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HighlightThe relationship between an adenine isopentenyltransferase transgene, reactive oxygen species (ROS) content and the ROS-scavenging system, mechanistically contributing to improved root growth under drought stress in creeping bentgrass, is discussed.

Leaf senescence is characterized by decreased chlorophyll content in leaves. The objectives of this study were to determine whether heat‐induced chlorophyll decline is due to inhibited chlorophyll synthesis or accelerated chlorophyll degradation and to determine whether genetic variations in heat tolerance of bentgrass (Agrostis spp.) species were associated with differential chlorophyll‐enzymatic responses to heat stress. Five turfgrass lines, including two transgenic creeping bentgrass (A. stolonifera L.) lines overexpressing isopentenyl transferase (ipt) gene ligated to a senescence‐activated promoter (SAG12) or heat shock promoter (HSP18.2) for controlling cytokinin synthesis, two thermal bentgrass (A. scabra Willd.) lines, and a wild‐type (WT) creeping bentgrass (‘Penncross’) were subjected to heat (38/33°C, day/night) or optimal temperature (22/18°C, day/night) (nonstress) for 42 d in growth chambers. The physiological parameters of turf quality, chlorophyll content, chlorophyll index, and dark green color index were measured. The data suggested significant genetic variations in the level of heat‐induced leaf senescence among the bentgrass lines. The enzyme activity of a key chlorophyll‐synthesizing enzyme, porphobilinogen deaminase, did not differ significantly across all the lines after 42 d of heat stress. The activities of chlorophyll‐degrading enzymes, including chlorophyllase and chlorophyll‐degrading peroxidase, increased significantly after heat stress, whereas pheophytinase activity was unchanged. Heat‐tolerant transgenic lines and thermal bentgrass maintained significantly lower activities of chlorophyll‐degrading enzymes than the WT under heat stress. Heat‐induced chlorophyll loss in bentgrass could be mainly due to accelerated chlorophyll degradation. Selecting for low chlorophyll degradation enzyme activity will help to facilitate the development of elite stay‐green bentgrass lines.

Arthropod abundance, biomass and taxonomic diversity are key metrics often used to assess the efficacy of restoration efforts. Gathering these metrics is a slow and laborious process, quantified by an expert manually sorting and weighing arthropod specimens. We present a tool to accelerate bulk arthropod classification and biomass estimates utilizing machine learning methods for computer vision. Our approach requires pre‐sorted arthropod samples to create a training dataset. We construct a dataset considering 18 terrestrial arthropod functional groups collected in southern Ontario, Canada. The dataset contains 517 high‐resolution images with approximately 20 individuals per image taken from either a petri dish or a bulk tray. Our tool uses the watershed algorithm to obtain precisely cropped individuals without any object annotations. After manually sorting cropped images of biological ‘debris’ and petri dish edges, three classifiers, DenseNet121, ResNet101 and MobileNetv2, each with trade‐offs of computational efficiency versus accuracy, are trained and compared to predict arthropod functional groups for each cropped individual. To calculate biomass, we compare seven linear and nonlinear models considering the arthropod pixel masks obtained using the watershed algorithm, in combination with images of a single function group with recorded weights, to calculate the per pixel density per functional group. From our experimentation, we recommend using DenseNet121 as it had the highest top‐1 functional group classification accuracy, likely a result of being the model with the largest number of parameters, with 86.14% considering the 20 labelled classes (18 arthropods plus debris and petri dish edge) in comparison to ResNet101 (85.10%) and MobileNetv2 (84.94%). For biomass estimation, we recommend using the average per pixel density which had the highest ranked performance considering both total error, 0.043 g (0.855% error), and cumulative class‐specific error, 1.62 g (40.67% average error across all classes), in comparison to the total ground truth biomass of 5.10 g. Our estimated Simpson's Index of Diversity was 0.9404 in comparison to the ground truth 0.9408. Our method simultaneously classifies >1,000 arthropods to functional groupings while estimating total and class specific biomass, without any computer vision bounding box or mask annotations, all from a single photo. We release our code and dataset to further research efforts in computer vision for arthropod classification.

The atmospheric carbon dioxide level has increased and is predicted to continue increasing, which may affect various aspects of plant growth. The objective of this study was to investigate the effects of doubling the carbon dioxide level on the growth and physiological activities of a widely utilized cool-season turfgrass species, creeping bentgrass (Agrostis stolonifera L. ‘Penncross’). ‘Penncross’ plants were established in fritted clay medium and maintained under well-irrigated and well-fertilized conditions in growth chambers. The plants were exposed to either ambient carbon dioxide concentrations (400±10 µmol L−1) or elevated carbon dioxide concentrations (800±10 µmol L−1) for 12 weeks. Plants grown under elevated carbon dioxide displayed a significantly faster growth rate of their lateral stems (stolons) and increased shoot and root dry weight but a reduced specific leaf area compared to those plants at ambient carbon dioxide levels. Fast stolon growth is a highly desirable trait for turfgrass establishment and recovery from physical damage. The root length and surface area were also increased due to the elevated CO2, which may facilitate water uptake and serve critical drought-avoidance roles when irrigation water is limited. Elevated carbon dioxide caused an increase in the leaf net photosynthetic rate but a reduction in the stomatal conductance and transpiration rate, contributing to improved water use efficiency in creeping bentgrass. Efficient water use is especially important for turfgrass plant survival when irrigation water is limited. Our results suggested that cool-season turfgrass species may greatly benefit from increasingly elevated carbon dioxide concentrations via growth promotion and increasing water use efficiency.