Climate-related Variables Research Articles

The Use of a Production Function to Evaluate Caribbean Ocean Health Conflicts and Economic Development: Policy Implications.

Central American and Caribbean (CAC) countries enjoy diverse marine environments. The oceans that enclose these coastlines contribute significantly to their economic growth. Industrial expansion and tourism place pressure on the marine ecosystems causing a decline in ocean health. To evaluate the cause of ocean health changes we investigated the factors influencing CAC ocean health using a health production function. Using Rank-Based Regression and the set of extractive, cultural and human well-being and services goals measuring Ocean Health Index (OHI), data from the World Bank, and the Human Development Index we developed a production function for CAC countries ocean health. Results show that all regional OHI scores, but Tourism and Recreation, the main income earning industry for most of the CAC countries, are less than the global score with four of the goals less than 40. The production function shows that all the goals, but the biodiversity sub-goal species, positively influencing OHI. Two climatic related variables, Nitrous Oxide and Carbon Dioxide negatively influenced OHI. The results are important to policy makers as they decide on the need to make greater effort towards improving sustainable contribution of CAC ocean resources to the blue economy.

Air Pollution and the Effectiveness of Positive Airway Pressure Therapy in Individuals With Sleep Apnea: A Retrospective Community-Based Repeated-Measures Longitudinal Study

Ambient air pollution may affect the severity of untreated OSA, but it is unknown whether air pollution adversely impacts the effectiveness of positive airway pressure (PAP) therapy. Do short-term changes in outdoor air pollution adversely impact adults with OSA using PAP therapy? We conducted a retrospective community-based repeated-measures longitudinal study of adults with OSA who purchased a PAP device from a registered equipment provider between 2013 and 2017 (Ontario, Canada) and had data on the daily device-derived residual apnea-hypopnea index (AHIFlow). We linked daily PAP-derived data to air pollution databases using postal codes. The primary exposures were mean nocturnal (8 pm to 8 am) residential concentrations of ozone, fine particulate matter, nitrogen dioxide, carbon monoxide, sulfur dioxide, and the Air Quality Health Index (AQHI). Potential confounders considered were demographics, season and year of exposure, initial OSA severity, other PAP parameters, and climate-related variables. Eight thousand one hundred forty-eight adults were analyzed with a median of 89days (interquartile range [IQR], 29-302days) of observation during which PAP was used for≥ 4 h. The median daily AHIFlow was 1.2/h (IQR, 0.5-2.5/h). In mixed multivariate regression analyses, an increase in air pollution was associated with a statistically significant increase in AHIFlow for most statistical models. The largest effect was for the AQHI: an increase in AHIFlow while comparing highest vslowest quartiles was 0.07/h (95%CI, 0.05-0.10/h). We demonstrated a modest but statistically significant increase in residual respiratory events during PAP therapy associated with an increase in air pollution concentrations.

Using climate risk and vulnerability assessments to prioritise Caribbean road investments

Climate risk and vulnerability assessments were undertaken in the Caribbean for national ministries, with funding from the Caribbean Development Bank. The studies evaluated the impacts of climate-related natural hazards on road transport infrastructure and identified measures to strengthen resilience. Geographic information systems were used to map infrastructure assets and structural condition, and climate-related variables were then integrated to model current and future hazard scenarios. The data sets were analysed to identify hotspot areas of the road network where adaptation interventions were proposed and to prioritise climate-resilient investments. The assessment findings helped decision makers understand system resilience, adaptive capacity and how critical risk hotspot areas could be mitigated. Improved road infrastructure design and a more proactive approach to road and structure maintenance will help increase the resilience of the road networks by reducing vulnerability to hazards and minimising the impact of climate change.

Analysis of flash droughts in China using machine learning

Abstract. The term “flash drought” describes a type of drought with rapid onset and strong intensity, which is co-affected by both water-limited and energy-limited conditions. It has aroused widespread attention in related research communities due to its devastating impacts on agricultural production and natural systems. Based on a global reanalysis dataset, we identify flash droughts across China during 1979–2016 by focusing on the depletion rate of weekly soil moisture percentile. The relationship between the rate of intensification (RI) and nine related climate variables is constructed using three machine learning (ML) technologies, namely, multiple linear regression (MLR), long short-term memory (LSTM), and random forest (RF) models. On this basis, the capabilities of these algorithms in estimating RI and detecting droughts (flash droughts and traditional slowly evolving droughts) were analyzed. Results showed that the RF model achieved the highest skill in terms of RI estimation and flash drought identification among the three approaches. Spatially, the RF-based RI performed best in southeastern China, with an average CC of 0.90 and average RMSE of the 2.6 percentile per week, while poor performances were found in the Xinjiang region. For drought detection, all three ML technologies presented a better performance in monitoring flash droughts than in conventional slowly evolving droughts. Particularly, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of flash drought derived from RF were 0.93, 0.15, and 0.80, respectively, indicating that RF technology is preferable in estimating the RI and monitoring flash droughts by considering multiple meteorological variable anomalies in adjacent weeks to drought onset. In terms of the meteorological driving mechanism of flash drought, the negative precipitation (P) anomalies and positive potential evapotranspiration (PET) anomalies exhibited a stronger synergistic effect on flash droughts compared to slowly developing droughts, along with asymmetrical compound influences in different regions of China. For the Xinjiang region, P deficit played a dominant role in triggering the onset of flash droughts, while in southwestern China, the lack of precipitation and enhanced evaporative demand almost contributed equally to the occurrence of flash drought. This study is valuable to enhance the understanding of flash droughts and highlight the potential of ML technologies in flash drought monitoring.

Regional-scale investigation of pile bearing capacity for Canadian permafrost regions in a warmer climate

Climate change is being experienced particularly intensely in the Arctic and therefore adaptation of engineering systems for this region cannot be further delayed. However, one of the major barriers to studies focused on adapting northern engineering systems is the lack of information at the spatial and temporal scales required for engineering applications. This study investigates pile bearing capacity for selected pile configurations for the Canadian permafrost regions (Nunavut and Northwest Territories), for current and future climates, using the very first ultra-high resolution (4 km) climate change simulation developed for the region using the Global Environmental Multiscale (GEM) model, for a high emission scenario.Comparison of the ultra-high-resolution GEM simulation, driven by reanalysis, with available observations confirms the model's ability in representing near-surface permafrost and related climate variables. The estimated adfreeze contribution to the total bearing capacity, for current climate, informed by the reanalysis-driven GEM simulation, for a 5-m cement pile, is found to be of the order of 15% for regions with shallow bedrock and 80% for regions with deeper bedrock. Application of the GEM climate change simulation outputs, for RCP8.5 scenario, suggest decreases to adfreeze contribution in the 5–30% range by 2040, with the largest differences noted for regions with deeper bedrock. For steel piles of same configuration, although the adfreeze contributions are only about 70% of that for cement piles, the projected relative changes are of similar magnitude.Further downscaling to 250 m resolution using the land model of GEM for the Slave Geological-Grays Bay corridor, where future developments are planned, including an all-season road, enables better estimation of bearing capacity for realistic pile scenarios such as those for bridges (in thick layer of sediments) used for river crossings. Due to the wide variation of pile materials, lengths and installation methods, site specific information can be developed from the framework developed in this study. The results of this study, including the ultra-high resolution climate change information, will thus form the basis for additional detailed investigations on climate-infrastructure interactions and climate resiliency studies.

Ageism, Job Engagement, Negative Stereotypes, Intergenerational Climate, and Life Satisfaction among Middle-Aged and Older Employees in a University Setting.

This study examined whether age-related discrimination, negative age-related stereotypes about declining abilities due to age, job engagement (cognitive, physical, and emotional), and workplace intergenerational climate in terms of positive intergenerational affect (PIA) and workplace intergenerational inclusiveness (WIG) correlated with life satisfaction in a university setting. The analysis was based on 115–117 faculty and staff, 50 years or older. A Principal Axis factor analysis with Promax rotation on the job-related variable revealed three factors: Experiencing Ageism (discrimination and negative stereotypes), Work Climate (PIA and WIG), and Job Engagement (physical, emotional, and cognitive). The factor-based regression scores on the three-factor-based scores were correlated with life satisfaction and also subjected to hierarchical regression analyses with age, sex, and education entered on the first step and the three factors on the second step. The results of both the correlational and hierarchical regression analysis indicated that experiencing ageism was significantly predictive of life satisfaction, and that ageism may play a more primary role than job engagement and work climate-related variables in accounting for life satisfaction.

Effects of stand factors on tree growth of Chinese fir in the subtropics of China depends on climate conditions from predictions of a deep learning algorithm: A long-term spacing trial

Stand and climate related variables are the main driving forces controlling individual tree growth. Two machine learning algorithms called deep learning and random forest were used to explore how annual diameter growth varied with stand and climatic variables. Data was obtained from a long-term spacing trail of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations in four provinces of southern China. Results from model comparisons showed the deep learning model with 8 hidden layers and 90 neurons in each hidden layer achieved the best performance, and the RF model ranked 4th among 9 selected models. In addition, sensitivity analysis showed that individual tree growth increased with an increase in Gini coefficient, while growth decreased with an increase in stand age (A) and the basal area of larger trees (BAL). The relationships between diameter growth and summer mean maximum temperature (SMMT), as well as winter mean minimum temperature (WMMT) and annual precipitation (AP) were not constant, which depended on the range of values of each climate factor. BAL had the greatest influence on diameter growth among all the variables. From an interaction analysis, we found that climate factors exacerbated the negative effects of competition on growth. Climate change promoted the growth of younger trees but restrained the growth of older trees. With climate variables considered, tree growth under high and middle stand structural heterogeneity were similar, and observably higher than that with low stand structural heterogeneity. Positive influences of climate tended to promote tree growth under lower competition and older individuals were more vulnerable to WMMT changes. Our findings enhance our understanding of the mechanisms driving individual Chinese fir growth in southern China in the face of future climate uncertainty.

Impact of climate variability on grain yields of spring and summer maize

Crop yield is greatly impacted by climate change, and a systemic assessment of its impacts on crop yields is essential. Aiming to investigate the impact of climate change on spring and summer maize yields in main maize growing areas of China, the observed meteorological and maize yield data over 1988–2017 at the 121 sites (including 85 sitess for spring maize and 36 sites for summer maize) in main maize growing areas of China were collected. The first-order difference, Sen’s slopes and trend test, multi-collinearity detection, Pearson correlation, stepwise linear and nonlinear regression methods were used, and the best statistical regression models between maize yield and climate variables have been established. Of these, the Sen′s slopes quantify the trend magnitude of the related climate variables and spring/summer maize yields during maize growth period. The Pearson correlation coefficients assess the relationship between pairs of climatic variables and maize yields, while the multi-collinearity analysis determines the mutually independent climatic variables with maize yields. The stepwise multi-variate linear and nonlinear regressions were conducted to obtain the best functions of the one-order-differences of spring (summer)maize yields at the 85 (36) sites. The results indicated that: (1) Generally, the precipitation and temperature during growth seasons was rising, while relative air humidity and sunshine hours was declining. Both the yields of spring and summer maize showed increasing trends. (2) Spring maize yields were more related to relative humidity, sunshine hours and precipitation, while summer maize yields were more related to precipitation and temperature. (3) The multivariate nonlinear functions performed better than the linear relationship. Based on the coefficient of determination, climate change has explained 5.8–87.6% variability of spring maize yield and 6.6–78.5% variability of summer maize yield. (4) The contribution importance rank of climate variables to yields of spring and summer maize was precipitation > relative humidity > sunshine hours > minimum temperature > maximum temperature > average temperature. The wet-cold and wet-warm climate, especially the former, had positive effects on maize yield. In conclusion, climate variables affect spring and summer maize yields and their best relationships were site-specific in China. Our research provides new insights for maize planting management under climate change.

Spatio–temporal variation of vegetation heterogeneity in groundwater dependent ecosystems within arid environments

Climate change, land cover change and the over–abstraction of groundwater threaten the existence of Groundwater-Dependent Ecosystems (GDE), despite these environments being regarded as biodiversity hotspots. The vegetation heterogeneity in GDEs requires routine monitoring in order to conserve and preserve the ecosystem services in these environments. However, in–situ monitoring of vegetation heterogeneity in extensive, or transboundary, groundwater resources remain a challenge. Inherently, the Spectral Variation Hypothesis (SVH) and remotely-sensed data provide a unique way to monitor the response of GDEs to seasonal or intra–annual environmental stressors, which is the key for achieving the national and regional biodiversity targets. This study presents the first attempt at monitoring the intra–annual, spatio–temporal variations in vegetation heterogeneity in the Khakea–Bray Transboundary Aquifer, which is located between Botswana and South Africa, by using the coefficient of variation derived from the Landsat 8 OLI Operational Land Imager (OLI). The coefficient of variation was used to measure spectral heterogeneity, which is a function of environmental heterogeneity. Heterogenous environments are more diverse, compared to homogenous environments, and the vegetation heterogeneity can be inferred from the heterogeneity of a landscape. The coefficient of variation was used to calculate the α- and β measures of vegetation heterogeneity (the Shannon–Weiner Index and the Rao's Q, respectively), whilst the monotonic trends in the spatio–temporal variation (January–December) of vegetation heterogeneity were derived by using the Mann–Kendall non–parametric test. Lastly, to explain the spatio–temporal variations of vegetation heterogeneity, a set of environmental variables were used, along with a machine-learning algorithm (random forest). The vegetation heterogeneity was observed to be relatively high during the wet season and low during the dry season, and these changes were mainly driven by landcover- and climate–related variables. More specifically, significant changes in vegetation heterogeneity were observed around natural water pans, along roads and rivers, as well as in cropping areas. Furthermore, these changes were better predicted by the Rao's Q (MAE = 5.81, RMSE = 6.63 and %RMSE = 42.41), than by the Shannon–Weiner Index (MAE = 30.37, RMSE = 33.25 and %RMSE = 63.94). These observations on the drivers and changes in vegetation heterogeneity provide new insights into the possible effects of future landcover changes and climate variability on GDEs. This information is imperative, considering that these environments are biodiversity hotspots that are capable of supporting many livelihoods. More importantly, this work provides a spatially explicit framework on how GDEs can be monitored to achieve Sustainable Development Goal (SDG) Number 15.

Coverage of In Situ Climatological Observations in the World's Mountains

Many mountainous environments and ecosystems around the world are responding rapidly to ongoing climate change. Long-term climatological time-series from such regions are crucial for developing improving understanding of the mechanisms driving such changes and ultimately delivering more reliable future impact projections to environmental managers and other decision makers. Whilst it is already established that high elevation regions tend to be comparatively under-sampled, detailed spatial and other patterns in the coverage of mountain climatological data have not yet been comprehensively assessed on a global basis. To begin to address this deficiency, we analyse the coverage of mountainous records from the Global Historical Climatological Network-Daily (GHCNd) inventory with respect to space, time, and elevation. Three key climate-related variables—air temperature, precipitation, and snow depth—are considered across 292 named mountain ranges. Several additional datasets are also introduced to characterize data coverage relative to topographic, hydrological, and socio-economic factors. Spatial mountain data coverage is found to be highly uneven, with station densities in several “Water Tower Units” that were previously identified as having great hydrological importance to society being especially low. Several mountainous regions whose elevational distribution is severely undersampled by GHCNd stations are identified, and mountain station density is shown to be only weakly related to the human population or economic output of the corresponding downstream catchments. Finally, we demonstrate the capabilities of a script (which is provided in the Supplementary Material) to produce detailed assessments of individual records' temporal coverage and measurement quality information. Overall, our contribution should help international authorities and regional stakeholders identify areas, variables, and other monitoring-related considerations that should be prioritized for infrastructure and capacity investment. Finally, the transparent and reproducible approach taken will enable the analysis to be rapidly repeated for subsequent versions of GHCNd, and could act as a basis for similar analyses using other spatial reporting boundaries and/or environmental monitoring station networks.

Applying a WASH Risk Assessment Tool in a Rural South African Setting to Identify Risks and Opportunities for Climate Resilient Communities.

Climate change threatens the health and well-being of populations. We conducted a risk assessment of two climate-related variables (i.e., temperature and rainfall) and associated water, sanitation and hygiene (WASH)-related exposures and vulnerabilities for people living in Mopani District, Limpopo province, South Africa. Primary and secondary data were applied in a qualitative and quantitative assessment to generate classifications of risk (i.e., low, medium, or high) for components of hazard/threat, human exposure, and human vulnerability. Climate-related threats were likely to impact human health due to the relatively high risk of waterborne diseases and WASH-associated pathogens. Vulnerabilities that increased the susceptibility of the population to these adverse outcomes included environmental, human, physical infrastructure, and political and institutional elements. People of low socio-economic status were found to be least likely to cope with changes in these hazards. By identifying and assessing the risk to sanitation services and water supply, evidence exists to inform actions of government and WASH sector partners. This evidence should also be used to guide disaster risk reduction, and climate change and human health adaptation planning.

Quantitative Analysis of Winter Wheat Growth and Yields Responding to Climate Change in Xinjiang, China

The knowledge of climate change effects on variations of winter wheat yields are crucial for productions. Our objectives were to investigate the relationship between yield-related indices of winter wheat and the related climatic variables (selected using variance inflation factors) at the 20 sites of Xinjiang, China over 1981–2017. The background of climate and yield changes was analyzed from temporal and spatial respects. The number of independent climatic variables was selected with the variance inflation factor method to remove the multicollinear feature. The Pearson correlation was conducted between the first difference values of climatic variables and yield-related indices of winter wheat (namely plant height, growth period duration, 1000-kernel weight, kernel number per ear, biomass and yield) to find the key climatic variables that impacted winter wheat growth and yields. The multi-variate linear and nonlinear functions were established step by step using the selected key climatic variables. The best function was determined for each site (significant for p < 0.05). From the results, there were general wetter and warmer trends of the climatic variables. Correspondingly, shortened winter wheat phenology and increased growth and yields were observed for most sites. Still, the climatic trends had mixed effects on winter wheat yields. The effects of precipitation, mean air temperature and relative humidity on plant height and growth period duration agreed well. Different sites had different major climatic drivers for winter wheat growth or yields, and the best functions of growth and yields could be linearly or nonlinearly, mostly described by multi-variate functions. The winter wheat growth or yield indices were also found to be closely connected with the soil water content status at the eight sites. The relationship between winter wheat growth or yield and climate provided useful references for forecasting crop production and for projecting the impact of future climate changes.

Indigenous impacts on north Australian savanna fire regimes over the Holocene

Fire is an essential component of tropical savannas, driving key ecological feedbacks and functions. Indigenous manipulation of fire has been practiced for tens of millennia in Australian savannas, and there is a renewed interest in understanding the effects of anthropogenic burning on savanna systems. However, separating the impacts of natural and human fire regimes on millennial timescales remains difficult. Here we show using palynological and isotope geochemical proxy records from a rare permanent water body in Northern Australia that vegetation, climate, and fire dynamics were intimately linked over the early to mid-Holocene. As the El Niño/Southern Oscillation (ENSO) intensified during the late Holocene, a decoupling occurred between fire intensity and frequency, landscape vegetation, and the source of vegetation burnt. We infer from this decoupling, that indigenous fire management began or intensified at around 3 cal kyr BP, possibly as a response to ENSO related climate variability. Indigenous fire management reduced fire intensity and targeted understory tropical grasses, enabling woody thickening to continue in a drying climate.

Long-term series demonstrate small-scale differences in trends within fish assemblages explained by climate variability

Over 30 years (1985–2018) of environmental monitoring data in the Belgian part of the North Sea allowed us to study common and individual species trends within three spatially distinct demersal and benthopelagic fish assemblages in relation to climate-related variability. A combination of multivariate (dynamic factor analysis) and univariate modelling techniques (Trendspotter and Spearman rank correlations) showed that the observed trends in fish densities in two coastal (mud and fine sand) assemblages were best explained by sea surface temperature (SST)-related variables and the Atlantic multidecadal oscillation winter index (AMOwinter). Higher fish densities seem to be linked with extreme cold winters in the year before (e.g. 1985, 1996, 2010), while warmer years may be linked to lower fish densities (fine sand assemblage) a few years later. Trends for the more offshore coarse sand fish assemblage were rather related to the North-Atlantic Oscillation index (NAO) (next to AMOwinter), coupled to the circulation and inflow of warmer saline waters through the English Channel. The temporal decrease in densities in this assemblage may partially reflect climate and subsequent biological regime shifts, as described for the broader North Sea region.At the species level, both positive, negative and varying trends in densities were noted, dependent on the fish assemblage. Overall, densities of most Lusitanean (warm water) species increased over the last decade, positively correlated with most SST-related variables and AMOwinter-values, indicating a northward expansion of their habitat range, which corroborates the findings of other studies. On the contrary, several Boreal (cold water) species were negatively correlated with SST and AMOwinter, with more or less stable trends in density between 1985 and 2000 followed by a decrease in the last decade, potentially explained by a relocation to deeper waters to cope with increasing seawater temperatures. For plaice Pleuronectes platessa, a commercially important species, a linear and significant decrease in length was observed over the 34-year period, most probably related to a combination of climatic influences and fishing induced selection. The current study shows that environmental, climate related drivers are important to explain part of the biological variability and should be taken into account when investigating the direct impact of human activities on the marine, demersal and benthopelagic ecosystem.

Is There a Relationship Between ADHD and COVID-19 Prevalence and Mortality Indices? An Analysis of Data From 156 Countries

Objective: To examine the relationship between nation-level prevalence of ADHD and COVID-19 prevalence and mortality indices. Method: Associations between nation-wise estimated prevalence, crude mortality rates and case-fatality ratios for COVID-19 and estimated prevalence rates for ADHD were examined, controlling for medical conditions known to be associated with COVID-19 outcome, as well as demographic, climate-related, and economic variables. Results: Prevalence of ADHD was positively correlated with COVID-19 prevalence and crude mortality rates on bivariate analyses, though the strength of this association was low. On multivariate regression, prevalence of ADHD was negatively associated with COVID-19 prevalence and crude mortality rates, though only the former finding was statistically significant. Conclusion: The association between ADHD and COVID-19 prevalence and mortality at a national level is inconsistent, modest, and may be largely due to confounding factors such as age, lifestyle factors, and medical comorbidities.

Did Hydro-climatic Extremes, Positive Checks, and Economic Fluctuations Modulate the Epidemics Outbreaks in Late Imperial China?

Empirical research has shown that climate-related variables, the decline in economic well-being, and the mutual reinforcement of positive checks are the primary drivers of epidemic outbreaks in recent human history. However, their relative importance in causing the outbreak of epidemics is rarely examined quantitatively in a single study. I sought to address this issue by analyzing the 1402 epidemic incidents in China between 1841 and 1911, which partially overlaps partly with the Third Pandemic period. Fine-grained historical big data, multiple regression, and wavelet coherence analysis were employed. Statistical results show that economic fluctuations drove the country-wide epidemics outbreaks in China in inter-annual and decadal time scales. Economic fluctuations could cause short-term hardship and long-term impoverishment to the underprivileged social groups since a large portion of the Chinese population lived at the subsistence level in the past. The fluctuations might have sustained the repeated waves of epidemic outbreaks during the Third Pandemic period.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10745-021-00272-7.

Applications of a conceptual framework to assess climate controls of forest tree diseases

AbstractA conceptual framework for climate involvement in forest tree diseases was applied to seven examples to demonstrate its suitability for different disease types: cases where climate favours pathogen biology which then leads to tree mortality or where diseases are caused primarily by climate‐driven physiological injury or stress to trees. Hypotheses for climate involvement are derived from detection and monitoring data to express associations of weather or climate factors with disease development at several spatial and temporal scales. Research findings contribute to an understanding of temperature, precipitation and related climate variables that influence biotic and abiotic diseases. To demonstrate use of the framework, we accessed information from the literature which exposed data and information gaps. Among various simulated approaches to test associations of climate and disease, we found disease risk factor models that use climate inputs derived from monitoring and research provide the best understanding of climate–disease relationships. These model outputs project future disease scenarios that can be used to inform climate adaptation strategies. Conservation and management implications for current and likely future climatic conditions are provided for each disease example. The most common guidance in managed landscapes is to move the imperilled tree species to areas of lower projected climate risk and to favour non‐host, climate‐adapted tree species where the disease is occurring.

Modelling wildfire occurrence at regional scale from land use/cover and climate change scenarios

Wildfire occurrence is expected to increase in future climate and Land Use Land Cover (LULC) change scenarios, especially in vulnerable areas as the European Mediterranean Basin. In this study future probability of wildfire occurrence was estimated for a 20-year time period (2041–2060, centered on 2050) by applying a statistically-based regression model using LULC-derived contact areas with the forest cover (interfaces) as proxy for the human-related factor and a combination of Live Fuel Moisture Content and seasonal climate-related variables as predictors. Future wildfire occurrence was mapped under RCP 8.5 high emissions scenario in four Spanish regions with heterogeneous socioeconomic, LULC and natural fire-related characteristics at 1 km2 target spatial resolution. Results showed increased wildfire probability in ∼19–73% of 1 km2 cells, observing regional differences in the variable effects. This approach could be applied to other spatial scales offering tools for planning and management actions and to obtain different possible future scenarios.

Macroinvertebrate functional trait responses to environmental gradients and anthropogenic disturbance in arid-land streams of North Africa

We analyzed the influence of land use and water physical-chemical characteristics on the trait composition of benthic macroinvertebrates in arid-land streams of North-East Algeria. Macroinvertebrates were sampled in the spring season of 2015, 2017 and 2018 at 36 sampling sites distributed along 5 streams of the Belezma biosphere reserve. Samples were taken from the various substratum types using a Surber net. Most of the variability of the trait-environment relationship was explained by increasing temperature and conductivity along the downstream gradient. Whilst agriculture at higher elevations did not have a great influence on the functional trait composition of macroinvertebrate communities, agriculture and urbanization at lower elevations generated significant deviations from predictable functional structures. Owing to the natural downstream decrease in community diversity in streams of the study region, entire taxa and/or functional groups were more likely to be wiped out in response to anthropogenic perturbations at lower elevations. Despite human activities, climate-related variables in arid lands play a major role on hydrological regimes that effect instream habitats, water chemistry, and macroinvertebrate communities. Given the environmental constraints in arid-land streams of North Africa, even slight increases in anthropogenic pressure can have negative effects on the taxonomic and functional composition of macroinvertebrate communities.

Assessing Smallholder Farmers’ Perception on Climate Variability in Relation to Climatological Evidence: A Case Study of Benguene in the Sudanian Zone of Mali

Sudanian zone is one of the regions in West Africa most confronted with high climate variability, challenging agricultural sustainability and rural livelihoods. Despite multiple research efforts devoted to exploring how farmers in this region perceive climate variability and how perceptions relate to adaptive responses, much remains to be done. This study examined farmers’ perception of climate variability compared with scientific evidence in the terroir of Benguene, between 1983 and 2018. Data was collected from twenty-nine (29) farmland heads (representing 49% of the total farmland heads in the village) through a survey conducted in October 2019. Monthly temperature data from ground measurement and daily rainfall data extracted from the gridded African Rainfall Climatology version 2 (ARC-2) time series was used. Descriptive statistic was used to analyse survey data. Climate data analysis included Mann Kendall trend analysis and computation of growing season parameters (Onset, Duration and Cessation) and its trend. In the end, a conformity index was used to compare farmers’ perceptions to climatological evidence. Conformity graph showed that apart from temperature, the farmers have low, to a weak perception of the other variables used to assess their perception. Results showed that many smallholder farmers have observed a rise in temperature (90%), delay onset (93%) and early cessation (93%) of rainfall. The analysis of the climate data showed high variability in the climate during the study period as observed in the increasing trends in temperature (p-value of 0.0007) and in the rainfall (p-value of 0.0001). Concerning the season parameters, the results show increasingly early-onset (June 1st ± 22 days) increasingly late cessation (October 09th ± 14 days) and consequently a long duration of the rainy season (130 ± 27 days). Conversely, farmers thought the opposite. These results imply the urgent need to increase smallholder farmers awareness of climate variability and change by providing climate information for better adaptation