integrated, context-specific approaches, greater investment is needed in supportive international institutions that function as trusted in-region ‘innovation brokers.’ This paper calls for a structured global network to advance adaptation and evolution of essential tools like the IASI methodology in support of the One CGIAR mandate and in service of positive agri-food systems transformation.

Introduction Climate change affects livestock production and productivity, which could threaten livestock-based food security in pastoral and agro-pastoral production systems of the tropics and sub-tropics. Boran cattle breed is one of the hardiest Zebu cattle reared by Borana Oromo pastoralists for milk and meat production. However, there is limited comprensive information on production, reproduction and adaption traits of the Boran cattle in Ethiopia. Thus, this paper aims to compile the main production, reproduction and some adaptation traits of Boran cattle based on systematic review and meta-analysis of peer reviewed published and unpublished literature. Methodology A combination of systematic review and meta-analysis based on PRISMA guideline was employed. Accordingly, out of 646 recorded articles identified through database searching, 64 were found to be eligible for production, reproduction and adaptation characteristics of the Boran cattle, 28 articles were included in qualitative systematic review while 36 articles were used for quantitative meta-analysis. Result The Boran cattle breed has the ability to survive, produce and reproduce under high ambient temperature, utilize low quality forage resources, and resist water shortage or long watering intervals and tick infestations. The review revealed that the breed employs various adaptation responses (morphological, physiological, biochemical, metabolic, cellular and molecular responses) to cope with harsh environmental conditions including climate change, rangeland degradation, seasonal feed and water shortages and high incidences of tick infestations. The meta-analysis using a random-effects model allowed provision of pooled estimates of heritability and genetic correlations for reproduction and production traits, which could be used to solve genetic prediction equations under a population level in purebred Boran cattle. In addition, heritability and genetic-correlation estimates found in the present study suggest that there is high genetic variability for most traits in Boran cattle, and that genetic progress is possible for all studied traits in this breed. Conclusion The Boran cattle breed has the ability to survive, produce and reproduce under high ambient temperature, utilize low quality forage resources, and resist water shortage or long watering intervals and tick infestations. However, currently there are several challenges such as recurrent droughts, pasture deterioration and lack of systematic selection and breeding programs that play to undermine the realization of the potential of the breed. Thus, we recommend systematic selection for enhancing the reproductive and production performances without compromising the adaptation traits of the breed coupled with improved management of rangelands.

It is estimated that about 1/4th of all greenhouse gas (GHG) emissions may be caused by the global food system. Reducing the GHG emissions from food production is a major challenge in the context of the projected growth of the world’s population, which is increasing demand for food. In this context, the goal should be to achieve the lowest possible emission intensity of the food production system, understood as the amount of GHG emissions per unit of output. The study aimed to calculate the emission intensity of food production systems and to specify its determinants based on a panel regression model for 14 countries, which accounted for more than 65% of food production in the world between 2000 and 2014. In this article, emission intensity is defined as the amount of GHG emissions per value of global output. Research on the determinants of GHG emissions related to food production is well documented in the literature; however, there is a lack of research on the determinants of the emission intensity ratio for food production. Hence, the original contribution of this paper is the analysis of the determinants of GHG emissions intensity of food production systems. The study found the decreased of emission intensity from an average of more than 0.68 kg of CO2 equivalent per USD 1 worth of food production global output in 2000 to less than 0.46 in 2014. The determinants of emission intensity decrease included the yield of cereals, the use of nitrogen fertilizers, the agriculture material intensity, the Human Development Index, and the share of fossil fuel energy consumption in total energy use. The determinants of growth of emission intensity of food production systems included GDP per capita, population density, nitrogen fertilizer production, utilized agriculture area, share of animal production, and energy use per capita.

This paper investigates the correlation between climate, environment and human land use in the Westernmost Mediterranean on both sides of the Strait of Gibraltar during the Late Glacial. Using a multi-proxy approach on a sample of 300 sites from the Solutrean and Magdalenian of the Iberian Peninsula and from the Iberomaurusian in Morocco, we find evidence for significant changes in settlement patterns and site density after the Last Glacial Maximum. In Southern Iberia, during Heinrich Stadial 1, hyperarid zones expanded drastically from the south-eastern coast to the West through the Interior. This aridification process heavily affected Magdalenian settlement in the South and caused a strong decline of hunter-gatherer population. Southern Iberia during Heinrich Stadial 1 turned out to be a high-risk environment when compared to Northern Iberia. At the same time, the Late Iberomaurusian of Morocco, although considered to be situated in a high-risk environment as well, experiences an increase of sites and expansion of settlement area.

In this paper, we introduce a scalable machine learning approach accompanied by open-source software for identifying violent and peaceful forms of political protest participation using social media data. While violent political protests are statistically rare events, they often shape public perceptions of political and social movements. This is, in part, due to the extensive and disproportionate media coverage which violent protest participation receives relative to peaceful protest participation. In the past, when a small number of media conglomerates served as the primary information source for learning about political and social movements, viewership and advertiser demands encouraged news organizations to focus on violent forms of political protest participation. Consequently, much of our knowledge about political protest participation is derived from data collected about violent protests, while less is known about peaceful forms of protest. Since the early 2000s, the digital revolution shifted attention away from traditional news sources toward social media as a primary source of information about current events. This, along with developments in machine learning which allow us to collect and analyze data relevant to political participation, present us with unique opportunities to expand our knowledge of peaceful and violent forms of political protest participation through social media data.

A critical examination of the US Environmental Protection Agency’s (US EPA’s) Greenhouse Gas Reporting Program (GHGRP) database provided an opportunity for the largest evaluation to date of landfilled waste decomposition kinetics with respect to different US climate regimes. In this paper, 5–8 years of annual methane collection data from 114 closed landfills located in 29 states were used to estimate site-specific waste decay rates (k) and methane collection potentials (Lc). These sites account for approximately 9% of all landfills required to report GHG emissions to the US EPA annually. The mean methane collection potential (Lc) for the sites located in regions with less than 635 mm (25 in) annual rainfall was significantly (p3 methane Mg-1 waste, respectively). This finding suggests that a fraction of the in-place biodegradable waste may not be decomposing, potentially due to a lack of adequate moisture content of landfills located in arid regions. The results of this evaluation offer insight that challenges assumptions of the traditional landfill methane estimation approach, especially in arid climates, that all methane corresponding to the total methane generation potential of the buried solid waste will be produced. Decay rates showed a significant correlation with annual precipitation, with an average k of 0.043 year-1 for arid regions (< 508 mm (20 in) year-1), 0.074 year-1 for regions with 508–1,016 mm (20–40 in) annual precipitation, and 0.09 year-1 in wet regions (> 1,016 mm (40 in) year-1). The data suggest that waste is decaying faster than the model default values, which in turn suggests that a larger fraction of methane is produced during a landfill’s operating life (relative to post-closure).

Coral bleaching driven by ocean warming is one of the most visible ecological impacts of climate change and perhaps the greatest threat to the persistence of reefs in the coming decades. In the absence of returning atmospheric greenhouse gas concentrations to those compatible with ocean temperatures below the mass coral bleaching temperature thresholds, the most straightforward means to reduce thermal-stress induced bleaching is to cool water at the seabed. The feasibility of reducing the seabed temperature through cool-water injections is considered first by analysing the feasibility of doing so on 19 reefs with differing physical environments using a simple residence time metric in 200 m resolution hydrodynamic model configurations. We then concentrate on the reefs around Lizard Island, the most promising candidate of the 19 locations, and develop a 40 m hydrodynamic model to investigate the effect of the injection of cool water at differing volumetric rates. Injecting 27°C seawater at a rate of 5 m3 s-1 at 4 sites in early 2017 cooled 97 ha of the reef by 0.15°C or more. The power required to pump 5 m3 s-1 through a set of pipes over a distance of 3 km from a nearby channel is ∼466 kW. This power applied at 4 sites for 3 months achieves a 2 Degree Heating Weeks (DHWs) reduction on 97 ha of reef. A more precise energy costing will require further expert engineering design of the pumping equipment and energy sources. Even for the most physically favourable reefs, cool-water transported through pipes and injected at a reef site is energy expensive and cannot be scaled up to any meaningful fraction of the 3,100 reefs of the GBR. Should priority be given to reducing thermal stress on one or a few high value reefs, this paper provides a framework to identify the most promising sites.

Emission trading scheme (ETS), the most popular market-based instrument, is widely used to solve carbon emission problems in the world. With the development of carbon market, carbon asset has been a popular financial product to invest and the risk management becomes important for government, regulated enterprises and other investors. As carbon prices have tail characteristics, this paper explores the extremal risks between carbon markets in US, Europe and China using tail dependence correlation coefficients. The empirical analysis demonstrates the tail dependence structure between carbon markets in US. and Europe is the same sign, which indicates that it is unwise to hold these two carbon assets as a portfolio. Moreover, the co-movements between European Union Emission Trade Scheme (EU ETS) and China's carbon markets are partly significant, and the operation mechanisms in China should be improved. In addition, the tail dependence test among the carbon pilots in China shows diversity. Hubei carbon trading pilot, located in central China, has extremal dependence with all other selected pilots for its regulatory program operation. The findings give insight to the carbon market regulars to improve the operation mechanism and are also useful for the investors to manage their portfolios, policymakers to make practically applicable regulations, and relevant organizations to develop procedures.

Perennial vegetables are a neglected and underutilized class of crops with potential to address 21st century challenges. They represent 33–56% of cultivated vegetable species, and occupy 6% of world vegetable cropland. Despite their distinct relevance to climate change mitigation and nutritional security, perennial vegetables receive little attention in the scientific literature. Compared to widely grown and marketed vegetable crops, many perennial vegetables show higher levels of key nutrients needed to address deficiencies. Trees with edible leaves are the group of vegetables with the highest levels of these key nutrients. Individual “multi-nutrient” species are identified with very high levels of multiple nutrients for addressing deficiencies. This paper reports on the synthesis and meta-analysis of a heretofore fragmented global literature on 613 cultivated perennial vegetables, representing 107 botanical families from every inhabited continent, in order to characterize the extent and potential of this class of crops. Carbon sequestration potential from new adoption of perennial vegetables is estimated at 22.7–280.6 MMT CO2-eq/yr on 4.6–26.4 Mha by 2050.

Agricultural development projects have a poor track record of success mainly due to risks and uncertainty involved in implementation. Cost-benefit analysis can help allocate resources more effectively, but scarcity of data and high uncertainty makes it difficult to use standard approaches. Bayesian Networks (BN) offer a suitable modelling technology for this domain as they can combine expert knowledge and data. This paper proposes a systematic methodology for creating a general BN model for evaluating agricultural development projects. Our approach adapts the BN model to specific projects by using systematic review of published evidence and relevant data repositories under the guidance of domain experts. We evaluate a large-scale agricultural investment in Africa to provide a proof of concept for this approach. The BN model provides decision support for project evaluation by predicting the value-measured as net present value and return on investment-of the project under different risk scenarios.

This paper brings advances in weather data collection and modeling, and developments in socioeconomic climate microsimulations to bear on the analysis of the implications of climate change (CC) in the design of public policies to combat food insecurity. It uses new downscaled predictions of future climate in 2050, derived from three Earth System Models calibrated with a new historical weather station dataset for Peru. This climate data is used in a three-stage socioeconomic microsimulation model that includes climate risk, and deals with the endogeneity of incomes and simultaneity of expected food consumption and its variability. We estimate the impact of CC on agricultural yields, and find results consistent and fully bounded within what the global simulations literature has found, with yields falling up to 13% in some regions. However, we show that these drops (and increases) in yields translate to much smaller changes in food consumption, and also surprisingly, to very minor impacts on vulnerability to food insecurity. The document explores what explains this surprising result, showing that in addition to characteristics that are specific to Peru, there are household and market mediating mechanisms that are available in all countries, which explain how changes in yields, and corresponding farm incomes have a reduced impact in vulnerability to food insecurity. Finally, in light of these findings, we explore which policies might have greater impact in reducing food insecurity in contexts of hunger prevalence.

Unidad de excelencia María de Maeztu CEX2019-000940-M Participatory planning networks made of government agencies, stakeholders, citizens and scientists are receiving attention as a potential pathway to build resilient landscapes in the face of increased wildfire impacts due to suppression policies and land-use and climate changes. A key challenge for these networks lies in incorporating local knowledge and social values about landscape into operational wildfire management strategies. As large wildfires overcome the suppression capacity of the fire departments, such strategies entail difficult decisions about intervention priorities among different regions, values and socioeconomic interests. Therefore there is increasing interest in developing tools that facilitate decision-making during emergencies. In this paper we present a method to democratize wildfire strategies by incorporating social values about landscape in both suppression and prevention planning. We do so by reporting and critically reflecting on the experience from a pilot participatory process conducted in a region of Catalonia (Spain). There, we built a network of researchers, practitioners and citizens across spatial and governance scales. We combined knowledge on expected wildfires, landscape co-valuation by relevant actors, and citizen participation sessions to design a wildfire strategy that minimized the loss of social values. Drawing on insights from political ecology and transformation science, we discuss what the attempt to democratize wildfire strategies entails in terms of power relationships and potential for social-ecological transformation. Based on our experience, we suggest a trade-off between current wildfire risk levels and democratic management in the fire-prone regions of many western countries. In turn, the political negotiation about the landscape effects of wildfire expert knowledge is shown as a potential transformation pathway towards lower risk landscapes that can re-define agency over landscape and foster community re-learning on fire. We conclude that democratizing wildfire strategies ultimately entails co-shaping the landscapes and societies of the future.

Recent studies highlight a growing concern over the limited adoption of climate smart agricultural (CSA) practices despite their potential benefits on adaptation, mitigation and productivity. Literature indicates several factors behind the lack of adoption including socio-demographic and economic conditions, agro-ecological scales and the nature of the practices. This paper examines to what extent and under which conditions such factors influence the adoption of CSA practices at farm, household and community level across three study sites in different continents: Vietnam, Nicaragua and Uganda. While cost benefit analysis (CBA) is employed to assess the farm-level profitability of CSA practices, the aggregate community impact disaggregated by different groups of farmer typologies with specific socio-economic features is derived from the adoption rate estimated by the relative advantage of practices and the income level of each group. Our main findings show great variation of farm-profitability of CSA practices across scales. Similar practices could generate different profitability depending on crop typologies, input access and prices, household types and local context. Regarding the aggregate profitability of CSA practices at regional scale, we found that under particular conditions, relevant factors of adoption matter to the adoption pattern and thereby affects the ranking. Such conditions include (i) high income inequality, (ii) large profitability gap of prioritized CSA practices, and (iii) large proportion of cost and benefit of the practices in the level of income. This study contributes to enhancing the prioritization process of CSA practices and provides practical guidance for research and policy to tailor the investment to appropriate end-users to assure the greatest impact for the community.

Understanding trends in the diverse resources provided by large rivers will help balance tradeoffs among stakeholders and inform strategies to mitigate the effects of landscape scale stressors such as climate change and invasive species. Absent a cohesive coordinated effort to assess trends in important large river resources, a logical starting point is to assess our ability to draw inferences from existing efforts. In this paper, we use a common analytical framework to analyze data from five disparate fish monitoring programs to better understand the nature of spatial and temporal trends in large river fish assemblages. We evaluated data from programs that monitor fishes in the Colorado, Columbia, Illinois, Mississippi, and Tallapoosa rivers using non-metric dimensional scaling ordinations and associated tests to evaluate trends in fish assemblage structure and native fish biodiversity. Our results indicate that fish assemblages exhibited significant spatial and temporal trends in all five of the rivers. We also document native species diversity trends that were variable within and between rivers and generally more evident in rivers with higher species richness and programs of longer duration. We discuss shared and basin-specific landscape level stressors. Having a basic understanding of the nature and extent of trends in fish assemblages is a necessary first step towards understanding factors affecting biodiversity and fisheries in large rivers.

This paper applies the detrended fluctuation analysis (DFA) method to investigate the long-range correlation of monthly mean temperatures from three typical measurement stations at Harbin, Changchun, and Shenyang in Northeast China from 1909 to 2014. The results reveal the memory characteristics of the climate system in this region. By comparing the temperatures from different time periods and investigating the variations of its scaling exponents at the three stations during these different time periods, we found that the monthly mean temperature has long-range correlation, which indicates that the temperature in Northeast China has long-term memory and good predictability. The monthly time series of temperatures over the past 106 years also shows good long-range correlation characteristics. These characteristics are also obviously observed in the annual mean temperature time series. Finally, we separated the centennial-length temperature time series into two time periods. These results reveal that the long-range correlations at the Harbin station over these two time periods have large variations, whereas no obvious variations are observed at the other two stations. This indicates that warming affects the regional climate system’s predictability differently at different time periods. The research results can provide a quantitative reference point for regional climate predictability assessment and future climate model evaluation.

The cotton mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), is a serious invasive species that significantly damages plants of approximately 60 families around the world. It is originally from North America and has also been introduced to other continents. Our goals were to create a current and future potential global distribution map for this pest under climate change with MaxEnt software. We tested the hypothesis of niche conservatism for P. solenopsis by comparing its native niche in North America to its invasive niches on other continents using Principal components analyses (PCA) in R. The potentially suitable habitat for P. solenopsis in its native and non-native ranges is presented in the present paper. The results suggested that the mean temperature of the wettest quarter and the mean temperature of the driest quarter are the most important environmental variables determining the potential distribution of P. solenopsis. We found strong evidence for niche shifts in the realized climatic niche of this pest in South America and Australia due to niche unfilling; however, a niche shift in the realized climatic niche of this pest in Eurasian owing to niche expansion.

Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months.

Significant progress has been made in the past few years in the development of recommendations, policies, and procedures for creating and promoting citations to data sets, software, and other research infrastructures like computing facilities. Open questions remain, however, about the extent to which referencing practices of authors of scholarly publications are changing in ways desired by these initiatives. This paper uses four focused case studies to evaluate whether research infrastructures are being increasingly identified and referenced in the research literature via persistent citable identifiers. The findings of the case studies show that references to such resources are increasing, but that the patterns of these increases are variable. In addition, the study suggests that citation practices for data sets may change more slowly than citation practices for software and research facilities, due to the inertia of existing practices for referencing the use of data. Similarly, existing practices for acknowledging computing support may slow the adoption of formal citations for computing resources.

This article builds on the premise that human consumption of goods, food and transport are the ultimate drivers of climate change. However, the nature of the climate change problem (well described as a tragedy of the commons) makes it difficult for individuals to recognise their personal duty to implement behavioural changes to reduce greenhouse gas emissions. Consequently, this article aims to analyse the climate change issue from a human-scale perspective, in which each of us has a clearly defined personal quota of CO2 emissions that limits our activity and there is a finite time during which CO2 emissions must be eliminated to achieve the "well below 2°C" warming limit set by the Paris Agreement of 2015 (COP21). Thus, this work's primary contribution is to connect an equal per capita fairness approach to a global carbon budget, linking personal levels with planetary levels. Here, we show that a personal quota of 5.0 tons of CO2 yr-1 p-1 is a representative value for both past and future emissions; for this level of a constant per-capita emissions and without considering any mitigation, the global accumulated emissions compatible with the "well below 2°C" and 2°C targets will be exhausted by 2030 and 2050, respectively. These are references years that provide an order of magnitude of the time that is left to reverse the global warming trend. More realistic scenarios that consider a smooth transition toward a zero-emission world show that the global accumulated emissions compatible with the "well below 2°C" and 2°C targets will be exhausted by 2040 and 2080, respectively. Implications of this paper include a return to personal responsibility following equity principles among individuals, and a definition of boundaries to the personal emissions of CO2.

Regulating ecosystem services provided by urban forests are of great importance for the quality of life among city dwellers. To reach a maximum contribution to well-being in cities, the urban regulating ecosystem services (URES) must match with the demands in terms of space and time. If we understand the matches or mismatches between the current urban dwellers' desired quality conditions (demand) and the supply of URES by urban forests (UF) in the cities, this will facilitate integrating the concepts of ecosystem services in urban planning and management, but such an assessment has suffered from major knowledge limitations. Since it is complex and problematic to identify the direct demands for URES and the spatiotemporal patterns therein, improving the demand indicators can help to determine the actual requirements. In this paper, a methodological approach based on indicators is presented and demonstrated for two important URES: air quality improvement and global climate change mitigation provided by urban trees and shrubs. Four air quality standards and greenhouse gas reduction targets were used and compared to supplies of the URES in Tabriz, Iran. Our results show that the mean contribution of the URES supply to air quality standards and greenhouse gas reduction targets is modest. Hence, in Tabriz, there is a strong mismatch between demand and supply. Mismatches at the city scale will have to be reduced by both a reduction in pollutant emissions and an increased provisioning of URES supply through urban greenery. The presented assessment approach and the results for Tabriz make it explicit how different the demands and supplies of the two studied URES are, and we expect similar mismatches in many other cities. Therefore, our approach, relatively simple but still realistic and easy-to-apply, can raise awareness about, and the utility of, the ecosystem services concepts for urban planning and policymaking.

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands-a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage-in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management.

In this work, we analyse factors explaining the distribution and range regression of Dupont’s lark in Spain, the only European country in which this threatened alaudid is present. Dupont’s lark is an extremely elusive and scarce species, distributed across a reduced and strongly fragmented range, showing a metapopulational structure with unknown dispersive and connective mechanisms. We used maximum entropy modelling (Maxent) on nearly 15,000 Dupont’s lark observations (1985–2015) to assess the probability of presence at a 1 km resolution across its European range. Moreover, we tested the probability of extinction by comparing pre- and post-2000 observations by means of a GLM over a subset of cells with presence-absence data. We obtained strong model fitting (AUC = 0.919), in which species occurrence was explained by low values of plant productivity (NDVI), climate (high temperature range and medium annual precipitation), land use (increasing with sclerophyllous scrubland), flat topography and human disturbance (associated with low human population density). The species also tolerates dry farming, but not other farm types or forest cover. The probability map identified two main regions known as the species’ core areas: the steppes of the Iberian System and the Ebro Valley. The North Plateau is characterised by a dispersed structure of small and very fragmented patches of suitable habitat, while a succession of discontinuous probability patches form an Eastern Corridor connecting the central core areas to the southernmost populations. Finally, the model identified small and isolated patches of high probability of presence along the eastern coastline. The species tends to occur in the best available areas but, at the same time, the model revealed a large area of suitable but unoccupied habitat. Our results correct the previous estimation of occupation area from 1,480 to 1,010.78 km2, a reduction of 26.22%. The current distribution of Dupont’s lark is almost completely covered by Important Bird Areas (IBAs), highlighting their importance for bird conservation, but only 44.89% is included in Natura 2000 Special Protection Areas (SPAs). A comparison of pre- and post-2000 periods revealed a range contraction of 44%. Probability of extinction increased with higher temperature range and lower annual precipitation, and with decreases in population density, which suggests that this species is extremely vulnerable to both climate change and rural abandonment, due to its dependence on traditional grazing. These results suggest the need for a re-evaluation of the conservation status of Dupont’s lark in Spain. They urge the preservation of not only current extant populations, but also the unoccupied suitable areas that could be critical for metapopulation structure, and the development of policies addressing the preservation of traditional grazing, Data used in this paper come from different projects, in particular the II National Census (2004-2006), granted by the Spanish Ministry of Environment; and the Dupont’s Lark Monitoring Program in Medinaceli Region, funded by Fundación Patrimonio Natural de Castilla y León. This is a contribution to the Excellence Network Remedinal 3CM (S2013/MAE-2719), supported by Comunidad de Madrid, and to the projects “Criteria for the management and conservation of the Spanish population of Dupont’s Lark, supported by Fundación Biodiversidad, of the Ministry of Agriculture, Food and Environment; LIFE Ricotí (LIFE15-NAT-ES-000802), supported by the European Commission; and BBVA-Dron Ricoti, funded by the BBVA Foundation. INECO provided support in the form of salary for author JHZ, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the ‘author contributions’ section

Marine ecosystems are changing rapidly as the oceans warm and become more acidic. The physical factors and the changes to ocean chemistry that they drive can all be measured with great precision. Changes in the biological composition of communities in different ocean regions are far more challenging to measure because most biological monitoring methods focus on a limited taxonomic or size range. Environmental DNA (eDNA) analysis has the potential to solve this problem in biological oceanography, as it is capable of identifying a huge phylogenetic range of organisms to species level. Here we develop and apply a novel multi-gene molecular toolkit to eDNA isolated from bulk plankton samples collected over a five-year period from a single site. This temporal scale and level of detail is unprecedented in eDNA studies. We identified consistent seasonal assemblages of zooplankton species, which demonstrates the ability of our toolkit to audit community composition. We were also able to detect clear departures from the regular seasonal patterns that occurred during an extreme marine heatwave. The integration of eDNA analyses with existing biotic and abiotic surveys delivers a powerful new long-term approach to monitoring the health of our world’s oceans in the context of a rapidly changing climate., Author summary All environments contain genetic remnants of the life they contain and support. For example, samples collected from the ocean contain biological material such as microscopic organisms, shed cells, excrement and saliva—the DNA from which reveals the surrounding marine biodiversity. Environmental DNA (eDNA) approaches have the ability to identify marine species that are notoriously difficult to identify using morphology alone. Here we develop, and apply, a DNA ‘toolkit’ to five years of samples collected from a single site in the Indian ocean. It is rare to find a temporal series of samples of this duration that are also suitable for DNA analysis. We show that eDNA techniques have the capacity to monitor ocean biology in fine detail. We demonstrate how the biological communities of plankton and fish respond to normal seasonal changes and, more importantly, to that of an uncharacteristic heatwave. The methods embodied in this paper are applicable to marine studies across the globe and, as such, pave the way for the design of long-term monitoring programs using eDNA.

This paper examines search and rescue and backcountry medical response constraints in the Canadian Arctic and potential for unmanned aerial vehicles (UAV) to aid in response and preparedness. Semi-structured interviews (n = 18) were conducted with search and rescue responders, Elders, and emergency management officials to collect data on current emergency response and potential for UAV use. UAV test flights (n = 17) were undertaken with community members. We analyzed five years of weather data to examine UAV flight suitability. Numerous challenges face Arctic search and rescue and backcountry emergency response. Changing social and environmental conditions were described as increasing vulnerability to backcountry emergencies. Responders desired additional first aid and emergency training. Legal and weather restrictions were found to limit where, when and who could fly UAVs. UAVs were demonstrated to have potential benefits for hazard monitoring but not for SAR or medical response due to legal restrictions, weather margins, and local capacity. We find that communities are ill-prepared for ongoing SAR demands, let alone a larger disaster. There are numerous limitations to the use of consumer UAVs by Arctic communities. Prevention of backcountry medical emergencies, building resilience to disasters, and first responder training should be prioritized over introducing UAVs to the response system.

In a previous article, we outlined the efforts of the Neglected Tropical Diseases (NTD) lobby to overcome the diseases' snub in the 2000 Millennium Development Goals (MDGs) and to campaign for inclusion in the post-2015 development agenda [1]. In doing so, we noted the extraordinary achievements made by the lobby despite its view from outside of the MDG juggernaut, which singled out just three diseases for special consideration (HIV/AIDS, Malaria, and, latterly, Tuberculosis), leaving a group of previously disparate diseases to be formulated within a plan for inclusion. The plan resulted in a three-pronged attack forged on advocacy, policy, and science. The NTD lobby has grown significantly since the original 2003 WHO/Deutsche Gesellschaft fur Technische Zusammenarbeit (GTZ) meetings were convened to float the idea that the NTDs should be reconceptualised and tackled as a group moving forward. The original core group—Peter J. Hotez, David Molyneux, and Alan Fenwick (along with WHO’s Lorenzo Savioli and others)—has since been joined by representation from academia (e.g., PLOS Neglected Tropical Diseases), the third sector (e.g., the Global Network, the Neglected Tropical Disease Non-Governmental Development Organization Network), the pharmaceutical industry, donor agencies, and the philanthropic sector (cf. Uniting to Combat NTDs). The lobby is also formally supported by the World Health Organisation (through the WHO Roadmap [2] and WHA Resolution 66.12 [3]) and by the disease endemic country governments that have signed the Addis Ababa NTD Commitment. Yet, despite boasting swelling ranks and a number of sizeable achievements in the intervening period—for instance the landmark London Declaration—the NTD lobby has remained interested in the more mainstream development agenda. The advocacy goal has always been the formal inclusion of NTDs in the successor framework to the Millennium Development Goals: the post-2015 Sustainable Development Agenda. On 1 January 2016, the Sustainable Development Goals (SDGs or Global Goals) came into force. The particulars of the goals were finalised at the United Nations summit in September 2015, and, as hoped, NTDs gained a special mention in goal 3.3: "By 2030, end the epidemics of AIDS, tuberculosis, malaria, and neglected tropical diseases and combat hepatitis, waterborne diseases, and other communicable diseases" (p 16) [4]. But what is this mention worth and what are its implications? The SDGs are similar to the MDGs in that they represent time-bound targets and retain a focus on ending world poverty, but they break with the original eight MDGs in terms of their colossal scope. Composed of 17 goals and 169 targets, the SDGs are the result of an extensive three-year consultation involving multiple perspectives from government, civil society, expert groups, the private sector, and individuals. The MDGs were regularly critiqued as restricted in focus and top-down, something which cannot be levelled against the SDGs. But might the warning of the High-Level Panel that originally drafted the framework document have been ignored—"The international community will need to ensure that a single, sustainable framework agenda is not overloaded with too many priorities. A product of compromise rather than decisions…"? (p 14) [5]. The post-2015 vision—as articulated through the SDGs—is to create a single universal agenda in which the social, economic, and environmental dimensions of sustainability are integrated. Whereas the MDGs were criticised for targeting the low-hanging fruit, the post-2015 agenda is underpinned by the tenet “leave no one behind.” As the UN summit drew closer in 2015, we noted how this principle, coupled with the post-2015 focus on environmental issues, provided a number of advocacy issues behind which the NTD lobby began to position itself: equity, poverty, water sanitation and hygiene (WASH), climate change, quality education, NTD-related disability, and nutrition. In this paper, we reflect on the NTD lobby’s efforts to gain traction in the wide-ranging post-2015 agenda by making the case that NTDs are crosscutting and by lobbying for NTD indicators.

Species distribution models (SDMs) are an effective way of predicting the potential distribution of species and their response to environmental change. Most SDMs apply presence data to a relatively generic set of predictive variables such as climate. However, this weakens the modelling process by overlooking the responses to more cryptic predictive variables. In this paper we demonstrate a means by which data gathered from an intensive animal trapping study can be used to enhance SDMs by combining field data with bioclimatic modelling techniques to determine the future potential distribution for the koomal (Trichosurus vulpecula hypoleucus). The koomal is a geographically isolated subspecies of the common brushtail possum, endemic to south-western Australia. Since European settlement this taxon has undergone a significant reduction in distribution due to its vulnerability to habitat fragmentation, introduced predators and tree/shrub dieback caused by a virulent group of plant pathogens of the genus Phytophthora. An intensive field study found: 1) the home range for the koomal rarely exceeded 1 km in in length at its widest point; 2) areas heavily infested with dieback were not occupied; 3) gap crossing between patches (>400 m) was common behaviour; 4) koomal presence was linked to the extent of suitable vegetation; and 5) where the needs of koomal were met, populations in fragments were demographically similar to those found in contiguous landscapes. We used this information to resolve a more accurate SDM for the koomal than that created from bioclimatic data alone. Specifically, we refined spatial coverages of remnant vegetation and dieback, to develop a set of variables that we combined with selected bioclimatic variables to construct models. We conclude that the utility value of an SDM can be enhanced and given greater resolution by identifying variables that reflect observed, species-specific responses to landscape parameters and incorporating these responses into the model.

This study aims at conducting the first science mapping analysis of the Mediterranean forest research in order to elucidate its research structure and evolution. We applied a science mapping approach based on co-term and citation analyses to a set of scientific publications retrieved from the Elsevier's Scopus database over the period 1980-2014. The Scopus search retrieved 2,698 research papers and reviews published by 159 peer-reviewed journals. The total number of publications was around 1% (N = 17) during the period 1980-1989 and they reached 3% (N = 69) in the time slice 1990-1994. Since 1995, the number of publications increased exponentially, thus reaching 55% (N = 1,476) during the period 2010-2014. Within the thirty-four years considered, the retrieved publications were published by 88 countries. Among them, Spain was the most productive country, publishing 44% (N = 1,178) of total publications followed by Italy (18%, N = 482) and France (12%, N = 336). These countries also host the ten most productive scientific institutions in terms of number of publications in Mediterranean forest subjects. Forest Ecology and Management and Annals of Forest Science were the most active journals in publishing research in Mediterranean forest. During the period 1980-1994, the research topics were poorly characterized, but they become better defined during the time slice 1995-1999. Since 2000s, the clusters become well defined by research topics. Current status of Mediterranean forest research (20092014) was represented by four clusters, in which different research topics such as biodiversity and conservation, land-use and degradation, climate change effects on ecophysiological responses and soil were identified. Basic research in Mediterranean forest ecosystems is mainly conducted by ecophysiological research. Applied research was mainly represented by land-use and degradation, biodiversity and conservation and fire research topics. The citation analyses revealed highly cited terms in the Mediterranean forest research as they were represented by fire, biodiversity, carbon sequestration, climate change and global warming. Finally, our analysis also revealed the multidisciplinary role of climate change research. This study provides a first holistic view of the Mediterranean forest research that could be useful for researchers and policy makers as they may evaluate and analyze its historical evolution, as well as its structure and scientific production. We concluded that Mediterranean forest research represents an active scientific field.

The dry subtropics are subject to a rapid expansion of crops and pastures over vast areas of natural woodlands and savannas. In this paper, we explored the effect of this transformation on vegetation productivity (magnitude, and seasonal and long-term variability) along aridity gradients which span from semiarid to subhumid conditions, considering exclusively those areas with summer rains (>66%). Vegetation productivity was characterized with the proxy metric "Enhanced Vegetation Index" (EVI) (2000 to 2012 period), on 6186 natural and cultivated sampling points on five continents, and combined with a global climatology database by means of additive models for quantile regressions. Globally and regionally, cultivation amplified the seasonal and inter-annual variability of EVI without affecting its magnitude. Natural and cultivated systems maintained a similar and continuous increase of EVI with increasing water availability, yet achieved through contrasting ways. In natural systems, the productivity peak and the growing season length displayed concurrent steady increases with water availability, while in cultivated systems the productivity peak increased from semiarid to dry-subhumid conditions, and stabilized thereafter giving place to an increase in the growing season length towards wetter conditions. Our results help to understand and predict the ecological impacts of deforestation on vegetation productivity, a key ecosystem process linked to a broad range of services. Fil: Baldi, Germán. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina Fil: Texeira González, Marcos Alexis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía; Argentina Fil: Murray, Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina Fil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina

Climate change adds an additional layer of complexity to existing sustainable development and biodiversity conservation challenges. The impacts of global climate change are felt locally, and thus local governance structures will increasingly be responsible for preparedness and local responses. Ecosystem-based adaptation (EbA) options are gaining prominence as relevant climate change solutions. Local government officials seldom have an appropriate understanding of the role of ecosystem functioning in sustainable development goals, or access to relevant climate information. Thus the use of ecosystems in helping people adapt to climate change is limited partially by the lack of information on where ecosystems have the highest potential to do so. To begin overcoming this barrier, Conservation South Africa in partnership with local government developed a socio-ecological approach for identifying spatial EbA priorities at the sub-national level. Using GIS-based multi-criteria analysis and vegetation distribution models, the authors have spatially integrated relevant ecological and social information at a scale appropriate to inform local level political, administrative, and operational decision makers. This is the first systematic approach of which we are aware that highlights spatial priority areas for EbA implementation. Nodes of socio-ecological vulnerability are identified, and the inclusion of areas that provide ecosystem services and ecological resilience to future climate change is innovative. The purpose of this paper is to present and demonstrate a methodology for combining complex information into user-friendly spatial products for local level decision making on EbA. The authors focus on illustrating the kinds of products that can be generated from combining information in the suggested ways, and do not discuss the nuance of climate models nor present specific technical details of the model outputs here. Two representative case studies from rural South Africa demonstrate the replicability of this approach in rural and peri-urban areas of other developing and least developed countries around the world.

This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published African pollen dataset and a global ecosystem classification scheme. To test the applicability of traditional and machine-learning based classification models for the task of biome prediction from high dimensional modern pollen data, we train a total of eight classification models, including Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Classification Decision Tree, Random Forest, Neural Network, and Support Vector Machine. The ability of each model to predict biomes from pollen data is statistically tested on an independent test set. The Random Forest classifier outperforms other models in its ability correctly classify biomes given pollen data. Out of the eight models, the Random Forest classifier scores highest on all of the metrics used for model evaluations and is able to predict four out of five biome classes to high degree of accuracy, including arid, montane, tropical and subtropical closed and open systems, e.g. forests and savanna/grassland. The model has the potential for accurate reconstructions of past biomes and awaits application to fossil pollen sequences. The Random Forest model may be used to investigate vegetation changes on both long and short time scales, e.g. during glacial and interglacial cycles, or more recent and abrupt climatic anomalies like the African Humid Period. Such applications may contribute to a better understanding of past shifts in vegetation cover and ultimately provide valuable information on drivers of climate change.

The role of forest management in mitigating climate change is a central concern for the Canadian province of British Columbia. The successful implementation of forest management activities to achieve climate change mitigation in British Columbia will be strongly influenced by public support or opposition. While we now have increasingly clear ideas of the management opportunities associated with forest mitigation and some insight into public support for climate change mitigation in the context of sustainable forest management, very little is known with respect to the levels and basis of public support for potential forest management strategies to mitigate climate change. This paper, by describing the results of a web-based survey, documents levels of public support for the implementation of eight forest carbon mitigation strategies in British Columbia's forest sector, and examines and quantifies the influence of the factors that shape this support. Overall, respondents ascribed a high level of importance to forest carbon mitigation and supported all of the eight proposed strategies, indicating that the British Columbia public is inclined to consider alternative practices in managing forests and wood products to mitigate climate change. That said, we found differences in levels of support for the mitigation strategies. In general, we found greater levels of support for a rehabilitation strategy (e.g. reforestation of unproductive forest land), and to a lesser extent for conservation strategies (e.g. old growth conservation, reduced harvest) over enhanced forest management strategies (e.g. improved harvesting and silvicultural techniques). We also highlighted multiple variables within the British Columbia population that appear to play a role in predicting levels of support for conservation and/or enhanced forest management strategies, including environmental values, risk perception, trust in groups of actors, prioritized objectives of forest management and socio-demographic factors.

Understanding Earth surface responses in terms of sediment dynamics to climatic variability and tectonics forcing is hindered by limited ability of current models to simulate long-term evolution of sediment transfer and associated morphological changes. This paper presents pyBadlands, an open-source python-based framework which computes over geological time (1) sediment transport from landmasses to coasts, (2) reworking of marine sediments by longshore currents and (3) development of coral reef systems. pyBadlands is cross-platform, distributed under the GPLv3 license and available on GitHub (http://github.com/badlands-model). Here, we describe the underlying physical assumptions behind the simulated processes and the main options already available in the numerical framework. Along with the source code, a list of hands-on examples is provided that illustrates the model capabilities. In addition, pre and post-processing classes have been built and are accessible as a companion toolbox which comprises a series of workflows to efficiently build, quantify and explore simulation input and output files. While the framework has been primarily designed for research, its simplicity of use and portability makes it a great tool for teaching purposes.