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1. Redundant functions of miR156 ‐targeted SQUAMOSA PROMOTER BINDING PROTEIN ‐ LIKE transcription factors in promoting cauline leaf identity

   https://doi.org/10.1111/nph.70210  

   Manuela, Darren; Du, Liren; Zhang, Qi; Liao, Yifei; Hu, Tieqiang; Fouracre, Jim P; Xu, Mingli (July 2025, New Phytologist)

   Summary Cauline leaf development represents an intermediate phase between vegetative and reproductive stages. While extensive research has been conducted on the genetic and environmental factors that determine cauline leaf number, less attention has been given to the regulation of their morphology and the establishment of cauline leaf identity.In this study, we report that miR156‐targeted SQUAMOSA PROMOTER BINDING PROTEIN‐LIKE (SPL) transcription factors, including SPL2, SPL9, SPL10, SPL11, SPL13, and SPL15, redundantly regulate cauline leaf identity, affecting both cauline leaf shape and the number of leaves on secondary inflorescences. This function is distinct from that of floral meristem identity genes, which affect the number of cauline leaves by promoting floral fate.We further show that the inducers of reproductive development SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) and FRUITFUL (FUL) directly bind to and activateSPL9andSPL15, linking floral induction pathways to the regulation of cauline leaf identity. Additionally, we demonstrate that the brassinosteroid receptor BRASSINOSTEROID INSENSITIVE 1 (BRI1) is co‐expressed with miR156‐targetedSPLsin cauline leaves and is a direct target of SPL9.Together, this study uncovers a SOC1/FUL‐SPL‐BRI1 module that governs cauline leaf identity, providing new insights into the regulatory networks that control plant inflorescence architecture. 

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2. Using accelerometer‐based behavioural classification to enhance scavenger conservation

   https://doi.org/10.1111/1365-2664.70180  

   Vaadia, Gideon; Acácio, Marta; Agassi, Tal; Anglister, Nili; Miller, Yigal; Hatzofe, Ohad; Mateo‐Tomás, Patricia; Rodríguez‐Pérez, Jorge; Fernández‐García, María; Olea, Pedro P; et al (November 2025, Journal of Applied Ecology)

   Abstract Human activities are endangering animal species globally, and implementing effective conservation strategies requires understanding animal behaviour and ecology. Advancements in GPS tracking technology, accelerometry and machine learning algorithms are allowing the in situ study of animal movement and behaviour remotely. However, the challenge of building supervised machine learning algorithms and collecting the large datasets required to train them is hampering the widespread use of these tools. Additionally, the reliability of these models in classifying unobserved behaviours is rarely validated, resulting in possible classification errors.We built a supervised accelerometer‐based behavioural classification model for griffon vultures (Gyps fulvus). Similarly to most other avian scavenger populations worldwide, griffons are critically endangered in Israel and neighbouring countries, mostly due to feeding on poisoned carcasses. Thus, identifying this scavenger's feeding behaviour and foraging areas is crucial for their conservation.We trained a Random Forest model on acceleration data of 14 captive and 17 free‐roaming griffons. We classified 5783 behavioural observations into 6 classes: feeding, lying, standing, other ground behaviours, flapping and soaring flight. The model performed well (0.96 accuracy, 0.89 precision and 0.82 recall) and, importantly, feeding behaviours were accurately classified (0.87 precision, 0.92 recall). We calculated an observation‐specific confidence score and demonstrated its effectiveness in identifying true‐ and false‐positive classifications, in both captive and free‐roaming individuals. Finally, we used our model to reliably identify feeding hotspots, where vultures can be at higher risk of poisoning.Synthesis and applications.We provide a tool to help identify vulture feeding hotspots, supporting carcass management efforts to prevent poisoning. Integrated with near real‐time tracking, our model can support global efforts to combat scavenger poisoning. The training dataset, model and codes are provided in a user‐friendly platform, along with a conceptual framework, to encourage use by ecologists and conservation practitioners. 

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3. Social networks and transformative behaviours in a grassland social‐ecological system

   https://doi.org/10.1002/pan3.10695  

   Nesbitt, Holly K; Metcalf, Alexander L; Floyd, Theresa M; Uden, Daniel R; Chaffin, Brian C; Gulab, Sabrina; Banerjee, Simanti; Vallury, Sechindra; Hamlin, Samantha L; Metcalf, Elizabeth Covelli; et al (October 2024, People and Nature)

   Abstract Social connections among individuals are essential components of social‐ecological systems (SESs), enabling people to take actions to more effectively adapt or transform in response to widespread social‐ecological change. Although scholars have associated social connections and cognitions with adaptive capacity, measuring actors' social networks may further clarify pathways for bolstering resilience‐enhancing actions.We asked how social networks and socio‐cognitions, as components of adaptive capacity, and SES regime shift severity affect individual landscape management behaviours using a quantitative analysis of ego network survey data from livestock producers and landcover data on regime shift severity (i.e. juniper encroachment) in the North American Great Plains.Producers who experienced severe regime shifts or perceived high risks from such shifts were not more likely to engage in transformative behaviour like prescribed burning. Instead, we found that social network characteristics explained significant variance in transformative behaviours.Policy implications: Our results indicate that social networks enable behaviours that have the potential to transform SESs, suggesting possible leverage points for enabling capacity and coordination toward sustainability. Particularly where private lands dominate and cultural practices condition regime shifts, clarifying how social connections promote resilience may provide much needed insight to bolster adaptive capacities in the face of global change. Read the freePlain Language Summaryfor this article on the Journal blog. 

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4. A multimodal approach to support teacher, researcher and AI collaboration in STEM +C learning environments

   https://doi.org/10.1111/bjet.13518  

   Cohn, Clayton; Snyder, Caitlin; Fonteles, Joyce Horn; Ashwin, TS; Montenegro, Justin; Biswas, Gautam (March 2025, British Journal of Educational Technology)

   AbstractRecent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross adifficulty threshold,and (2) thepoint of intervention, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback. Practitioner notesWhat is already known about this topicCollaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains.Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments.Hybrid human‐AI approaches can help address collaborating students' STEM+C difficulties by combining the domain knowledge, emotional intelligence and social awareness of human experts with the general knowledge and efficiency of AI.What this paper addsWe extend a previous human‐AI collaboration framework using a hybrid intelligence approach to characterize the human component of the partnership as a researcher‐teacher partnership and present our approach as a teacher‐researcher‐AI collaboration.We adapt an AI‐generated multimodal timeline to actualize our human‐AI collaboration by pairing the timeline with videos of students encountering difficulties, engaging in active discussions with a high school teacher while watching the videos to discern the timeline's utility in the classroom.From our discussions with the teacher, we define two types ofinflection pointsto address students' STEM+C difficulties—thedifficulty thresholdand theintervention point—and discuss how thefeedback latency intervalseparating them can inform educator interventions.We discuss two ways in which our teacher‐researcher‐AI collaboration can help teachers support students encountering STEM+C difficulties: (1) teachers using the multimodal timeline to guide feedback for students, and (2) researchers using teachers' input to iteratively refine the multimodal timeline.Implications for practice and/or policyOur case study suggests that timeline gaps (ie, disengaged behaviour identified by off‐screen students, pauses in discourse and lulls in environment actions) are particularly important for identifying inflection points and formulating formative feedback.Human‐AI collaboration exists on a dynamic spectrum and requires varying degrees of human control and AI automation depending on the context of the learning task and students' work in the environment.Our analysis of this human‐AI collaboration using a multimodal timeline can be extended in the future to support students and teachers in additional ways, for example, designing pedagogical agents that interact directly with students, developing intervention and reflection tools for teachers, helping teachers craft daily lesson plans and aiding teachers and administrators in designing curricula. 

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5. Patterns and drivers of pollen co‐transport network structure vary across pollinator functional groups

   https://doi.org/10.1111/1365-2745.14397  

   Carneiro, Liedson Tavares; Williams, Jessica Nicole; Barker, Daniel A; Anderson, Joseph W; Martel, Carlos; Arceo‐Gomez, Gerardo (October 2024, Journal of Ecology)

   Abstract The patterns and drivers of pollen transport on insect bodies can have important consequences for plant reproductive success and floral evolution; however, they remain little studied. Recently, pollinator bodies have been further described as pollen competitive arenas, where pollen grains can compete for space, with implications for the evolution of pollen dispersal strategies and plant community assembly. However, the identity, strength, and diversity of pollen competitive interactions and how they vary across pollinator functional groups is not known. Evaluating patterns and drivers of the pollen co‐transport landscape and how these vary across different pollinator groups is central to further our understanding of floral evolution and co‐flowering community assembly.Here, we integrate information on the number and identity of pollen grains on individual insect pollen loads with network analyses to uncover novel pollen co‐transport networks and how these vary across pollinator functional groups (bees and bee flies). We further evaluate differences in pollen load size, species composition, diversity and phylogenetic diversity among insect groups and how these relate to body size and gender.Pollen co‐transport networks were diverse and highly modular in bees, with groups of pollen species interacting more often with each other on insect bodies. However, the number, identity and frequency of competitors that pollen grains encounter on insect bodies vary between some pollinator functional groups. Other aspects of pollen loads such as their size, richness and phylogenetical diversity were shaped by bee size or gender, with females carrying larger but less phylogenetically diverse pollen loads than males.Synthesis. Our results show that the number, identity and phylogenetic relatedness of pollen competitors changes as pollen grains travel on the body of different pollinators. As a result, pollinator groups impose vastly different interaction landscapes during pollen transport, with so far unknown consequences for plant reproductive success, floral evolution and community assembly. 

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