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Construction healthy city is a global strategy advocated by the WHO to address the social and environmental problems brought about by urbanization worldwide, and is also an important grip for China to promote the strategy of Beautiful China and Healthy China in the new era. This study constructed an evaluation system for health development efficiency (HDE) of Chinese cities from an input–output perspective, evaluated the HDE of 284 prefecture-level and above cities in China from 2009 to 2019 using the super-efficient SBM model, and further explored the dynamic transfer characteristics, spatial spillover effects, and future development trends of urban HDE in depth using Markov chain model and spatial Markov chain model. The main findings were as follows. (1) From 2009 to 2019, the average level of HDE in Chinese cities generally showed a “W-shaped” fluctuating upward trend, increasing from 0.6106 to 0.6538, an overall increase of 7 %. However, the overall input–output efficiency was still relatively low, with only 27.11 % of cities in a high efficiency state in 2019. (2) The spatial distribution pattern of HDE of cities nationwide showed a gradually decreasing pattern with the urban cluster cities as the core, specifically “eastern cities > western cities > central cities” and “mega cities > super cities > large cities > medium cities > small cities”. (3) There was a significant spatial spillover effect on the HDE of Chinese cities. When a city was adjacent to a region with low HDE, the probability of its HDE type shifting downward increased, and when it was adjacent to a region with high HDE, the probability of its HDE type shifting upward increased. (4) In the long term, the distribution of urban HDE in China gradually shifted from intermediate (ML and MH) to high (H) or low (L) state over time, with the greatest possibility (27.40 %) of dynamic shift to high (H) states. After further classification and prediction based on spatial neighborhood states, heterogeneity in the future evolutionary impact of different neighborhood contexts on urban HDE was found. These findings can provide references for improving the quality of healthy cities work and optimizing healthy cities guidance policies.

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence are often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this "paradox of predictability." We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative-genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding about how evolution works within and across timescales., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Evolutionary Biology.)

Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder’s equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. Previous research showed that the expected value of these prediction errors is often not zero, so predictions are systematically biased. Here, we propose that this bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a method to predict evolution, which is built on three key innovations. First, the method predicts change as the breeder’s equation plus a bias term. Second, the method combines information from the breeder’s equation and from the record of past changes in the mean to predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a learning algorithm on the record of past changes. We compare the method to the breeder’s equation in two artificial selection experiments, one using the wing of the fruit fly and another using simulations that include a complex mapping of genotypes to phenotypes. The proposed method outperforms the breeder’s equation, particularly when traits under selection are omitted from the analysis, when data are noisy, and when additive genetic variance is estimated inaccurately or not estimated at all. The proposed method is easy to apply, requiring only the trait means over past generations. [ABSTRACT FROM AUTHOR]

Phage therapy is the use of bacterial viruses (phages) to treat bacterial infections, a medical intervention long abandoned in the West but now experiencing a revival. Currently, therapeutic phages are often chosen based on limited criteria, sometimes merely an ability to plate on the pathogenic bacterium. Better treatment might result from an informed choice of phages. Here we consider whether phages used to treat the bacterial infection in a patient may specifically evolve to improve treatment on that patient or benefit subsequent patients. With mathematical and computational models, we explore in vivo evolution for four phage properties expected to influence therapeutic success: generalized phage growth, phage decay rate, excreted enzymes to degrade protective bacterial layers, and growth on resistant bacteria. Within-host phage evolution is strongly aligned with treatment success for phage decay rate but only partially aligned for phage growth rate and growth on resistant bacteria. Excreted enzymes are mostly not selected for treatment success. Even when evolution and treatment success are aligned, evolution may not be rapid enough to keep pace with bacterial evolution for maximum benefit. An informed use of phages is invariably superior to naive reliance on within-host evolution.

Rapid environmental change currently presents a major threat to global biodiversity and ecosystem functions, and understanding impacts on individual populations is critical to creating reliable predictions and mitigation plans. One emerging tool for this goal is high-throughput sequencing technology, which can now be used to scan the genome for signs of environmental selection in any species and any system. This explosion of data provides a powerful new window into the molecular mechanisms of adaptation, and although there has been some success in using genomic data to predict responses to selection in fields such as agriculture, thus far genomic data are rarely integrated into predictive frameworks of future adaptation in natural populations. Here, we review both theoretical and empirical studies of adaptation to rapid environmental change, focusing on areas where genomic data are poised to contribute to our ability to estimate species and population persistence and adaptation. We advocate for the need to study and model evolutionary response architectures, which integrate spatial information, fitness estimates, and plasticity with genetic architecture. Understanding how these factors contribute to adaptive responses is essential in efforts to predict the responses of species and ecosystems to future environmental change. [ABSTRACT FROM AUTHOR]

• Evaluation system of urban health development efficiency (HDE) was constructed. • Urban HDE from 2009 to 2019 was measured using the super-efficient SBM model. • Transfer probability matrix was constructed to examine the transfer status of HDE. • Spatial Markov chain model was used to identify the spillover effects of HDE. • Markov chain model was used to forecast the long-term evolution trend of HDE. Construction healthy city is a global strategy advocated by the WHO to address the social and environmental problems brought about by urbanization worldwide, and is also an important grip for China to promote the strategy of Beautiful China and Healthy China in the new era. This study constructed an evaluation system for health development efficiency (HDE) of Chinese cities from an input–output perspective, evaluated the HDE of 284 prefecture-level and above cities in China from 2009 to 2019 using the super-efficient SBM model, and further explored the dynamic transfer characteristics, spatial spillover effects, and future development trends of urban HDE in depth using Markov chain model and spatial Markov chain model. The main findings were as follows. (1) From 2009 to 2019, the average level of HDE in Chinese cities generally showed a "W-shaped" fluctuating upward trend, increasing from 0.6106 to 0.6538, an overall increase of 7 %. However, the overall input–output efficiency was still relatively low, with only 27.11 % of cities in a high efficiency state in 2019. (2) The spatial distribution pattern of HDE of cities nationwide showed a gradually decreasing pattern with the urban cluster cities as the core, specifically "eastern cities > western cities > central cities" and "mega cities > super cities > large cities > medium cities > small cities". (3) There was a significant spatial spillover effect on the HDE of Chinese cities. When a city was adjacent to a region with low HDE, the probability of its HDE type shifting downward increased, and when it was adjacent to a region with high HDE, the probability of its HDE type shifting upward increased. (4) In the long term, the distribution of urban HDE in China gradually shifted from intermediate (ML and MH) to high (H) or low (L) state over time, with the greatest possibility (27.40 %) of dynamic shift to high (H) states. After further classification and prediction based on spatial neighborhood states, heterogeneity in the future evolutionary impact of different neighborhood contexts on urban HDE was found. These findings can provide references for improving the quality of healthy cities work and optimizing healthy cities guidance policies. [ABSTRACT FROM AUTHOR]

In an age of rapid global change, it is imperative that we continue to improve our understanding of factors that govern genetic differentiation in plants to inform biologically reasonable predictions for the future and enlighten conservation and restoration practices. In this special issue, we have assembled a set of original research and reviews that employ diverse approaches, both classic and contemporary, to illuminate patterns of phenotypic and genetic variation, probe the underlying evolutionary processes that have contributed to these patterns, build predictive models, and test evolutionary hypotheses. Our goal was to underscore the unique insights that can be obtained through the complementary and distinct studies of plant populations across species' geographic ranges. [ABSTRACT FROM AUTHOR]

Phage therapy is the use of bacterial viruses (phages) to treat bacterial infections, a medical intervention long abandoned in the West but now experiencing a revival. Currently, therapeutic phages are often chosen based on limited criteria, sometimes merely an ability to plate on the pathogenic bacterium. Better treatment might result from an informed choice of phages. Here we consider whether phages used to treat the bacterial infection in a patient may specifically evolve to improve treatment on that patient or benefit subsequent patients. With mathematical and computational models, we explore in vivo evolution for four phage properties expected to influence therapeutic success: generalized phage growth, phage decay rate, excreted enzymes to degrade protective bacterial layers, and growth on resistant bacteria. Within-host phage evolution is strongly aligned with treatment success for phage decay rate but only partially aligned for phage growth rate and growth on resistant bacteria. Excreted enzymes are mostly not selected for treatment success. Even when evolution and treatment success are aligned, evolution may not be rapid enough to keep pace with bacterial evolution for maximum benefit. An informed use of phages is invariably superior to naive reliance on within-host evolution. [ABSTRACT FROM AUTHOR]

We present several predictions concerning the expression of genetic variation in, and covariation among, gender-related traits in perfect-flowered plant taxa with different breeding systems. We start with the inference that the pollen:ovule (P/O) ratio in obligately autogamous species (in which the ovules in a flower are fertilized only by the pollen it produces) should be under much stronger stabilizing selection than in outcrossing taxa. Consequently, we predict that obligately autogamous taxa should exhibit lower genetic coefficients of variation in the P/O ratio. Nevertheless, genetic variation in both pollenand ovule production per flower might persist within autogamous as well as outcrossing populations. In autogamous taxa, genotypes with relatively few pollen grains and ovules per flower (but producing relatively high numbers of flowers) and genotypes with comparatively high numbers of gametes per flower (but producing relatively few flowers) could co-exist if lifetime flower production is selectively neutral. In contrast, in outcrossers, the maintenance of genetic variation in ovule and pollen production per flower might result predominantly from their ability to maintain variation in phenotypic and functional gender. Given genetic variation in primary sexual traits, we predict that the genetic correlation between investment in male and female gametes per flower should qualitatively differ between selfers and outcrossers. We predict a positive genetic correlation between pollen and ovule production per flower in obligately autogamous taxa, primarily because strong stabilizing selection on the P/O ratio should select against the gender specialists that would be necessary to effect a negative genetic correlation between mean pollen and ovule production perflower. Moreover, the fact that autogamous individuals are 50% female and 50% male means that gender-biased phenotypes cannot be functionally gender-biased, preventing selection from favoring phenotypic extre [ABSTRACT FROM AUTHOR]