The mangrove forest ecosystem acts as a shield against the destructive tidal waves, preventing the coastal areas and other properties nearby from severe damages; this protective function certainly deserves attention from researchers to undertake further investigation and exploration. Mangrove forest provides different goods and services. The unique environmental factors affecting the growth of mangrove forest are as follows: distance from the sea or the estuary bank, frequency and duration of tidal inundation, salinity, and composition of the soil. These crucial factors may under certain circumstances turn into obstacles in accessing and managing the mangrove forest. One effective method to circumvent this shortcoming is by using remotely sensed imagery data, which offers a more accurate way of measuring the ecosystem and a more efficient tool of managing the mangrove forest. This paper attempts to review and discuss the usage of remotely sensed imagery data in mangrove forest management, and how they will improve the accuracy and precision in measuring the mangrove forest ecosystem. All types of measurements related to the mangrove forest ecosystem, such as detection of land cover changes, species distribution mapping and disaster observation should take advantage of the advanced technology; for example, adopting the digital image processing algorithm coupled with high-resolution image available nowadays. Thus, remote sensing is a highly efficient, low-cost and time-saving technique for mangrove forest measurement. The application of this technique will further add value to the mangrove forest and enhance its in-situ conservation and protection programmes in combating the effects of the rising sea level due to climate change.
Late-spring frosts (LSFs) affect the performance of plants and animals across the world's temperate and boreal zones, but despite their ecological and economic impact on agriculture and forestry, the geographic distribution and evolutionary impact of these frost events are poorly understood. Here, we analyze LSFs between 1959 and 2017 and the resistance strategies of Northern Hemisphere woody species to infer trees' adaptations for minimizing frost damage to their leaves and to forecast forest vulnerability under the ongoing changes in frost frequencies. Trait values on leaf-out and leaf-freezing resistance come from up to 1,500 temperate and boreal woody species cultivated in common gardens. We find that areas in which LSFs are common, such as eastern North America, harbor tree species with cautious (late-leafing) leaf-out strategies. Areas in which LSFs used to be unlikely, such as broad-leaved forests and shrublands in Europe and Asia, instead harbor opportunistic tree species (quickly reacting to warming air temperatures). LSFs in the latter regions are currently increasing, and given species' innate resistance strategies, we estimate that ∼35% of the European and ∼26% of the Asian temperate forest area, but only ∼10% of the North American, will experience increasing late-frost damage in the future. Our findings reveal region-specific changes in the spring-frost risk that can inform decision-making in land management, forestry, agriculture, and insurance policy.
Island health differs from other health care systems, particularly in that there are limited resources and referral faculties available. With globalisation and climate change, island populations have become increasingly vulnerable to natural disasters and global pandemics. This study will identify, explore, compare and report on island health issues facing in the western Pacific, before making appropriate recommendations. A review of selected health indicators in Pacific islands was collected from the World Health Organization (WHO) and other publicly available resources. In the Pacific region, 15 islands saw lower health expenditure (
The successive flood-heat extreme (SFHE) event, which threatens the securities of human health, economy, and building environment, has attracted extensive research attention recently. However, the potential changes in SFHE characteristics and the global population exposure to SFHE under anthropogenic warming remain unclear. Here, we present a global-scale evaluation of the projected changes and uncertainties in SFHE characteristics (frequency, intensity, duration, land exposure) and population exposure under the Representative Concentration Pathway (RCP) 2.6 and 6.0 scenarios, based on the multi-model ensembles (five global water models forced by four global climate models) within the Inter-Sectoral Impact Model Intercomparison Project 2b framework. The results reveal that, relative to the 1970-1999 baseline period, the SFHE frequency is projected to increase nearly globally by the end of this century, especially in the Qinghai-Tibet Plateau (>20 events/30-year) and the tropical regions (e.g., northern South America, central Africa, and southeastern Asia, >15 events/30-year). The projected higher SFHE frequency is generally accompanied by a larger model uncertainty. By the end of this century, the SFHE land exposure is expected to increase by 12 % (20 %) under RCP2.6 (RCP6.0), and the intervals between flood and heatwave in SFHE tend to decrease by up to 3 days under both RCPs, implying the more intermittent SFHE occurrence under future warming. The SFHE events will lead to the higher population exposure in the Indian Peninsula and central Africa (<10 million person-days) and eastern Asia (<5 million person-days) due to the higher population density and the longer SFHE duration. Partial correlation analysis indicates that the contribution of flood to the SFHE frequency is greater than that of heatwave for most global regions, but the SFHE frequency is dominated by the heatwave in northern North America and northern Asia.
Grassland degradation poses a serious threat to biodiversity, ecosystem services, and human well-being. In this study, we investigated grassland degradation in Zhaosu County, China, between 2001 and 2020, and analyzed the impacts of climate change and human activities using the Miami model. The actual net primary productivity (ANPP) obtained with CASA (Carnegie-Ames-Stanford Approach) modeling, showed a decreasing trend, reflecting the significant degradation that the grasslands in Zhaosu County have experienced in the past 20 years. Grassland degradation was found to be highest in 2018, while the degraded area continuously decreased in the last 3 years (2018-2020). Climatic factors for found to be the dominant factor affecting grassland degradation, particularly the decrease in precipitation. On the other hand, human activities were found to be the main factor affecting improvement of grasslands, especially in recent years. This finding profoundly elucidates the underlying causes of grassland degradation and improvement and helps implement ecological conservation and restoration measures. From a practical perspective, the research results provide an important reference for the formulation of policies and management strategies for sustainable land use.
Global climate change has contributed to the intensity, frequency, and duration of heatwave events. The association between heatwaves and elderly mortality is highly researched in developed countries. In contrast, heatwave impact on hospital admissions has been insufficiently studied worldwide due to data availability and sensitivity. In our opinion, the relationship between heatwaves and hospital admissions is worthwhile to explore as it could have a profound impact on healthcare systems. Therefore, we aimed to investigate the associations between heatwaves and hospitalisations for the elderly by age group in Selangor, Malaysia, from 2010 to 2020. We further explored the impact of heatwaves on the risks of cause-specific hospital admissions across age groups within the elderly. This study applied generalized additive models (GAMs) with the Poisson family and distributed lag models (DLMs) to estimate the effect of heatwaves on hospitalisations. According to the findings, there was no significant increase in hospitalisations for those aged 60 and older during heatwaves; however, a rise in mean apparent temperature (ATmean) by 1 °C significantly increased the risk of hospital admission by 12.9%. Heatwaves had no immediate effects on hospital admissions among elderly patients, but significant delay effects were identified for ATmean with a lag of 0-3 days. The hospital admission rates of the elderly groups started declining after a 5-day average following the heatwave event. Females were found to be relatively more vulnerable than males during heatwave periods. Consequently, these results can provide a reference to improve public health strategies to target elderly people who are at the greatest risk of hospitalisations due to heatwaves. Development of early heatwave and health warning systems for the elderly would assist with preventing and reducing health risks while also minimising the burden on the whole hospital system in Selangor, Malaysia.
Elucidating the physiological mechanisms of the irregular yet concerted flowering rhythm of mass flowering tree species in the tropics requires long-term monitoring of flowering phenology, exogenous and endogenous environmental factors, as well as identifying interactions and dependencies among these factors. To investigate the proximate factors for floral initiation of mast seeding trees in the tropics, we monitored the expression dynamics of two key flowering genes, meteorological conditions and endogenous resources over two flowering events of Shorea curtisii and Shorea leprosula in the Malay Peninsula. Comparisons of expression dynamics of genes studied indicated functional conservation of FLOWERING LOCUS T (FT) and LEAFY (LFY) in Shorea. The genes were highly expressed at least 1 month before anthesis for both species. A mathematical model considering the synergistic effect of cool temperature and drought on activation of the flowering gene was successful in predicting the observed gene expression patterns. Requirement of both cool temperature and drought for floral transition suggested by the model implies that flowering phenologies of these species are sensitive to climate change. Our molecular phenology approach in the tropics sheds light on the conserved role of flowering genes in plants inhabiting different climate zones and can be widely applied to dissect the flowering processes in other plant species.
A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949-2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07-0.85, 0.08-0.76, 0.062-0.80 and 0.042-0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.
Global climate change has led to an increase in both the frequency and magnitude of extreme events around the world, the risk of which is especially imminent in tropical regions. Developing hydrological models with better capabilities to simulate streamflow, especially peak flow, is urgently needed to facilitate water resource planning and management as well as climate change mitigation efforts in the tropics. In view of the need, this paper explores the feasibility of improving streamflow simulation performance in the tropical Kelantan River Basin (KRB) of Peninsular Malaysia through coupling a conceptual process-based hydrological model - Soil and Water Assessment Tool (SWAT) with a deep learning model - Bidirectional Long Short-Term Memory (Bi-LSTM) in two ways. All SWAT parameters were set as their default values in one hybrid model (SWAT-D-LSTM), whereas three most sensitive SWAT parameters were calibrated in the other hybrid model (SWAT-T-LSTM). Comparison of daily streamflow simulation results have shown that SWAT-T-LSTM consistently performs better than SWAT-D-LSTM as well as the stand-alone SWAT and Bi-LSTM model throughout the simulation period. Particularly, SWAT-T-LSTM performs considerably better than the other three models in simulating daily peak flow. Based on the latest projection results of five GCMs from the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) under three emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), the best-performed SWAT-T-LSTM was run to assess the potential impacts of climate change on streamflow in the KRB. Ensemble assessment results have concluded that both average and extreme streamflow is much likely to increase considerably in the already wet northeast monsoon season from November to January, which has surely raised the alarm for more frequent flood occurrence in the KRB.
The impact of global warming has received much international attention in recent decades. To meet climate-change mitigation targets, environmental policy instruments have been designed to transform the way goods and services are produced as well as alter consumption patterns. The government of Malaysia is strongly committed to reducing CO2gas emissions as a proportion of GDP by 40% from 2005 levels by the year 2020. This study evaluates the economy-wide impacts of implementing two different types of CO2emission abatement policies in Malaysia using market-based (imposing a carbon tax) and command-and-control mechanism (sectoral emission standards). The policy simulations conducted involve the removal of the subsidy on petroleum products by the government. A carbon emission tax in conjunction with the revenue neutrality assumption is seen to be more effective than a command-and-control policy as it provides a double dividend. This is apparent as changes in consumption patterns lead to welfare enhancements while contributing to reductions in CO2emissions. The simulation results show that the production of renewable energies is stepped up when the imposition of carbon tax and removal of the subsidy is augmented by revenue recycling. This study provides an economy-wide assessment that compares two important tools for assisting environment policy makers evaluate carbon emission abatement initiatives in Malaysia.
The study estimates the long-run dynamics of a cleaner environment in promoting the gross domestic product of E7 and G7 countries. The recent study intends to estimate the climate change mitigation factor for a cleaner environment with the GDP of E7 countries and G7 countries from 2010 to 2018. For long-run estimation, second-generation panel data techniques including augmented Dickey-Fuller (ADF), Phillip-Peron technique and fully modified ordinary least square (FMOLS) techniques are applied to draw the long-run inference. The results of the study are robust with VECM technique. The outcomes of the study revealed that climate change mitigation indicators significantly affect the GDP of G7 countries than that of E7 countries. The GDP of both E7 and G7 countries is found depleting due to less clean environment. However, green financing techniques helps to clean the environment and reinforce the confidence of policymakers on the elevation of green economic growth in G7 and E7 countries. Furthermore, study results shown that a 1% rise in green financing index improves the environmental quality by 0.375% in G7 countries, while it purifies 0.3920% environment in E7 countries. There is a need to reduce environmental pollution, shift energy generation sources towards alternative, innovative and green sources.The study also provides different policy implications for the stakeholders guiding to actively promote financial hedging for green financing. So that climate change and envoirnmental pollution reduction could be achieved effectively. The novelty of the study lies in study framework.
Groundwater provides critical freshwater supply, particularly in dry regions where surface water availability is limited. Climate change impacts on GWS (groundwater storage) could affect the sustainability of freshwater resources. Here, we used a fully-coupled climate model to investigate GWS changes over seven critical aquifers identified as significantly distressed by satellite observations. We assessed the potential climate-driven impacts on GWS changes throughout the 21st century under the business-as-usual scenario (RCP8.5). Results show that the climate-driven impacts on GWS changes do not necessarily reflect the long-term trend in precipitation; instead, the trend may result from enhancement of evapotranspiration, and reduction in snowmelt, which collectively lead to divergent responses of GWS changes across different aquifers. Finally, we compare the climate-driven and anthropogenic pumping impacts. The reduction in GWS is mainly due to the combined impacts of over-pumping and climate effects; however, the contribution of pumping could easily far exceed the natural replenishment.
Both community composition changes due to species redistribution and within-species size shifts may alter body-size structures under climate warming. Here we assess the relative contribution of these processes in community-level body-size changes in tropical moth assemblages that moved uphill during a period of warming. Based on resurvey data for seven assemblages of geometrid moths (>8000 individuals) on Mt. Kinabalu, Borneo, in 1965 and 2007, we show significant wing-length reduction (mean shrinkage of 1.3% per species). Range shifts explain most size restructuring, due to uphill shifts of relatively small species, especially at high elevations. Overall, mean forewing length shrank by ca. 5%, much of which is accounted for by species range boundary shifts (3.9%), followed by within-boundary distribution changes (0.5%), and within-species size shrinkage (0.6%). We conclude that the effects of range shifting predominate, but considering species physiological responses is also important for understanding community size reorganization under climate warming.
Drought affects vegetation growth to a large extent. Understanding the dynamic changes of vegetation during drought is of great significance for agricultural and ecological management and climate change adaptation. The relations between vegetation and drought have been widely investigated, but how vegetation loss and restoration in response to drought remains unclear. Using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) data, this study developed an evaluation framework for exploring the responses of vegetation loss and recovery to meteorological drought, and applied it to the humid subtropical Pearl River basin (PRB) in southern China for estimating the loss and recovery of three vegetation types (forest, grassland, cropland) during drought using the observed NDVI changes. Results indicate that vegetation is more sensitive to drought in high-elevation areas (lag time 8 months). Vegetation loss (especially in cropland) is found to be more sensitive to drought duration than drought severity and peak. No obvious linear relationship between drought intensity and the extent of vegetation loss is found. Regardless of the intensity, drought can cause the largest probability of mild loss of vegetation, followed by moderate loss, and the least probability of severe loss. Large spatial variability in the probability of vegetation loss and recovery time is found over the study domain, with a higher probability (up to 50 %) of drought-induced vegetation loss and a longer recovery time (>7 months) mostly in the high-elevation areas. Further analysis suggests that forest shows higher but cropland shows lower drought resistance than other vegetation types, and grassland requires a shorter recovery time (4.2-month) after loss than forest (5.1-month) and cropland (4.8-month).
The anthropogenic emission of carbon dioxide (CO2) into the atmosphere is recognized as the main contributor to global climate change. To date, scientists have developed various strategies, including CO2 utilization technologies, to reduce global carbon emissions. This paper presents the global scientific landscape of the CO2 utilization research from 1995 to 2019 based on a bibliometric analysis of 1875 publications extracted from Web of Science. The findings indicate a major increase in the number of publications and citations received from 2015 to 2019, denoting a fast-emerging research trend. The dynamics of global CO2 utilization research is partly driven by China's policies and research funding to promote low-carbon economic development. Applied Energy is recognized as a core journal in this research topic. The utilization of CO2 is a multidisciplinary topic that has progressed by multidimensional collaborations at the country and organizations levels, while the formation of co-authorship networks at the individual level is mostly influenced by the authors' affiliations. Keyword co-occurrence analysis reveals a rapid evolution in the CO2 utilization strategies from chemical fixation in carbonates and epoxides to pilot-scale testing of power-to-gas technologies in Europe and the USA. The development of efficient power-to-fuel technologies and biological utilization routes (using microalgae and bacteria) will probably be the next research priorities in CO2 utilization research.
Global warming and ozone depletion, and the resulting increase of ultraviolet radiation (UVR), have far-reaching impacts on biota, especially affecting the algae that form the basis of the food webs in aquatic ecosystems. The aim of the present study was to investigate the interactive effects of temperature and UVR by comparing the photosynthetic responses of similar taxa of Chlorella from Antarctic (Chlorella UMACC 237), temperate (Chlorella vulgaris UMACC 248) and tropical (Chlorella vulgaris UMACC 001) environments. The cultures were exposed to three different treatments: photosynthetically active radiation (PAR; 400-700 nm), PAR plus ultraviolet-A (320-400 nm) radiation (PAR + UV-A) and PAR plus UV-A and ultraviolet-B (280-320 nm) radiation (PAR + UV-A + UV-B) for one hour in incubators set at different temperatures. The Antarctic Chlorella was exposed to 4, 14 and 20°C. The temperate Chlorella was exposed to 11, 18 and 25°C while the tropical Chlorella was exposed to 24, 28 and 30°C. A pulse-amplitude modulated (PAM) fluorometer was used to assess the photosynthetic response of microalgae. Parameters such as the photoadaptive index (Ek) and light harvesting efficiency (α) were determined from rapid light curves. The damage (k) and repair (r) rates were calculated from the decrease in ΦPSIIeff over time during exposure response curves where cells were exposed to the various combinations of PAR and UVR, and fitting the data to the Kok model. The results showed that UV-A caused much lower inhibition than UV-B in photosynthesis in all Chlorella isolates. The three isolates of Chlorella from different regions showed different trends in their photosynthesis responses under the combined effects of UVR (PAR + UV-A + UV-B) and temperature. In accordance with the noted strain-specific characteristics, we can conclude that the repair (r) mechanisms at higher temperatures were not sufficient to overcome damage caused by UVR in the Antarctic Chlorella strain, suggesting negative effects of global climate change on microalgae inhabiting (circum-) polar regions. For temperate and tropical strains of Chlorella, damage from UVR was independent of temperature but the repair constant increased with increasing temperature, implying an improved ability of these strains to recover from UVR stress under global warming.
Current agricultural production depends on very limited species grown as monocultures that are highly vulnerable to climate change, presenting a threat to the sustainability of agri-food systems. However, many hundreds of neglected crop species have the potential to cater to the challenges of climate change by means of resilience to adverse climate conditions. Proso millet (Panicum miliaceum L.), one of the underutilised minor millets grown as a rainfed subsistence crop, was selected in this study as an exemplary climate-resilient crop. Using a previously calibrated version of the Agricultural Production Systems Simulator (APSIM), the sensitivity of the crop to changes in temperature and precipitation was studied using the protocol of the Coordinated Climate Crop Modelling Project (C3MP). The future (2040-2069) production was simulated using bias-corrected climate data from 20 general circulation models of the Coupled Model Intercomparison Project (CMIP5) under RCP4.5 and 8.5 scenarios. According to the C3MP analysis, we found a 1°C increment of temperature decreased the yield by 5-10% at zero rainfall change. However, Proso millet yields increased by 5% within a restricted climate change space of up to 2°C of warming with increased rainfall. Simulated future climate yields were lower than the simulated yields under the baseline climate of the 1980-2009 period (mean 1707 kg ha-1) under both RCP4.5 (-7.3%) and RCP8.5 (-16.6%) though these changes were not significantly (p > 0.05) different from the baseline yields. Proso millet is currently cultivated in limited areas of Sri Lanka, but our yield mapping shows the potential for expansion of the crop to new areas under both current and future climates. The results of the study, indicating minor impacts from projected climate change, reveal that Proso millet is an excellent candidate for low-input farming systems under changing climate. More generally, through this study, a framework that can be used to assess the climate sensitivity of underutilized crops was also developed.
We aimed to reparameterize and validate an existing dengue model, comprising an entomological component (CIMSiM) and a disease component (DENSiM) for application in Malaysia. With the model we aimed to measure the effect of importation rate on dengue incidence, and to determine the potential impact of moderate climate change (a 1 °C temperature increase) on dengue activity. Dengue models (comprising CIMSiM and DENSiM) were reparameterized for a simulated Malaysian village of 10 000 people, and validated against monthly dengue case data from the district of Petaling Jaya in the state of Selangor. Simulations were also performed for 2008-2012 for variable virus importation rates (ranging from 1 to 25 per week) and dengue incidence determined. Dengue incidence in the period 2010-2012 was modelled, twice, with observed daily weather and with a 1 °C increase, the latter to simulate moderate climate change. Strong concordance between simulated and observed monthly dengue cases was observed (up to r = 0·72). There was a linear relationship between importation and incidence. However, a doubling of dengue importation did not equate to a doubling of dengue activity. The largest individual dengue outbreak was observed with the lowest dengue importation rate. Moderate climate change resulted in an overall decrease in dengue activity over a 3-year period, linked to high human seroprevalence early on in the simulation. Our results suggest that moderate reductions in importation with control programmes may not reduce the frequency of large outbreaks. Moderate increases in temperature do not necessarily lead to greater dengue incidence.
Arthropod-borne viruses (arboviruses) are important causes of human disease nearly worldwide. All arboviruses circulate among wild animals, and many cause disease after spillover transmission to humans and agriculturally important domestic animals that are incidental or dead-end hosts. Viruses such as dengue (DENV) and chikungunya (CHIKV) that have lost the requirement for enzootic amplification now produce extensive epidemics in tropical urban centers. Many arboviruses recently have increased in importance as human and veterinary pathogens using a variety of mechanisms. Beginning in 1999, West Nile virus (WNV) underwent a dramatic geographic expansion into the Americas. High amplification associated with avian virulence coupled with adaptation for replication at higher temperatures in mosquito vectors, has caused the largest epidemic of arboviral encephalitis ever reported in the Americas. Japanese encephalitis virus (JEV), the most frequent arboviral cause of encephalitis worldwide, has spread throughout most of Asia and as far south as Australia from its putative origin in Indonesia and Malaysia. JEV has caused major epidemics as it invaded new areas, often enabled by rice culture and amplification in domesticated swine. Rift Valley fever virus (RVFV), another arbovirus that infects humans after amplification in domesticated animals, undergoes epizootic transmission during wet years following droughts. Warming of the Indian Ocean, linked to the El Niño-Southern Oscillation in the Pacific, leads to heavy rainfall in east Africa inundating surface pools and vertically infected mosquito eggs laid during previous seasons. Like WNV, JEV and RVFV could become epizootic and epidemic in the Americas if introduced unintentionally via commerce or intentionally for nefarious purposes. Climate warming also could facilitate the expansion of the distributions of many arboviruses, as documented for bluetongue viruses (BTV), major pathogens of ruminants. BTV, especially BTV-8, invaded Europe after climate warming and enabled the major midge vector to expand is distribution northward into southern Europe, extending the transmission season and vectorial capacity of local midge species. Perhaps the greatest health risk of arboviral emergence comes from extensive tropical urbanization and the colonization of this expanding habitat by the highly anthropophilic (attracted to humans) mosquito, Aedes aegypti. These factors led to the emergence of permanent endemic cycles of urban DENV and CHIKV, as well as seasonal interhuman transmission of yellow fever virus. The recent invasion into the Americas, Europe and Africa by Aedes albopictus, an important CHIKV and secondary DENV vector, could enhance urban transmission of these viruses in tropical as well as temperate regions. The minimal requirements for sustained endemic arbovirus transmission, adequate human viremia and vector competence of Ae. aegypti and/or Ae. albopictus, may be met by two other viruses with the potential to become major human pathogens: Venezuelan equine encephalitis virus, already an important cause of neurological disease in humans and equids throughout the Americas, and Mayaro virus, a close relative of CHIKV that produces a comparably debilitating arthralgic disease in South America. Further research is needed to understand the potential of these and other arboviruses to emerge in the future, invade new geographic areas, and become important public and veterinary health problems.