Nova Reader - Subject

Greenhouse gas emissions intensity of food production systems and its determinants

2021-04-30T00:00

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.

Long-term variations in solar radiation, diffuse radiation, and diffuse radiation fraction caused by aerosols in China during 1961–2016

2021-05-03T00:00

The effects of atmospheric aerosols on the terrestrial climate system are more regional than those of greenhouse gases, which are more global. Thus, it is necessary to examine the typical regional effects of how aerosols affect solar radiation in order to develop a more comprehensive understanding. In this study, we used global AErosol RObotic NETwork (AERONET) data and robust radiation observational evidence to investigate the impact of aerosols on total radiation, diffuse radiation, and the diffuse radiation fraction in China from 1961 to 2016. Our results showed that there were different temporal changes in the aerosol optical depth (AOD), total solar radiation, diffuse radiation and diffuse radiation fraction over the past 56 years. Specifically, the 550 nm AOD from 2005 to 2016 decreased significantly, with annual average AOD of 0.51. Meanwhile, the average total solar radiation reduced by 2.48%, while there was a slight increase in average diffuse radiation at a rate of 3.10 MJ·m^-2·yr^-1. Moreover, the spatial heterogeneities of AOD, total radiation, diffuse radiation, and the diffuse radiation fraction in China were significant. Aerosol particle emissions in the developed eastern and southern regions of China were more severe than those in the western regions, resulting in higher total radiation and diffuse radiation in the western plateau than in the eastern plain. In addition, aerosols were found to have negative effects on total radiation and sunshine hours, and positive effects on diffuse radiation and diffuse radiation fraction. Further, the diffuse radiation fraction was negatively correlated with sunshine hours. However, there was a positive correlation between AOD and sunshine hours. These results could be used to assess the impacts of climate change on terrestrial ecosystem productivity and carbon budgets.

Spatial variation characteristics of vegetation phenology and its influencing factors in the subtropical monsoon climate region of southern China

2021-04-28T00:00

Understanding the response mechanism of ecosystems to climate change and human disturbance can be improved by analyzing the spatial patterns of vegetation phenology and its influencing factors. Because the diverse phenological patterns are impacted by cloud cover contamination issues in the satellite observations, there are few remote sensing phenological research data in subtropical monsoon climate regions. To better understand the horizontal and vertical changes of vegetation phenology in these regions and how it may be affected by climatic factors and topographical features, we first extracted vegetation phenological information (such as start of growth season (SOS), end of growth season (EOS) and length of growth season (LEN)) from a reconstructed MODIS EVI time-series data. We then used geographic detectors to identify the influencing factors of phenology in different elevation zoning areas. We have found that in the Xiangjiang River Basin: 1) gradual changes in the longitudinal or latitudinal gradient of vegetation phenology were not obvious. Instead of horizontal changes, the variation pattern of phenology was similar to the striped river network of the Xiangjiang River. Earlier SOS mainly appeared in the areas far away from the river; later SOS appeared in the midstream and downstream reaches.2) Elevation played an important role in the regional differentiation of phenology. Boundaries at elevations of 320 m and 520 m distinctly separated the region into plain, hilly, and mountain vegetation phenological characteristics. 3) The impacts of climatic factors were quite different in the three vertical zoning areas. Precipitation was the most crucial factor affecting SOS both in plain and mountain areas. There was no significant factor affecting EOS in the plain area, but temperature had an essential effect on EOS in the mountain area. The hilly areas had a concentrated growth period with no significant factors affecting phenology. These findings highlight the importance of elevation in phenology at a watershed scale, enhance our understanding of the impact of climate changes on subtropical ecosystems, and provide a reference for further land-use change monitoring.

Climate change and the emergence of fungal pathogens

2021-04-29T00:00

Species-specific thermal classification schemes can improve climate related marine resource decisions

2021-04-28T00:00

Global climate change increasingly contributes to large changes in ecosystem structure. Timely management of rapidly changing marine ecosystems must be matched with methods to rapidly quantify and assess climate driven impacts to ecological communities. Here we create a species-specific, classification system for fish thermal affinities, using three quantifiable datasets and expert opinion. Multiple sources of information limit potential data bias and avoid misclassification. Using a temperate kelp forest fish community in California, USA as a test case for this new methodology, we found the majority of species had high classification agreement across all four data sources (n = 78) but also a number of low agreement species (2 sources disagree from the others, n = 47). For species with low agreement, use of just one dataset to classify species, as is commonly done, would lead to high risk of misclassification. Differences in species classification between individual datasets and our composite classification were apparent. Applying different thermal classifications, lead to different conclusions when quantifying ‘warm’ and ‘cool’ species density responses to a marine heatwave. Managers can use this classification approach as a tool to generate accurate, timely and simple information for resource management.

Spatial distribution of rural population using mixed geographically weighted regression: Evidence from Jiangxi Province in China

2021-04-26T00:00

On the basis of the spatial panel data of 2000, 2005, 2010, and 2015, this study uses a mixed geographically weighted regression model to explore the spatial distribution characteristics and influencing factors of the rural (permanent) population in Jiangxi Province, China. Results show that residents in the county area have a significant spatial positive autocorrelation, especially in the lake and mountain areas and the global Moran’ I index is more than 0.05. The influence of social and economic factors presents spatial homogeneity. The effect of urbanization and per capita disposable income is negative, whereas that of agricultural output value and rural electricity consumption is positive. The influence of climate factors presents spatial heterogeneity. The influence coefficient of rainfall in 2015 ranges from [-0.061, 0.133], which has a negative effect on the southwest mountain areas and a positive effect on the northeast lake areas., The influence coefficient of temperature in 2015 ranges from [-0.110, 0.094], which has a positive effect on the southwest mountain areas and a negative effect on the northeast lake areas. The influence coefficients of wind speed and relative humidity range from [-0.090, 0.153] and [-0.069, 0.130] in 2015 respectively, which further reinforce this effect. Therefore, scholars should pay attention to the universal adaptability of economic and social factors. Moreover, they should consider the spatial difference of climatic factors to promote urbanization following the local conditions. Finally, policymakers and concerned non-governmental institutions should fully understand the sensitivity of the rural population in underdeveloped mountain areas to climate factors to promote their rational distribution.

Willingness to help climate migrants: A survey experiment in the Korail slum of Dhaka, Bangladesh

2021-04-22T00:00

Bangladesh faces a severe rural to urban migration challenge, which is accentuated by climate change and the Rohingya crisis. These migrants often reside in urban slums and struggle to access public services, which are already short in supply for existing slum dwellers. Given the inadequacy of governmental efforts, nonprofits have assumed responsibility for providing essential services such as housing, healthcare, and education. Would local slum-dwellers in Dhaka be willing to support such nonprofits financially? We deploy an in-person survey experiment with three frames (generic migrants, climate migrants, and religiously persecuted Rohingya migrants) to assess Dhaka slum-dwellers’ willingness to support a humanitarian charity that provides healthcare services to migrants. Bangladesh is noted as a climate change hotspot and its government is vocal about the climate issue in international forums. While we expected this to translate into public support for climate migrants, we find respondents are 16% less likely to support climate migrants in relation to the generic migrants. However, consistent with the government’s hostility towards Rohingya, we find that respondents are 9% less likely to support a charity focused on helping Rohingya migrants. Our results are robust even when we examine subpopulations such as recent arrivals in Dhaka and those who have experienced floods (both of which could be expected to be more sympathetic to climate migrants), as well as those who regularly follow the news (and hence are well informed about the climate and the Rohingya crisis).

Ideological biases in social sharing of online information about climate change

2021-04-23T00:00

Exposure to media content is an important component of opinion formation around climate change. Online social media such as Twitter, the focus of this study, provide an avenue to study public engagement and digital media dissemination related to climate change. Sharing a link to an online article is an indicator of media engagement. Aggregated link-sharing forms a network structure which maps collective media engagement by the user population. Here we construct bipartite networks linking Twitter users to the web pages they shared, using a dataset of approximately 5.3 million English-language tweets by almost 2 million users during an eventful seven-week period centred on the announcement of the US withdrawal from the Paris Agreement on climate change. Community detection indicates that the observed information-sharing network can be partitioned into two weakly connected components, representing subsets of articles shared by a group of users. We characterise these partitions through analysis of web domains and text content from shared articles, finding them to be broadly described as a left-wing/environmentalist group and a right-wing/climate sceptic group. Correlation analysis shows a striking positive association between left/right political ideology and environmentalist/sceptic climate ideology respectively. Looking at information-sharing over time, there is considerable turnover in the engaged user population and the articles that are shared, but the web domain sources and polarised network structure are relatively persistent. This study provides evidence that online sharing of news media content related to climate change is both polarised and politicised, with implications for opinion dynamics and public debate around this important societal challenge.

Relative snowpack response to elevation, temperature and precipitation in the Crown of the Continent region of North America 1980-2013

2021-04-13T00:00

Water availability in western Canada and the United States is dependent on the accumulation of snowpack in the montane regions and threatened by increased winter temperature and more precipitation as rain linked to climate change. In order to make reasoned decisions to adapt to climate change managers require knowledge of the role of temperature and precipitation in SWE development and data to distinguish the relative retention response of snowpack regions to expected temperature and precipitation regime shifts at the watershed scale. Using the Daymet interpolated 1 km² dataset, effects of elevation, temperature (T_max, T_min and T_avg) and precipitation on April 1 SWE in the Crown of the Continent were tested by linear regression and Kendall correlation. Changes in Daymet estimated snow water equivalent (SWE) in response to increased temperatures and changes in precipitation were estimated in two ways: 1) comparing April 1SWE in the 11 warmest (mean T_max February) and driest (mean precipitation January to March) years with the 22 cooler/wetter years 1981–2013 and 2) SWE retention from April 1 to June 1 over the period 1980 to 2013 across 120 watersheds in a major continental headwater region, the Crown of the Continent of North America. Historical analysis of period warm year April 1 SWE was assumed to indicate the recent impact of warmer winter temperatures. Changes in snowpack April 1 to June 1 reflected likely effects on peak runoff and were, therefore, also relevant for future climate change adaptation considerations. Winter (JFM) precipitation proved more influential than temperature in shaping April 1 SWE response at the regional scale. Of the three factors, elevation was most positively associated with April 1 SWE at the watershed scale. Temperature and precipitation influenced SWE accumulation and persistence at the watershed scale, but higher precipitation was more closely associated with higher April 1 SWE retention. Ranking of watershed snowpack retention in warm and dry years, combined with spring snowpack retention offers data to assist identification of watersheds with greatest snowpack persistence in the face of anticipated climate change effects.

Balancing the need for seed against invasive species risks in prairie habitat restorations

2021-04-07T00:00

Adequate diversity and abundance of native seed for large-scale grassland restorations often require commercially produced seed from distant sources. However, as sourcing distance increases, the likelihood of inadvertent introduction of multiple novel, non-native weed species as seed contaminants also increases. We created a model to determine an “optimal maximum distance” that would maximize availability of native prairie seed from commercial sources while minimizing the risk of novel invasive weeds via contamination. The model focused on the central portion of the Level II temperate prairie ecoregion in the Midwest US. The median optimal maximum distance from which to source seed was 272 km (169 miles). In addition, we weighted the model to address potential concerns from restoration practitioners: 1. sourcing seed via a facilitated migration strategy (i.e., direct movement of species from areas south of a given restoration site to assist species’ range expansion) to account for warming due to climate change; and 2. emphasizing non-native, exotic species with a federal mandate to control. Weighting the model for climate change increased the median optimal maximum distance to 398 km (247 miles), but this was not statistically different from the distance calculated without taking sourcing for climate adaptation into account. Weighting the model for federally mandated exotic species increased the median optimal maximum distance only slightly to 293 km (182 miles), so practitioners may not need to adjust their sourcing strategy, compared to the original model. This decision framework highlights some potential inadvertent consequences from species translocations and provides insight on how to balance needs for prairie seed against those risks.

Impact of different heat wave definitions on daily mortality in Bandafassi, Senegal

2021-04-05T00:00

Objective

The aim of this study is to find the most suitable heat wave definition among 15 different ones and to evaluate its impact on total, age-, and gender-specific mortality for Bandafassi, Senegal.

Methods

Daily weather station data were obtained from Kedougou situated at 17 km from Bandafassi from 1973 to 2012. Poisson generalized additive model (GAM) and distributed lag non-linear model (DLNM) are used to investigate the effect of heat wave on mortality and to evaluate the nonlinear association of heat wave definitions at different lag days, respectively.

Results

Heat wave definitions, based on three or more consecutive days with both daily minimum and maximum temperatures greater than the 90^th percentile, provided the best model fit. A statistically significant increase in the relative risk (RRs 1.4 (95% Confidence Interval (CI): 1.2–1.6), 1.7 (95% CI: 1.5–1.9), 1.21 (95% CI: 1.08–1.3), 1.2 (95% CI: 1.04–1.5), 1.5 (95% CI: 1.3–1.8), 1.4 (95% CI: 1.2–1.5), 1.5 (95% CI: 1.07–1.6), and 1.5 (95% CI: 1.3–1.8)) of total mortality was observed for eight definitions. By using the definition based on the 90^th percentile of minimum and maximum temperature with a 3-day duration, we also found that females and people aged ≥ 55 years old were at higher risks than males and other different age groups to heat wave related mortality.

Conclusion

The impact of heat waves was associated with total-, age-, gender-mortality. These results are expected to be useful for decision makers who conceive of public health policies in Senegal and elsewhere. Climate parameters, including temperatures and humidity, could be used to forecast heat wave risks as an early warning system in the area where we conduct this research. More broadly, our findings should be highly beneficial to climate services, researchers, clinicians, end-users and decision-makers.

Spatial distribution of rural population from a climate perspective: Evidence from Jiangxi Province in China

2021-03-04T00:00

The research on rural population distribution from a climate perspective is rare. Therefore, this study adopts this perspective and uses the ordinary least squares and spatial econometric models to explore the spatial distribution characteristics of the rural population in the Poyang Lake ecological economic zone. Results show that (1) a significant spatial autocorrelation is present in the distribution of rural population, and a spatial correlation exists between the population distribution and climatic factors, (2) the influence of climatic factors on the distribution of rural population in the Poyang Lake ecological economic zone is greater than that of economic factors, and (3) the annual average sunshine and annual average rainfall have a significant negative effect on the distribution of the regional rural population, which is contrary to the expectations., so we then analyze this negative effect on the regional rural population distribution. It is found that (1) the influence of climate factors on the distribution of rural population in lake area is far more than that of economic factors, and more consideration should be given to the influence of climate factors on the population distribution in the lake area, (2) different geographical capital and natural resource endowment, the influence of climate on micro-regional population distribution may be different from the general law, (3) the spatial measurement model which takes spatial dependence into account can reveal the influence of climate on rural population distribution more accurately.

Climate change impacts on population growth across a species’ range differ due to nonlinear responses of populations to climate and variation in rates of climate change

2021-03-03T00:00

Impacts of climate change can differ substantially across species’ geographic ranges, and impacts on a given population can be difficult to predict accurately. A commonly used approximation for the impacts of climate change on the population growth rate is the product of local changes in each climate variable (which may differ among populations) and the sensitivity (the derivative of the population growth rate with respect to that climate variable), summed across climate variables. However, this approximation may not be accurate for predicting changes in population growth rate across geographic ranges, because the sensitivities to climate variables or the rate of climate change may differ among populations. In addition, while this approximation assumes a linear response of population growth rate to climate, population growth rate is typically a nonlinear function of climate variables. Here, we use climate-driven integral projection models combined with projections of future climate to predict changes in population growth rate from 2008 to 2099 for an uncommon alpine plant species, Douglasia alaskana, in a rapidly warming location, southcentral Alaska USA. We dissect the causes of among-population variation in climate change impacts, including magnitude of climate change in each population and nonlinearities in population response to climate change. We show that much of the variation in climate change impacts across D. alaskana’s range arises from nonlinearities in population response to climate. Our results highlight the critical role of nonlinear responses to climate change impacts, suggesting that current responses to increases in temperature or changes in precipitation may not continue indefinitely under continued changes in climate. Further, our results suggest the degree of nonlinearity in climate responses and the shape of responses (e.g., convex or concave) can differ substantially across populations, such that populations may differ dramatically in responses to future climate even when their current responses are quite similar.

Toward a Monte Carlo approach to selecting climate variables in MaxEnt

2021-03-03T00:00

MaxEnt is an important aid in understanding the influence of climate change on species distributions. There is growing interest in using IPCC-class global climate model outputs as environmental predictors in this work. These models provide realistic, global representations of the climate system, projections for hundreds of variables (including Essential Climate Variables), and combine observations from an array of satellite, airborne, and in-situ sensors. Unfortunately, direct use of this important class of data in MaxEnt modeling has been limited by the large size of climate model output collections and the fact that MaxEnt can only operate on a relatively small set of predictors stored in a computer’s main memory. In this study, we demonstrate the feasibility of a Monte Carlo method that overcomes this limitation by finding a useful subset of predictors in a larger, externally-stored collection of environmental variables in a reasonable amount of time. Our proposed solution takes an ensemble approach wherein many MaxEnt runs, each drawing on a small random subset of variables, converges on a global estimate of the top contributing subset of variables in the larger collection. In preliminary tests, the Monte Carlo approach selected a consistent set of top six variables within 540 runs, with the four most contributory variables of the top six accounting for approximately 93% of overall permutation importance in a final model. These results suggest that a Monte Carlo approach could offer a viable means of screening environmental predictors prior to final model construction that is amenable to parallelization and scalable to very large data sets. This points to the possibility of near-real-time multiprocessor implementations that could enable broader and more exploratory use of global climate model outputs in environmental niche modeling and aid in the discovery of viable predictors.

The gap of water supply—Demand and its driving factors: From water footprint view in Huaihe River Basin

2021-03-04T00:00

Climate change, population growth, the development of industrialization and urbanization are increasing the demand for water resources, but the water pollution is reducing the limited water supply. In recent years, the gap between water supply and demand which shows water scarcity situation is becoming more serious. Clear knowing this gap and its main driving factors could help us to put forward water protection measures correctly. We take the data of Huaihe River Basin from 2001 to 2016 as an example and use ecological water footprint to describe the demand, with the water carrying capacity representing the supply. We analyze the water supply-demand situation of Huaihe River Basin and its five provinces from footprint view in time and space. Then we apply the Logarithmic Mean Divisia Index model to analyze the driving factors of the ecological water footprint. The results show that: (1) the supply and demand balance of Huaihe River Basin was only achieved in year 2003 and 2005. There is also a large difference between Jiangsu province and other provinces in Huaihe River basin, most years in Jiangsu province per capital ecological footprint of water is more than 1 hm²/person except the years of 2003, 2015, and 2016. But other provinces are all less than 1 hm²/person. (2) Through the decomposition of water demand drivers, we concluded that economic development is the most important factor, with an annual contribution of more than 60%. Our study provides countermeasures and suggestions for the management and optimal allocation of water resources in Huaihe River Basin, and also provides reference for the formulation of water-saving policies in the world.

Thermal performance of the Chagas disease vector, Triatoma infestans, under thermal variability

2021-02-11T00:00

Vector-borne diseases (VBD) are particularly susceptible to climate change because most of the diseases’ vectors are ectotherms, which themselves are susceptible to thermal changes. The Chagas disease is one neglected tropical disease caused by the protozoan parasite, Trypanosoma cruzi. One of the main vectors of the Chagas disease in South America is Triatoma infestans, a species traditionally considered to be restricted to domestic or peridomestic habitats, but sylvatic foci have also been described along its distribution. The infestation of wild individuals, together with the projections of environmental changes due to global warming, urge the need to understand the relationship between temperature and the vector’s performance. Here, we evaluated the impact of temperature variability on the thermal response of T. infestans. We acclimated individuals to six thermal treatments for five weeks to then estimate their thermal performance curves (TPCs) by measuring the walking speed of the individuals. We found that the TPCs varied with thermal acclimation and body mass. Individuals acclimated to a low and variable ambient temperature (18°C ± 5°C) exhibited lower performances than those individuals acclimated to an optimal temperature (27°C ± 0°C); while those individuals acclimated to a low but constant temperature (18°C ± 0°C) did not differ in their maximal performance from those at an optimal temperature. Additionally, thermal variability (i.e., ± 5°C) at a high temperature (30°C) increased performance. These results evidenced the plastic response of T. infestans to thermal acclimation. This plastic response and the non-linear effect of thermal variability on the performance of T. infestans posit challenges when predicting changes in the vector’s distribution range under climate change.

The Chagas disease is transmitted by the infected feces of kissing bugs, such as Triatoma infestans, when it feeds on human blood. This species is an ectotherm and its physical activity level, behavior, and survival respond to environmental changes in temperature. Climate change predicts changes in environmental temperature and its variability. These changes may affect the distribution and survival of T. infestans, generating difficulties for eradication programs. Here, we evaluated the effects of temperature, and its variability on the performance of T. infestans’ individuals maintained at or acclimated to six different temperatures, which capture the essence of climate change predictions regarding temperature (i.e., temperatures displaying changes in mean and in variance). We found that T. infestans exhibited a plastic response to thermal conditions, with individuals performing worse at a low mean temperature (18°C) with variance (± 5°C), while individuals acclimated to the same low temperature (18°C) without variance (± 0°C) performed as good as individuals acclimated to their optimal temperature (27°C). Nevertheless, thermal variation at a high mean temperature (30°C ± 5°C) increased performance. Thus, we concluded that the thermal variability affects the performance of this species, in a non-linear way. From our results, the difficulty to predict climate change effects on populations of T. infestans becomes evident, as well as the need to include putative thermal plasticity in future predictions.

Visualizing changes to US federal environmental agency websites, 2016–2020

2021-02-25T00:00

Websites have become the primary means by which the US federal government communicates about its operations and presents information for public consumption. However, the alteration or removal of critical information from these sites is often entirely legal and done without the public’s awareness. Relative to paper records, websites enable governments to shape public understanding in quick, scalable, and permissible ways. During the Trump administration, website changes indicative of climate denial prompted civil society organizations to develop tools for tracking online government information sources. We in the Environmental Data & Governance Initiative (EDGI) illustrate how five data visualization techniques can be used to document and analyze changes to government websites. We examine a large sample of websites of US federal environmental agencies and show that between 2016 and 2020: 1) the use of the term “climate change” decreased by an estimated 38%; 2) access to as much as 20% of the Environmental Protection Agency’s website was removed; 3) changes were made more to Cabinet agencies’ websites and to highly visible pages. In formulating ways to visualize and assess the alteration of websites, our study lays important groundwork for both systematically tracking changes and holding officials more accountable for their web practices. Our techniques enable researchers and watchdog groups alike to operate at the scale necessary to understand the breadth of impact an administration can have on the online face of government.

Potential impacts of climate change on geographical distribution of three primary vectors of African Trypanosomiasis in Tanzania’s Maasai Steppe: G. m. morsitans, G. pallidipes and G. swynnertoni

2021-02-11T00:00

In the Maasai Steppe, public health and economy are threatened by African Trypanosomiasis, a debilitating and fatal disease to livestock (African Animal Trypanosomiasis -AAT) and humans (Human African Trypanosomiasis—HAT), if not treated. The tsetse fly is the primary vector for both HAT and AAT and climate is an important predictor of their occurrence and the parasites they carry. While understanding tsetse fly distribution is essential for informing vector and disease control strategies, existing distribution maps are old and were based on coarse spatial resolution data, consequently, inaccurately representing vector and disease dynamics necessary to design and implement fit-for-purpose mitigation strategies. Also, the assertion that climate change is altering tsetse fly distribution in Tanzania lacks empirical evidence. Despite tsetse flies posing public health risks and economic hardship, no study has modelled their distributions at a scale needed for local planning. This study used MaxEnt species distribution modelling (SDM) and ecological niche modeling tools to predict potential distribution of three tsetse fly species in Tanzania’s Maasai Steppe from current climate information, and project their distributions to midcentury climatic conditions under representative concentration pathways (RCP) 4.5 scenarios. Current climate results predicted that G. m. morsitans, G. pallidipes and G swynnertoni cover 19,225 km², 7,113 km² and 32,335 km² and future prediction indicated that by the year 2050, the habitable area may decrease by up to 23.13%, 12.9% and 22.8% of current habitable area, respectively. This information can serve as a useful predictor of potential HAT and AAT hotspots and inform surveillance strategies. Distribution maps generated by this study can be useful in guiding tsetse fly control managers, and health, livestock and wildlife officers when setting surveys and surveillance programs. The maps can also inform protected area managers of potential encroachment into the protected areas (PAs) due to shrinkage of tsetse fly habitats outside PAs.

Spatial variation of African Trypanosomiasis burden depends on distribution of biotopes necessary for tsetse flies to thrive. Therefore, mapping the occurrence of the tsetse fly species is a useful predictor of African Trypanosomiasis transmission risk areas. Climate is a major determining factor for occurrence and survival of tsetse flies, the vector responsible for both HAT and AAT. Since resources for prevention and control of tsetse fly species and the disease they transmit are generally scarce in endemic settings, understanding potential impacts of climate change on tsetse fly species distribution in space and time is essential for informing coherent strategies for vector and disease control at a local scale.

Enhancing integrated analysis of national and global goal pursuit by endogenizing economic productivity

2021-02-25T00:00

Analysis with integrated assessment models (IAMs) and multisector dynamics models (MSDs) of global and national challenges and opportunities, including pursuit of Sustainable Development Goals (SDGs), requires projections of economic growth. In turn, the pursuit of multiple interacting goals affects economic productivity and growth, generating complex feedback loops among actions and objectives. Yet, most analysis uses either exogenous projections of productivity and growth or specifications endogenously enriched with a very small set of drivers. Extending endogenous treatment of productivity to represent two-way interactions with a significant set of goal-related variables can considerably enhance analysis. Among such variables incorporated in this project are aspects of human development (e.g., education, health, poverty reduction), socio-political change (e.g., governance capacity and quality), and infrastructure (e.g. water and sanitation and modern energy access), all in conditional interaction with underlying technological advance and economic convergence among countries. Using extensive datasets across countries and time, this project broadly endogenizes total factor productivity (TFP) within a large-scale, multi-issue IAM, the International Futures (IFs) model system. We demonstrate the utility of the resultant open system via comparison of new TFP projections with those produced for Shared Socioeconomic Pathways (SSP) scenarios, via integrated analysis of economic growth potential, and via multi-scenario analysis of progress toward the SDGs. We find that the integrated system can reproduce existing SSP projections, help anticipate differential economic progress across countries, and facilitate extended, integrated analysis of trade-offs and synergies in pursuit of the SDGs.

Trends in vegetation productivity related to climate change in China’s Pearl River Delta

2021-02-24T00:00

Climate change will be a powerful stressor on ecosystems and biodiversity in the second half of the 21^st century. In this study, we used the satellite-derived Normalized Difference Vegetation Index (NDVI) to examine a 34-year trend along with the response of vegetation to climate indicators surrounding the world’s largest megacity: the Pearl River Delta (PRD) of China. An overall increasing trend is observed in vegetation productivity metrics over the study period 1982 to 2015. Increase in winter productivity in both natural ecosystems and croplands is more related to increasing temperatures (r = 0.5–0.78), than to changes in rainfall. For growing season productivity, negative correlations with temperature were observed in cropland regions, and some forests in the northern part of PRD region, suggesting high-temperature stress on crop production and forest vegetation. However, increased winter and spring temperatures provide higher opportunities for cropping in winter. During the decade 1995–2004, vegetation productivity metrics showed a reversal in the upward trend. The geographical and biological complexity of the region under significant climatic and development impacts suggests causative factors would be synergistic. These include our observed decrease in sunshine hours, increasing cloud cover associated with atmospheric aerosols from industrial and urban development, direct pollution effects on plant growth, and exceedance of high temperature growth thresholds.

Dynamic graph embedding for outlier detection on multiple meteorological time series

2021-02-18T00:00

Existing dynamic graph embedding-based outlier detection methods mainly focus on the evolution of graphs and ignore the similarities among them. To overcome this limitation for the effective detection of abnormal climatic events from meteorological time series, we proposed a dynamic graph embedding model based on graph proximity, called DynGPE. Climatic events are represented as a graph where each vertex indicates meteorological data and each edge indicates a spurious relationship between two meteorological time series that are not causally related. The graph proximity is described as the distance between two graphs. DynGPE can cluster similar climatic events in the embedding space. Abnormal climatic events are distant from most of the other events and can be detected using outlier detection methods. We conducted experiments by applying three outlier detection methods (i.e., isolation forest, local outlier factor, and box plot) to real meteorological data. The results showed that DynGPE achieves better results than the baseline by 44.3% on average in terms of the F-measure. Isolation forest provides the best performance and stability. It achieved higher results than the local outlier factor and box plot methods, namely, by 15.4% and 78.9% on average, respectively.

Visual art inspired by climate change—An analysis of audience reactions to 37 artworks presented during 21st UN climate summit in Paris

2021-02-19T00:00

This paper suggests and tests a psychological model of environmental art perception and subsequent support for climate change policies. The model is based on findings from art perception and environmental psychology, which indicate that the response of the viewer to the artwork is (1) first an emotional reaction, which can be positive and/or negative. The emotional activation leads to (2) evaluation of the perceived quality of the artwork. This forms the first impression of the artwork the viewer gets, which then triggers (3) reflections on the artwork that are finally related to support for climate policies. The model test uses data collected at the ArtCOP21 that accompanied the 21^st UN climate summit in Paris. At 37 connected events, the research team collected 883 audience responses with a brief quantitative paper-pencil questionnaire. The data were analyzed using a multilevel-structural equation modeling approach. Results support the suggested theoretical model. Moreover, the effect of reflections on the artwork on support for climate policies is moderated by environmental attitudes, meaning the lower the environmental attitudes, the higher the effect of reflections on policy support. Finally, artwork features like color, size, displaying something personal, etc., could be identified that had a significant relation to differences on the artwork level regarding the first impression of the artwork and the reflections elicited. The study shows that being confronted with climate change-related artwork relates at least in the short run to increased climate policy support, which is mostly channeled through an emotional activation with following cognitive processing. Features of the artwork relate to how strongly and which emotions are activated.

Fish harvesting advice under climate change: A risk-equivalent empirical approach

2021-02-19T00:00

The rate of climate change (CC) has accelerated to the point where it now affects the mid- to long-term sustainability of fishing strategies. Therefore, it is important to consider practical and effective ways to incorporate CC into fisheries advice so that the advice can be considered conditioned to CC. We developed a model to characterise the empirical relationship between a variable affected by climate and fish production. We then used model projections as a foundation for a risk analysis of CC effects on harvesting of Greenland halibut Reinhardtius hippoglossoides in the Gulf of St Lawrence, Canada. The risk-based approach quantified a) the relative change in risk of a status quo fishing strategy under various CC scenarios, and b) the change in fishery exploitation rates required to achieve a management objective over a specified time period at a level of risk considered acceptable (risk equivalent fishery exploitation advice). This empirical approach can be used to develop risk-based advice for any other external variable that affects stock production in addition to climate-related variables and it can be applied in most situations where there is an index of stock biomass and fisheries catch. Shifting the focus from process-based understanding of the responses of fish stocks to CC to quantification of how CC-contributed uncertainty can alter the risks associated with different fishing strategies and/or management options, can ensure timely delivery of robust scientific advice for fisheries under non-stationary environmental conditions.

Mores of the customer base for ecotourism industry: Development and validation of a new measurement scale

2021-02-18T00:00

To date, there is no such scale that may precisely measure mores of the customer base for the ecotourism industry. Therefore, a thematic analysis of literature has been conducted by examining various good quality research works on intrinsic characteristics eliciting pro-environmental actions. Based upon the thematic analysis, a new scale of measure has been proposed with the help of 17 scholars and 15 practitioners hailing from different countries by mutually agreed intended meanings and breadth of the theoretical concepts. The new scale has 4 dimensions comprising a pool of 32 items, which has been empirically validated through the data collected from 268 Malaysian tourists. The dimensions are: sense of obligation to care for the natural environment, sense of obligation to practice eco-friendly activities, sense of obligation to purchase eco-friendly products, and sense of obligation to support eco-friendly inventions. The theoretical and managerial implications together with research limitations have been discussed.

Estimating spatial variation in the effects of climate change on the net primary production of Japanese cedar plantations based on modeled carbon dynamics

2021-02-17T00:00

Spatiotemporal prediction of the response of planted forests to a changing climate is increasingly important for the sustainable management of forest ecosystems. In this study, we present a methodology for estimating spatially varying productivity in a planted forest and changes in productivity with a changing climate in Japan, with a focus on Japanese cedar (Cryptomeria japonica D. Don) as a representative tree species of this region. The process-based model Biome-BGC was parameterized using a plant trait database for Japanese cedar and a Bayesian optimization scheme. To compare productivity under historical (1996–2000) and future (2096–2100) climatic conditions, the climate scenarios of two representative concentration pathways (i.e., RCP2.6 and RCP8.5) were used in five global climate models (GCMs) with approximately 1-km resolution. The seasonality of modeled fluxes, namely gross primary production, ecosystem respiration, net ecosystem exchange, and soil respiration, improved after two steps of parameterization. The estimated net primary production (NPP) of stands aged 36–40 years under the historical climatic conditions of the five GCMs was 0.77 ± 0.10 kgC m^-2 year^-1 (mean ± standard deviation), in accordance with the geographical distribution of forest NPP estimated in previous studies. Under the RCP2.6 and RCP8.5 scenarios, the mean NPP of the five GCMs increased by 0.04 ± 0.07 and 0.14 ± 0.11 kgC m^-2 year^-1, respectively. The increases in annual NPP were small in the southwestern region because of the decreases in summer NPP and the small increases in winter NPP under the RCP2.6 and RCP8.5 scenarios, respectively. Under the RCP2.6 scenario, Japanese cedar was at risk in the southwestern region, in accordance with previous studies, and monitoring and silvicultural practices should be modified accordingly.

Climate change, hunger and rural health through the lens of farming styles: An agent-based model to assess the potential role of peasant farming

2021-02-11T00:00

Undernutrition is a major contributor to the global-burden of disease, and global-level health impact models suggest that climate change-mediated reductions in food quantity and quality will negatively affect it. These models, however, capture just some of the processes that will shape future nutrition. We adopt an alternative standpoint, developing an agent-based model in which producer-consumer smallholders practice different ‘styles of farming’ in the global food system. The model represents a hypothetical rural community in which ‘orphan’ (subsistence) farmers may develop by adopting an ‘entrepreneurial’ style (highly market-dependent) or by maintaining a ‘peasant’ style (agroecology). We take a first look at the question: how might patterns of farming styles—under various style preference, climate, policy, and price transmission scenarios—impact on hunger and health-supporting conditions (incomes, work, inequality, ‘real land productivity’) in rural areas? imulations without climate change or agricultural policy found that style preference patterns influence production, food price, and incomes, and there were trade-offs between them. For instance, entrepreneurial-oriented futures had the highest production and lowest prices but were simultaneously those in which farms tended towards crisis. Simulations with climate change and agricultural policy found that peasant-orientated agroecology futures had the highest production, prices equal to or lower than those under entrepreneurial-oriented futures, and better supported rural health. There were, however, contradictory effects on nutrition, with benefits and harms for different groups. Collectively the findings suggest that when attempting to understand how climate change may impact on future nutrition and health, patterns of farming styles—along with the fates of the households that practice them—matter. These issues, including the potential role of peasant farming, have been neglected in previous global-level climate-nutrition modelling but go to the heart of current debates on the future of farming: thus, they should be given more prominence in future work.

The effects of a temporal framing manipulation on environmentalism: A replication and extension

2021-02-11T00:00

Recent research promotes comparing the current state of the environment with the past (and not the future) to increase the pro-environmental attitudes of those on the political right. We aimed to replicate this temporal framing effect and extend on research in this area by testing the potential drivers of the effect. Across two large-scale replication studies, we found limited evidence that past comparisons (relative to future comparisons) increase pro-environmentalism among those with a more conservative political ideology, thus precluding a full investigation into the mediators of the effect. Where the effect was present, it was not consistent across studies. In Study One, conservatives reported greater certainty that climate change was real after viewing past comparisons, as the environmental changes were perceived as more certain. However, in Study Two, the temporal framing condition interacted with political orientation to instead undermine the certainty about climate change among political liberals in the past-focused condition. Together, these studies present the first evidence of backfire from temporal frames, and do not support the efficacy of past comparisons for increasing conservatives’ environmentalism. We echo recent calls for open science principles, including preregistration and efforts to replicate existing work, and suggest the replication of other methods of inducing temporal comparisons.

Climate change risk perception in the USA and alignment with sustainable travel behaviours

2021-02-03T00:00

In an online survey of 1071 Americans conducted in October 2016, we found technological optimism, environmental beliefs, and gender to be better predictors of climate change concern than respondents’ perceived ability to visualize the year 2050 and their future optimism. An important finding from this study is that in October 2016, just before the 2016 Presidential election, 74% of responding Americans were concerned about climate change. Climate change ranked as their second most serious global threat (behind terrorism). However, when asked to describe travel in the year 2050 only 29% of participants discussed lower carbon options, suggesting that actively envisioning a sustainable future was less prevalent than climate change concern. Enabling expectations and active anticipation of a low carbon future may help facilitate mitigation efforts.

Multi-criteria suitability analysis for neglected and underutilised crop species in South Africa

2021-01-19T00:00

Several neglected and underutilised species (NUS) provide solutions to climate change and creating a Zero Hunger world, the Sustainable Development Goal 2. Several NUS are drought and heat stress-tolerant, making them ideal for improving marginalised cropping systems in drought-prone areas. However, owing to their status as NUS, current crop suitability maps do not include them as part of the crop choices. This study aimed to develop land suitability maps for selected NUS [sorghum, (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta)] using Analytic Hierarchy Process (AHP) in ArcGIS. Multidisciplinary factors from climatic, soil and landscape, socio-economic and technical indicators overlaid using Weighted Overlay Analysis. Validation was done through field visits, and area under the curve (AUC) was used to measure AHP model performance. The results indicated that sorghum was highly suitable (S1) = 2%, moderately suitable (S2) = 61%, marginally suitable (S3) = 33%, and unsuitable (N1) = 4%, cowpea S1 = 3%, S2 = 56%, S3 = 39%, N1 = 2%, amaranth S1 = 8%, S2 = 81%, S3 = 11%, and taro S1 = 0.4%, S2 = 28%, S3 = 64%, N1 = 7%, of calculated arable land of SA (12 655 859 ha). Overall, the validation showed that the mapping exercises exhibited a high degree of accuracies (i.e. sorghum AUC = 0.87, cowpea AUC = 0.88, amaranth AUC = 0.95 and taro AUC = 0.82). Rainfall was the most critical variable and criteria with the highest impact on land suitability of the NUS. Results of this study suggest that South Africa has a huge potential for NUS production. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Also, the maps can be used to design appropriate production guidelines and to support existing policy frameworks which advocate for sustainable intensification of marginalised cropping systems through increased crop diversity and the use of stress-tolerant food crops.

Tourist willingness to pay for local green hotel certification

2021-02-08T00:00

This study aims to understand tourists’ willingness to pay a price premium for a local green hotel certification, and is one of only a few in the literature for small-island tourism destinations in emerging economies with their unique and pressing sustainability challenges. In a survey of 535 tourists visiting Gili Trawangan, Indonesia, facing numerous waste management and coral reef conservation issues, the willingness to pay extra for sustainable hotel services was elicited. There were five discrete pricing levels across the surveys that ranged from $0.75 USD to $7.50 USD extra per night. We examined the relationship of the respondents’ payment choice to their socio-demographic attributes and attitudes regarding environmental issues such as climate change. The main findings and practical implications of the study are: (1) to demonstrate the broad willingness to pay for sustainable hotel services. Findings indicate at all price levels (between $0.75 USD and $7.50 USD), more than 50% of tourists are willing to pay. (2) To estimate a lower bound mean willingness to pay per night for a local green hotel certificate of $1.55USD and 1.34€ EUR, and (3) To identify individual attributes that influence willingness to pay. Findings indicate environmental knowledge and preferences play a role. These results can be used generally to incorporate evidence-based practices into the development of a green hotel marketing strategy, and to help define the target market for small-scale green hotel certification. Additionally, we propose a finance strategy for funding local and sustainable initiatives that support the hotel industry and the island’s infrastructure through the premiums collected from the ’Gili Green Award’ certificate.

Mediating artificial intelligence developments through negative and positive incentives

2021-01-26T00:00

The field of Artificial Intelligence (AI) is going through a period of great expectations, introducing a certain level of anxiety in research, business and also policy. This anxiety is further energised by an AI race narrative that makes people believe they might be missing out. Whether real or not, a belief in this narrative may be detrimental as some stake-holders will feel obliged to cut corners on safety precautions, or ignore societal consequences just to “win”. Starting from a baseline model that describes a broad class of technology races where winners draw a significant benefit compared to others (such as AI advances, patent race, pharmaceutical technologies), we investigate here how positive (rewards) and negative (punishments) incentives may beneficially influence the outcomes. We uncover conditions in which punishment is either capable of reducing the development speed of unsafe participants or has the capacity to reduce innovation through over-regulation. Alternatively, we show that, in several scenarios, rewarding those that follow safety measures may increase the development speed while ensuring safe choices. Moreover, in the latter regimes, rewards do not suffer from the issue of over-regulation as is the case for punishment. Overall, our findings provide valuable insights into the nature and kinds of regulatory actions most suitable to improve safety compliance in the contexts of both smooth and sudden technological shifts.

Rainfall decrease and red deer rutting behaviour: Weaker and delayed rutting activity though higher opportunity for sexual selection

2021-01-20T00:00

In the last decades, climate change has caused an increase in mean temperatures and a reduction in average rainfall in southern Europe, which is expected to reduce resource availability for herbivores. Resource availability can influence animals' physical condition and population growth. However, much less is known on its effects on reproductive performance and sexual selection. In this study, we assessed the impact of three environmental factors related to climate change (rainfall, temperature and vegetation index) on Iberian red deer Cervus elaphus hispanicus reproductive timing and sexual behaviour, and their effects on the opportunity for sexual selection in the population. We measured rutting phenology as rut peak date, the intensity of male rutting activity as roaring rate, and the opportunity for sexual selection from the distribution of females among harem holding males in Doñana Biological Reserve (Southwest Spain), from data of daily observations collected during the rut over a period of 25 years. For this study period, we found a trend for less raining and hence poorer environmental conditions, which associated with delayed rutting season and decreased rutting intensity, but that appeared to favour a higher degree of polygyny and opportunity for sexual selection, all these relationships being modulated by population density and sex ratio. This study highlights how climate change (mainly rainfall reduction in this area) can alter the conditions for mating and the opportunity for sexual selection in a large terrestrial mammal.

Challenges and benefits of using unstructured citizen science data to estimate seasonal timing of bird migration across large scales

2021-02-04T00:00

Millions of bird observations have been entered on online portals in the past 20 years either as checklists or arbitrary individual entries. While several hundred publications have been written on a variety of topics based on bird checklists worldwide, unstructured non-checklist observations have received little attention and praise by academia. In the present study we tested the suitability of non-checklist data to estimate key figures of large-scale migration phenology in four zones covering the whole of Finland. For that purpose, we analysed 10 years of ornithological non-checklist data including over 400 million. individuals of 115 bird species. We discuss bird- and human-induced effects to be considered in handling non-checklist data in this context and describe applied methodologies to address these effects. We calculated 5%, 50% and 95% percentile dates of spring and autumn migration period for all species in all four zones. For validation purposes we compared the temporal distributions of 43 bird species with migration phenology from standardized long-term ringing data in autumn of which 24 species (56%) showed similar medians. In a model approach, non-checklist data successfully revealed latitudinal migration progression in spring and autumn. Overall, non-checklist data proved to be well suited to determine descriptors of migration phenology in Northern Europe which are challenging to attain by any other currently available means. The effort-to-yield ratio of data processing was commensurate to the outcomes. The unprecedented spatiotemporal coverage makes non-checklist data a valuable complement to current migration databases from bird observatories. The basic concept of the present methodology is applicable to data from other bird portals, if combined with local field ornithological knowledge and literature. Species-specific descriptors of migration phenology can be potentially used in climate change studies and to support echo interpretation in radar ornithology.

Mapping climate discourse to climate opinion: An approach for augmenting surveys with social media to enhance understandings of climate opinion in the United States

2021-01-14T00:00

Surveys are commonly used to quantify public opinions of climate change and to inform sustainability policies. However, conducting large-scale population-based surveys is often a difficult task due to time and resource constraints. This paper outlines a machine learning framework—grounded in statistical learning theory and natural language processing—to augment climate change opinion surveys with social media data. The proposed framework maps social media discourse to climate opinion surveys, allowing for discerning the regionally distinct topics and themes that contribute to climate opinions. The analysis reveals significant regional variation in the emergent social media topics associated with climate opinions. Furthermore, significant correlation is identified between social media discourse and climate attitude. However, the dependencies between topic discussion and climate opinion are not always intuitive and often require augmenting the analysis with a topic’s most frequent n-grams and most representative tweets to effectively interpret the relationship. Finally, the paper concludes with a discussion of how these results can be used in the policy framing process to quickly and effectively understand constituents’ opinions on critical issues.

Genetic diversity of native and cultivated Ugandan Robusta coffee (Coffea canephora Pierre ex A. Froehner): Climate influences, breeding potential and diversity conservation

2021-02-08T00:00

Wild genetic resources and their ability to adapt to environmental change are critically important in light of the projected climate change, while constituting the foundation of agricultural sustainability. To address the expected negative effects of climate change on Robusta coffee trees (Coffea canephora), collecting missions were conducted to explore its current native distribution in Uganda over a broad climatic range. Wild material from seven forests could thus be collected. We used 19 microsatellite (SSR) markers to assess genetic diversity and structure of this material as well as material from two ex-situ collections and a feral population. The Ugandan C. canephora diversity was then positioned relative to the species’ global diversity structure. Twenty-two climatic variables were used to explore variations in climatic zones across the sampled forests. Overall, Uganda’s native C. canephora diversity differs from other known genetic groups of this species. In northwestern (NW) Uganda, four distinct genetic clusters were distinguished being from Zoka, Budongo, Itwara and Kibale forests A large southern-central (SC) cluster included Malabigambo, Mabira, and Kalangala forest accessions, as well as feral and cultivated accessions, suggesting similarity in genetic origin and strong gene flow between wild and cultivated compartments. We also confirmed the introduction of Congolese varieties into the SC region where most Robusta coffee production takes place. Identified populations occurred in divergent environmental conditions and 12 environmental variables significantly explained 16.3% of the total allelic variation across populations. The substantial genetic variation within and between Ugandan populations with different climatic envelopes might contain adaptive diversity to cope with climate change. The accessions that we collected have substantially enriched the diversity hosted in the Ugandan collections and thus contribute to ex situ conservation of this vital genetic resource. However, there is an urgent need to develop strategies to enhance complementary in-situ conservation of Coffea canephora in native forests in northwestern Uganda.

Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates

2021-01-20T00:00

While the international pet trade and habitat destruction have been extensively discussed as major threats to the survival of the pancake tortoise (Malacochersus tornieri), the impact of climate change on the species remains unknown. In this study, we used species distribution modelling to predict the current and future distribution of pancake tortoises in Zambezian and Somalian biogeographical regions. We used 224 pancake tortoise occurrences obtained from Tanzania, Kenya and Zambia to estimate suitable and stable areas for the pancake tortoise in all countries present in these regions. We also used a protected area network to assess how many of the suitable and stable areas are protected for the conservation of this critically endangered species. Our model predicted the expansion of climatically suitable habitats for pancake tortoises from four countries and a total area of 90,668.75 km² to ten countries in the future and an area of 343,459.60–401,179.70 km². The model also showed that a more significant area of climatically suitable habitat for the species lies outside of the wildlife protected areas. Based on our results, we can predict that pancake tortoises may not suffer from habitat constriction. However, the species will continue to be at risk from the international pet trade, as most of the identified suitable habitats remain outside of protected areas. We suggest that efforts to conserve the pancake tortoise should not only focus on protected areas but also areas that are unprotected, as these comprise a large proportion of the suitable and stable habitats available following predicted future climate change.

Population dynamics of sugarcane borers, Diatraea spp., under different climatic scenarios in Colombia

2021-01-15T00:00

Seasonal temperature and precipitation patterns on a global scale are main factors to which insects and plants adapt through natural selection, although periodic outbreaks in insect populations may occur in areas where they had not been previously reported, a phenomenon considered as a consequence of global warming. In this study, we estimate the distribution of sugarcane borers, Diatraea spp., under different climate scenarios (rcp26, rcp45, rcp60 and rcp85.) Insects were collected weekly in four sugarcane fields from four different towns in the department of Caldas (Colombia) during 2017, and also in several sugarcane fields in the Cauca River Valley (CRV) between 2010 and 2017. The influence of climatic variables on different agro-ecological zones of the CRV sugarcane fields was defined by climatic data between 2010 and 2017 (maximum and minimum daily temperatures, and accumulated precipitation). The estimate of an optimal niche for Diatraea spp. includes temperatures between 20°C and 23°C, accumulated annual rainfall between 1200 and 1500 mm, dry months with precipitations below 50 mm, slopes of less than 0.05 degrees, crop heterogeneity with an index of 0.2 and primary production values of 1.0. Data suggests Diatraea population is considerably influenced by adverse climate change effects, under the premise of an increase in local and global temperatures, reducing its population niches as well as the number of individuals.

Awareness of climate change's impacts and motivation to adapt are not enough to drive action: A look of Puerto Rican farmers after Hurricane Maria

2021-01-27T00:00

Understanding how perceptions around motivation, capacity, and climate change’s impacts relate to the adoption of adaptation practices in light of experiences with extreme weather events is important in assessing farmers’ adaptive capacity. However, very little of this work has occurred in islands, which may have different vulnerabilities and capacities for adaptation. Data of surveyed farmers throughout Puerto Rico after Hurricane Maria (n = 405, 87% response rate) were used in a structural equation model to explore the extent to which their adoption of agricultural practices and management strategies was driven by perceptions of motivation, vulnerability, and capacity as a function of their psychological distance of climate change. Our results show that half of farmers did not adopt any practice or strategy, even though the majority perceived themselves capable and motivated to adapt to climate change, and understood their farms to be vulnerable to future extreme events. Furthermore, adoption was neither linked to these adaptation perceptions, nor to their psychological distance of climate change, which we found to be both near and far. Puerto Rican farmers’ showed a broad awareness of climate change’s impacts both locally and globally in different dimensions (temporal, spatial, and social), and climate distance was not linked to reported damages from Hurricane Maria or to previous extreme weather events. These results suggest that we may be reaching a tipping point for extreme events as a driver for climate belief and action, especially in places where there is a high level of climate change awareness and continued experience of compounded impacts. Further, high perceived capacity and motivation are not linked to actual adaptation behaviors, suggesting that broadening adaptation analyses beyond individual perceptions and capacities as drivers of climate adaptation may give us a better understanding of the determinants to strengthen farmers’ adaptive capacity.

Collapse and continuity: A multi-proxy reconstruction of settlement organization and population trajectories in the Northern Fertile Crescent during the 4.2kya Rapid Climate Change event

2021-01-11T00:00

The rise and fall of ancient societies have been attributed to rapid climate change events. One of the most discussed of these is the 4.2kya event, a period of increased aridity and cooling posited as the cause of societal changes across the globe, including the collapse of the Akkadian Empire in Mesopotamia. Studies seeking to correlate social and climatic changes around the 4.2kya event have tended to focus either on highly localized analyses of specific sites or surveys or more synthetic overviews at pan-continental scales, and temporally on the event and its aftermath. Here we take an empirical approach at a large spatial scale to investigate trends in population and settlement organization across the entirety of Northern Fertile Crescent (Northern Mesopotamia and the Northern Levant) from 6,000 to 3,000 cal BP. We use Summed Probability Distributions of radiocarbon dates and data from eighteen archaeological surveys as proxies for population, and a dataset of all settlements over ten hectares in size as a proxy for the degree of urbanization. The goal is to examine the spatial and temporal impact of the 4.2kya event and to contextualize it within longer term patterns of settlement. We find that negative trends are visible during the event horizon in all three proxies. However, these occur against a long-term trend of increased population and urbanization supported through unsustainable overshoot and the exploitation of a drier zone with increased risk of crop failure. We argue that the 4.2kya event occurred during a period of unprecedented urban and rural growth which may have been unsustainable even without an exogenous climate forcing.

Predicting the current and future distribution of the western black-legged tick, Ixodes pacificus, across the Western US using citizen science collections

2021-01-05T00:00

In the twenty-first century, ticks and tick-borne diseases have expanded their ranges and impact across the US. With this spread, it has become vital to monitor vector and disease distributions, as these shifts have public health implications. Typically, tick-borne disease surveillance (e.g., Lyme disease) is passive and relies on case reports, while disease risk is calculated using active surveillance, where researchers collect ticks from the environment. Case reports provide the basis for estimating the number of cases; however, they provide minimal information on vector population or pathogen dynamics. Active surveillance monitors ticks and sylvatic pathogens at local scales, but it is resource-intensive. As a result, data are often sparse and aggregated across time and space to increase statistical power to model or identify range changes. Engaging public participation in surveillance efforts allows spatially and temporally diverse samples to be collected with minimal effort. These citizen-driven tick collections have the potential to provide a powerful tool for tracking vector and pathogen changes. We used MaxEnt species distribution models to predict the current and future distribution of Ixodes pacificus across the Western US through the use of a nationwide citizen science tick collection program. Here, we present niche models produced through citizen science tick collections over two years. Despite obvious limitations with citizen science collections, the models are consistent with previously-predicted species ranges in California that utilized more than thirty years of traditional surveillance data. Additionally, citizen science allows for an expanded understanding of I. pacificus distribution in Oregon and Washington. With the potential for rapid environmental changes instigated by a burgeoning human population and rapid climate change, the development of tools, concepts, and methodologies that provide rapid, current, and accurate assessment of important ecological qualities will be invaluable for monitoring and predicting disease across time and space.

What’s the temperature in tropical caves?

2020-12-31T00:00

Hourly temperature was measured for approximately one year at 17 stations in three caves in Quintana Roo, Mexico. Thirteen of these stations were in the extensive twilight zones of all three caves. All seventeen stations showed seasonality in temperature with a 3°C drop during the Nortes season. Two of the caves, Muévelo Sabrosito and Muévelo Rico, showed greater variability during the winter months while in Río Secreto (Tuch) variability was greatest during the rainy season. Río Secreto is less open to the surface than the other two. All sites also showed a daily temperature cycle, although it was very faint in some Río Secreto (Tuch) sites. While temperature variability is diminished relative to surface variation, its temporal pattern is worthy of further study.

Global risk of invasion by Bactrocera zonata: Implications on horticultural crop production under changing climatic conditions

2020-12-23T00:00

The peach fruit fly Bactrocera zonata (Saunders) (Diptera: Tephritidae) is an important invasive species causing substantial losses to the horticulture industry worldwide. Despite the severe economic impact caused by this pest in its native and invaded range, information on its potential range expansion under changing climate remains largely unknown. In this study, we employed maximum entropy (MaxEnt) modeling approach to predict the global potential climatic suitability of B. zonata under current climate and four Representative Concentration Pathways (RCPs) for the year 2050. Outputs from MaxEnt were merged with Spatial Production Allocation Model. A natural dispersal model using Gaussian dispersal kernel was developed. The Areas Under Curves generated by MaxEnt were greater than 0.92 for both current and future climate change scenarios, indicating satisfactory performances of the models. Mean temperature of the coldest quarter, precipitation of driest month and temperature seasonality significantly influenced the potential establishment of B. zonata. The models indicated high climatic suitability in tropical and subtropical areas in Asia and Africa, where the species has already been recorded. Suitable areas were predicted in West, East and Central Africa and to a lesser extent in Central and South America. Future climatic scenarios models, RCP 4.5 and 8.5 show significant potential range expansion of B. zonata in Western Sahara, while RCP 4.5 highlighted expansion in Southern Africa. Contrarily, RCP 2.6 showed considerable decrease in B. zonata range expansion in Central, East and West Africa. There was increased climatic suitability of B. zonata in Egypt and Middle East under RCP 6.0. The dispersal model revealed that B. zonata could spread widely within its vicinity with decreasing infestation rates away from the source points. Our findings can help to guide biosecurity agencies in decision-making and serve as an early warning tool to safeguard against the pest invasion into unaffected areas.

Climate change and primary production: Forty years in a bunchgrass prairie

2020-12-23T00:00

Over the past 109 years, a Montana intermountain bunchgrass prairie annually became warmer (0.7°C) and drier (27%). The temperature and precipitation trends continued since 1978, as we studied nitrogen availability, annual aboveground primary production (ANPP), plant phenology and species composition. Given the annual increase in temperature and decrease in precipitation, ANPP might be expected to decline; however, it increased by 110%, as the period of greatest production (late-May–June) became wetter and cooler, counter to the annual pattern, and this was strongest at lower elevations. Grass production increased by 251%, while dicot production declined by 65%, which increased grass relative abundance by 54%. Summer temperatures increased 12.5% which increased plant senescence by 119% and decreased fall plant regrowth by 68%. More intense summer senescence changed plant species composition in favor of more drought tolerant species. The greater ANPP and summer senescence may increase susceptibility for fire, but fire tolerance of the plant species composition did not change. Invasive plant species increased 108% over the study with annual grasses accounting for >50% of this increase, which further increased summer plant senescence. Therefore, seasonal climate changes at a smaller geographical scale (local), rather than average annual climate changes over a larger geographical scale (regional), may better reflect plant community responses, and this makes ecological forecasting of climate change more difficult.

Planning priority conservation areas for biodiversity under climate change in topographically complex areas: A case study in Sichuan province, China

2020-12-23T00:00

Identifying priority conservation areas plays a significant role in conserving biodiversity under climate change, but uncertainties create challenges for conservation planning. To reduce uncertainties in the conservation planning framework, we developed an adaptation index to assess the effect of topographic complexity on species adaptation to climate change, which was incorporated into the conservation framework as conservation costs. Meanwhile, the species distributions were predicted by the Maxent model, and the priority conservation areas were optimized during different periods in Sichuan province by the Marxan model. Our results showed that the effect of topographic complexity was critical for species adaptation, but the adaptation index decreased with the temperature increase. Based on the conservation targets and costs, the distributions of priority conservation areas were mainly concentrated in mountainous areas around the Sichuan Basin where may be robust to the adaptation to climate change. In the future, the distributions of priority conservation areas had no evident changes, accounting for about 26% and 28% of the study areas. Moreover, most species habitats could be conserved in terms of conservation targets in these priority conservation areas. Therefore, our approach could achieve biodiversity conservation goals and be highly practical. More importantly, quantifying the effect of topography also is critical for options for planning conservation areas in response to climate change.

The Interconnected Arctic — UArctic Congress 2016

2015-06-14T00:00

This open access book presents the most current research results and knowledge from five multidisciplinary themes: Vulnerability of Arctic Environments, Vulnerability of Arctic Societies, Local and Traditional Knowledge, Building Long-term Human Capacity, New Markets for the Arctic, including tourism and safety. The themes are those discussed at the first ever UArctic Congress Science Section, St. Petersburg, Russia, September 2016. The book looks at the Arctic from a holistic perspective; how the environment (both marine and terrestrial) and communities can adapt and manage the changes due to climate change. The chapters provide examples of the state-of-the-art research, bringing together both scientific and local knowledge to form a comprehensive and cohesive volume.