Climatic Limits Research Articles

The effects of biophysical and anthropogenic factors on the recent upper forest‐cover upward shift in the Romanian Carpathians

AbstractAimTo estimate the effects of environmental and human drivers on upper forest‐cover upward shift over the period 1945–2018.LocationRomanian Carpathians.MethodsUsing historical topographic maps and high‐resolution Sentinel‐2A satellite images, we delineated the location of the upper forest‐cover limit in 1945 and 2018 to obtain a binary raster with the “forest gains” and “no‐forest gains” for 1945–2018. The maps were overlapped over 12 independent variables to estimate how they affected the recent upper forest‐cover upward shift, and to predict its probability of expansion in the near future. To achieve this, logistic regression models were applied and then integrated into the GIS spatial analysis.ResultsForest gains of almost 35,000 ha have been detected at the upper forest limit in the study sites, leading to an average upward movement of 30 m (0.5 m per year), mainly at the altitudinal bands between 1,400 and 1,800 m a.s.l. (72%); only 9.7% of the gain was detected above the estimated potential climatic limit. The process was significantly inversely related to elevation (−17.3 ≤ β ≤ −5.7, p < 0.01), slope aspect (−1.5 ≤ β ≤ −0.4, p < 0.01), sheepfold numbers (−18.1 ≤ β ≤ −0.7, p < 0.01), and sheep (−18.3 ≤ β ≤ −0.6, p < 0.01) and cattle (−16.4 ≤ β ≤ −0.8, p < 0.01) populations. Strong direct effects were found for the slope angle (1.3 ≤ β ≤ 10.6, p < 0.01) and distance to sheepfolds (0.5 ≤ β ≤ 12.8, p < 0.01). A low contribution was indicated for the trend in mean air temperature (−1.1 ≤ β ≤ 4.3, p < 0.01), growing season length (−2.1 ≤ β ≤ 5.1, p < 0.01) and protective measures (−1.1 ≤ β ≤ 2.4, p < 0.01, for national parks; −1.8 ≤ β ≤ 1.2, p < 0.01, for natural parks).ConclusionsThe analysis demonstrated that decreased land‐use intensity and, in particular, declining traditional practices were the foremost mechanisms of the recent upper forest‐cover upward shift, whereas the effect of climate warming remains not so obvious.

ТРАНСФОРМАЦІЯ УЯВЛЕНЬ ПРО АРХІТЕКТУРНУ ФОРМУ В КОНТЕКСТІ РОЗВИТКУ МЕТАВСЕСВІТНОГО СЕРЕДОВИЩА

The article examines some problems of ideas transformation on architectural form in the conditions of metaverse space. Some factors that, in this regard, affect the change of design priorities is considered. The connection of the emerging new architectural language with the worldview of deconstructivism and postmodernism is traced. The purpose of the article is to trace the transformation of ideas about architectural form in the context of the development of the metaverse space. On the basis of the analysis of projects created by well-known architectural firms for the metaverse environment, two content types of data related to form-creation were selected; they can be conventionally called perceptual and evolutional. Perceptual changes are understood as changes in morphological characteristics that are associated with the disappearance of climatic and gravitational conditions limitations that determined the key parameters of architecture in the real environment. Evolutional type refers to the connection of metaversal morphology with the historical ontology of architectural form in general. Within the framework of the perceptual type of data, the following form regularities are highlighted: a) significant inertia of traditional gravitational statics; b) experiments with the absence of fencing and insurance structures; c) experiments with the illumination of space, which can simultaneously exist in different light modes; d)lack of binding of material imitation to its physical properties; e) tendency to unique attractiveness. Within the limits of the evolutional type, premonitions regarding metaversal form in such tendencies as postmodernism and deconstructivism are considered. In the context of the first, such features as the relativization of time and the understanding of space as a set of events oriented to the communicative effect are highlighted. In the context of the second – attention to astatic and agravating masses, individualization of choice and perception of architecture is emphasized not only as a language where the designer is the “speaker” and the user is the interpreter, but as an equal communication of all participants. The hypothesis is expressed that in the future the main feature of the space of metaverses will be the ability to cause a communicative response, that is, to be the subject of discussion and reflection of its inhabitants. Probably, gradually, this feature will constitute the category of value of metaverse architecture – both artistic and economic.

Green algae monitoring via ground-based GNSS-R observations

Outbreaks of harmful algal blooms (HABs) exhibit high frequency, large range and damage aggravation characteristics, but existing monitoring methods, such as artificial and optical near-infrared remote sensing, cannot accommodate these characteristics. We propose a new method for monitoring green algae using Global Navigation Satellite System Reflectometry (GNSS-R) observations. The basic principle states that changes in the seawater dielectric constant and sea surface roughness due to the emergence of green algae lead to an increase in brightness temperature, which can be inverted based on the reflection time delay waveform. Shipboard reflection waveform data collected during an Enteromorpha prolifera outbreak in the Qingdao sea area were used for model development and validation of the detection and estimation performance. The results indicated that the root mean square error of GNSS-R-based inversion of the green algae density was 6.74%, indicating the potential of GNSS-R technology for rapid preliminary monitoring of green algae. Moreover, the advantages of a low cost, short return time and no climatic limitations support GNSS-R technology as a new and efficient means of green algae monitoring.

Nutrient-doped synthetic silicates for enhanced weathering, remineralization and fertilization on agricultural lands of global cold regions- A perspective on the research ahead.

There is now a dire demand for negative emissions technologies (which sequester CO2 from the atmosphere) that can be rapidly deployed, are scalable, and are demonstrably safe and effective. Enhanced weathering of silicate minerals has demonstrated a significant potential for CO2 capture and sequestration by the formation of pedogenic carbonates in soils, subsoils, and sediments. This technique has also been shown to deliver fruitful results in terms of improving soil health, and in turn plant health, through remineralization. The silicate minerals that possess the highest weathering rates (e.g., wollastonite), are relatively rare in nature, whereas the abundant ones (e.g., anorthite and forsterite) have a slower pace of weathering, especially in colder and drier climates such as found in the extensive agricultural lands of Western Canada and the Western United States. Herein, we offer a perspective on the opportunities for computational studies targeting atomic-scale interaction of CO2 with silicates and synthesis of fast-weathering silicates (such as larnite and bredigite), whose composition can be tuned to also support soil fertilization and remineralization, and whose production must be integrated with green and carbon-neutral technologies to ensure net-negative life cycle emissions.

Douglas Fir Growth Is Constrained by Drought: Delineating the Climatic Limits of Timber Species under Seasonally Dry Conditions

There is debate on which tree species can sustain forest ecosystem services in a drier and warmer future. In Europe, the use of non-native timber species, such as Douglas fir (Pseudotsuga menziesii [Mirb.] Franco), is suggested as a solution to mitigate climate change impacts because of their high growth resilience to drought. However, the biogeographical, climatic and ecological limits for widely planted timber species still need to be defined. Here, we study the growth response to climate variables and drought of four Douglas fir plantations in northern Spain subjected to contrasting climate conditions. Further, we measure wood density in one of the sites to obtain a better understanding of growth responses to climate. Correlative analyses and simulations based on the Vaganov–Shaskin process-based model confirm that growth of Douglas fir is constrained by warm and dry conditions during summer and early autumn, particularly in the driest study site. Minimum wood density increased in response to dry spring conditions. Therefore, planting Douglas fir in sites with a marked summer drought will result in reduced growth but a dense earlywood. Stands inhabiting dry sites are vulnerable to late-summer drought stress and can act as “sentinel plantations”, delineating the tolerance climate limits of timber species.

Transitional rock glaciers at sea level in northern Norway

Abstract. Rock glaciers are geomorphological expressions of permafrost. Close to sea level in northernmost Norway, in the subarctic Nordkinn peninsula, we have observed several rock glaciers that appear to be active now or were active in the recent past. Active rock glaciers at this elevation have never before been described in Fennoscandia, and they are outside the climatic limits of present-day permafrost according to models. In this study, we have investigated whether or not these rock glaciers are active under the current climate situation. We made detailed geomorphological maps of three rock glacier areas in Nordkinn and investigated the regional ground dynamics using synthetic aperture radar interferometry (InSAR). One of the rock glaciers, namely the Ivarsfjorden rock glacier, was investigated in more detail by combining observations of vertical and horizontal changes from optical images acquired by airborne and terrestrial sensors and terrestrial laser scans (TLSs). The subsurface of the same rock glacier was investigated using a combination of electrical resistivity tomography (ERT) and refraction seismic tomography (RST). We also measured ground surface temperatures between 2016 and 2020, complemented by investigations using an infrared thermal camera, and a multi-decadal climatic analysis. We mapped the rock glaciers in the innermost parts of Store and Lille Skogfjorden as relict, while the more active ones are in the mouths of both fjords, fed by active talus in the upper slopes. Several of the rock glaciers cross over both the Younger Dryas shoreline (25 m a.s.l.) and the Early to Mid-Holocene shoreline at 13 m a.s.l. Both InSAR and optical remote sensing observations reveal low yearly movement rates (centimetres to millimetres per year). The ERT and RST suggest that there is no longer permafrost and ground ice in the rock glacier, while temperature observations on the front slope indicate freezing conditions also in summer. Based on the in situ temperature measurements and the interpolated regional temperature data, we show that the mean annual air temperature (MAAT) of the region has risen by 2 ∘C since the late 19th century to about 1.5 ∘C in the last decade. MAATs below 0 ∘C 100–150 years ago suggest that new rock glacier lobes may have formed at the end of the Little Ice Age (LIA). These combined results indicate that the Nordkinn rock glaciers are transitioning from active to relict stages. The study shows that transitional rock glaciers are still affected by creep, rock falls, snow avalanches, etc., and are not entirely dynamically dead features. Our contrasting results concerning permafrost presence and rock glacier activity show the importance of a multi-methodological approach when investigating slope processes in the edge zones of permafrost influence.

Enviromic prediction is useful to define the limits of climate adaptation: A case study of common bean in Brazil

Ongoing changes in the global environmental conditions foster plant breeding research to develop climate-smart cultivars as fast as possible. Data analytics are essential for achieving this goal, especially the so-called science of enviromics (large-scale environmental characterization of crop growing conditions) that could be used to pinpoint the relevant environment impacts driving the adaptation of a certain specie in a breeding framework. Here we quantified the effects of diverse climate factors on the current adaptation of elite common bean germplasm in Brazil. To capture the non-linearity of those impacts across a wide range of environments, we developed an “enviromic prediction” approach by combining Generalized Additive Models (GAM), environmental covariates (EC), and grain yield (GY) from 18 years of historical breeding trials. Then, we predicted the optimum limits for ECs at each production scenario (four regions, three seasons, and two grain types) and its respective predictions of GY adaptation. Our results indicate that the nonlinear influence of air temperature, solar radiation, and rainfall led to a huge interaction of the impacts among the development stages, seasons, and regions. This revealed that seasonality differently affected the vegetative and reproductive stages, which its impact drastically vary according to the region and season, which makes unfeasible the development of a breeding strategy for selecting for broad adaptation. Conversely, with our approach it was possible to pinpoint the effects of the region- or season-specific impacts, which helped identify the “climate limits” and critical development phases for each possible production scenario. This could allow breeders to design crop ideotypes while directing efforts to develop climate-smart varieties. Furthermore, enviromics prediction is a cost-effective way to use EC as a data analytics tool to support the visualization of regional breeding gaps for specific growing conditions. • We verified the ability of GAM-based models to determine climate drivers that explain grain yield variation. • Climatic limitations for cropping common bean in Brazil were identified across diverse seasons and regions. • “Optimum” predicted values for climate variables in different common bean production regions were obtained.

Tree mixtures mediate negative effects of introduced tree species on bird taxonomic and functional diversity

Abstract Recent biodiversity loss has emphasized the necessity to critically evaluate the consequences of human alterations of forest ecosystems. Stand diversification via tree species mixtures and the use of non‐native trees are two such alterations currently gaining importance as climate change adaptations. However, the effects of local versus regional tree mixing on associated biodiversity and notably the modifying role of tree species growing outside their natural range remain poorly understood. We assessed how monocultures and mixtures of native and introduced tree species influence the taxonomic and functional diversity of bird communities at stand and landscape scales in north‐west Germany. We focused on the dominant natural tree species (Fagus sylvatica) and economically important conifer species planted outside their natural range (the native Picea abies and non‐native Pseudotsuga menziesii). We found that bird species richness and functional diversity were generally higher in pure and mixed stands of native F. sylvatica than in pure conifer stands, especially in comparison to non‐native P. menziesii. These differences were particularly strong at the landscape scale. Pure conifer stands harboured only a reduced set of functionally similar bird species. Structural diversity based on tree microhabitat availability emerged as a key predictor of bird diversity. Synthesis and applications. Our study suggests that tree species mixtures do not necessarily increase bird diversity compared to pure stands of native trees, but can promote bird diversity relative to pure stands of species planted outside their natural range. Moreover, local mixtures, rather than a mosaic of pure stands, may promote bird diversity also at the landscape scale. By contrast, pure stands of tree species planted outside their natural range can increase biotic homogenization of forest birds. Promoting structural diversity of microhabitats via tree retention and ensuring that non‐native trees are planted in mixtures with native trees may alleviate potential limitations of climate change‐oriented management for biodiversity.

Canopy self-replacement in Pinus sylvestris rear-edge populations following drought-induced die-off and mortality

In recent years, Pinus sylvestris die-off and mortality events have occurred across all its range of distribution, usually associated with recurrent droughts induced by climate change. A shift in canopy dominance towards other better adapted co-existing species can be expected, especially in populations located close to their climatic tolerance limits. Herein, we tested, along a local elevational gradient, whether canopy opening resulting from die-off and mortality favours the growth of a non-dominant co-existing tree species (Quercus pubescens) established in the sub-canopy, in comparison to P. sylvestris sub-canopy trees. We also tested whether the growth of both species is associated with local climatic suitability for these species (extracted from SDMs) or, alternatively, with direct measures of micro-climatic variables. Finally, the effect on tree growth of other micro-local factors such as competition, canopy closure and micro-topography was also tested. Sub-canopy tree growth was enhanced overall by canopy opening resulting from P. sylvestris canopy die-off, but this response was stronger in P. sylvestris trees, reinforcing the self-replacement of this species after die-off. This higher growth rate is related to modifications in the micro-local climate (higher temperatures in the wettest quarter). Conversely, Q. pubescens is less sensitive to micro-local climate conditions but it can grow faster than P. sylvestris on stands with no canopy die-off or mortality. In contrast, climatic suitability extracted from SDMs was negatively related to sub-canopy P. sylvestris growth and had no effect on Q. pubescens. These contrasting results support observations at plot scale that P. sylvestris self-replacement is better explained by local environmental conditions than by values of climatic suitability obtained from regional-scale data-sets. Nevertheless, these climatic suitability measures remain consistent with the overall pattern of low seedling recruitment observed in previous works at the rear edge of species' distribution. This study reveals that short-term shifts in species dominance at a local scale will not necessarily occur in the studied P. sylvestris forests following die-off. This finding endorses the notion that micro-local environment and species traits (i.e., light and temperature tolerance, life-history strategies) modulate the capacity for resilience in rear-edge populations that would probably be prone to collapse otherwise.

Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations

Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives.

Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China

Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze-Huaihe River Basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB.

Soil texture and climate limit cultivation of the arsenic hyperaccumulator Pteris vittata for phytoextraction in a long-term field study

Long term field studies are required to bridge gaps between research and practical application of arsenic phytoextraction with the arsenic-hyperaccumulating fern Pteris vittata. In a 4-year field study, we investigated the effects of nutrient application (compost, inorganic or organic nitrogen, inorganic or organic phosphorus) and soil texture (13 % and 35 % clay) on arsenic phytoextraction with P. vittata in moderately contaminated soils (74–79 mg As/kg in the 0–15 cm depth interval). We found the highest phytoextraction rates, 5 ± 1 kg As/ha/y, in a coarse-textured compost-amended soil after 2 years of phytoextraction. Phytoextraction rates decreased over time, likely due to decreased root growth in mature stands, indicating plants should be replaced every 2–3 years to maintain phytoextraction efficiency. Across soil textures, nitrogen or phosphorus application led to a 60 % decrease in mean frond arsenic concentrations, leading to mean phytoextraction rates 54 % lower than in control ferns. In the fine-textured soil, frond arsenic concentrations were 54 % lower than in the coarse-textured soil, and fewer ferns survived from year 3 to 4. Across soil textures, compost application increased fern survival. We show that phytoextraction with P. vittata is limited to specific soil and climate conditions, narrower than those under which P. vittata grows in the wild.

Climatic Control on Spatial Distribution of Water Storage at the Catchment Scale: A Framework for Unifying Saturation Excess Runoff Models

AbstractThis paper aims to investigate the connection between TOPography‐based hydrological model (TOPMODEL) and Variable Infiltration Capacity (VIC) model through virtual experiments from the perspective of water table and storage at the catchment scale. A simple finite‐difference groundwater flow model was built for a hypothetical catchment forced by a sequence of recharges. A steady‐state water table under a low recharge rate is used as the climatic lower limit, above which the pore space is considered as the maximum storage capacity. When the water table is shallow, the land surface is a good proxy of the water table as assumed in the original TOPMODEL, and the underlying water storage distribution curve is similar as the maximum storage capacity distribution curve. When the water table is deep, the climatic lower limit is a good proxy of water table, and the storage is approximately spatially uniform over the unsaturated area as assumed in the VIC model. The systematic variation of water table and storage distribution potentially provides a framework for unifying the TOPMODEL and VIC model.

Non-linear modelling reveals a predominant moisture limit on juniper growth across the southern Tibetan Plateau.

Tree growth in plateau forests is critically limited by harsh climatic conditions. Many mathematical statistical methods have been used to identify the relationships between tree growth and climatic factors, but there is still uncertainty regarding the relative importance of these factors across different regions. We tested major climatic limits at 30 sites to provide insights into the main climatic limits for juniper trees (Juniperus tibetica Kom.) across the southern Tibetan Plateau. We analysed the linear and non-linear relationships between tree growth and climatic factors using Pearson correlation statistics and a process-based forward Vaganov-Shashkin-Lite (VS-Lite) model, respectively. These relationships were used to identify the strength of the influence of different climatic factors throughout the species' growing season and to identify the main climatic factors limiting tree growth. Growth of juniper trees began in April and ended in October in the study area. The radial growth of juniper trees was limited by soil moisture throughout the summer (June-August) of the current year at 24 sampling sites and was limited by temperature at the other six sites on the southern Tibetan Plateau. Soil moisture limited juniper growth at the majority of sites. Temperature in the current summer limited the growth of juniper trees at a few sampling sites in the western part of the study area. Local climate conditions may contribute to different limiting factors in the growth response of trees on the southern Tibetan Plateau. These findings may contribute to our understanding of divergent forest dynamics and to sustainable forest management under future climate scenarios.

Heteropogon‐Themeda grasses evolve to occupy either tropical grassland or wetland biomes

AbstractSpecies of the Heteropogon‐Themeda clade are ecologically important grasses distributed across the tropics, including widespread species, such as the pantropical Heteropogon contortus and Themeda triandra, and range‐restricted species such as Heteropogon ritchiei and Themeda anathera. Here, we examine habitat preferences of the grassland/savanna and wetland species by describing bioclimatic niche characteristics, characterizing functional traits, and investigating the evolution of functional traits of 31 species in the Heteropogon‐Themeda clade in relation to precipitation and temperature. The climatic limits of the clade are linked to mean annual precipitation and seasonality that also distinguish seven wetland species from 24 grassland/savanna species. Tests of niche equivalency highlighted the unique bioclimatic niche of the wetland species. However, climatic factors do not fully explain species geographic range, and other factors are likely to contribute to their distribution ranges. Trait analyses demonstrated that the wetland and grassland/savanna species were separated by culm height, leaf length, leaf area, awn length, and awn types. Phylogenetic analyses showed that the wetland species had tall stature with long and large leaves and lack of hygroscopic awns, which suggest selective pressures in the shift between savanna/grassland and wetland. The two most widespread species, H. contortus and T. triandra, have significantly different bioclimatic niches, but we also found that climatic niche alone does not explain the current geographic distributions of H. contortus and T. triandra. Our study provides a new understanding of the biogeography and evolutionary history of an ecologically important clade of C4 tropical grasses.

Predicting global terrestrial biomes with the LeNet convolutional neural network

Abstract. A biome is a major regional ecological community characterized by distinctive life forms and principal plants. Many empirical schemes such as the Holdridge life zone (HLZ) system have been proposed and implemented to predict the global distribution of terrestrial biomes. Knowledge of physiological climatic limits has been employed to predict biomes, resulting in more precise simulation; however, this requires different sets of physiological limits for different vegetation classification schemes. Here, we demonstrate an accurate and practical method to construct empirical models for biome mapping: a convolutional neural network (CNN) was trained by an observation-based biome map, as well as images depicting air temperature and precipitation. Unlike previous approaches, which require assumption(s) of environmental constrain for each biome, this method automatically extracts non-linear seasonal patterns of climatic variables that are relevant in biome classification. The trained model accurately simulated a global map of current terrestrial biome distribution. Then, the trained model was applied to climate scenarios toward the end of the 21st century, predicting a significant shift in global biome distribution with rapid warming trends. Our results demonstrate that the proposed CNN approach can provide an efficient and objective method to generate preliminary estimations of the impact of climate change on biome distribution. Moreover, we anticipate that our approach could provide a basis for more general implementations to build empirical models of other climate-driven categorical phenomena.

Assessing the vulnerability of plant functional trait strategies to climate change

AbstractAimOur ability to understand how species may respond to changing climate conditions is hampered by a lack of high‐quality data on the adaptive capacity of species. Plant functional traits are linked to many aspects of species life history and adaptation to environment, with different combinations of trait values reflecting alternate strategies for adapting to varied conditions. If the realized climate limits of species can be partially explained by plant functional trait combinations, then a new approach of using trait combinations to predict the expected climate limits of species trait combinations may offer considerable benefits.LocationAustralia.Time periodCurrent and future.MethodsUsing trait data for leaf size, seed mass and plant height for 6,747 Australian native species from 27 plant families, we model the expected climate limits of trait combinations and use future climate scenarios to estimate climate change impacts based on plant functional trait strategies.ResultsFunctional trait combinations were a significant predictor of species climate niche metrics with potentially meaningful relationships with two rainfall variables (R2 = .36 & .45) and three temperature variables (R2 = .21, .28, .30). Using this method, the proportion of species exposed to conditions across their range that are beyond the expected climate limits of their trait strategies will increase under climate change.Main conclusionsOur new approach, called trait strategy vulnerability, includes three new metrics. For example, the climate change vulnerability (CCV) metric identified a small but important proportion of species (4.3%) that will on average be exposed to conditions beyond their expected limits for summer temperature in the future. These potentially vulnerable species could be high priority targets for deeper assessment of adaptive capacity at the genomic or physiological level. Our methods can be applied to any suite of co‐occurring plants globally.

Valuation of Climate Performance of a Low-Tech Greenhouse in Costa Rica

The expansion of protected agriculture has technological, climatic, and topographic limitations. The agricultural regions of Costa Rica use the greenhouse concept and adapt it to its conditions. The objective of this work was to describe the variation in temperature and humidity in a greenhouse ventilated passively and on land with a more than 45% slope. To evaluate the environment inside the greenhouse, temperature and humidity variations were measured with a weather station installed outside of the greenhouse to measure the external environment. Inside the greenhouse, 17 sensors were placed to measure the temperature (T) and relative humidity (RH). During data recording inside the greenhouse, tomato crops were in the fruit formation stage, and pepper was less than one week old. Six scenarios were tested to determine the air temperature and humidity dynamic under different climatic conditions. An evaluation of the greenhouse environment was carried out employing an analysis of variance of temperature and RH to establish if there are significant differences in the direction of the slope of the cross-section. The uniformity of temperature and RH do not present stratifications derived from wind currents that can affect the effective production of these crops.

Mineral Protection and Resource Limitations Combine to Explain Profile‐Scale Soil Carbon Persistence

AbstractThe fate of soil carbon (C) is largely controlled by microbial oxidation of organic matter (OM), which is constrained by a variety of mechanisms. OM association with soil minerals provides pronounced protection against microbial decomposition. However, factors such as climate, occlusion, and resource limitations also contribute to OM preservation. We explore the factors explaining C distribution and age within an upland rainforest soil in Hawaiʻi, a site with abundant preferential flow paths (PFPs) and high short‐range order (SRO) mineral content. We characterized lateral and vertical changes in ∆14C, SRO mineral content, C‐functional group chemistry, and microbial community composition to elucidate the contributions of multiple protection mechanisms to OM preservation. Consistent with our expectation, SRO mineral content and ∆14C were strongly correlated (R2 = 0.95), indicating strong mineral protection of OM throughout the profile. Surprisingly, distance from PFP was also a significant predictor of ∆14C and improved model fit, particularly in the shallow horizons (R2 = 0.97). Elevated C/N ratios, decreased microbial abundance, and greater SRO mineral content suggest nitrogen limitations and enhanced mineral protection constrain OM turnover with distance from PFPs in deep, subsurface mineral horizons. Steady microbial abundance, increasing putative anaerobe abundance, and changes in C‐functional group chemistry indicate oxygen limitations constrain OM turnover in the matrix of shallow mineral horizons. Given that oxygen and nutrient limitations contribute to OM preservation in this high SRO system—an exemplar of mineral protection—resource limitations may play an even more important role in OM preservation in other well‐structured soils.

Remote-sensing monitoring of rubber plantations using object-oriented characteristics from the vigorous period

Natural rubber is one of the four major industrial raw materials in China, and the demand for it is increasing rapidly in China. However, due to geographic and climatic limitations, the rate of natural rubber production in China is significantly less than required to satisfy this demand. Therefore, to ensure the healthy development of China’s rubber industry, it is urgent to develop a method to rapidly and accurately monitor the planting and distribution of rubber forests in China. Existing studies have exploited the unique phenological characteristics of rubber plantations manifested by the spectral characteristics of the vigorous growth period and the deciduous period to identify rubber plantations through remote sensing. Unfortunately, the cloudy and rainy climate of rubber-growing regions makes it is difficult to obtain remote-sensing data in the optimal period. Therefore, the present study uses Planet images as the basic data and extracts typical spectral and textural features of rubber forests during the single vigorous growth period to construct a remote-sensing method to monitor rubber forests based on the object-oriented random forest algorithm, which uses all the spectral and texture features. The results show that the proposed random forest classification model achieves a high classification accuracy: the total classification accuracy reaches 89%, and the rubber producer’s accuracy and user’s accuracy both exceed 92%. This study can effectively solve the problem of lack image data with cloudless from deciduous period and provides a good theoretical basis for remote-sensing monitoring of China’s rubber forests, thereby facilitating the development of China’s rubber industry.