Individual Plant Scale Research Articles

Biomass and Carbon Stock Capacity of Robinia pseudoacacia Plantations at Different Densities on the Loess Plateau

Forests make an important contribution to the global carbon cycle and climate regulation. Caijiachuan watershed false acacia (Robinia pseudoacacia Linn.) plantation forests have been created for 30 years, but a series of problems have arisen due to the irrationality of the density involved at that time. To precisely assess the contribution of R. pseudoacacia plantations with different densities to this cycle, we measured the diameter at breast height (DBH), tree height (H), biomass, and carbon stocks in trees, shrubs, herbs, litter, and soil across different density ranges, denoted as D1 = 900–1400, D2 = 1401–1900, D3 = 1901–2400, D4 = 2401–2900, and D5 = 2901–3400 trees ha−1. In order to achieve the purpose of accurately estimating the biomass, carbon stocks and the contribution rate of each part in different densities of R. pseudoacacia plantations were measured. The results are as follows: (1) Both DBH and H decreased with increasing density, and field surveys were much more difficult and less accurate for H than DBH. Based on the two allometric growth models, it was found that the determination coefficient of the biomass model that incorporated both H and DBH (0.90) closely resembled that of the model using only DBH (0.89), with an error margin of only 0.04%. (2) At the sample scale, stand density significantly affected R. pseudoacacia stem biomass and total biomass. At the individual plant scale, stand density significantly affected R. pseudoacacia organ biomass. Increasing stand densities promoted the accumulation of vegetation biomass within the sample plot but did not improve the growth of individual R. pseudoacacia trees. The stem biomass constituted the majority of the total R. pseudoacacia biomass (58.25%–60.62%); the total R. pseudoacacia biomass represented a significant portion of the vegetation biomass (93.02%–97.37%). (3) The total carbon stock in the sample plots tended to increase with increasing stand density, indicating a positive correlation between density and the carbon stock of the whole plantation forest ecosystem. Hence, in future R. pseudoacacia plantations, appropriate densities should be selected based on specific objectives. For wood utilization, a planting density of 900–1400 trees ha−1 should be controlled. For carbon fixation, an initial planting density of 2900–3400 trees ha−1 should be selected for R. pseudoacacia. This study provides theoretical support for local forest management and how to better sequester carbon.

Fluvial biogeomorphological feedbacks from plant traits to the landscape: Lessons from selected French rivers in line with A.M. Gurnell's influential contribution

AbstractResearch in fluvial biogeomorphology largely aims to promote our understanding of the interactions between riparian vegetation and fluvial morphodynamics within riverine ecosystems. Starting at the end of the last century, Angela M. Gurnell has made a major contribution to fluvial geomorphology by considering water flow and sediment transport in combinaton with riparian and later also aquatic vegetation and thus significantly promoting the fluvial biogeomorphological approach from its early days until today. The objective of the present paper is to present a set of studies and results obtained over the last 20 years mainly by the authors and many collaborators, including Angela M. Gurnell, on a panel of French rivers: the Tech, Garonne, Isère and Allier. In particular, feedback mechanisms between fluvial morphodynamics and riparian vegetation dynamics were investigated directly in the field and by using high‐resolution remote sensor systems at the scale of individual plants, populations, communities and landscapes as well as during semi‐controlled ex‐situ experiments at the scale of individual plants. Collectively, the authors' research conducted over the past 20 years has helped to elucidate some key aspects of the feedback dynamics between the lowest and highest levels of the riparian ecosystem organization. This article reviews and discusses those key aspects. The gradually obtained results have contributed to a better understanding and quantification of feedback between river morphodynamics and vegetation at nested spatiotemporal scales, from plant species traits to the riverine landscape. Furthermore, the biogeomorphological approach advocated for more than 20 years now has clearly facilitated the extension of the discipline of geomorphology to ecology in general and evolutionary ecology in particular and thus contributed to a more integrative vision of earth surface processes.

Species distribution and habitat attributes guide translocation planning of a threatened short-range endemic plant

The success of plant translocations depends on defining habitat attributes critical for establishment and survival, and on locating this habitat in the landscape. We used species distribution modelling, coupled with fine-scale characterisation of local habitat attributes to characterise and identify potential sites for translocation of Tetratheca erubescens J.P.Bull (Elaeocarpaceae), a threatened shrub restricted to cliff faces on one banded ironstone range in semi-arid south-west Western Australia. Here, we 1) constructed a maximum entropy species distribution model (SDM) from known occurrence locations and environmental data, and projected this onto a broader surrounding landscape to seek suitable unoccupied habitat; 2) characterised the local habitat attributes (LHA) of both occupied and predicted but unoccupied habitat (locality types), determining potential characteristics of habitat suitability at the scale of individual plants (microsites) not captured by the landscape-scale models and; 3) assessed unoccupied areas of modelled high habitat suitability to identify ‘potential translocation sites’ that may have the characteristics to support T. erubescens. The SDM resolved two management considerations by identifying suitable habitat outside the known extent of threatened species to locate areas to search for additional natural populations (none found) or to verify as ‘potential translocation sites’ (24 sites identified). Using field surveys to assess LHA at a finer-scale, we detected differences between plant microsites and other random points in occupied habitat. As such, T. erubescens was more likely to grow in wider rock cracks, located in relatively water gaining points in the cliff profile, in soils with organic (rather than mineral) content. Our approach systematically addressed the complex challenges involved in identifying and selecting sites for translocation of a threatened plant species, a first for a semi-arid environment. We used the outcomes of this study to present a conceptual model for practitioners and regulators that outlines our approach for identifying ‘potential translocation sites’ and ranking their suitability within the context of three assessment filters: species requirements; management-operational constraints and regulatory considerations. This application is relevant to translocation programs that seek to return plant species into natural habitat areas.

Modeling of summer maize transpiration considering morphogenesis

Plant transpiration research is the foundation for improving the efficiency of agricultural water resource utilization and ensuring food production safety. It is an important content of agricultural production and water resource utilization research. However, spatial heterogeneities of leaf size and positions affect the estimation of transpiration. In this study, summer maize on the North China Plain was taken as the research object. Based on the measured data of the photosynthetic apparatus, morphogenesis and stem flow meter, the transpiration variation rule of summer maize leaves was analyzed, and the spatial distribution model of photosynthetically active radiation based on plant morphogenesis was constructed. Considering the spatial heterogeneities of leaf size and positions, the individual-plant scale transpiration model was upgraded. The reliability of transpiration model at individual plant scale was verified. The results showed that: i) the transpiration rate of leaves with different leaf positions, positive and negative leaves at the same leaf position and different parts of the same leaf were significantly different. ii) Based on the measured data, the structure of summer maize with spatial coordinates was obtained, and a calculation model for solar radiation reaching the top of the plants and a calculation model for photosynthetically active radiation reaching the surface of the leaves were constructed. Based on the different absorption, reflection, and transmission of radiation by summer maize leaves, the photosynthetically active radiation of each part of the leaves was characterized. iii) A plant transpiration model was constructed by combining the leaf panel transpiration model with the spatial distribution model of photosynthetically active radiation in summer maize plants, and, the determination coefficients between the simulated and the measured values of the individual plant scale transpiration rate were all above 0.95, the consistency index was close to 1.0, and the RMSE was in range of 12.41–16.64 g h−1. The individual plant transpiration obtained after simulated accumulation could meet the simulation accuracy. The results achieved the scale improvement of the leaf transpiration to individual plant transpiration.

Using repeat UAV-based laser scanning and multispectral imagery to explore eco-geomorphic feedbacks along a river corridor

Abstract. Vegetation plays a critical role in the modulation of fluvial process and morphological evolution. However, adequately capturing the spatial and temporal variability and complexity of vegetation characteristics remains a challenge. Currently, most of the research seeking to address these issues takes place at either the individual plant scale or via larger-scale bulk roughness classifications, with the former typically seeking to characterise vegetation–flow interactions and the latter identifying spatial variation in vegetation types. Herein, we devise a method which extracts functional vegetation traits using UAV (uncrewed aerial vehicle) laser scanning and multispectral imagery and upscale these to reach-scale functional group classifications. Simultaneous monitoring of morphological change is undertaken to identify eco-geomorphic links between different functional groups and the geomorphic response of the system. Identification of four groups from quantitative structural modelling and two further groups from image analysis was achieved and upscaled to reach-scale group classifications with an overall accuracy of 80 %. For each functional group, the directions and magnitudes of geomorphic change were assessed over four time periods, comprising two summers and winters. This research reveals that remote sensing offers a possible solution to the challenges in scaling trait-based approaches for eco-geomorphic research and that future work should investigate how these methods may be applied to different functional groups and to larger areas using airborne laser scanning and satellite imagery datasets.

Improving agricultural robot patch-spraying accuracy and precision through combined error adjustment

Site-specific weed management (SSWM) involves precision spraying operations based on the spatial variability of weeds in the field. High spraying precision enables extensive economic and environmental benefits. We developed a high-precision patch-spraying robot system based on weed maps to conduct patch-spraying operations at the individual plant scale. The patch-spraying droplet deposition effect is evaluated by post-spraying droplet deposition images. The agricultural robot mainly consists of a walking chassis, spraying, and control systems. The walking chassis provides walking power and support for the suspended spraying system. Combining the static structural and dynamic error models of the robot improves patch-spraying accuracy and precision. Twelve patch-spraying simulations were carried out on the leveling site. Different correction methods and driving speeds were tested, with driving rates of 0.5, 1.0, and 1.5 m/s. Aerial images of the patch-spraying process were evaluated using pixel point-based discriminant statistics after operation completion. The droplet deposition coverage accuracy ranged from 95.9 to 98.8 %. The center of droplet deposition offset distance was 3.7–7.2 cm. Field results showed that the patch-spraying weed control rate based on individual plant scales (spraying precision ≤ 15 cm) was 94.2–98.1 %. In summary, the developed agricultural robot performs patch-spraying operations at the individual plant scale. Patch-spraying accuracy and precision are improved by modeling corrections for random errors. A general error correction method for the deposition position of fog droplets is explored to achieve precise droplet delivery in the walking state of plant protection operation equipment.

DECO2—An Open-Source Energy System Decarbonisation Planning Software including Negative Emissions Technologies

The deployment of CO2 capture and storage (CCS) and negative emissions technologies (NETs) are crucial to meeting the net-zero emissions target by the year 2050, as emphasised by the Glasgow Climate Pact. Over the years, several energy planning models have been developed to address the temporal aspects of carbon management. However, limited works have incorporated CCS and NETs for bottom-up energy planning at the individual plant scale, which is considered in this work. The novel formulation is implemented in an open-source energy system software that has been developed in this work for optimal decarbonisation planning. The DECarbonation Options Optimisation (DECO2) software considers multiperiod energy planning with a superstructural model and was developed in Python with an integrated user interface in Microsoft Excel. The software application is demonstrated with two scenarios that differ in terms of the availabilities of mitigation technologies. For the more conservative Scenario 1, in which CCS is only available in later years, and other NETs are assumed not to be available, all coal plants were replaced with biomass. Meanwhile, only 38% of natural gas plants are CCS retrofitted. The remaining natural gas plants are replaced with biogas. For the more aggressive Scenario 2, which includes all mitigation technologies, once again, all coal plants undergo fuel substitution. However, close to half of the natural gas plants are CCS retrofitted. The results demonstrated the potential of fuel substitutions for low-carbon alternatives in existing coal and natural gas power plants. Additionally, once NETs are mature and are available for commercial deployment, their deployment is crucial in aiding CO2 removal in minimal investment costs scenarios. However, the results indicate that the deployment of energy-producing NETs (EP-NETs), e.g., biochar and biomass with CCS, are far more beneficial in CO2 removal versus energy-consuming NETs (EC-NETs), e.g., enhanced weathering. The newly developed open-source software demonstrates the importance of determining the optimal deployment of mitigation technologies in meeting climate change targets for each period, as well as driving the achievement of net-zero emissions by mid-century.

An automated, high-performance approach for detecting and characterizing broccoli based on UAV remote-sensing and transformers: A case study from Haining, China

• A lightweight UAV platform for detecting and characterizing broccoli was developed. • Transformers was chosen because of its success in natural language processing. • Existing TransUet and PCT provide a good balance between the accuracy and speed. • Our integrated framework is robust against variations of environmental conditions. Accurate canopy mapping and head-volume estimation of large areas of broccoli is an important prerequisite for precision farming since it provides important phenotypic traits associated with field management, environmental control, and yield prediction. Currently, the detection and characterization of broccoli mostly rely on ground surveys and human interpretation, which is often time- and labor-intensive. Recent developments based on unmanned aerial vehicle (UAV) remote sensing offer low cost, timely, and flexible data acquisition, thereby providing a potential alternative technique to enhance in situ field surveys. The combination of UAV data and deep learning has led to a series of breakthroughs in rapid and automated collection of simultaneous multisensor and multimodal plant phenotyping data. However, their application for monitoring broccoli remains problematic when faced with the significant spatial scale involved and the variety of vegetation species. To address this problem, we propose herein a fast and reliable semi-automatic workflow based on deep learning to process UAV RGB imagery and LiDAR point clouds and thereby remotely detect and characterize broccoli canopy and heads. First, we explore the use of TransUNet to differentiate canopy and non-canopy regions in RGB images at the individual-plant scale. The results demonstrate that TransUNet consistently achieves the highest accuracy (average returned Precision, Recall, F1 score, and IoU of 0.917, 0.864, 0.901, and 0.895, respectively) compared with three CNN-based and two shallow learning-based approaches. In addition, TransUNet performs best in terms of robustness against variations in training samples. Subsequently, to estimate the volume of broccoli heads, a point cloud transformer (PCT) network is developed for point cloud segmentation. Improving upon the results of three existing methods PointNet, PointNet++, and K-means that were applied to the same datasets, the best-performing PCT produced a precision of 0.914, an overall recall of 0.899, an overall F1 score of 0.901, and an overall IoU of 0.879. A regression analysis indicates that the PCT estimates had R 2 = 0.875, RMSE = 18.62, and rRMSE = 3.64 %, which is also superior to the results from other comparison approaches. Collectively, the wide application of such technology would facilitate applied research in plant phenotyping and precision agro-ecological applications and field management.

: On growth patterns and mechanisms in arctic-alpine shrubs

Arctic-alpine ecosystems are considered hot-spots of environmental change, with rapidly warming conditions causing massive alterations in vegetational structure. These changes and their environmental controls are highly complex and variable across spatial and temporal scales. Yet, despite their numerous implications for the global climate system, the underlying physiological processes and mechanisms at the individual plant scale are still little explored. Using hourly recordings of shrub stem diameter change provided by dendrometers, paired with on-site environmental conditions, enabled us to shed light on these processes. In this way, growth patterns in three widely distributed shrub species were assessed and linked to thermal and hygric conditions. We started our analysis with a close examination of one evergreen species under extreme environmental conditions, followed by a comparison of evergreen and deciduous species, and, finally, a comparative look at growth patterns across local micro-habitats. The results revealed distinct growth strategies, closely linked to species-specific water-use dynamics and cambial rhythms. Within the heterogenous alpine landscape these conditions were mainly attributed to the variation in local micro-habitats, defined by fine-scale topography and consequent variation in snow conditions and exposure. Thus, the overall growth success was mainly controlled by complex seasonal dynamics of soil moisture availability, snow conditions, and associated freeze–thaw cycles. It was therefore in many cases decoupled from governing regional climate signals. At the same time, exceedingly high summer temperatures were limiting shrub growth during the main growing season, resulting in more or less pronounced bimodal growth patterns, indicating potential growth limitation with on-going summer warming. While shrubs are currently able to maximize their growth success through a high level of adaptation to local micro-site conditions, their continued growth under rapidly changing environmental conditions is uncertain. However, our results suggest a high level of heterogeneity across spatial and temporal scales. Thus, broad-scale vegetational shifts can not be explained by a singular driver or uniform response pattern. Instead, fine-scale physiological processes and on-site near-ground environmental conditions have to be incorporated into our understanding of these changes.

Quantifying contributions of leaf area and longevity to leaf area duration under increased planting density and nitrogen input regimens during maize yield improvement

Leaf area and its longevity are the key factors driving dry matter accumulation and yield formation in maize (Zea mays. L). Both factors are influenced by cultivation management measures. The objective of this study was to utilize a quantitative framework to better understand the contribution of leaf area and longevity to leaf area duration (LAD) and yield in maize plants at varying plant densities and nitrogen application rates. A two-year field experiment was conducted using a factorial combination of two plant densities and four nitrogen levels. Leaf area and longevity were measured at each leaf position in two different maize cultivars. We calculated the LAD at the single-leaf, individual-plant, and whole-population scales and quantitatively analyzed the contribution of leaf area and longevity to LAD. The results showed that in the upper leaves, leaf area had a larger proportional contribution to LAD (54.1–95.7%), whereas in the lower leaves, leaf longevity contributed more to LAD (51.8–74.1%). Both were greatly affected by variations in plant density and nitrogen application. At the individual-plant scale, the proportional contribution of leaf area to LAD (57.3%) was similar to that of leaf longevity (57.2%); at the whole-population scale, leaf area accounted for a larger contribution to LAD (87.0%). The increase in LAD (which integrating increases in leaf area and longevity) was positively associated with an increase in dry matter at maturity and grain yield. Yield gain was achieved by reasonable increases in density and nitrogen fertilizer application to promote LAD. The quantitative evaluation of leaf area and longevity described in this study provides a theoretical basis for increasing maize yield under increased plant density and nitrogen input conditions.

Aridity and High Salinity, Rather Than Soil Nutrients, Regulate Nitrogen and Phosphorus Stoichiometry in Desert Plants from the Individual to the Community Level

The stoichiometric characteristics of plant nitrogen (N) and phosphorus (P) and their correlations with soil properties are regarded as key for exploring plant physiological and ecological processes and predicting ecosystem functions. However, quantitative studies on the relative contributions of water–salt gradients and nutrient gradients to plant stoichiometry are limited. In addition, previous studies have been conducted at the plant species and individual levels, meaning that how community-scale stoichiometry responds to soil properties is still unclear. Therefore, we selected typical sample strips from 13 sampling sites in arid regions to assess the leaf N and P levels of 23 species of desert plants and measure the corresponding soil water content, total salt content, total nitrogen content, and total phosphorus content. The aim was to elucidate the main soil properties that influence the stoichiometric characteristics of desert plants and compare the individual and community responses to those soil properties. Our results indicated that the growth of desert plants is mainly limited by nitrogen, with individual plant leaf nitrogen and phosphorus concentrations ranging from 4.08 to 31.39 mg g−1 and 0.48 to 3.78 mg g−1, respectively. Community stoichiometry was significantly lower than that of individual plants. A significant correlation was observed between the mean N concentration, P concentration, and N:P ratio of plant leaves. At the individual plant scale, aridity significantly reduced leaf N and P concentrations, while high salt content significantly increased leaf N concentrations. At the community scale, aridity had no significant effects on leaf nitrogen or phosphorus stoichiometry, while high salinity significantly increased the leaf N:P ratio and there were no significant interactions between the aridity and salinity conditions. No significant effects of soil nutrient gradients were observed on plant N and P stoichiometric characteristics at the individual or community levels. These results suggest that individual desert plants have lower leaf N and P concentrations to adapt to extreme drought and only adapt to salt stress through higher leaf N concentrations. The N and P stoichiometric characteristics of desert plant communities are not sensitive to variations in aridity and salinity in this extreme habitat. The results of this study could enhance our perceptions of plant adaptation mechanisms to extreme habitats within terrestrial ecosystems.

Use of gravimetric measurements to calibrate thermal dissipation probes with stem segments

AbstractTree transpiration is an important part of the global water cycle, and it is frequently represented by sap flow measurements at the individual plant scale. The thermal dissipation probes (TDPs) are commonly employed to measure and calculate sap flows, for which Granier equation is generally used. However, the original Granier equation has been found to underestimate sap flow rates. Calibration methods using different materials (i.e., whole trees or stem segments) have been proposed to recalibrate TDPs; however, these methods are often either expensive or complicated. This study proposes a new device that uses the gravimetric measurement method (GMM) to recalibrate TDPs. The GMM is relatively simple and easy to conduct. The proposed device uses a tube and siphon to maintain a constant pressure; an electronic balance is used to measure the actual sap flow. Four plant species with different stem porosity types, Pyrus pyrifolia (diffuse‐porous), Quercus fabri (ring‐porous), Platycarya strobilacea (ring‐porous) and Kalopanax septemlobus (ring‐porous), were selected to examine the performance of the GMM. The results showed that: (1) good correlations were obtained between the actual sap flow (from gravimetric measurements) and sap flow estimates obtained using the original TDP technique; (2) the results of the GMM‐recalibrated TDP measurement equation greatly differed from those of the original Granier calibration equation; and (3) the recalibration equation derived from the GMM outperformed the original Granier equation. This study suggests that the developed GMM can be used to recalibrate TDPs, and that using this method can help to improve sap flow measurements, thereby improving estimates of tree water consumption and transpiration.

Scaling-up trait covariation: Coordination and trade-offs within and among plant species in alpine meadow communities

<p indent="0mm">Functional traits and their covariation could reflect specific ecological strategies of organisms in response and adaptation to changing environment as the basis of understanding organism-environment relations at the individual, population, and community levels. Many studies report trait coordination and trade-offs among species over broad scales, especially for the leaf economic spectrum at regional and global scales. An increasing number of studies have attempted to explain and predict the functional (trait) structure of populations, communities, and vegetation in response to environmental change, but the mechanisms underpinning trait coordination and trade-offs may differ across ecological levels. We know little about this trait coordination and trade-offs within and among species in local communities, lacking not only relevant theoretical frameworks but also experimental evidence. Merging trait covariation into theory for community assembly, we predict: (1) Significant trait coordination or trade-offs among coexisting species occur when coexisting species occupy distinct ecological niches, similar to the pattern induced by specialization of species niches on a broad scale and (2) if trait covariations are induced by functional balance and/or structural optima, then consistent trait coordination and trade-offs within and among species will occur and scale up to the community and ecosystem levels. In this study, we experimentally test these predictions in local communities and discuss the potential mechanisms underpinning trait coordination and trade-offs from the scale of individual plants to vegetation. In Tibetan alpine meadows with high plant species richness but low environmental heterogeneity, we sampled two leaf stoichiometric traits (leaf nitrogen and phosphorus content), two leaf morphological traits (specific leaf area and leaf dry matter content) and mature plant height for three to five individuals for all coexisting species within each of 36 plant communities (plots) distributed along a gradient of environmental harshness. In each plot, we averaged trait values of individual samples of each species at the population-level, and used the mean of all populations of each species to indicate the species-level traits. We assessed pairwise trait correlation within and among 29 frequent species by standardized major axis regression and trait coordination and trade-offs among multiple traits by principal component analysis. Similar to the broad-scale pattern, we found that leaf nitrogen and phosphorus contents were significantly positively correlated with specific leaf area (negatively correlated with leaf dry matter content) among 29 coexisting species, but these trade-offs were significant at a population level in only a few species. Both among species and within most species, (1) leaf nitrogen content was significantly positively correlated with phosphorus content, likely induced by functional balance, (2) specific leaf area was negatively correlated with leaf dry matter content, indicating a structural optimum, (3) neither leaf stoichiometric nor morphological traits significantly correlated with plant height. Trait coordination and trade-offs were stronger for multiple trait relationships than for bivariate correlations. These results support our predictions and provide a basis and experimental evidence for scaling up trait covariations from individuals to communities.

Real-Time Detection of Hogweed: UAV Platform Empowered by Deep Learning

The Hogweed of Sosnowskyi (lat. Heracleum sosnówskyi) is poisonous for humans, dangerous for farming crops, and local ecosystems. This plant is fast-growing and has already spread all over Eurasia: from Germany to the Siberian part of Russia, and its distribution expands year-by-year. In-situ detection of this harmful plant is a tremendous challenge for many countries. Meanwhile, there are no automatic systems for detection and localization of hogweed. In this article, we report on an approach for fast and accurate detection of hogweed. The approach includes the Unmanned Aerial Vehicle (UAV) with an embedded system on board running various Fully Convolutional Neural Networks (FCNN). We propose the optimal architecture of FCNN for the embedded system relying on the trade-off between the detection quality and frame rate. We propose a model that achieves ROC AUC 0.96 in the hogweed segmentation task, which can process 4K frames at 0.46 FPS on NVIDIA Jetson Nano. The developed system can recognize the hogweed on the scale of individual plants and leaves. This system opens up a wide vista for obtaining comprehensive and relevant data about the spreading of harmful plants allowing for the elimination of their expansion.

Leachate and Irrigation Sensor Development and Performance in Container Nursery Production

HighlightsUsing leaching fraction to schedule irrigation is recommended yet no automated measurement system exists.Sensors were developed to automatically measure leachate and irrigation within a sensor network.There was no difference between sensor measured and manually captured volume for sensors deployed in a nursery.After deployment in commercial nurseries, sensors accurately measured leachate and irrigation within 10% margin.Abstract. Nursery crops are often over-irrigated, resulting in wasted water and agrochemical inputs. Irrigating based on leaching fraction is recommended, yet an automated system for measuring and recording nursery container effluent (leachate) does not exist. The objective of this research was to develop and test a sensor-based system for real-time leachate and irrigation measurement in outdoor commercial nurseries. Sensors were developed to automatically measure irrigation and leachate volume in container nurseries that use overhead irrigation with the goal of facilitating the development of an automated leaching fraction-based irrigation system. Sensors were built using readily available components, including tipping bucket mechanisms calibrated to either 4.7 or 8.2 mL per tip, and were designed and constructed to function with commonly used 3.8-, 11.4-, and 14.5-L nursery containers. Sensor networks were developed in order to collect data from the sensors. Sensors were deployed at three commercial nurseries and tested using closed- and open-loop tests. Initially, a closed-loop test was performed on a subset of the sensors to test the integrity of the sensor-container system when subjected to an overhead irrigation delivery system. Following closed-loop tests, sensors were subjected to tests utilizing directed applications of water to compare sensor measurements with the volume of water applied and to compare sensor measurements over time (pre- and post-season). There was no difference between leachate measured by sensors and leachate captured and measured manually in closed-loop tests (p = 0.0570). In directed applications, sensors measured water flow with less than 3% margin at the beginning of the season (p = 0.0485) and less than 10% margin at the end of the season (p = 0.0390) regardless of container size. Pre- and post-season comparisons showed equivalence at the 10% margin for the 4.7-mL tipping bucket size (p = 0.0043) and at 5% for those calibrated to 8.2 mL per tip (p = 0.0198). Sensors deployed in commercial nurseries accurately measured leachate and irrigation within a 10% margin in real-time, on an individual plant scale, making them a viable option for a leaching fraction-based irrigation schedule. Keywords: Container effluent, Container-grown plants, Leaching fraction, Irrigation schedule, Sensor network.

Satellite Observed Positive Impacts of Fog on Vegetation

AbstractFog is an important water source for many ecosystems, especially in drylands. Most fog‐vegetation studies focus on individual plant scale; the relationship between fog and vegetation function at larger spatial scales remains unclear. This hinders an accurate prediction of climate change impacts on dryland ecosystems. To this end, we examined the effect of fog on vegetation utilizing both optical and microwave remote sensing‐derived vegetation proxies and fog observations from two locations at Gobabeb and Marble Koppie within the fog‐dominated zone of the Namib Desert. Significantly positive relationships were found between fog and vegetation attributes from optical data at both locations. The positive relationship was also observed for microwave data at Gobabeb. Fog can explain about 10%–30% of variability in vegetation proxies. These findings suggested that fog impacts on vegetation can be quantitatively evaluated from space using remote sensing data, opening a new window for research on fog‐vegetation interactions.

The dual effect of sand-covered biocrusts on annual plants: Increasing cover but reducing individual plant biomass and fecundity

It is generally accepted that biological soil crusts promote the growth of vascular plants. Nevertheless, contradictory results were recently found for annual plants in the extreme desert dunefield of the Negev (average precipitation of 95 mm) during extreme drought years, explained by the lower available water content (AWC) under the low-albedo biocrusted surfaces at the interdune (ID). Trying to assess whether the biomass and fecundity of the annual plants is higher at the non-crusted dune crest (CR) in comparison to ID also during years with near-average precipitation, surface stability, rain, AWC, fine (silt and clay) content (FC), and soil organic carbon (SOC) were measured in a pair of plots established in CR and ID in two dunes and interdunes. The research was conducted for three years (2009/2010, 2013/14 and 2014/15), having a mean precipitation of 109.4 mm. At the end of each winter, sampling of the annual plants (cover, density, species composition, biomass) took place at CR and ID at 30 cm-diameter miniplots (miniplot-scale), while the biomass and fecundity of 50 randomly-chosen individuals that abundantly inhabited both habitats during the same year were measured at parallel transects (individual plant-scale). Significantly higher cover and generally higher diversity characterized the plants at ID at the miniplot-scale, explained by the higher surface stability. Although characterized by lower FC and SOC, significantly higher biomass and fecundity characterized the individual plants at CR, explained by the higher AWC. Biocrusts were found to have a dual effect on the annual plants: positive due to increased surface stability (cover and for most years also diversity) at the miniplot-scale, and negative due to reduced AWC (low biomass and fecundity) at the individual plant-scale. The higher biomass and fecundity of the individual plants at CR point to the cardinal role played by water (rather than SOC) in this low-nutrient extreme desert. As far the annual plants are concerned, these findings also point at the fact that moderate crust destruction (such as by light trampling) may not necessarily be hazardous to the ecosystem, as it may increase fecundity and subsequently germination success.

Unmanned aerial systems measure structural habitat features for wildlife across multiple scales

AbstractAssessing habitat quality is a primary goal of ecologists. However, evaluating habitat features that relate strongly to habitat quality at fine‐scale resolutions across broad‐scale extents is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging the gap between relatively high spatial resolution, low spatial extent field‐based habitat quality measurements and lower spatial resolution, higher spatial extent satellite‐based remote sensing. Our goal in this study was to evaluate the potential forUASstructure from motion (SfM) to estimate several dimensions of habitat quality that provide potential security from predators, and forage for pygmy rabbits (Brachylagus idahoensis) in a sagebrush steppe environment.At the plant and patch scales, we comparedUAS‐derived estimates of vegetation height, volume (estimate of food availability) and canopy cover to estimates from ground‐based terrestrial laser scanning (TLS) and field‐based measurements. Then, we mapped habitat features across two sagebrush landscapes in Idaho,USA, using point clouds derived fromUASSfM.At the individual plant scale, theUAS‐derived estimates matched those fromTLSfor height (R2 = 0.85), volume (R2 = 0.94) and canopy cover (R2 = 0.68). However, there was less agreement with field‐based measurements of height (R2 = 0.67), volume (R2 = 0.31) and canopy cover (R2 = 0.29). At the patch scale,UAS‐derived estimates provided a better fit to field‐based measurements (R2 = 0.51–0.75) than at the plant scale. Landscape‐scale maps created fromUASwere able to distinguish structural heterogeneity between key patch types.Our work demonstrates thatUASwas able to accurately estimate habitat heterogeneity for a key terrestrial vertebrate at multiple spatial scales. Given that many of the vegetation metrics we focus on are important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring fine‐resolution habitat quality across broad landscapes for use in studies of animal ecology, conservation and land management.

Flammability as an ecological and evolutionary driver

SummaryWe live on a flammable planet yet there is little consensus on the origin and evolution of flammability in our flora.We argue that part of the problem lies in the concept of flammability, which should not be viewed as a single quantitative trait or metric. Rather, we propose that flammability has three major dimensions that are not necessarily correlated: ignitability, heat release and fire spread rate. These major axes of variation are controlled by different plant traits and have differing ecological impacts during fire.At the individual plant scale, these traits define three flammability strategies observed in fire‐prone ecosystems: the non‐flammable, the fast‐flammable and the hot‐flammable strategy (with low ignitability, high flame spread rate and high heat release, respectively). These strategies increase the survival or reproduction under recurrent fires, and thus, plants in fire‐prone ecosystems benefit from acquiring one of them; they represent different (alternative) ways to live under recurrent fires.Synthesis. This novel framework based on different flammability strategies helps us to understand variability in flammability across scales, and provides a basis for further research.

Ecosystem heterogeneity determines the ecological resilience of the Amazon to climate change

Amazon forests, which store ∼ 50% of tropical forest carbon and play a vital role in global water, energy, and carbon cycling, are predicted to experience both longer and more intense dry seasons by the end of the 21st century. However, the climate sensitivity of this ecosystem remains uncertain: several studies have predicted large-scale die-back of the Amazon, whereas several more recent studies predict that the biome will remain largely intact. Combining remote-sensing and ground-based observations with a size- and age-structured terrestrial ecosystem model, we explore the sensitivity and ecological resilience of these forests to changes in climate. We demonstrate that water stress operating at the scale of individual plants, combined with spatial variation in soil texture, explains observed patterns of variation in ecosystem biomass, composition, and dynamics across the region, and strongly influences the ecosystem's resilience to changes in dry season length. Specifically, our analysis suggests that in contrast to existing predictions of either stability or catastrophic biomass loss, the Amazon forest's response to a drying regional climate is likely to be an immediate, graded, heterogeneous transition from high-biomass moist forests to transitional dry forests and woody savannah-like states. Fire, logging, and other anthropogenic disturbances may, however, exacerbate these climate change-induced ecosystem transitions.