Abstract Fluctuations in population abundances are often correlated through time across multiple locations, a phenomenon known as spatial synchrony. Spatial synchrony can exhibit complex spatial structures, termed ‘geographies of synchrony’, that can reveal mechanisms underlying population fluctuations. However, most studies have focused on spatial extents of 10s to 100s of kilometres, making it unclear how synchrony concepts and approaches should apply to dynamics at finer spatial scales.We used network analyses, multiple regression on similarity matrices, and wavelet coherence analyses to examine micro‐scale synchrony and geographies of synchrony, over distances up to 30 m, in a serpentine grassland plant community.We found that species' populations exhibited a geography of synchrony even over such short distances. Often, well‐synchronized populations were geographically separate, a spatial structure that was shaped mainly by gopher disturbance and dispersal limitation, and to a lesser extent by relationships with other plant species. Precipitation was a significant driver of site‐ and community‐wide temporal dynamics. Gopher disturbance appeared to drive synchrony on 2‐ to 6‐year timescales, and we detected coherent fluctuations among pairs of focal plant taxa.Synthesis. Micro‐geographies of synchrony are an intriguing phenomenon that may also help us better understand community dynamics. Additionally, the related geographies of synchrony and coherent temporal dynamics among some species pairs indicate that incorporating interspecific interactions can improve understanding of population spatial synchrony.
more »
« less
Mapping the uneven geographies of digital phenomena: The case of blockchain
Key messages Grounding practices within the materiality of geography is an important technique for studying the complexity of digital phenomena.The DIGO (Discourses, Infrastructures, Groupings, and Outcomes) framework uses these categories to guide data selection for locating digital phenomenon in material geographies.This article applies the DIGO framework to blockchain (using data about tweets, miners, firms, and ICOs) to show how this digital practice connects to and across material geographies.
more »
« less
- Award ID(s):
- 1853718
- PAR ID:
- 10363819
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Canadian Geographies / Géographies canadiennes
- Volume:
- 66
- Issue:
- 1
- ISSN:
- 0008-3658
- Page Range / eLocation ID:
- p. 23-36
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Model calibration is crucial for optimizing the performance of complex computer models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological advancement through the integration of advanced digital technologies into industrial processes, model calibration plays a key role in advancing digital twin technology, ensuring alignment between digital representations and real‐world systems. This comprehensive review focuses on the Kennedy and O'Hagan (KOH) framework (Kennedy and O'Hagan, Journal of the Royal Statistical Society: Series B 2001; 63(3):425–464). In particular, we explore recent advancements addressing the challenges of the unidentifiability issue while accommodating model inadequacy within the KOH framework. In addition, we explore recent advancements in adapting the KOH framework to complex scenarios, including those involving multivariate outputs and functional calibration parameters. We also delve into experimental design strategies tailored to the unique demands of model calibration. By offering a comprehensive analysis of the KOH approach and its diverse applications, this review serves as a valuable resource for researchers and practitioners aiming to enhance the accuracy and reliability of their computer models. This article is categorized under:Statistical Models > Semiparametric ModelsStatistical Models > Simulation ModelsStatistical Models > Bayesian Modelsmore » « less
-
Abstract Capturing evidence for dynamic changes in self‐regulated learning (SRL) behaviours resulting from interventions is challenging for researchers. In the current study, we identified students who were likely to do poorly in a biology course and those who were likely to do well. Then, we randomly assigned a portion of the students predicted to perform poorly to a science of learning to learn intervention where they were taught SRL study strategies. Learning outcome and log data (257 K events) were collected fromn = 226 students. We used a complex systems framework to model the differences in SRL including the amount, interrelatedness, density and regularity of engagement captured in digital trace data (ie, logs). Differences were compared between students who were predicted to (1) perform poorly (control,n = 48), (2) perform poorly and received intervention (treatment,n = 95) and (3) perform well (not flagged,n = 83). Results indicated that the regularity of students' engagement was predictive of course grade, and that the intervention group exhibited increased regularity in engagement over the control group immediately after the intervention and maintained that increase over the course of the semester. We discuss the implications of these findings in relation to the future of artificial intelligence and potential uses for monitoring student learning in online environments. Practitioner notesWhat is already known about this topicSelf‐regulated learning (SRL) knowledge and skills are strong predictors of postsecondary STEM student success.SRL is a dynamic, temporal process that leads to purposeful student engagement.Methods and metrics for measuring dynamic SRL behaviours in learning contexts are needed.What this paper addsA Markov process for measuring dynamic SRL processes using log data.Evidence that dynamic, interaction‐dominant aspects of SRL predict student achievement.Evidence that SRL processes can be meaningfully impacted through educational intervention.Implications for theory and practiceComplexity approaches inform theory and measurement of dynamic SRL processes.Static representations of dynamic SRL processes are promising learning analytics metrics.Engineered features of LMS usage are valuable contributions to AI models.more » « less
-
Summary Lycopodiaceae are one of three surviving families of lycopsids, a lineage of vascular plants with a fossil history dating to at least the Early Devonian or perhaps the Late Silurian (c. 415 Ma). Many fossils have been linked to crown Lycopodiaceae, but the lack of well‐preserved material has hindered definitive recognition of this group in the paleobotanical record.New, exceptionally well‐preserved permineralized lycopsid fossils from the Early Cretaceous (125.6 ± 1.0 Ma) of Inner Mongolia, China, were examined in detail using acetate peel and micro‐computed tomography techniques. The anatomy of extant Lycopodiaceae was analyzed for comparison using fluorescence microscopy. Phylogenetic relationships of the new fossil to extant Lycopodiaceae were evaluated using parsimony and maximum likelihood analyses.Lycopodicaulis oellgaardiigen. et sp. nov. provides the earliest unequivocal and best‐documented evidence of crown Lycopodiaceae and Lycopodioideae, based on anatomically‐preserved fossil material.Recognition ofLycopodicaulisin Asia during the Early Cretaceous indicates the presence of crown Lycopodiaceae at this time, and striking similarities of stem anatomy with extant species provide a framework for the understanding of the interaction of branching and vascular anatomy in crown‐group lycopsids.more » « less
-
Abstract The costs of foraging can be high while also carrying significant risks, especially for consumers feeding at the top of the food chain.To mitigate these risks, many predators supplement active hunting with scavenging and kleptoparasitic behaviours, in some cases specializing in these alternative modes of predation.The factors that drive differential utilization of these tactics from species to species are not well understood.Here, we use an energetics approach to investigate the survival advantages of hunting, scavenging and kleptoparasitism as a function of predator, prey and potential competitor body sizes for terrestrial mammalian carnivores.The results of our framework reveal that predator tactics become more diverse closer to starvation, while the deployment of scavenging and kleptoparasitism is strongly constrained by the ratio of predator to prey body size.Our model accurately predicts a behavioural transition away from hunting towards alternative modes of predation with increasing prey size for predators spanning an order of magnitude in body size, closely matching observational data across a range of species.We then show that this behavioural boundary follows an allometric power‐law scaling relationship where the predator size scales with an exponent nearing 3/4 with prey size, meaning that this behavioural switch occurs at relatively larger threshold prey body size for larger carnivores.We suggest that our approach may provide a holistic framework for guiding future observational efforts exploring the diverse array of predator foraging behaviours.more » « less
An official website of the United States government
