Search for: All records

Bird migration has fascinated natural historians and scientists for centuries. While the timing of migration is known to vary by species, population, sex, and individual, identifying the cause of this variation can be challenging. Here we investigate factors underlying migratory timing in a long- distance migratory bird, the Common Yellowthroat (Geothlypas trichas), using a population genomic approach. We begin by creating a map of genetic variation across geographic space (a “genoscape”) using lcWGS from across the breeding range. We then utilize genetic assays to assign 249 wintering and 1050 northward migrating birds to genetically distinct breeding populations. Additionally, we estimate the expected spring onset date in each predicted breeding region and calculate the remaining migratory distance for northward migrating birds. Our findings indicate that when population genetic structure is not a factor in the analysis, it appears that birds captured early in the season are migrating to breeding grounds where spring arrives later, which contrasts with prior research. However, when we incorporate population structure into our analysis, our results align with predictions, indicating that birds captured earlier in the season are indeed heading to breeding grounds where spring arrives earlier. Further analysis revealed that the disparity between results obtained with and without population genetic structure can be attributed to the fact that individuals from the western genetic group migrate three times the distance to the west, despite breeding at the same latitude. Our findings suggest that categorizing large numbers of migrating birds into genetically distinct groups can reveal population-specific patterns in migratory timing and shed light on the relative contributions of different selective forces responsible for the observed patterns. 

Abstract Robotic systems often struggle to adapt to dynamic, unstructured environments due to top-down design constraints based on human assumptions. Inspired by biological morphogenesis, this study introduces a cellular plasticity model based on Turing patterns, enabling multi-cellular robots to self-organize their cell phenotypes in response to environmental stimuli. The model leverages reaction-diffusion dynamics to capture key cellular plasticity phenomena observed in muscle cells, neurons, and stem cells. Analytical analysis explores equilibrium points, stability, and conditions for emergent Turing patterns, while simulations examine parametric influences on system behavior. Physical experiments with the Loopy platform demonstrate that its cells dynamically self-organize mechanical properties in response to behavioral and environmental demands. This response enables Loopy to achieve similar performance to empirically optimized static parameters in obstacle-free environments and outperform the static configuration in an environment with limited space. This work advances morphogenetic robotics, presenting a scalable framework for decentralized, dynamic adaptation in unmodeled environments. 

We demonstrate the ability of a non-heme iron containingl-lysine dioxygenase to perform sequential oxidation on its native substrate and computationally explore structural elements that promote this reactivity. 

Squeezed light, with its quantum noise reduction capabilities, has emerged as a powerful resource in quantum information processing and precision metrology. To reach noise reduction levels such that a quantum advantage is achieved, off-chip squeezers are typically used. The development of on-chip squeezed light sources, particularly in nanophotonic platforms, has been challenging. We report 3.7±0.2dB of directly detected nanophotonic quantum squeezing using foundry-fabricated silicon nitride (Si₃N₄) microrings with an inferred squeezing level of 10.2 dB on-chip. The squeezing level is robust across multiple devices and pump detunings, and is consistent with the overcoupling degree without noticeable degradation from excess classical noise. We also offer insights to mitigate thermally induced excess noise, which typically degrades squeezing, by using small-radius rings with a larger free spectral range (450 GHz) and consequently lower parametric oscillation thresholds. Our results demonstrate that Si₃N₄is a viable platform for strong quantum noise reduction in a CMOS-compatible, scalable architecture. 

Most hosts contain few parasites, whereas few hosts contain many. This pattern, known as aggregation, is well-documented in macroparasites where parasite intensity distribution among hosts affects host–parasite dynamics. Infection intensity also drives fungal disease dynamics, but we lack a basic understanding of host–fungal aggregation patterns, how they compare with macroparasites and if they reflect biological processes. To begin addressing these gaps, we characterized aggregation of the fungal pathogenBatrachochytrium dendrobatidis(Bd) in amphibian hosts. Utilizing the slope of Taylor’s Power law, we found Bd intensity distributions were more aggregated than many macroparasites, conforming closely to lognormal distributions. We observed that Bd aggregation patterns are strongly correlated with known biological processes operating in amphibian populations, such as epizoological phase (i.e. invasion, post-invasion and enzootic), and intensity-dependent disease mortality. Using intensity-dependent mathematical models, we found evidence of evolution of host resistance based on aggregation shifts in systems persisting with Bd following disease-induced declines. Our results show that Bd aggregation is highly conserved across disparate systems and contains signatures of potential biological processes of amphibian–Bd systems. Our work can inform future modelling approaches and be extended to other fungal pathogens to elucidate host–fungal interactions and unite host–fungal dynamics under a common theoretical framework. 

Abstract The 1991–2020 climate normals for sea surface temperature (SST) are computed based on the NOAA Daily Optimum Interpolation SST dataset. This is the first time that high‐resolution SST normals with global coverage can be achieved in the satellite SST era. Normals are one of the fundamental parameters in describing and understanding weather and climate and provide decision‐making information to industry, public, and scientific communities. This product suite includes SST mean, standard deviation, count and extreme parameters at daily, monthly, seasonal and annual time scales on 0.25° spatial grids. The main feature of the SST mean state revealed by the normals is that in the Tropics, the Indo‐Pacific Ocean is dominated by the warm pool (SST ≥ 28°C) while the eastern Pacific is characterized by the cold tongue (SST ≤ 24°C); in the midlatitudes, SSTs are in zonal patterns with high meridional gradients. Daily SST standard deviations are generally small (<1.0°C) except in frontal zones (>1.5°C) mostly associated with ocean currents such as the Gulf Stream, Kuroshio and Equatorial Currents. Compared to the 1982–2011 climatology, the 1991–2020 mean SSTs increased over most global areas but obvious cooling is seen in the Southern Ocean, eastern tropical South Pacific Ocean and North Atlantic warming hole. The Indo‐Pacific warm pool (IPWP) is found to have strengthened in both intensity and coverage since 1982–2011. By a count parameter criterion of ≥300 days annually with SST ≥ 28°C, the IPWP coverage increased 33% from 1982–2011 to 1991–2020. The global mean SST of 1991–2020 is warmer than that of 1982–2011, and the warming rate over 1991–2020 doubles that over 1901–2020. 

Abstract Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SSTs are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or preprocessed into common coarse grids. The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (i) when the reference SSTs are averaged onto 0.25° × 0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (ii) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25° × 0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products’ original grids, showing the advantage of those high-resolution analyses for resolving finer-scale SSTs; (iii) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (iv) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons. Significance StatementIntercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for.