Climatic Events Research Articles

Substantial invasion of Antarctic Intermediate Water into the Arabian Sea during Younger Dryas and Heinrich Stadials

Antarctic Intermediate Water (AAIW) is an important component of the global thermohaline circulation and an essential limb of the Atlantic Meridional Overturning Circulation (AMOC), which redistributes heat, oxygen, and nutrients in the global ocean. Understanding the dynamics of intermediate water circulation over a millennial timeframe is essential to determine the impact of AMOC changes on ocean heat transport during abrupt climatic events. However, the precise relationship between global ocean intermediate water circulation and abrupt climate events, such as the Younger Dryas (YD) and Heinrich Stadials (HS), is not yet fully understood, particularly in tropical regions. Here, we present a ∼29 ka high-resolution record of Neodymium isotope (ƐNd) in authigenic phases, a water mass tracer, of a sediment core collected from the intermediate depth (840 m) in the eastern Arabian Sea (off Goa) to understand the past variability in AAIW circulation in the northern Indian Ocean on a millennial time scale. Our new ƐNd record reveals the pronounced temporal variation (−9.5 to −6.1) throughout the core, marked with enhanced radiogenic Nd signature during the YD, HS1, and HS2. These episodes of enhanced radiogenic Nd signatures signify an increased northward penetration of Antarctic Intermediate Water into the Northern Indian Ocean. The intervals of northward progression of AAIW coincide with the Northern Hemisphere cold events which could have resulted from the enhanced formation of AAIW in the Southern Ocean that occurred due to the warming-induced deceleration of the AMOC. This correlation underscores a robust connection between the formation of North Atlantic Deep Water (NADW) and Southern Ocean climate dynamics through the "bipolar seesaw" mechanism. Our study emphasizes that tracking changes in AAIW in the Arabian Sea can provide insights into the past and future variations in AMOC.

Effects of climate and soil on morphological diversity of Moringa oleifera L. Germplasm in Punjab Pakistan

Effects of climate and soil on morphological diversity of Moringa oleifera L. Germplasm in Punjab Pakistan

Disaster Induced Agricultural Productivity in Coastal Regions of Bangladesh: A Stochastic Frontier Analysis Approach

The production and technical efficiency of the rice sector in Bangladesh’s coastal areas are vulnerable to a wide range of natural disasters and climate change issues. These regions face recurring threats from climatic events such as salinity intrusion, inundation, rising sea levels, and cyclones, which can significantly disrupt agricultural activities. In this paper, we conduct a comparative analysis of the rice producers’ technical efficiency based on their exposure to disaster-induced impacts over the past five years. Using the Stochastic Frontier Analysis, we examine factors influencing household-level productive efficiency in three coastal districts—Patuakhali, Cox’s Bazar, and Khulna. Our findings reveal that rice-producing households affected by natural disasters between 2014 and 2018 exhibited, on average, an 8.29 percentage point higher productive efficiency compared to unaffected households. When controlling for other confounding variables, such as household characteristics and external conditions, the efficiency gain rises to 19.8%. This suggests that households exposed to adverse climatic events may have adapted their farming practices to become more resilient, thus improving their productive efficiency in the long term. Moreover, our results indicate that a larger household size enhances efficiency by 6.7%. Households where rice farming is the primary occupation also tend to be more efficient, likely because of greater specialization and focus on improving agricultural practices. Finally, the age of the producer is positively associated with efficiency, reflecting the accumulation of farming experience and knowledge over time.

Cropland Exposure to Extreme Dryness and Wetness in China Under Shared Socioeconomic Pathways

ABSTRACTGlobal warming by human activities have exacerbated the occurrence of extreme climatic events, which have taken a huge toll on human production and livelihoods. Predicting future changes in extreme wetness and dryness, along with the extent of cropland exposure to these conditions under various scenarios, is essential for effective climate adaptation and achieving sustainable development goals. This study employs the Standardised Antecedent Precipitation Evapotranspiration Index (SAPEI) method to identify extreme dryness and extreme wetness in China for future projections (2021–2100), and to analyse the characteristics of changes in the pixel‐day, intensity, and affected area by extreme dryness and wetness, as well as the cropland exposures to them under different shared socioeconomic pathways (SSPs). We find that the intensity and spatial extent of extreme dryness and wetness significantly increase in future climate projections, especially under high‐emission scenario compared to low‐emission scenario. Under the scenarios with increased emissions, the cropland exposure increased in most parts of China. Therefore, it is particularly urgent to keep the low‐emission scenario in order to minimise the cropland damage caused by extreme drought and wetness in China in the future.

Assessment of extreme climate stress across China’s maize harvest region in CMIP6 simulations

Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China’s maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI. The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change.

Does farm mechanization improve farm performance and ensure food availability at household level? Empirical evidence from Pakistan

Agriculture has a significant role in the wellbeing of the rural households of developing countries. Still, its productivity is very low in these countries due to the low use of mechanization at the farm level. In addition, extreme climate events and labor shortages at required intervals have made decisive pitch for the promotion and adoption of farm mechanization for ensuring sustainable farm performance and food security in developing countries. However, limited empirical evidence is available from Pakistan, about the adoption of farm mechanization and its impact on farm performance and food availability. Therefore, primary data were collected from 384 farmers from cotton–wheat cropping system of Punjab, Pakistan, by using a multi-stage sampling procedure. The endogenous switching regression (ESR) model was employed to estimate the determinants of farm mechanization adoption and their associated impacts on farmers’ livelihood, measured by farm performance and food availability. The findings indicate that the adoption of full mechanization across all farming operations enhances overall farm performance and improves food availability. In addition, full mechanization leads to a substantial increase in farm performance by up to 55% and boosts food availability by approximately 125%. Our study demonstrates that the adoption of farm mechanization is strongly influenced by factors such as education, household size, landholding, off-farm employment, access to credit, and extension services.

Determinants of plant species richness along elevational gradients: insights with climate, energy and water–energy dynamics

BackgroundUnderstanding the patterns and processes of species distributions has long remained a central focus of biogeographical and ecological research. While the evidence for elevational patterns in species richness is widespread, our understanding of underlying causes and mechanisms remains limited. Therefore, this study aimed to entangle the influence of environmental variables on plant species richness along elevational gradients in the Western Himalayas.MethodsWe compiled elevational distribution for about 1150 vascular plants using the published literature and available database. The species richness was estimated in 100-m elevational bands using the range interpolation method. We used the generalised linear model and structural equation modelling (SEM) framework to identify the direct and indirect effects of climatic factors on species richness.ResultsOur results indicated that primary environmental correlates of species richness varied with elevational gradients. Climatic variables combined with energy and water availability were more important than the topographic heterogeneity. Further, the direct and interaction effects of climatic variables were more substantial than their indirect effects. The indirect effects of climate are more strongly mediated by water–energy dynamics than the energy alone.ConclusionsOverall, our findings emphasise the importance of considering direct effects and interactions among environmental variables while studying the underlying mechanisms governing elevational biodiversity gradients. Species richness appeared to be shaped by climatic tolerances rather than habitat heterogeneity at regional scales. This information can have implications for biodiversity dynamics under environmental change.

Diminished water yield coefficient of glacial catchments in Northwest China

Study region19 glacial catchments located in Northwest China. Study focusThe water yield coefficient (denoted as WYc) of glacier catchments in Northwest China has changed over the past six decades, closely linked to shifts in land cover dynamics. However, quantifying the impacts of climate and land cover changes on glacier catchments remains challenging due to the complexity of glacier hydrological processes and the scarcity of long-term glacier runoff data. In this study, the Budyko equation was coupled with a glacier runoff model to enable quantitative analysis of the factors driving changes in water yield (WY). Path analysis was then employed to assess the direct and indirect effects of climate and landscape changes on the WYc. New hydrological insight for the regionThe results indicated that 14 out of 19 catchments experienced a decline in WYc, particularly in those with greater glacier coverage. Glacier retreat and increased vegetation greenness adversely affected WYc by increasing the catchment parameter, with effects of −26.42 % and −25.21 %, respectively. Although overall WY in the catchments has increased—driven primarily by increased precipitation and glacier storage loss, with contribution rates of 100.66 % and 25.05 %, respectively—the diminished WYc is expected to decelerate the rate of WY increase. This trend poses significant challenges for water resource management in arid and water-scarce regions.

Potential distribution of Detarium microcarpum under different climate change scenarios in Burkina Faso

Climate change and human activities are major drivers influencing species distribution, posing significant challenges for woody species that provide essential ecosystem goods and services. Therefore, a thorough understanding of climate impacts on species distribution is crucial for more effective management. This study aims to assess the effect of climate change on the current and future distribution of Detarium microcarpum. This study was conducted in Burkina Faso. The maximum entropy (MaxEnt) approach was used to simulate the current and future distributions of the species. The future distribution of the species was assessed through four global climate models: HadGEM3-GC3.1-LL, ACCESS-CM2, CNRM-CM6-1 and MIROC6 under two Shared Socioeconomic Pathways (SSPs) scenarios, SSP2-4.5 and SSP5-8.5 at the horizons 2061–2080 and 2081–2100. The area under the curve (AUC ˃ 0.80) and True Skill Statistic (TSS=0.7) revealed a high level of prediction for the current and future scenarios. The results showed that the mean temperature of the Wettest Quarter (bio8) and Isothermality (bio3) were the most important environmental variables which affect D. microcarpum distribution. The results show that the current suitability area for the species covers 65.6 % of the country area with respectively high suitability (36.73 %) and low suitability (29.16 %). Based on the outcomes of the prediction, the high suitable areas for D. microcarpum conservation will decrease on average by 5,92 % of their current area under future climate change, regardless of the scenario and model used, except for the MIROC6 model under the SSP5-8.5 scenario, which will increase its current area from 0.56 % to 1.7 %. The result of this study underscores the significant effect of the future climate on the suitable area of D. microcarpum. Specific actions should be taken to ensure the suitable conservation of this valuable multipurpose species.

Fast generation of high-dimensional spatial extremes

Widespread extreme climate events cause many fatalities, economic losses and have a huge impact on critical infrastructure. It is therefore of utmost importance to estimate the frequency and associated consequences of spatially concurrent extremes. Impact studies of climate extremes are severely hampered by the lack of extreme observations, and even large ensembles of climate simulations often do not include enough extreme or record-breaking climate events for robust analysis. On the other hand, weather generators specifically fitted to extreme observations can quickly generate many physically or statistically plausible extreme events, even with intensities that have never been observed before. We propose a Fourier-based algorithm for generating high-resolution synthetic datasets of rare events, using essential concepts of classical modelling of (spatial) extremes. Here, the key feature is that the stochastically generated datasets have the same spatial dependence as the observed extreme events. Using high-resolution gridded precipitation and temperature datasets, we show that the new algorithm produces realistic spatial patterns, and is particularly attractive compared to other existing methods for spatial extremes. It is exceptionally fast, easy to implement, scalable to high dimensions and, in principle, applicable for any spatial resolution. We generated datasets with 10000 gridpoints, a number that can be increased without difficulty. Since current impact models often require high-resolution climate inputs, the new algorithm is particularly useful for improved impact and vulnerability assessment.

Bird communities in the Dry Chaco of South America: vegetation structure and climate effects

Species lead to a complex and dynamic environment affected by external processes. Better understanding the importance of these factors is particularly urgent for the world’s tropical dry forest, which is understudied, highly threatened and rapidly disappearing. Building on a unique, field-based bird community dataset, we used multivariate analysis and generalized linear models to test the effects of climate and vegetation structure on bird composition and richness in forest corridors. Our analyses revealed the importance of forest corridors that not only connect the landscape but may facilitate the movement of species, having a high potential for management and connectivity planning. We found significant differences in bird communities to environmental changes when focusing on all birds or when analyzing dry-forest birds only. For all birds, composition revealed preferences of habitat. Birds of open habitats were positively associated with canopy openness, temperature, and relative humidity, while birds to avoid open habitats were positively associated with higher canopy density. The most important variables explaining variations of dry-forest birds were understory and canopy density. Richness increases with temperature for the entire community, yet higher temperatures during the day decrease bird activity. Overall, we showed that bird composition differences were associated with canopy changes, yet richness increased with understory cover. Likewise, our study highlights the importance of maintaining a microenvironment based on local requirements for composition or richness. Moreover, the conservation strategies should be consistent to those requirements to promote the viability of corridors uses that potentially connect the landscape.

Fire management now and in the future: Will today's solutions still apply tomorrow?

Climate change and fire management actions are the two key drivers of fire regime changes now and into the future. The predicted effects of these drivers vary between regions and global climate projections; however, it is expected that fire regimes globally are likely to intensify. Increased wildfire extent, frequency and severity mean impacts to people, property, infrastructure, production and the environment are also likely to increase under worsening climate conditions. Fire management programs aim to reduce the influence of worsening climatic conditions on wildfires and risk to assets now and into the future However, given the pace of changes to fire regimes, trade-offs between assets are increasingly likely. Therefore, understanding the cost-effectiveness of fire management in the form of both fuel management and suppression is critical for managers to make informed decisions regarding resource allocation. We develop and test a Bayesian Decision Network (BDN) incorporating data from ~1200 fire regime simulations capturing 16 management strategies across six regions and six climate models. We quantify the effects of management and climate on fire size and risk to environmental, infrastructure, and production assets, as well as people and property. We calculate the overall cost-effectiveness of the management scenario based on the cost of implementing the program and the subsequent cost of impacts caused by wildfires. We found that costs increased under future climate conditions for all management scenarios in most regions. Despite some regional variation in the cost-effectiveness of management scenarios we were able to identify key scenarios which consistently had high cost-effectiveness. These were combinations of prescribed burning and suppression. Importantly, the model clearly demonstrates the risk of a do-nothing approach and highlights that action is needed to prevent high impacts now and into the future and to reduce the overall costs of wildfires.

Vegetation Changes in the Arctic: A Review of Earth Observation Applications

The Arctic, characterised by severe climatic conditions and sparse vegetation, is experiencing rapid warming, with temperatures increasing by up to four times the global rate since 1979. Extensive impacts from these changes have far-reaching consequences for the global climate and energy balance. Satellite remote sensing is a valuable tool for monitoring Arctic vegetation dynamics, particularly in regions with limited ground observations. To investigate the ongoing impact of climate change on Arctic and sub-Arctic vegetation dynamics, a review of 162 studies published between 2000 and November 2024 was conducted. This review analyses the research objectives, spatial distribution of study areas, methods, and the temporal and spatial resolution of utilised satellite data. The key findings reveal circumpolar tendencies, including Arctic greening, lichen decline, shrub increase, and positive primary productivity trends. These changes impact the carbon balance in the tundra and affect specialised fauna and local communities. A large majority of studies conducted their analysis based on multispectral data, primarily using AVHRR, MODIS, and Landsat sensors. Although the warming of the Arctic is linked to greening trends, increased productivity, and shrub expansion, the diverse and localised ecological shifts are influenced by a multitude of complex factors. Furthermore, these changes can be challenging to observe due to difficult cloud cover and illumination conditions when acquiring optical satellite data. Additionally, the difficulty in validating these changes is compounded by the scarcity of in situ data. The fusion of satellite data with different spatial–temporal characteristics and sensor types, combined with methodological advancements, may help mitigate data gaps. This may be particularly crucial when assessing the Arctic’s potential role as a future carbon source or sink.

Influence of climatic variables on biome transitions in the COLOMBIAN and PANAMANIAN Caribbean region

Disentangling the environmental determinants of tropical biomes is crucial for understanding their response to climate change. This study investigated the effect of climate and soil-related variables on biome transitions in the Caribbean region of Colombia and Panama, focusing on xerophytic forest (XF), tropical dry forest (TDF), and tropical rainforest (TRF). We analyzed the climatic variables at different time scales (daily, seasonal, and annual) and their interaction with soil properties. We performed an ordinal logistic regression to assess the combined effect of the most important variables in biome transitions. Our results showed that climate variables are major discriminators in our study region, particularly precipitation at a seasonal and annual scale. The ordinal logistic regression highlighted the significance of annual precipitation and dry-season length in biome transitions, with maximum temperature impacting TDF-TRF transitions. Soil differences, although present (e.g., higher sand content in XF), played a marginal role. Overall, our findings emphasize the dominance of climate over soil in shaping tropical biome distributions in the northern Caribbean part of South America. These findings contribute to a deeper understanding of tropical biome responses to climate change.

Impact of an extreme drought event on clonal reproduction and the acclimation capacity of the succulent plant Sempervivum tectorum L.

Functional traits have been defined as those that affect organismal performance, that is growth and development, reproduction and survival, so they have been generally associated with acclimation and adaptation. Here, we aimed to study the impact of an extreme drought event on clonal reproduction and hormonal mechanisms underlying acclimation of houseleek (Sempervivum tectorum L.), a plant adapted to survive harsh environments. We also explored the validity of growth- and stress-related phytohormones as functional traits to evaluate stress acclimation responses in the field. We compared the response of plants, considering both mother rosettes and newly produced clones, to a very extreme summer drought event occurring in small cliffs in Les Guilleries mountains (NE Spain). We measured various stress makers in the field together with hormonal profiling through a metabolomic approach using liquid chromatography coupled to tandem mass spectrometry. Results showed that clonal propagation was arrested during the study period and revealed a 100-fold increase in abscisic acid content from spring to summer both in mothers and new clones, concomitantly with reductions in relative water content, which decreased by 20% only. The stress-related bioactive jasmonate, jasmonoyl-isoleucine increased simultaneously with abscisic acid, while growth-related hormones, including bioactive cytokinins (2-isopentenyl adenine and trans-zeatin) decreased from spring to summer, which was consistent with growth arrest. It is concluded that S. tectorum adjusts recruitment of new clones during periods of low water availability and withstands extreme drought events during the summer (preventing severe cell turgor loss at soil water contents below 2% and temperatures above 43 ºC) by successfully activating a complex hormonal response that underlies the great capacity of this species to survive extreme climatic events.

Multi-proxy reconstructions of paleotemperature in the southern South China Sea since the last deglaciation

The accuracy of paleothermometers is a prerequisite for understanding the past sea surface temperature (SST) changes in the tropical seas. Here, we analyzed the SST estimates reconstructed by four lipid proxies with common linear and newly advanced models in parallel in a sediment core collected from the southern South China Sea (SCS). After excluding the impact of terrestrial input, all of the four proxies-inferred SSTs displayed a gradually warming pattern since 18.3 ka. Our long-chain alkenones-derived annual SST at seawater depth of 0–30 m (SST 0–30 m) record closely matched the regional synthetic SST record from the entire southern SCS, corresponding to high-latitude climate events during the deglaciation. The temperatures reconstructed by long-chain diols (LCDs) showed an upper limit of 27 °C, and we thus proposed that they reflected the optimal survival temperature for organisms producing LCDs when SST was higher than 27 °C. Isoprenoid and hydroxy glycerol dialkyl glycerol tetraethers (iGDGTs and OH-GDGTs)-derived temperatures likely reflected the subsurface temperature (subT) at seawater depth of 30–125 m and SST towards the warm season in the tropical sea, respectively.

Responses of vegetation low-growth to Extreme Climate Events on the Mongolian Plateau

Since the 21st century began, the frequency and intensity of extreme climate events have significantly increased globally, becoming a widely recognized phenomenon of global change. These extreme events, including droughts and heatwaves, have profound impacts on the structure and function of ecosystems. This study focuses on the Mongolian Plateau. It utilizes meteorological and remote sensing vegetation data, combined with consistency and sensitivity analyses. These approaches aim to provide an in-depth understanding of the relationship between various extreme climate events and vegetation low-growth. The study found that extreme drought and extreme heat events are the primary drivers affecting vegetation low-growth on the Mongolian Plateau. The analysis results indicate that the sensitivity of vegetation to these extreme climate events is regulated by regional hydrothermal conditions, with vegetation in long-term drought areas being more susceptible to the suppression of extreme drought, while humid areas exhibit some resistance. As the temperature gradient increases, the sensitivity of vegetation to extreme high temperatures increases, while sensitivity to extreme low temperatures decreases. Furthermore, the study also revealed differences in the responses of different vegetation types to extreme events under the same climatic conditions, highlighting the ecological basis of ecosystem resilience and adaptability. This research not only enhances our understanding of vegetation dynamics under the influence of extreme climate events but also provides scientific evidence for ecological management and climate adaptation in the Mongolian Plateau region.

Collapse of an insular bird species driven by a decrease in rainfall

Arid island environments harbour a unique biota characterised to have adaptive features that enable them to thrive in such harsh habitats. However, our understanding of how anthropogenic climate change compromises the biodiversity and sustainability of these ecosystems is greatly unknown. Here we used fine-grained field data to evaluate the effects of extreme weather on the population size, distribution, and habitat preferences of an endemic bird species inhabiting an arid Atlantic island, across two temporal windows spanning approximately 20 years (2005–2024). Population size declined sharply (63 %–70 %) between periods, according to a distance-based sampling design and a habitat suitability modelling approach, with the number of individuals estimated in 2024 being 4650 (CI 95 %: 3600–5950) and 4150 (CI 95 %: 3600–4800) respectively. The density of this species in 2024 was reduced by approximately three times compared to the previous study period. The results revealed that in 2024 a larger island area (246 km2 and 514 km2) was necessary to encompass 50 % and 75 % of all individuals of this species, respectively, compared to the previous period (195 km2 and 434 km2). Such a population collapse was associated with the decrease in rainfall on the island, which is closely related to the reproductive success in this species. We recorded a pattern of continuous decrease in rainfall since 2005–2006, which included several extremely dry years immediately before the 2024 study. We also found strong evidence for the population decline of other native bird species of the same foraging guild. Overall, our data emphasizes the significance of recurrent and extreme climatic events on arid island bird species in driving population declines and reducing their distribution ranges; and highlights how fragile such unique taxa can be under longer dry periods, with uncertain consequences for their future viability.

Comparative analysis of artificial intelligence models for real-time and future forecasting of environmental conditions: A wood-frame historic building case study

Precise environmental monitoring within historic buildings is crucial for ensuring optimal conditions that guarantee the proper preservation of these structures, thereby maintaining their integrity and cultural legacy. Moreover, outdoor environmental conditions can potentially impact the indoor environment, especially in historic structures with deficient insulation materials and damaged envelopes. As climate change points to more recurrent and severe extreme climatic events in the coming years, a current and future comprehensive evaluation of indoor microclimate in historic structures is essential. This study investigated the utilization of machine learning and deep learning algorithms for real-time and future forecasting of the indoor microclimate, including air temperature, relative humidity, and dew point, in a historic building located in San Antonio, Texas, USA. In situ monitored data from April 2022 to January 2023 were used to train different predictive algorithms. The results indicated that Multi-Layer Perceptrons and Support Vector Machine models yielded the most accurate values in terms of real-time forecasting of the indoor microclimate, while Extreme Gradient Boosting model excelled in convergence time. Additionally, Long Short-Term Memory models were the most accurate in predicting indoor microclimate using future weather data for the current century, specifically for 2050 and 2080. The methodology developed in this study can be applied to different construction types and locations globally. As it enables the prediction of environmental conditions crucial for historic preservation, the results have the potential to assist experts in making informed decisions about conserving historic structures, both now and in the future.

Extreme heat and drought did not affect interspecific interactions between dune grasses

The frequency of extreme climatic events, such as storm and heatwaves, is predicted to increase because of climate change. Understanding interactions between species in environmental extremes plays a vital role in predicting ecosystem resilience. In this study, we examined how heat and drought combined with interspecific interactions between pioneer dune builder sand couch (Thinopyrum junceiforme) and primary foredune builder marram grass (Calamagrostis arenaria) affected growth and survival of the latter species in an embryonic dune system. In a 4-week field experiment, we transplanted marram grass within sand couch patches or on bare sediment. This plant interaction treatment was combined with a compound heat and drought treatment that was simulated with greenhouses that inhibited rainfall and increased temperatures (average daily maximum temperature +4°C). Results show that the presence of sand couch significantly reduced growth (i.e., formation of new shoots, shoot and root length and aboveground biomass) of marram grass. By contrast, the heat and drought treatment had no significant effects on growth or survival of marram grass, irrespective of species interactions. The neutral response suggests that even in its early establishment marram grass is highly heat and drought resistant. Since the competitive interaction between sand couch and establishing marram grass did not change under pressure of an extreme heat and drought event, we expect that these factors do not affect embryonic dune development.