Predicted Effects Of Climate Change Research Articles

Large Differences in Bud Burst and Senescence between Low- and High-Altitude European Beech Populations along an Altitudinal Transect in the South-Eastern Carpathians

Large Differences in Bud Burst and Senescence between Low- and High-Altitude European Beech Populations along an Altitudinal Transect in the South-Eastern Carpathians

Modeling climate change effects on transport and fate of pathogens in an urban coastal area of Lake Michigan

Abstract Infrastructure renewal and public health efforts require prediction of climate change effects on the occurrence of pathogens in the Great Lakes' urban coastal waters. This paper presents an investigation that addressed the climate change effects on transport and the fate of bacteria in Milwaukee's urban coastal area. This investigation was part of a study on climate change risks and impacts that included downscaling of climate change data for meteorological stations around Lake Michigan, and implementation of a hydrologic model that predicts tributary flows and bacteria loads. A method to select scenarios appropriate to link watershed and lake transport processes is presented. For the watershed, the sensitivity of bacterial loads with respect to changes in spring-season precipitation and air temperature is critical, while for lake transport, the most important driver is the wind field. Watershed and lake processes are linked by using spring-season watershed loading in the simulation of coastal transport. Scenarios for hydrodynamic modeling were developed by selecting climate projections that yielded high-and-low percentile projected spring-season wind speed. The patterns of bacteria transport showed significant changes under climate change conditions, and the changes in fecal coliform concentration at critical locations were explained by changes in current vector fields.

Microorganism community structure: A characterisation of agrosystems from Madeira Archipelago.

Microbial diversity profoundly influences soil ecosystem functions, making it vital to monitor community dynamics to comprehend its structure. Our study focused on six agrosystems in Madeira Archipelago, analysing bacteria, archaea, fungi and AMF through classical microbiology and molecular techniques. Despite distinct edaphoclimatic conditions and management practices, bacterial structures exhibited similarities, with Alphaproteobacteria at 18%-20%, Bacilli at 11%-18% and Clostridia at 9%-14%. The predominance of copiothrophic groups suggested that soil nutrient content was the driver of these communities. Regarding archaea, the communities changed among sites, and it was evident that agrosystems provided niches for methanogens. The Crenarchaeota varied between 15% and 29%, followed by two classes of Euryarchaeota, Methanomicrobia (17%-25%) and Methanococci (4%-32%). Fungal communities showed consistent composition at the class level but had differing diversity indices due to management practices and soil texture. Sordaryomycetes (21%-28%) and Agaricomycetes (15%-23%) were predominant. Conversely, AMF communities appeared to be also influenced by the agrosystem, with Glomus representing over 50% of the community in all agrosystems. These insights into microbial groups' susceptibilities to environmental conditions are crucial for maintaining healthy soil and predicting climate change effects on agrosystems' productivity, resilience and sustainability. Additionally, our findings enable the development of more robust prediction models for agricultural practices.

Metagenomic insights into microbial diversity and carbon cycling related genes along the elevational gradient in an arid mountain ecosystem

Understanding the elevational patterns of soil microbial carbon (C) metabolic potentials is instrumental for predicting changes in soil organic carbon (SOC) stocks in the face of climate change. However, such patterns remain uncertain in arid mountain ecosystems, where climosequences are quite different from other ecosystems. To address this gap, this study investigated the distribution determinants of microbial communities, C-cycling functional genes, and SOC fractions along the elevational gradient (1 707−3 548 m, a mean annual precipitation (MAP) range of 38 to 344 mm) of the north slope of Central Kunlun Mountain, using a metagenomic approach. Results showed that elevation significantly influenced the α-diversity (Shannon index) and composition of microbial communities as well as the C-cycling functional genes. The α-diversity of microbial taxonomy and C-cycling functional genes linearly increased with the increase in MAP along the elevational gradient. The elevational patterns of genes encoding glycoside hydrolases and glycosyltransferases (GTs) were mainly driven by electrical conductivity (EC), mean average temperature (MAT), MAP, and plant diversity. Furthermore, mineral-associated organic carbon (MAOC), particulate organic carbon (POC), and their sum generally increased with elevation. However, the MAOC/POC ratio followed a unimodal pattern, suggesting greater stability of the SOC pool in the mid-elevation regions. This unimodal pattern was likely influenced by the abundance of Actinobacteria and genes encoding GTs and carbohydrate esterases (CEs) and threshold effects of EC and MAT. In summary, our findings indicate that the distribution patterns of microbial communities and C-cycling functional genes along the elevational gradient in an arid ecosystem are distinct from those of regions with higher MAP, facilitating the prediction of climate change effects on SOC metabolism under more arid conditions. Soil salinity, plant diversity, precipitation, and temperature are the main regulatory factors of microbial C metabolism processes, and they potentially play a central role in mediating SOC pool stability.

A talajkímélő művelési rendszerek áttekintése az éghajlatváltozás hatásához alkalmazkodni képes talajok kialakítása és fenntartása céljából a szubszaharai Afrikában

Change in hydrological intensity in the form of rainfall is predicted to worsen in Southern Africa. Compounded by consequences of higher global temperatures such as heatwaves, may render many Southern African regions prone to drought. The agricultural sectors of the latter regions are faced with a greater threat. In view of such concerns, the aim of this study is to review the available literature to evaluate climate-smart agricultural practices suitable for the mitigation of climate change challenges faced by farmers. Climate-smart agriculture practices are advocated as alternatives for mitigating and managing climate change problems, increasing food production, and improving rural people's livelihoods. This review paper is aimed at the bestowed benefits of climate-smart agricultural practices on soil physical, chemical, and biological and its association to mitigate or manage predicted climate change effects in SSA.

Study of the Genetic Adaptation Mechanisms of Siberian Larch (Larix sibirica Ledeb.) Regarding Climatic Stresses Based on Dendrogenomic Analysis

Dendrogenomics is a new interdisciplinary approach that allows joint analysis of dendrological and genomic data and opens up new ways to study the temporal dynamics of forest treelines, delineate spatial and temporal population structures, decipher individual tree responses to abiotic and biotic stresses, and evaluate the adaptive genetic potential of forest tree populations. These data are needed for the prediction of climate change effects and mitigation of the negative effects. We present here an association analysis of the variation of 27 individual tree traits, including adaptive dendrophenotypes reflecting the individual responses of trees to drought stress, such as the resistance (Rt), recovery (Rc), resilience (Rs), and relative resilience (RRs) indexes measured in 136 Siberian larch trees in 5 populations in the foothills of the Batenevsky Ridge (Kuznetsk Alatau, Republic of Khakassia, Russia), with variation of 9742 SNPs genotyped using ddRADseq in the same trees. The population structure of five closely located Siberian larch populations was relatively weak (FST = 0.018). We found that the level of individual heterozygosity positively correlated with the Rc and RR indices for the five studied drought periods and partly with the Rs indices for three drought periods. It seems that higher individual heterozygosity improves the adaptive capabilities of the tree. We also discovered a significant negative relationship between individual heterozygosity and the Rt index in four out of five periods, which means that growth slows down during droughts more in trees with higher individual heterozygosity and is likely associated with energy and internal resource reallocation toward more efficient water and energy usage and optimization of larch growth during drought years. We found 371 SNPs with potentially adaptive variations significantly associated with the variation of adaptive dendrophenotypes based on all three different methods of association analysis. Among them, 26 SNPs were located in genomic regions carrying functional genes: 21 in intergenic regions and 5 in gene-coding regions. Based on the obtained results, it can be assumed that these populations of Siberian larch have relatively high standing adaptive genetic variation and adaptive potential underlying the adaptations of larch to various climatic conditions.

The Impact of Climate Change on California Rangelands and Livestock Management

On a global scale, rangelands occupy approximately half of the world’s land base surface; have a critical role in carbon sequestration and biodiversity; and support a diverse and critical economy, but at the same time, are under threat by many factors, including climate change. California rangelands, which are no exception to these aforementioned characteristics, are also unique socio-ecological systems that provide a broad range of ecosystem services and support a >$3 billion annual cattle ranching industry. However, climate change both directly and indirectly poses significant challenges to the future sustainability of California rangelands and, ultimately, the management of livestock, which has important economic implications for the state’s agricultural economy. In this study, we examined the changes in overall climate exposure and climatic water deficit (CWD), which was used as a physiological plant water stress gauge, to evaluate potential impacts of climate change on various rangeland vegetation types across California. We used two downscaled global climate models, MIROC and CNRM, under the ‘business-as-usual’ emissions scenario of RCP8.5 at a mid-century time horizon of 2040–2069 and known vegetation–climate relationships. Using the models, we predicted climate change effects using metrics and spatial scales that have management relevance and that can support climate-informed decision making for livestock managers. We found that more than 80% of the area of the rangeland vegetation types considered in this study will have higher CWD by 2040–2069. We evaluated these results with beef cattle inventory data from the U.S. Department of Agriculture by county and found that, on average, 71.6% of rangelands in the top 30 counties were projected to be highly climate-stressed. We found that current proactive and reactive ranching practices such as resting pastures, reducing herd size, and rotational grazing may need to be expanded to include additional strategies for coping with declining plant productivity.

Climate change does not equally affect temporal patterns of natural selection on reproductive timing across populations in two songbird species.

Climate change has led to changes in the strength of directional selection on seasonal timing. Understanding the causes and consequences of these changes is crucial to predict the impact of climate change. But are observed patterns in one population generalizable to others, and can spatial variation in selection be explained by environmental variation among populations? We used long-term data (1955-2022) on blue and great tits co-occurring in four locations across the Netherlands to assess inter-population variation in temporal patterns of selection on laying date. To analyse selection, we combine reproduction and adult survival into a joined fitness measure. We found distinct spatial variation in temporal patterns of selection which overall acted towards earlier laying, and which was due to selection through reproduction rather than through survival. The underlying relationships between temperature, bird and caterpillar phenology were however the same across populations, and the spatial variation in selection patterns is thus caused by spatial variation in the temperatures and other habitat characteristics to which birds and caterpillars respond. This underlines that climate change is not necessarily equally affecting populations, but that we can understand this spatial variation, which enables us to predict climate change effects on selection for other populations.

Zonal Concept: Landscape Level Parameters and Application

Abstract Zonal concept is a traditional approach in land assessment. Although its principles have been known for over a hundred years, they have not yet been thoroughly evaluated using modern analytical approaches. Assessing the empirically established parameters for characterising a zonal site, establishing threshold values of significant environmental factors, along with assessing the applicability of the zonal concept, were the goals of this study. The data analysed were obtained from the robust and objective Czech National Forest Inventory database. Regression, indirect ordination, hierarchical clustering and spatial analyses of geo-information systems were used. The study revealed seven crucial environmental factors: Slope, Slope Height, Terrain Surface Texture, Negative Openness, Multi-Resolution Index of Valley Bottom Flatness, Soil Type and Soil Subtype. A graphical model of zonal/azonal sites was constructed based on calculated threshold values of the factors. This methodic approach introduces significant geomorphological information that are otherwise problematically detectable in field mapping. We suggest it is possible to use the zonal concept as a base layer for general landscape assessment. Zonal site classification can become a part of a precise land management practice, consisting of valuable empiricism of traditional landscape ecological classifications enriched by modelling in disturbance ecology and prediction of climate change effects.

Variations and patterns of C and N stoichiometry in the first five root branch orders across 218 woody plant species.

Despite the vital role in carbon (C) sequestration and nutrient retention, variations and patterns in root C and nitrogen (N) stoichiometry of the first five root orders across woody plant species remains unclear. We compiled a dataset to explore variations and patterns of root C and N stoichiometry in the first five orders of 218 woody plant species. Across the five orders, root N concentrations were greater in deciduous, broadleaf, and arbuscular mycorrhizal species than in evergreen, coniferous species, and ectomycorrhizal association species, respectively. Contrasting trends were found for root C:N ratios. Most root branch orders showed clear latitudinal and altitudinal trends in root C and N stoichiometry. There were opposite patterns in N concentrations between latitude and altitude. Such variations were mainly driven by plant species, and climatic factors together. Our results indicate divergent C and N use strategies among plant types and convergence and divergence in the patterns of C and N stoichiometry between latitude and altitude across the first five root orders. These findings provide important data on the root economics spectrum and biogeochemical models to improve understanding and prediction of climate change effects on C and nutrient dynamics in terrestrial ecosystems.

Reproductive Phenology of the Eastern Oyster, Crassostrea virginica (Gmelin, 1791), Along a Temperate Estuarine Salinity Gradient

Low salinity can negatively affect reproduction in estuarine bivalves. The spatial and temporal extents of these effects are important to inform models of population dynamics, environmental risk assessments, restoration efforts, and predictions of climate change effects. A hypothesis of delayed gametogenesis for oysters at low salinity sites was tested relative to their higher salinity counterparts in downstream experimental cages. In 2018, the timing of gametogenesis and spawning was observed June–August for 2-year-old oysters from three distinct ancestries (native, hatchery, aquaculture), outplanted at age 1 month along the salinity gradient (3–30 psu) of a temperate estuary. A second season of data was collected in 2019 from a 3-year-old aquaculture line and mixed-age native adult oysters dredged and transplanted 1 year prior. Dermo was tested in 2019 and prevalence was 1.3% (n = 240). Gametogenesis and spawning were retarded for all ancestries at low salinity relative to higher salinity sites during July and August. The reverse pattern was found in June, with low salinity sites having more advanced gonad index than at a high salinity site. This difference in average gonad index was 2.65 vs 1.46, respectively, for the native line and 2.62 vs 2.08 for aquaculture. Low salinity seemed to not only induce earlier gametogenesis in June, but also extended the reproductive season relative to higher salinity sites. Among oyster ancestries, the aquaculture line stood out as having 30–48% lower gametogenic synchrony within sites, but only in 2018. Because the native oysters used in this study have been restricted to low salinity conditions for many generations, demonstration of their reproductive plasticity across salinities is notable and broadens the range of potential future restoration strategies.

Physics-Guided Multitask Learning for Estimating Power Generation and CO2 Emissions From Satellite Imagery

Fossil fuel combustion produces large quantities of carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), a major Greenhouse Gas (GHG), which is one of the main drivers of Climate Change. A quantitative assessment of GHG emissions is fundamental to predicting climate change effects, enforcing emission regulations, and monitoring pollution trading schemes. Unfortunately, the reporting of GHG emissions is only required in some countries, resulting in insufficient global coverage. At the same time, the transition from fossil fuels to zero carbon in order to limit climate change is at the heart of several ecological movements, hence the need for quantifying energy production, as well. In this work, we propose an end-to-end method to estimate power generation rates for fossil fuel power plants from satellite images, based on which we approximate GHG (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) emission rates. We present a physics-guided multitask deep learning approach able to simultaneously predict from a single satellite image of a power plant: (i) the pixel-area covered by plumes, (ii) the type of fired fuel, and (iii) the power generation rate. To ensure physically realistic predictions from our model we account for environmental conditions and empirical physical constraints. We then convert the predicted power generation rate into estimates for the rate at which CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is being emitted, using a fuel-dependent conversion factor. Experimental results show that our multitask learning approach improves the power generation estimation MAE by 23% compared to a single-task network trained on the same dataset. Code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> utilized in this work are publicly available.

Prediction of Climate Change Effects on Siberian Crane (Grus leucogeranus) Habitat Suitability by Using Ensemble Modeling in Asia Wetlands

The Siberian Crane (Grus leucogeranus) is the third rarest and the most endangered crane species in the world. This study aimed at predicting the effect of climate change on Siberian Crane habitat suitability of breeding range and wintering range in Asia Wetlands by using ensemble modeling under future climate scenarios before the year 2050. In this regard, we used 4 modeling methods, Surface Range Envelop (SRE), Random Forest (RF), Classification Tree Analysis (CTA) and Flexible Discriminant Analysis (FDA) to determine the relationships between the species occurrence and bioclimatic variables under the ensemble framework by using Biomod2 and R software. The results showed that the AUC values were greater than 0.9 and functioning of all models was excellent. The Temperature Seasonality and Temperature Annual Range in the breeding range and Temperature Seasonality and Mean Temperature of Coldest Quarter in the wintering range had the most important role for habitat suitability of this species and respectively 105.64% and 136.27% changes was justified in Siberian Crane habitat suitability. Under RCP2.6 and RCP8.5 climate scenario for Siberian Crane breeding and wintering range, it is possible that climate change will cause a 100% loss of suitable habitat in West Sibera, and a 25.28% loss in Iran and China by 2050. The results of this study can be used in planning and conservation of crane species.

Variability in the discharge of the Mississippi River and tributaries from 1817 to 2020.

There are conflicting predictions of climate change effects and landuse on the discharge of the Mississippi River-the largest river in North America. Are discharges becoming higher or lower, and if they did change, then when? To address these uncertainties I compiled a two-hundred-year long dataset of the annual average, minimum, and maximum discharges at five stations draining the Mississippi River watershed: at Clinton, IA, Herman, MO, St. Louis, MO, Louisville, KY, and Vicksburg, MS. A spline/Lowess analysis tested for trends and inflection points. All three discharge metrics increased, and the minimum annual discharge increased faster than either the annual maximum discharge or annual average discharge. A regression analysis of variations in average discharges from 1950 to 2020 at these five locations demonstrates correlations to the air pressure differentials represented in the North Atlantic Oscillation (NAO) Index for January, February and March. The longest data set, for the Mississippi River at Vicksburg, demonstrates a similar direct relationship with the NAO Index from 1826 to 1969. After 1969, however, the relationship between discharge and the NAO Index is insignificant even though the range of Index values overlap for the two intervals. A breakpoint and rise in discharge ca. 1970 is consistent with well-documented land cover and land use changes occurring then that resulted in reduced evapotranspiration as homogenous cropping systems were established, and a higher percent of precipitation was routed into groundwater and baseflow. The Bonnet Carré Spillway at New Orleans, LA, is being opened more frequently to reduce flood threats as the river's stage increasingly reaches the threshold for opening it. Significant water quality impairments in the coastal zone will appear or be sustained with these openings. These data may be useful for climate change assessments through modeling or synthetic assessments in combination with other data sets.

Prediction of climate change effects on the potential distribution of cycad blue butterfly (Luthrodes pandava) in South Korea

AbstractLuthrodes pandava (Lepidoptera: Lycaenidae) is a major pest of the genus Cycas (Cycadales: Cycadaceae) and mainly inhabits low latitudes of Asia. Recently, L. pandava has gradually spread to other regions. L. pandava was first recorded in South Korea in 2006 and has been continuously observed on Jeju Island ever since. Adequate management and mitigation plans are necessary to prevent further spread of L. pandava throughout Korea. Therefore, this study used a species distribution model to predict the potential distribution of L. pandava under current and future climate conditions. A weighted ensemble model that incorporated the accuracy of 10 species distribution models was used to improve the forecast certainty. The potential distribution areas of L. pandava were predicted in units of 20 years from the present to 2,100 using a climate change scenario (SSP5–8.5). Under current and future climate conditions, the ensemble model predicted that L. pandava would be extensively distributed on Jeju Island and along the southern seaboard, west coast, and east coast, inhabiting 50–70% of Korea's land area by 2,100. Additionally, many sources of food for L. pandava, excluding those in the Cycas genus, have been observed in Korea. Thus, trading ports and the inland of the Korean Peninsula must be continuously monitored for outbreaks of L. pandava. The findings of this study provide essential necessary data to help mitigate the outbreak and spread of L. pandava in Korea and their associated damage to the Cycas genus.

Predicting Climate Change Effects on the Potential Distribution of Two Invasive Cryptic Species of the Bemisia tabaci Species Complex in China.

Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED) are two invasive cryptic species of the Bemisia tabaci species complex (Hemiptera: Aleyrodidae) that cause serious damage to agricultural and horticultural crops worldwide. To explore the possible impact of climate change on their distribution, the maximum entropy (MaxEnt) model was used to predict the potential distribution ranges of MEAM1 and MED in China under current and four future climate scenarios, using shared socioeconomic pathways (SSPs), namely SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, over four time periods (2021-2040, 2041-2060, 2061-2080, and 2081-2100). The distribution ranges of MEAM1 and MED were extensive and similar in China under current climatic conditions, while their moderately and highly suitable habitat ranges differed. Under future climate scenarios, the areas of suitable habitat of different levels for MEAM1 and MED were predicted to increase to different degrees. However, the predicted expansion of suitable habitats varied between them, suggesting that these invasive cryptic species respond differently to climate change. Our results illustrate the difference in the effects of climate change on the geographical distribution of different cryptic species of B. tabaci and provide insightful information for further forecasting and managing the two invasive cryptic species in China.

Soil disturbance and invasion magnify CO2 effects on grassland productivity, reducing diversity.

Climate change, disturbance, and plant invasion threaten grassland ecosystems, but their combined and interactive effects are poorly understood. Here, we examine how the combination of disturbance and plant invasion influences the sensitivity of mixed-grass prairie to elevated carbon dioxide (eCO2 ) and warming. We established subplots of intact prairie and disturbed/invaded prairie within a free-air CO2 enrichment (to 600 ppmv) by infrared warming (+1.5°C day, 3°C night) experiment and followed plant and soil responses for 5 years. Elevated CO2 initially led to moderate increases in biomass and plant diversity in both intact and disturbed/invaded prairie, but these effects shifted due to strong eCO2 responses of the invasive forb Centaurea diffusa. In the final 3 years, biomass responses to eCO2 in disturbed/invaded prairie were 10 times as large as those in intact prairie (+186% vs. +18%), resulting in reduced rather than increased plant diversity (-17% vs. +10%). At the same time, warming interacted with disturbance/invasion and year, reducing the rate of topsoil carbon recovery following disturbance. The strength of these interactions demonstrates the need to incorporate disturbance into predictions of climate change effects. In contrast to expectations from studies in intact ecosystems, eCO2 may threaten plant diversity in ecosystems subject to soil disturbance and invasion.

Testing Crop Models to Predict Climate Change Effects

Testing Crop Models to Predict Climate Change Effects

Associations and burdens of relative humidity with cause-specific mortality in three Chinese cities.

This study aimed to investigate the association between relative humidity (RH) and various cause of mortality, and then quantify the RH-related mortality fraction of low and high RH under the assumption that causal effects exist. Daily cause-specific mortality counts from 2008 to 2011, and contemporaneous meteorological data in three Chinese cities were collected. Distributed lag nonlinear models were adopted to quantify the nonlinear and delayed effects of RH on mortality risk. Low and high RH were defined as RH lower or higher than the minimum mortality risk RH (MMRH), respectively. Corresponding RH-related mortality fractions were calculated in the explanatory analysis. From the three cities, 736,301 deaths were collected. RH (mean ± standard deviation) were 50.9 ± 20.0 for Beijing, 75.5 ± 8.6 for Chengdu, and 70.8 ± 14.6 for Nanjing. We found that low RH in Beijing and high RH (about 80-90%) in Chengdu was associated with increased all-cause mortality risk. Both low and high RH may increase the CVD mortality risk in Beijing. Both low and high (about 80-85%) RH may increase the COPD mortality risk in Chengdu. Low RH (about < 45%) was associated with increased diabetes mortality risk in Nanjing. Effects of extreme low and extreme high RH were delayed in these cities, except that extreme low effects on COPD mortality appeared immediately in Chengdu. The effects of extreme low RH are higher than that of the extreme high RH in Beijing and Nanjing, while contrary in Chengdu. Finally, under the causal effect assumption, 6.80% (95% eCI: 2.90, 10.73) all-cause mortality and 12.48% (95% eCI: 7.17, 16.80) CVD deaths in Beijing, 9.59% (95% eCI: 1.38, 16.88) COPD deaths in Chengdu, and 23.79% (95% eCI: 0.92, 387.93) diabetes mortality in Nanjing were attributable to RH. Our study provided insights into RH-mortality risk, helped draw relative intervention policies, and is also significant for future predictions of climate change effects under different scenarios.

The other side of tropical forest drought: do shallow water table regions of Amazonia act as large-scale hydrological refugia from drought?

Tropical forest function is of global significance to climate change responses, and critically determined by water availability patterns. Groundwater is tightly related to soil water through the water table depth (WT), but historically neglected in ecological studies. Shallow WT forests (WT < 5 m) are underrepresented in forest research networks and absent in eddy flux measurements, although they represent c. 50% of the Amazon and are expected to respond differently to global-change-related droughts. We review WT patterns and consequences for plants, emerging results, and advance a conceptual model integrating environment and trait distributions to predict climate change effects. Shallow WT forests have a distinct species composition, with more resource-acquisitive and hydrologically vulnerable trees, shorter canopies and lower biomass than deep WT forests. During 'normal' climatic years, shallow WT forests have higher mortality and lower productivity than deep WT forests, but during moderate droughts mortality is buffered and productivity increases. However, during severe drought, shallow WT forests may be more sensitive due to shallow roots and drought-intolerant traits. Our evidence supports the hypothesis of neglected shallow WT forests being resilient to moderate drought, challenging the prevailing view of widespread negative effects of climate change on Amazonian forests that ignores WT gradients, but predicts they could collapse under very strong droughts.