Predictions Of Future Climate Change Research Articles

Predicted changes in distribution and grazing value of Stipa-based plant communities across the Eurasian steppe

The Eurasian steppe is one of the world's largest continuous areas of grassland and has an important role in supporting livestock grazing, the most ubiquitous land use on Earth. However, the Eurasian steppe is under threat, from irrational grazing utilization, climate change, and resource exploitation. We used an ensemble modeling approach to predict the current and future distribution of Stipa-dominated plant communities in three important steppe subregions; the Tibetan Alpine, Central Asian, and Black Sea-Kazakhstan subregions. We combined this with an assessment of the grazing value of 22 Stipa species, the dominant grassland species in the area, to predict how grazing value might change under future climate change predictions. We found that the effects of changing climates on grazing values differed across the three subregions. Grazing values increased in the Tibetan alpine steppe and to a lesser extent in Central Asia, but there were few changes in the Black Sea-Kazakhstan subregion. The response of different species to changing climates varied with environmental variables. Finally, our trait-based assessment of Stipa species revealed variations in grazing value, and this had major effects on the overall grazing value of the region. Our results reinforce the importance of trait-based characteristics of steppe plant species, how these traits affect grazing value, and how grazing values will change across different areas of the Eurasian steppe. Our work provides valuable insights into how different species will respond to changing climates and grazing, with important implications for sustainable management of different areas of the vast Eurasian steppe ecosystem.

Tracking δ13C and δ18O fluctuations uncovers stable modes and key patterns of paleoclimate

The examination of fluctuations in the correlations between δ13C and δ18O is of significant importance for the reconstruction of the Earth’s climate history. A key challenge in paleoclimatology is finding a suitable method to represent the correlated fluctuation system between δ13C and δ18O. The method must be able to handle data sets with missing or inaccurate values, while still retaining the full range of dynamic information about the system. The non-linear and complex correlations between δ13C and δ18O poses a challenge in developing reliable and interpretable approaches. The transition network, which involves embedding the δ13C and δ18O sequence into the network using phase space reconstruction, is a coarse-grained based approach. This approach is well-suited to nonlinear, complex dynamic systems, and is particularly adept at emerging knowledge from low-quality datasets. We have effectively represented the fluctuations in the correlation between δ13C and δ18O since 66 million years ago (Ma) using a system of complex network. This system, which has topological dynamical structures, is able to uncover the stable modes and key patterns in Cenozoic climate dynamics. Our findings could help to improve climate models and predictions of future climate change.

Assessment and Prediction of Future Climate Change in the Kaidu River Basin of Xinjiang under Shared Socioeconomic Pathway Scenarios

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China’s largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people’s daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase.

Projection and Uncertainty Analysis of Future Temperature Change over the Yarlung Tsangpo-Brahmaputra River Basin Based on CMIP6

Accurate predictions of future climate change are significant to both human social production and development. Accordingly, the changes in the daily maximum (Tmax) and minimum temperatures (Tmin) in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB), along with its three sub-regions (Tibetan Plateau—TP, Himalayan Belt—HB, and Floodplain—FP) were evaluated here using the Bayesian model average (BMA) results from nine climate models in the CMIP6 under four future scenarios, and the corresponding uncertainty of the projected results was analyzed. The results showed the following: (1) The BMA can simulate the Tmax and Tmin of the YBRB well. (2) Future Tmax and Tmin over the YBRB exhibited an overall fluctuating upward trend. Even under the most ideal sustainable development scenario examined (SSP126), the average Tmax (Tmin) over the YBRB was projected to increase by 3.53 (3.38) °C by the end of this century. (3) Although the future changes in the YBRB are predicted to fall below the global average, the future temperature difference in the YBRB will increase further. (4) The uncertainty increased with prediction time, while spatially, the regions with the uncertainty were the TP > HB > FP. These findings can provide a reference for the YBRB climate change adaptation strategies.

Sensitivity analysis of magnetic susceptibility under different climatic conditions during the late Pleistocene-Holocene period

Understanding the climate change experienced by the Loess Plateau in China from the end of the late Pleistocene to the Holocene is helpful for exploring global palaeoclimate change and predicting future climate change. However, sensitivity studies on palaeoclimate proxies under different geological periods and different climatic conditions are still lacking, which affects the accurate identification of palaeoclimate changes and the precision with which future climate change predictions can be made. Three soil profiles in Luochuan County were sampled, and the magnetic susceptibility (χlf), frequency-dependent susceptibility (χfd%), clay content (<0.005 mm), clay/coarse silt ratio, calcium carbonate content (CaCO3), and total content of organic carbon (TOC) of the profiles were analysed. The results showed that the climate changed from dry and cold to warm and wet to dry and cool since the end of the late Pleistocene. The precipitation (MAP) and temperature (MAT) since the end of the late Pleistocene were reconstructed by using the magnetic susceptibility of different soil particle size fractions (>2 mm, 2–1 mm, 1–0.5 mm, 0.5–0.25 mm, <0.25 mm), and the results were compared with those from other researchers. The results showed that the magnetic susceptibility of the fine fraction (<0.25 mm) was more sensitive at 0–3200 cal. yr. BP, 8500–13,000 cal. yr. BP, and 15,000–25,000 cal. yr. BP, while that of the coarse fraction (>0.25 mm) was more sensitive at 3200–8500 cal. yr. BP. Based on this finding, the precipitation and temperature of this period were reconstructed. This study provides a research method for selecting suitable climate proxies to describe climate changes in different geological periods to accurately conduct quantitative or semiquantitative assessments of climate change.

Dysregulation of microRNAs may contribute to neurosensory impairment in Arctic cod (Boreogadus saida) following CO2 exposure

MicroRNAs (miRNAs) are epigenetic markers with a key role in post-transcriptional gene regulation. Several studies have described the dysregulation of miRNAs in temperature and hypoxic stress responses of marine organisms, but their role in the response to acidification conditions has remained relatively underexplored. We investigated the differential expression of miRNAs in whole brain tissue of Arctic cod (Boregogadus saida) exposed to elevated aqueous CO2 levels representative of future climate change predictions. We detected the expression of 17 miRNAs of interest that are either directly or indirectly associated with reduced auditory performance; 12 of the 17 miRNAs showed significant differential expression in high treatment vs. low (control) aqueous CO2 conditions. Target gene predictions indicated that these miRNAs are likely involved with inner ear maintenance, hair cell degradation, age-related hearing loss, neural inflammation, and injury. The highest differential expression was observed in mir-135b, which is linked with increased neural inflammation and injury that may be associated with neurosensory dysfunction. Collectively, these results elucidate the contributions of miRNA mechanisms underlying CO2-induced sensory deficits in fishes facing abiotic environmental change and suggest strong potential for this approach to yield novel insights into the mechanistic effects of climate change on marine organisms.

Developmental temperature alters the thermal sensitivity of courtship activity and signal–preference relationships, but not mating rates

Mating behaviors are sensitive to novel or stressful thermal conditions, particularly for ectothermic organisms. An organism's sensitivity to temperature, which may manifest in altered mating outcomes, can be shaped in part by temperatures experienced during development. Here, we tested how developmental temperature shapes the expression of adult mating-related behaviors across different ambient conditions, with a focus on courtship behavior, mating rates, and mating signals and preferences. To do so, we reared treehoppers under two temperature regimes and then tested the expression of male and female mating behaviors across a range of ambient temperatures. We found that developmental temperature affects the thermal sensitivity of courtship behavior and mating signals for males. However, developmental temperature did not affect the thermal sensitivity of courtship or mate preferences in females. This sex-specific plasticity did not alter the likelihood of mating across ambient temperatures, but it did disrupt how closely mating signals and preferences matched each other at higher ambient temperatures. As a result, developmental temperature could alter sexual selection through signal-preference de-coupling. We further discuss how adult age may drive sex-specific results, and the potential for mismatches between developmental and mating thermal environments under future climate change predictions.

Sustainability Consequences of Making Land Change Decisions Based on Current Climatology in the Brazilian Cerrados

Brazil is one of the largest suppliers of commodities in the world, partly due to the agricultural expansion in the Brazilian savannas (also known as Cerrado) that began in the 1970s. However, as areas with better soil and climate for agriculture become scarce, farmers have been advancing to the ecotone between the savanna and xeric shrubland, where precipitation is less reliable for rainfed agriculture. The expected increase in temperature will lead to extended drought periods, with negative consequences for surface and groundwater resources. This study explores the hazards associated with making land-use decisions based on current climatology in regions where projected increases in temperature and reductions in water availability are anticipated to pose significant challenges to rainfed agriculture in the Brazilian Cerrado biome. We modeled future farmland expansion and how that matches with future climate change predictions (2016–2046). According to our estimates, at least 129 thousand km2 of cropland and 418 thousand km2 of pastures will be added in places with projected higher annual temperatures ranging from 26–30 °C. This is equivalent to ~60% of the current agricultural areas, and a novel agro-climatology will emerge for the Cerrado biome. Therefore, we discuss the agro-environmental policies that are pushing and pulling farmland expansion in the Cerrado. For instance, payments for environmental services could support the conservation of native vegetation on private land in regions with the highest temperature increases and deforestation risks. Moreover, in areas with expected reduced water yields, such as in the western Cerrado, the protection of riparian vegetation and strict regulation of water use could mitigate future risks to agriculture.

Authigenic beryllium isotopes reveal fluctuations in the East Asian monsoon over the past two millennia

Reconstructing climate variability beyond human records allows reconstruction of past and prediction of future climate change. However, a lack of high-resolution paleoclimate archives and effective climate proxies has hitherto limited reconstruction of the spatiotemporal evolution of the East Asian summer monsoon (EASM). In this study, the ratio of the meteoric cosmogenic 10Be (10Bemet) to silicate-weathered 9Be in annually laminated sediments from Maar Lake Xiaolongwan (NE China) was utilized to reconstruct EASM precipitation over the past two millennia. We find two apparent humid periods during the transitions of the Dark Age Cold Period–Medieval Climate Anomaly (DACP–MCA, ∼700 CE) and MCA–Little Ice Age (MCA–LIA, ∼1250 CE). The interval of these two humid periods is consistent with previous proposition of ∼500-yr oscillations of EASM in Northeast Asia. Although there are some phase variabilities, the pattern of temporal variation from 400 to 1600 CE are synchronous with other regional climate records from the EASM region. However, the spatial pattern shows an anti-phase synchronization between the EASM and the transition zone between the EASM and the Indian summer monsoon (ISM), revealing a “dry northeastern and wet southwestern” mode during the MCA. The climate change fluctuations derived here from 10Bemet/9Be in maar lake sediments are more straightforward than Chinese cave δ18O records and global temperature anomalies, providing a unique climate proxy for detecting variations of paleo-precipitation flux in specific regions. The overall slight weakening of EASM is consistent with a long-term cooling trend, which is likely regulated by solar irradiance. The apparent decrease of EASM during the transition from MCA to LIA is interpreted as the main control of explosive volcanic eruptions. The internal fluctuations and the spatiotemporal variability of precipitation are likely associated with the integrated effect of ISM and El Niño-Southern Oscillation.

Seasonal Influence of Biodiversity on Soil Respiration in a Temperate Forest

Soil respiration in forests contributes to significant carbon dioxide emissions from terrestrial ecosystems but it varies both spatially and seasonally. Both abiotic and biotic factors influence soil respiration but their relative contribution to spatial and seasonal variability remains poorly understood, which leads to uncertainty in models of global C cycling and predictions of future climate change. Here, we hypothesize that tree diversity, soil diversity, and soil properties contribute to local-scale variability of soil respiration but their relative importance changes in different seasons. To test our hypothesis, we conducted seasonal soil respiration measurements along a local-scale environmental gradient in a temperate forest in Northeast China, analyzed spatial variability of soil respiration and tested the relationships between soil respiration and a variety of abiotic and biotic factors including topography, soil chemical properties, and plant and soil diversity. We found that soil respiration varied substantially across the study site, with spatial coefficients of variation (CV) of 29.1%, 27.3% and 30.8% in spring, summer, and autumn, respectively. Soil respiration was consistently lower at high soil water content, but the influence of other factors was seasonal. In spring, soil respiration increased with tree diversity and biomass but decreased with soil fungal diversity. In summer, soil respiration increased with soil temperature, whereas in autumn, soil respiration increased with tree diversity but decreased with increasing soil nutrient content. However, soil nutrient content indirectly enhanced soil respiration via its effect on tree diversity across seasons, and forest stand structure indirectly enhanced soil respiration via tree diversity in spring. Our results highlight that substantial differences in soil respiration at local scales was jointly explained by soil properties (soil water content and soil nutrients), tree diversity, and soil fungal diversity but the relative importance of these drivers varied seasonally in our temperate forest.

Improvement of swelling soil by using lime sludge and sodium chloride

Construction on swelling soil is a major challenge to geotechnical engineers all around the world. This type of soil has a quite complex behavior as they undergo the category of problematic soils. The seasonal variations in the climate cause swelling and shrinkage, affecting soil’s water retention. The prediction of future climate change will worsen this problem, increasing the importance of the matter. The attention has been directed to experimental modeling and field investigations. In previous references, this problem was targeted by adding additives which are relatively expensive and non-environment friendly. In this study, waste and recycled materials are used in the lab to enhance the undesirable expansive soil characteristics to keep the environment clean and sustainable and reduce pollution. The additives are selected not only for its availability and cheapness but also to reduce the damage of environment due to disposal of waste. Therefore, the aim of study is to observe the behavior of expansive soils when mixing with various percentages of sodium chloride (NaCl) and lime sludge (LS). The percentages used in this work to determine the swelling properties of expansive soil were 1%, 2%, and 3% NaCl by weight with 3%, 6%, 9%, 10%, 12%, 15%, and 20% of LS by weight. The experimental tests held in the laboratory were liquid limit (LL), plastic limit (PL), free swell (FS), plasticity index (PI), swelling pressure, swelling and infiltration test, and physical models. The results show that by increasing the percentage of NaCl and LS, a significant improvement is obvious to the swelling properties of soils. Added to that, a decrease in PI of the swelling soil is clearly observed which reflects a decrease in swelling pressure and free swell of the swelling soil.

Temporally corrected long-term satellite solar-induced fluorescence leads to improved estimation of global trends in vegetation photosynthesis during 1995–2018

Terrestrial gross primary production (GPP) is one of the largest flux of carbon cycle and also introduces the largest uncertainties of the estimated global carbon budget. During the past decades, the emerging remotely-sensed solar-induced chlorophyll fluorescence (SIF) data have been rapidly developed as the novel proxy of GPP. Though satellite SIF datasets have been widely used for plenty of climatic and ecological studies, a lack of consistent long-term satellite SIF data has hindered its use for long-term assessments due to their temporal inconsistency induced by sensor degradation. Here, based on the time series of SIF data at the Sahara Desert as the benchmark, we present a practical approach to correct the temporal inconsistency of different satellites (GOME, SCIAMACHY and GOME-2) far-red SIF data. The temporally corrected SIF datasets were then spatially downscaled to a finer resolution of 0.05° with the light-use-efficiency (LUE) method. Then, the individual time series of SIF data from the three satellite sensors were fused together to generate a continuously long-term consistent global SIF dataset from July 1995 to December 2018 (termed as LT_SIFc*). We further validated and compared the spatial and temporal characteristics of LT_SIFc* with the ground-based long-term GPP observations from 15 flux sites, modelled global long-term GPP time series, and high-spatial resolution SIF from TROPOMI and OCO-2, respectively. Our results show that the LT_SIFc* can capture the temporal trends and inter-annual variations for both site- and global-GPP, and also the global spatial distributions of SIF at the spatial resolution of 0.05° validated with TROPOMI SIF (R2 = 0.76, RMSE = 0.11 mW m−2 nm−1 sr-1). Our temporally corrected and spatially downscaled global long-term SIF product has the potential for the better estimations of global spatial–temporal variations in GPP, and also could be helpful for the improvement in the predictions of future land carbon storage and climate change.

Long-term projection of future climate change over the twenty-first century in the Sahara region in Africa under four Shared Socio-Economic Pathways scenarios.

Climate change affects air quality and people's health. Therefore, accurate prediction of future climate change is of great significance for human beings to better adapt and mitigate climate change. Using the projection simulation dataset of the CMIP6 multi-model ensemble, the future climate change in the Sahara region under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) is analyzed. The results show that annual and seasonal average surface air temperature in the Sahara region will continue to rise throughout the twenty-first century relative to the baseline period 1995-2014 if greenhouse gas (GHG) concentrations continue increasing. Under the four SSPs scenarios, the warming in the Sahara region will be more pronounced than in the whole world through the twenty-first century. The annual maximum temperature (TX), the annual minimum temperature (TN), the annual count of days with maximum temperature above 35°C (TX 35), and the annual count of days with maximum temperature above 40°C (TX 40) in the Sahara region will continue to increase until the end of the twenty-first century under the four scenarios. The results of climate change prediction can provide scientific reference for climate policy-making.

Interactive effects of warming and managements on carbon fluxes in grasslands: A global meta-analysis

The prediction of future carbon(C) dynamics of grassland ecosystems requires knowledge of how warming and management practices (e.g., fertilization, grazing, and mowing) interact to affect C fluxes. In this study, we synthesized experimental data extracted from 350 observations from 64 publications to quantify the direction and magnitude of individual and interactive effects between climatic warming and grassland management practices on seven variables related to C fluxes. Warming significantly enhanced gross primary productivity (GPP) and ecosystem respiration (ER) by 9.81% and 16.43%, respectively. Similarly, fertilization significantly increased GPP, net primary productivity (NPP), aboveground net primary productivity (ANPP), ER, and net ecosystem exchange (NEE) by 29.51%, 24.79%, 33.52%, 21.81%, and 33.89%, respectively. In contrast, grazing and mowing reduced ANPP by 19.95% and 22.83%, respectively. Furthermore, the combination of warming and fertilization significantly increased GPP and ANPP by 38.09% and 22.89%, respectively. More importantly, the results showed that additive interactions dominated the response of most of the studied variables, while synergistic interactions were found only for ANPP and ER to warming and fertilization. The responses of C fluxes to warming and management were strongly dependent on the environmental factors, experimental duration, and grassland types. Our meta-analysis highlights the importance of the interactions between warming and management on ecosystem C exchange. Therefore, incorporating these key interactions into future prediction models should enhance the accuracy of prediction of future climate change impacts on grassland ecosystems and inform the development of sustainable grassland management strategies.

Basin-Scale Sea Level Budget from Satellite Altimetry, Satellite Gravimetry, and Argo Data over 2005 to 2019

Monitoring sea level changes and exploring their causes are of great significance for future climate change predictions and the sustainable development of mankind. This study uses multiple sets of satellite altimetry, satellite gravity, and ocean temperature and salinity data to study the basin-scale sea level budget (SLB) from 2005 to 2019. The basin-scale sea level rises significantly at a rate of 2.48–4.31 mm/yr, for which the ocean mass component is a main and stable contributing factor, with a rate of 1.77–2.39 mm/yr, while the steric component explains a ~1 mm/yr sea level rise in most ocean basins, except for the Southern Ocean. Due to the drift in Argo salinity since 2016, the residuals of basin-scale SLB are significant from 2016 to 2019. The worst-affected ocean is the Atlantic Ocean, where the SLB is no longer closed from 2005 to 2019. If halosteric sea level change trends from 2005 to 2015 are used to revise salinity data after 2016, the SLB on the ocean basin scale can be kept closed. However, the SLB on the global scale is still not closed and requires further study. Therefore, we recommend that Argo salinity products after 2016 should be used with caution.

Mesoscale simulation of typhoon-generated storm surge: methodology and Shanghai case study

Abstract. The increasing vulnerability of coastal megacities to storm surge inundation means both infrastructure and populations are subject to significant threat. Planning for further urban development should include consideration of the changing circumstances in coastal cities to ensure a sustainable future. A sustainable urban plan relies on sound preparedness and prediction of future climate change and multiple natural hazards. In light of these needs for urban planning, this paper develops a general method to simulate typhoon-generated storm surge at the mesoscale (1–100 km in length). Mesoscale simulation provides a general approach with reasonable accuracy that could be implemented for planning purposes while having a relatively low computation resource requirement. The case study of Shanghai was chosen to implement this method. The mesoscale simulations of two historical typhoons not only provides realistic typhoon storm surge inundation results at the city level but is also suitable for implementing a large amount of simulations for future scenario studies. The method will be generally applicable to all coastal cities around the world to examine the effect of future climate change on typhoon-generated storm surge even when historical observation data are inadequate or not available.

Nonlinear Mixed Effect Model Used in a Simulation of the Impact of Climate Change on Height Growth of Cyclobalanopsis glauca

Localized climate is sensitive to terrain, underlying surface material, building distribution, green coverage and CO2 emissions. The Regional Climate Model (RegCM) was used to make a statistical detailed analysis of the climate change data in a specific study area to obtain fine-scale distribution of climatic elements data over time. The effects of climate change factors on height growth trends of a climate-sensitive tree species (Cyclobalanopsis glauca) were simulated based on historical climate base line data (1961–2010) and future climate change (2010–2100) predictions. Cyclobalanopsis glauca growth trends were simulated and analyzed by using a nonlinear mixed effect model (NLME). The results showed that under the RCP8.5 emissions scenario, the growth promotion effect on the height growth of Cyclobalanopsis glauca will be obvious. Under RCP4.5 and RCP2.6 emissions scenarios, although the inhibition intensity is not exactly the same, height growth will still be inhibited to a certain extent, which may lead to the gradual extinction of this species, affecting the composition of dominant tree species in the study area. The results indirectly reflect the impact of climate change on tree species diversity in the future.

Spatiotemporal Variation of Osmanthus fragrans Phenology in China in Response to Climate Change From 1973 to 1996.

Climate change greatly affects spring and autumn plant phenology around the world consequently, and significantly impacts ecosystem function and the social economy. However, autumn plant phenology, especially autumn flowering phenology, has not been studied so far. In this study, we examined the spatiotemporal pattern of Osmanthus fragrans phenology, including both leaf phenology (the date of bud-bust, BBD; first leaf unfolding, FLD; and 50% of leaf unfolding, 50 LD) and flowering phenology (the date of first flowering, FFD; peak of flowering, PFD; and end of flowering, EFD). Stepwise multiple linear regressions were employed to analyze the relationships between phenophases and climatic factors in the long term phenological data collected by the Chinese Phenological Observation Network from 1973 to 1996. The results showed that spring leaf phenophases and autumn flowering phenophases were strongly affected by latitude. BBD, FLD, and 50LD of O. fragrans were delayed by 3.98, 3.93, and 4.40 days as per degree of latitude increased, while FFD, PFD and EFD in O. fragrans advanced 3.11, 3.26, and 2.99 days, respectively. During the entire study period, BBD was significantly delayed across the region, whereas no significant trends were observed either in FLD or 50LD. Notably, all flowering phenophases of O. fragrans were delayed. Both leaf and flowering phenophases negatively correlated with growing degree-days (GDD) and cold degree-days (CDD), respectively. BBD and FLD were negatively correlated with total annual precipitation. In addition to the effects of climate on autumn flowering phenology, we found that earlier spring leaf phenophases led to delayed autumn flowering phenophases. Our results suggest that future climate change and global warming might delay the phenological sequence of O. fragrans. Our findings also advanced the flowering mechanism study of autumn flowering plants, and facilitated the accurate prediction of future phenology and climate change.

Population niche breadth and individual trophic specialisation of fish along a climate-productivity gradient

A mechanistic understanding of how environmental change affects trophic ecology of fish at the individual and population level remains elusive. To address this, we conducted a space-for-time approach incorporating environmental gradients (temperature, precipitation and nutrients), lake morphometry (visibility, depth and area), fish communities (richness, competition and predation), prey availability (richness and density) and feeding (population niche breadth and individual trophic specialisation) for 15 native fish taxa belonging to different thermal guilds from 35 subarctic lakes along a marked climate-productivity gradient corresponding to future climate change predictions. We revealed significant and contrasting responses from two generalist species that are abundant and widely distributed in the region. The cold-water adapted European whitefish (Coregonus lavaretus) reduced individual specialisation in warmer and more productive lakes. Conversely, the cool-water adapted Eurasian perch (Perca fluviatilis) showed increased levels of individual specialism along climate-productivity gradient. Although whitefish and perch differed in the way they consumed prey along the climate-productivity gradient, they both switched from consumption of zooplankton in cooler, less productive lakes, to macrozoobenthos in warmer, more productive lakes. Species with specialist benthic or pelagic feeding did not show significant changes in trophic ecology along the gradient. We conclude that generalist consumers, such as warmer adapted perch, have clear advantages over colder and clear-water specialised species or morphs through their capacity to undergo reciprocal benthic–pelagic switches in feeding associated with environmental change. The capacity to show trophic flexibility in warmer and more productive lakes is likely a key trait for species dominance in future communities of high latitudes under climate change.

The spatial distribution of the woodland communities and their associated environmental drivers in the Golden Gate Highlands National Park, South Africa

The extreme variability in the topography, altitude and climatic conditions in the temperate Grassland Mountains of Southern Africa is associated with the complex mosaic of grassland communities with pockets of woodland patches. Understanding the relationships between plant communities and environmental parameters is essential in biodiversity conservation, especially for current and future climate change predictions. This article focused on the spatial distribution of woodland communities and their associated environmental drivers in the Golden Gate Highlands (GGHNP) National Park in South Africa. A generalized linear model (GLM) assuming a binomial distribution, was used to determine the optimal environmental variables influencing the spatial distribution of the woodland communities. The Coefficient of Variation (CV) was relatively higher for the topographic ruggedness index (68.78%), topographic roughness index (68.03), aspect (60.04%), coarse fragments (37.46%) and the topographic wetness index (31.33) whereas soil pH, bulk density, sandy and clay contents had relatively less variation (2.39%, 3.23%, 7.56% and 8.46% respectively). In determining the optimal number of environmental variables influencing the spatial distribution of woodland communities, roughness index, topographic wetness index, soil coarse fragments, soil organic carbon, soil cation exchange capacity and remote-sensing based vegetation condition index were significant (p 0.05) and positively correlated with the woodland communities. Soil nitrogen, clay content, soil pH, fire and elevation were also significant but negatively correlated with the woodland communities. The area under the curve (AUC) of the receiver operating characteristics (ROC) was 0.81. This was indicative of a Parsimonious Model with explanatory predictive power for determination of optimal environmental variables in vegetation ecology.Conservation implications: The isolated woodland communities are sources of floristic diversity and important biogeographical links between larger forest areas in the wider Drakensberg region. They provide suitable habitats for a larger number of forest species and harbour some of the endemic tree species of South Africa. They also provide watershed protection and other important ecosystem services. Understanding the drivers influencing the spatial distribution and persistence of these woodland communities is therefore key to conservation planning in the area.