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1. Scaling up biodiversity–ecosystem function relationships across space and over time

   https://doi.org/10.1002/ecy.3166  

   Qiu, Jiangxiao; Cardinale, Bradley J. (January 2020, Ecology)

   Understanding how to scale up effects of biological diversity on ecosystem functioning and services remains challenging. There is a general consensus that biodiversity loss alters ecosystem processes underpinning the goods and services upon which humanity depends. Yet, most of that consensus stems from experiments performed at small spatial-scales for short time-frames, which limits transferability of conclusions to longer-term, landscape-scale conservation policies and management. Here we develop quantitative scaling relationships linking 374 experiments that tested plant diversity effects on biomass production across a range of scales. We show that biodiversity effects increase by factors of 1.68 and 1.10 for each 10-fold increase in experiment temporal and spatial scales, respectively. Contrary to prior studies, our analyses suggest that the time scale of experiments, rather than their spatial scale, is the primary source of variation in biodiversity effects. But consistent with earlier research, our analyses reveal that complementarity effects, rather than selection effects, drive the positive space-time interactions for plant diversity effects. Importantly, we also demonstrate complex space-time interactions and nonlinear responses that emphasize how simple extrapolations from small-scale experiments are likely to underestimate biodiversity effects in real-world ecosystems. Quantitative scaling relationships from this research are a crucial step towards bridging controlled experiments that identify biological mechanisms across a range of scales. Predictions from scaling relationships like these could then be compared with observations for fine-tuning the relationships and ultimately improving their capacities to predict consequences of biodiversity loss for ecosystem functioning and services over longer time-frames across real-world landscapes. 

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2. Global patterns in copepod thermal tolerance

   https://doi.org/10.1093/plankt/fbab044  

   Sasaki, Matthew; Dam, Hans G (July 2021, Journal of Plankton Research)

   Abstract Copepods are key components of aquatic habitats across the globe. Understanding how they respond to warming is important for predicting the effects of climate change on aquatic communities. Lethal thermal limits may play an important role in determining responses to warming. Thermal tolerance can vary over several different spatial and temporal scales, but we still lack a fundamental understanding of what drives the evolution of these patterns in copepods. In this Horizons piece, we provide a synthesis of global patterns in copepod thermal tolerance and potential acclimatory capacities. Copepod thermal tolerance increases with maximum annual temperature. We also find that the effects of phenotypic plasticity on thermal tolerance are negatively related to the magnitude of thermal tolerance, suggesting a potential trade-off between these traits. Our ability to fully describe these patterns is limited, however, by a lack of spatial, temporal and phylogenetic coverage in copepod thermal tolerance data. We indicate several priority areas for future work on copepod thermal tolerance, and accompanying suggestions regarding experimental design and methodology. 

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3. Generation of multicellular spatiotemporal models of population dynamics from ordinary differential equations, with applications in viral infection

   https://doi.org/10.1186/s12915-021-01115-z  

   Sego, T. J.; Aponte-Serrano, Josua O.; Gianlupi, Juliano F.; Glazier, James A. (September 2021, BMC Biology)

   Abstract BackgroundThe biophysics of an organism span multiple scales from subcellular to organismal and include processes characterized by spatial properties, such as the diffusion of molecules, cell migration, and flow of intravenous fluids. Mathematical biology seeks to explain biophysical processes in mathematical terms at, and across, all relevant spatial and temporal scales, through the generation of representative models. While non-spatial, ordinary differential equation (ODE) models are often used and readily calibrated to experimental data, they do not explicitly represent the spatial and stochastic features of a biological system, limiting their insights and applications. However, spatial models describing biological systems with spatial information are mathematically complex and computationally expensive, which limits the ability to calibrate and deploy them and highlights the need for simpler methods able to model the spatial features of biological systems. ResultsIn this work, we develop a formal method for deriving cell-based, spatial, multicellular models from ODE models of population dynamics in biological systems, and vice versa. We provide examples of generating spatiotemporal, multicellular models from ODE models of viral infection and immune response. In these models, the determinants of agreement of spatial and non-spatial models are the degree of spatial heterogeneity in viral production and rates of extracellular viral diffusion and decay. We show how ODE model parameters can implicitly represent spatial parameters, and cell-based spatial models can generate uncertain predictions through sensitivity to stochastic cellular events, which is not a feature of ODE models. Using our method, we can test ODE models in a multicellular, spatial context and translate information to and from non-spatial and spatial models, which help to employ spatiotemporal multicellular models using calibrated ODE model parameters. We additionally investigate objects and processes implicitly represented by ODE model terms and parameters and improve the reproducibility of spatial, stochastic models. ConclusionWe developed and demonstrate a method for generating spatiotemporal, multicellular models from non-spatial population dynamics models of multicellular systems. We envision employing our method to generate new ODE model terms from spatiotemporal and multicellular models, recast popular ODE models on a cellular basis, and generate better models for critical applications where spatial and stochastic features affect outcomes. 

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4. Multimodal spectroscopic investigation of the conformation and local environment of biomolecules at an electrified interface

   https://doi.org/10.1039/d0tb01158d  

   Moonitz, Sasha A.; Shepard, Noah; Noriega, Rodrigo (August 2020, Journal of Materials Chemistry B)

   The complex and dynamic interfacial regions between biological samples and electronic components pose many challenges for characterization, including their evolution over multiple temporal and spatial scales. Spectroscopic probes of buried interfaces employing mid-infrared plasmon resonances and time-resolved fluorescence detection in the visible range are used to study the properties of polypeptides adsorbed at the surface of a working electrode. Information from these complementary spectroscopic probes reveals the interplay of solvation, electric fields, and ion concentration on their resulting macromolecular conformations. 

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5. Characterizing tree trait variance over spatiotemporal scales

   https://doi.org/10.1002/ecy.4126  

   Umaña, Maria Natalia; Hulshof, Catherine M. (June 2023, Ecology)

   Abstract Beyond the study of the mean, functional ecology lacks a concise characterization of trait variance patterns across spatiotemporal scales. Traits are measured in different ways, using different metrics, and at different spatial (and rarely temporal) scales. This study expands on previous research by applying a ubiquitous and widely used empirical model—Taylor's Power Law—to functional trait variance with the goal of identifying general patterns of trait variance scaling (the behavior of trait variance across scales). We compiled data on tree seedling communities monitored over 10 years across 213 2 m²plots and functional trait data from a subtropical forest in Puerto Rico. We examined trait‐based Taylor's Power Law at nested spatial and temporal scales. The scaling of variance with the mean was idiosyncratic across traits suggesting that the drivers of variation are likely to differ across traits that may make variance scaling theory elusive. However, slopes varied more in space than through time, suggesting that spatial environmental variability may have a larger role in driving trait variance than temporal variability. Empirical models that characterize taxonomic patterns across spatiotemporal scales, like Taylor's Power Law, can provide an insight into the scaling of functional traits, a necessary next step toward a more predictive trait‐based ecology. 

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