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"Anand, Madhur"
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Parasitic oscillations
\"A stunning new collection of poems that examine various aspects of living and practicing as both a poet and scientist in the Anthropocene during a time of unravelling. The poems in Madhur Anand's second collection interrogate the inevitability of undesired cyclic variation caused by feedback in the amplifying devices of both poetry and science. There are several interacting currents: the poet's own work between the arts and the sciences, living between North American and Indian cultures, as well as examining contemporary environments through the lag effects of the past. Weaving in a close reading of A.O. Hume's The Nests and Eggs of Indian Birds (1889), anti-colonial, intertextual, feminist, electronic, and diasporic relationships are examined against the backdrop of unprecedented ecological collapse. Here, birds are often no longer direct subjects of metaphor, but rather remain strange, sometimes silent, a kind of menacing and stray capacitance, but can still act as harbingers of discovery and hope. Fluctuating through extreme highs and lows, both emotional and environmental, while examining a myriad of philosophical and ethical dilemmas, Parasitic Oscillations is an enlightening, thought-provoking, and profoundly beautiful work that both informs and questions.\"-- Provided by publisher.
Book
Plant functional traits as measures of ecosystem service provision
Despite the relevance of ecosystem services (ES) to society and modern ecological research, current methods of measurement and mapping remain inconsistent and often lack primary data in estimating and modeling ES. A key player in our understanding of ES and their measurements are plant functional traits—chemical and physical aspects of plants—which are often cited as one of the drivers of ecosystem processes and functions. In order to better quantify the ES–plant functional trait indicators, we outline existing evidence of this relationship and identify gaps between the best predicted ES and the most valued ES. This study offers an up‐to‐date review of plant functional traits' direct or indirect relationships with ecosystem service provision and discusses the quantitative evidence these traits might hold as indicators. With this review, we seek to (1) offer a current summary of the quantitative evidence on ecosystem service–plant functional trait relationships, (2) identify which traits have been used to successfully indicate ecosystem services, and (3) identify research gaps, and ecosystem services or traits that receive little attention or have weak criteria as indicators. In a comprehensive literature review of the 19 services that were searched for, genetic materials, medicine, and cultural services had no relevant plant functional trait indicators, while the remaining 16 services had a range of traits associated with them. We found that functional traits showed varying relationships to ES, with some depending on the ecosystem type they were found in, while others appeared to remain consistent across ecosystems and conditions. This indicates that there could exist a subset of traits that are “universal” indicators across all ecosystem types, while others are ecosystem dependent. Our review suggests the need for more research on less clearly defined ES (such as cultural, educational, and refugium services) both by more careful definitions to make quantitative measures more applicable, and through increased quantitative and qualitative studies to better understand the nature of ES indicators for these services. This summary shows how plant functional traits can quantitatively and reliably predict and provide details on a subset of ES.
Journal Article
Local lockdowns outperform global lockdown on the far side of the COVID-19 epidemic curve
In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, “counties”). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.
Journal Article
Communicating sentiment and outlook reverses inaction against collective risks
Collective risks permeate society, triggering social dilemmas in which working toward a common goal is impeded by selfish interests. One such dilemma is mitigating runaway climate change. To study the social aspects of climate-change mitigation, we organized an experimental game and asked volunteer groups of three different sizes to invest toward a common mitigation goal. If investments reached a preset target, volunteers would avoid all consequences and convert their remaining capital into monetary payouts. In the opposite case, however, volunteers would lose all their capital with 50% probability. The dilemma was, therefore, whether to invest one’s own capital or wait for others to step in. We find that communicating sentiment and outlook helps to resolve the dilemma by a fundamental shift in investment patterns. Groups in which communication is allowed invest persistently and hardly ever give up, even when their current investment deficits are substantial. The improved investment patterns are robust to group size, although larger groups are harder to coordinate, as evidenced by their overall lower success frequencies. A clustering algorithm reveals three behavioral types and shows that communication reduces the abundance of the free-riding type. Climate-change mitigation, however, is achieved mainly by cooperator and altruist types stepping up and increasing contributions as the failure looms. Meanwhile, contributions from free riders remain flat throughout the game. This reveals that the mechanisms behind avoiding collective risks depend on an interaction between behavioral type, communication, and timing.
Journal Article
Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation
Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles.
The authors investigate the broad-scale climatological and soil properties that co-vary with major axes of plant functional traits. They find that variation in plant size is attributed to latitudinal gradients in water or energy limitation, while variation in leaf economics traits is attributed to both climate and soil fertility including their interaction.
Journal Article
Model-based projections for COVID-19 outbreak size and student-days lost to closure in Ontario childcare centres and primary schools
There is a pressing need for evidence-based scrutiny of plans to re-open childcare centres during the COVID-19 pandemic. Here we developed an agent-based model of SARS-CoV-2 transmission within a childcare centre and households. Scenarios varied the student-to-educator ratio (15:2, 8:2, 7:3), family clustering (siblings together versus random assignment) and time spent in class. We also evaluated a primary school setting (with student-educator ratios 30:1, 15:1 and 8:1), including cohorts that alternate weekly. In the childcare centre setting, grouping siblings significantly reduced outbreak size and student-days lost. We identify an intensification cascade specific to classroom outbreaks of respiratory viruses with presymptomatic infection. In both childcare and primary school settings, each doubling of class size from 8 to 15 to 30 more than doubled the outbreak size and student-days lost (increases by factors of 2–5, depending on the scenario. Proposals for childcare and primary school reopening could be enhanced for safety by switching to smaller class sizes and grouping siblings.
Journal Article
Predicting discrete-time bifurcations with deep learning
Many natural and man-made systems are prone to critical transitions—abrupt and potentially devastating changes in dynamics. Deep learning classifiers can provide an early warning signal for critical transitions by learning generic features of bifurcations from large simulated training data sets. So far, classifiers have only been trained to predict continuous-time bifurcations, ignoring rich dynamics unique to discrete-time bifurcations. Here, we train a deep learning classifier to provide an early warning signal for the five local discrete-time bifurcations of codimension-one. We test the classifier on simulation data from discrete-time models used in physiology, economics and ecology, as well as experimental data of spontaneously beating chick-heart aggregates that undergo a period-doubling bifurcation. The classifier shows higher sensitivity and specificity than commonly used early warning signals under a wide range of noise intensities and rates of approach to the bifurcation. It also predicts the correct bifurcation in most cases, with particularly high accuracy for the period-doubling, Neimark-Sacker and fold bifurcations. Deep learning as a tool for bifurcation prediction is still in its nascence and has the potential to transform the way we monitor systems for critical transitions.
Critical transitions and qualitative changes of dynamics in cardiac, ecological, and economical systems, can be characterized by discrete-time bifurcations. The authors propose a deep learning framework that provides early warning signals for critical transitions in discrete-time experimental data.
Journal Article
Early warning signals for bifurcations embedded in high dimensions
Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these tipping points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded in a much higher dimensional state space. In many cases of practical relevance, the form of this embedding is poorly understood or entirely unknown. This paper explores how measurement of the critical phenomena that generically precede such bifurcations can be used to make inferences about some properties of their embeddings, and, conversely, how prior knowledge about the mechanism of bifurcation can robustify predictions of an oncoming tipping event. These modes of analysis are first demonstrated on a simple fluid flow system undergoing a Hopf bifurcation. The same approach is then applied to data associated with the West African monsoon shift, with results corroborated by existing models of the same system. This example highlights the effectiveness of the methodology even when applied to complex climate data, and demonstrates how a well-resolved spatial structure associated with the onset of atmospheric instability can be inferred purely from time series measurements.
Journal Article
Spatial early warning signals of social and epidemiological tipping points in a coupled behaviour-disease network
The resurgence of infectious diseases due to vaccine refusal has highlighted the role of interactions between disease dynamics and the spread of vaccine opinion on social networks. Shifts between disease elimination and outbreak regimes often occur through tipping points. It is known that tipping points can be predicted by early warning signals (EWS) based on characteristic dynamics near the critical transition, but the study of EWS in coupled behaviour-disease networks has received little attention. Here, we test several EWS indicators measuring spatial coherence and autocorrelation for their ability to predict a critical transition corresponding to disease outbreaks and vaccine refusal in a multiplex network model. The model couples paediatric infectious disease spread through a contact network to binary opinion dynamics of vaccine opinion on a social network. Through change point detection, we find that mutual information and join count indicators provided the best EWS. We also show the paediatric infectious disease natural history generates a discrepancy between population-level vaccine opinions and vaccine immunity status, such that transitions in the social network may occur before epidemiological transitions. These results suggest that monitoring social media for EWS of paediatric infectious disease outbreaks using these spatial indicators could be successful.
Journal Article