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The scattering and absorption of light within biological tissue severely limits the penetration depth of optical imaging techniques. Recently, it has been found that water-soluble, strongly absorbing dye molecules, such as tartrazine, can achievein vivotissue transparency by increasing the refractive index of aqueous components in tissue, as predicted by the Lorentz oscillator model and Kramers–Kronig relations. In this study, we topically applied absorbing dye molecules to the abdominal skin of pigmented and nonpigmented mice to enhance the penetration depth of optical coherence tomography (OCT) and photoacoustic microscopy (PAM). In both types of mice, the penetration depth of OCT was significantly improved using tartrazine and 4-aminoantipyrine. As predicted by the Kramers–Kronig relations and absorption spectra of the dyes, mice treated with 4-aminoantipyrine showed significantly improved penetration depth compared to mice treated with tartrazine for the PAM system with 532 nm excitation. These findings further demonstrate the use of absorbing dye molecules for achieving tissue transparency to enhance the penetration depth of depth-resolved optical imaging modalities in skin, thus accelerating the translation of these technologies in clinical areas, such as dermatology. 

Abstract Pollinators are an essential component of terrestrial food webs and agricultural systems but are threatened by insufficient access to floral resources. Managed honey bees, as generalist foragers that hoard nectar as honey, can act as bioindicators of floral resources available to pollinators in a given landscape through their accumulation of honey. Honey yields across the United States have decreased appreciably since the 1990s, concurrent with shifts in climate, land-use, and large-scale pesticide application. While many factors can affect honey accumulation, this suggests that anthropogenic stressors may be having large-scale impacts on the floral resources that pollinators depend on for their nutrition. We used hierarchical partitioning on five decades of state-level data to parse the most important environmental factors and likely mechanisms associated with spatial and temporal variation in honey yields across the US. Climatic conditions and soil productivity were among the most important variables for estimating honey yields, with states in warm or cool regions with productive soils having the highest honey yields per colony. These findings suggest that foundational factors constrain pollinator habitat suitability and define ecoregions of low or high honey production. The most important temporally varying factors were change in herbicide use, land use (i.e. increase in intensive agriculture and reduction in land conservation programs that support pollinators) and annual weather anomalies. This study provides insights into the interplay between broad abiotic conditions and fine temporal variation on habitat suitability for honey bees and other pollinators. Our results also provide a baseline for investigating how these factors influence floral resource availability, which is essential to developing strategies for resilient plant–pollinator communities in the face of global change. 

Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood‐based approaches (e.g. packageslme4) and machine learning methods.These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journalNature Ecology & Evolution, and we found that the majority of published papers didnotreport any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods.Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journalMethods Ecology & Evolution. We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement.Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices. 

Changes in phenology in response to ongoing climate change have been observed in numerous taxa around the world. Differing rates of phenological shifts across trophic levels have led to concerns that ecological interactions may become increasingly decoupled in time, with potential negative consequences for populations. Despite widespread evidence of phenological change and a broad body of supporting theory, large-scale multitaxa evidence for demographic consequences of phenological asynchrony remains elusive. Using data from a continental-scale bird-banding program, we assess the impact of phenological dynamics on avian breeding productivity in 41 species of migratory and resident North American birds breeding in and around forested areas. We find strong evidence for a phenological optimum where breeding productivity decreases in years with both particularly early or late phenology and when breeding occurs early or late relative to local vegetation phenology. Moreover, we demonstrate that landbird breeding phenology did not keep pace with shifts in the timing of vegetation green-up over a recent 18-y period, even though avian breeding phenology has tracked green-up with greater sensitivity than arrival for migratory species. Species whose breeding phenology more closely tracked green-up tend to migrate shorter distances (or are resident over the entire year) and breed earlier in the season. These results showcase the broadest-scale evidence yet of the demographic impacts of phenological change. Future climate change–associated phenological shifts will likely result in a decrease in breeding productivity for most species, given that bird breeding phenology is failing to keep pace with climate change. 

Animals and plants are shifting the timing of key life events in response to climate change, yet despite recent documentation of escalating phenological change, scientists lack a full understanding of how and why phenological responses vary across space and among species. Here, we used over 7 million community-contributed bird observations to derive species-specific, spatially explicit estimates of annual spring migration phenology for 56 bird species across eastern North America. We show that changes in the spring arrival of migratory birds are coarsely synchronized with fluctuations in vegetation green-up and that the sensitivity of birds to plant phenology varied extensively. Bird arrival responded more synchronously with vegetation green-up at higher latitudes, where phenological shifts over time are also greater. Critically, species’ migratory traits explained variation in sensitivity to green-up, with species that migrate more slowly, arrive earlier and overwinter further north showing greater responsiveness to earlier springs. Identifying how and why species vary in their ability to shift phenological events is fundamental to predicting species’ vulnerability to climate change. Such variation in sensitivity across taxa, with long-distance neotropical migrants exhibiting reduced synchrony, may help to explain substantial declines in these species over the last several decades. 

Sex-related differences in mortality are widespread in the animal kingdom. Although studies have shown that sex determination systems might drive lifespan evolution, sex chromosome influence on aging rates have not been investigated so far, likely due to an apparent lack of demographic data from clades including both XY (with heterogametic males) and ZW (heterogametic females) systems. Taking advantage of a unique collection of capture–recapture datasets in amphibians, a vertebrate group where XY and ZW systems have repeatedly evolved over the past 200 million years, we examined whether sex heterogamy can predict sex differences in aging rates and lifespans. We showed that the strength and direction of sex differences in aging rates (and not lifespan) differ between XY and ZW systems. Sex-specific variation in aging rates was moderate within each system, but aging rates tended to be consistently higher in the heterogametic sex. This led to small but detectable effects of sex chromosome system on sex differences in aging rates in our models. Although preliminary, our results suggest that exposed recessive deleterious mutations on the X/Z chromosome (the “unguarded X/Z effect”) or repeat-rich Y/W chromosome (the “toxic Y/W effect”) could accelerate aging in the heterogametic sex in some vertebrate clades.