Abstract By dispersing seeds, animals provide ecological functions critical for the ecology, evolution, and conservation of plants. We review quantitative and empirical approaches and emerging technologies to quantify processes and patterns of animal‐mediated seed dispersal (zoochory) across its phases: from predispersal to postdispersal. In addition, we consider approaches to studying seed disperser behaviors and plant traits, both of which influence all dispersal phases of animal‐mediated dispersal. Finally, we discuss how we can use quantitative and empirical approaches to integrate across seed dispersal phases and address data gaps to improve our mechanistic understanding of zoochory and its consequences for ecology and conservation. To move towards generalization and predictability in seed dispersal ecology, we recommend the development of standardized protocols that can be widely implemented across systems with simultaneous and iterative development of theory and quantitative models. As approaches in studying animal‐mediated seed dispersal continue to advance, exciting opportunities present themselves to increase our understanding of seed dispersal ecology.

Abstract Premise Seed dispersal is a critical process for plant community assembly; however, natural rates of seed arrival are rarely quantified compared with other assembly mechanisms, especially in herbaceous communities. Methods Here we compare the utility of artificial grass carpet squares (“artificial grass”) for capturing seed rain with classic “sticky trap” methods. We placed paired sticky traps and artificial grass squares in two grassland ecosystems, added known numbers of seeds of multiple species to each trap, and recovered seeds at one‐week, one‐month, and two‐month intervals. Results We found that both trap types lost seeds through time at similar rates, but each trap type had advantages and disadvantages. Overall, sticky traps retained more seeds and measured primary dispersal, but recovering seeds was difficult and hindered by debris stuck to the traps. Alternatively, artificial grass traps measured effective dispersal as more seeds were lost through time to secondary dispersal and granivory, but recovered seeds could be handled easily and retained for long‐term storage and germination. Discussion We encourage the broad adoption of seed rain studies to improve links between theoretical and empirical community ecology. Both sticky traps and artificial grass traps are useful in measuring seed rain in grasslands but vary in the types of information they provide.

Abstract The complexity and variability of natural environments make quantitative studies of seed wind dispersal challenging. Wind tunnel experiments offer a controlled alternative to investigate the mechanisms of seed wind dispersal. This review focuses on wind tunnels and the associated technologies used for studying seed wind dispersal, including wind tunnel structure and its functionality in replicating natural dispersal scenarios. We discuss the selection and arrangement of key environmental factors, such as terrain, individual plants, vegetation, and substrates, as well as the real simulation of wind dispersal conditions, and provide a detailed description of critical experimental procedures. This paper emphasizes key factors for ensuring reliable and repeatable experimental results, including the use of representative seed materials, strict control of airflow characteristics, and maintaining standardized conditions. By integrating experimental experiences, this research offers valuable references for the application of wind tunnel technology in simulating the process of seed wind dispersal, providing strong support for understanding seed dispersal mechanisms.

Abstract Premise The censer seed dispersal mechanism, whereby mature fruits are retained on plants and seeds dispersed by mechanical shaking, is among the most specialized wind‐aided seed dispersal strategies employed by the flowering plants. An efficient, affordable, and easily repeatable protocol for determining whether a species uses this unusual mechanism of seed dispersal is lacking. Our focal species, Solanum tudununggae , is one of just two Solanum species (in a fleshy‐fruited genus of ca. 1200 species) hypothesized to use this dispersal mechanism as a means to move seeds farther from parent plants than would gravity alone. Methods and Results Live greenhouse plants were systematically shaken using a hand pulling technique. The proportion of seeds released and the distance of their movement were recorded and assessed using tools such as organza bags, bed sheets, a smartphone, ImageJ software, and a set of simple calculations. The results from this mentored undergraduate research project demonstrate that the protocol effectively identifies the use of the censer mechanism in S. tudununggae and quantifies its seed dispersal effectiveness. Conclusions The findings indicate that this protocol has confirmed experimentally, for the first time, the presence and effectiveness of the censer mechanism in the large and economically important genus Solanum . We believe that this protocol is useful for other taxa with similarly elongated and flexible growth habits.

Abstract Fleshy fruits attract animals to ingest fruit, swallow the seeds, and release them in the landscape, thus facilitating seed dispersal and plant regeneration. Attraction of animal dispersers is achieved via attractants such as color or scent, and rewards like sugars, lipids, and micronutrients. In addition to these attractants and rewards, a plethora of fruit and seed functional traits, including size, hardness, and chemistry, can affect the accessibility and attractiveness of fruits to seed‐dispersing animals. These functional traits form the interface between animals and fruits and, through trait matching, act as filters in dispersal networks. Yet, despite their critical roles in shaping seed dispersal networks, many fruit traits are not quantified in a standardized fashion, if they are quantified at all. As such, the existing databases of fruit traits used for syntheses lack many functional dimensions. We review the latest developments in fruit trait analysis, focusing on traits relevant for plant–frugivore interactions (i.e., morphology, color, chemistry), as well as some of their drivers (DNA, RNA, and microbial communities). We provide an overview of many existing methods, their advantages and disadvantages, and their application in field conditions.

PremiseDeep learning–based classification of herbarium images is hampered by background heterogeneity, which introduces noise and artifacts that can potentially mislead models and degrade their accuracy. Addressing these effects is essential to enhance overall performance.MethodsWe introduce PlantSAM, an automated segmentation pipeline that integrates YOLOv10 for plant region detection and the Segment Anything Model (SAM2) for segmentation. YOLOv10 generates bounding box prompts to guide SAM2, enhancing segmentation accuracy. Both models were fine‐tuned on herbarium images and evaluated using intersection over union (IoU) and Sørensen–Dice coefficient metrics.ResultsPlantSAM achieved state‐of‐the‐art segmentation performance, with an IoU of 0.94 and a Sørensen–Dice coefficient of 0.97. Incorporating segmented images into classification models led to consistent performance improvements across five tested botanical traits, with accuracy gains of up to 4.36% and F1 score improvements of 4.15%.ConclusionsOur findings highlight the importance of background removal in herbarium image analysis, as it significantly enhances classification performance by enabling models to focus more effectively on the foreground plant structures.

PremiseSalt marshes in the North Atlantic United States are dominated by grasses from the genus Sporobolus, which are perennial C4 plants known for tolerating saline, anoxic, and flooded coastal sediments. Establishing in vitro cultures of Sporobolus species remains a major challenge due to frequent seed contamination from ergot and endophytic bacteria in vegetative tissues.Methods and ResultsHere, we demonstrate that in vitro axenic cultures of Sporobolus alterniflorus can be established in four weeks and micropropagation chains in three months with a 75% success rate. Additionally, this method is easily transferable to other salt marsh grasses, such as S. cynosuroides.ConclusionsThe strength of our method lies in bypassing adventitious regeneration or somatic embryogenesis. Instead, we remove the hypocotyls and endosperm, primary sources of fungal contamination, and induce adventitious roots in the epicotyl. We also detail culture‐ and PCR‐based methods to screen for contamination in vitroplants of S. alterniflorus.

Abstract Premise As the sister clade to angiosperms, extant gymnosperms are crucial for reconstructing ancestral gene regulatory networks in seed plants. This highlights the need for model systems representing each of their distinct lineages. However, tools to quickly and effectively investigate gene function in gymnosperms are still limited due to the challenges of long life cycles and large genome sizes. Species within the xerophytic genus Ephedra (Gnetales) have comparatively smaller genomes and shrubby growth habits with shorter life spans, making them better suited for greenhouse cultivation and laboratory experiments. Methods We implement virus‐induced gene silencing (VIGS) to manipulate gene expression in Ephedra tweedieana via Agrobacterium ‐mediated vacuum infiltration of tobacco rattle virus (TRV1 and TRV2) into seedlings. Results Treatment resulted in highly efficient gene silencing of the E. tweedieana PHYTOENE DESATURASE ( PDS ) ortholog EtwPDS . The expected photobleaching phenotype was observed as early as two weeks, and lasted at least five months in stems, shoot tips, leaves, axillary meristems, and lateral branches of treated plants. Discussion We report on virus‐induced targeted gene silencing of PDS in a Gnetales representative to further enable functional studies of the genetic mechanisms underpinning adaptations in gymnosperms, an important and underrepresented lineage of seed plants.

PremiseStudies comparing seed longevity between species or genotypes commonly measure seed longevity as the time it takes for seed lot viability to drop to 50% (p50). However, p50 is influenced by the initial viability. Although standard protocols for comparative studies thus recommend using seed lots with similar and high initial viability (>85%), variation in viability strongly influences p50.MethodsWe simulated seed viability decline across a range of initial viabilities and rates of probit viability loss (σ) to illustrate how variation in initial viability affects p50 estimates and to test approaches for reducing this bias.ResultsFor hypothetical seed lots with identical rates of seed viability loss, variation in initial viability leads to a threefold variation in p50 estimates. Narrowing the initial viability (e.g., to 85–95%) reduced this bias. Alternatively, p50 can be recalculated to a standardized value of initial viability (e.g., 90%), which makes it proportional to the rate of probit viability loss. The most straightforward measure of seed longevity for comparative studies is the probit rate of viability loss itself, represented by σ from the viability equation.Conclusionsp50 is confounded by variation in initial seed lot viability and is suboptimal for comparative studies of seed longevity among species or genotypes. Robust measures of seed longevity include the rate of probit viability loss (σ) or p50 standardized to a certain initial seed viability.

PremiseDue to their small size and lack of easily visible macroscopic characters, the identification of cryptogam species has always been challenging. Here, the use of a machine learning computer vision method is explored for the identification of species of lichens and bryophytes from Australian biocrusts.MethodsThree models were trained using mostly images from herbarium specimens. The models were then evaluated based on statistics produced by Microsoft Azure Custom Vision and a bench‐test with the CSIRO Horama ID mobile app.ResultsDespite the small size and reduced habit of lichens and bryophytes, the Cryptogam (lichens and bryophytes) model performance value is just slightly lower than the performance of a vascular plant model of similar scope (64% accuracy for the Cryptogam model versus 70.3% for vascular plants from Costa Rica).DiscussionThe performance of our models suggested opportunities for improvement, including for bias issues caused by imbalanced datasets, white background, and mixed specimens, as well as the difficulty in stabilizing live images at high magnification when using a mobile device to deploy the model. Further opportunities to improve model performance for these small and character‐poor organisms, including data augmentation and image segmentation, are also discussed.