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Despite their critical role as reservoir hosts for many zoonotic diseases, the impact of land-use and land-cover changes (LCLUC) on flying foxes' interactions with humans remains unclear, posing a potential public health risk. To address this, we apply optimal foraging theory and individual-based modelling to simulate flying-fox movement and population dynamics under various LCLUC scenarios. After validating our model against available data, we analyze the effects of agriculturalization, urbanization, forest fragmentation, and reforestation on flying-fox densities across synthetic landscapes of urban, forest, orchard, and water-body habitats. Our findings indicate that habitat disruption—particularly fragmentation through urbanization—significantly increases the risk of zoonotic spillover events by increasing contacts between species. Scenarios of forest degradation reveal that ecologically degraded forest environments can further exacerbate this risk. Additionally, we find that reforestation can alleviate spillover risk. These results underscore the importance of conservation and habitat restoration as critical strategies for mitigating zoonotic disease transmission.

The case fatality ratio (CFR) is a vital metric for assessing the disease severity of novel pathogens. The widely used direct method of CFR estimation—the ratio of total confirmed deaths to total confirmed cases—is inherently simplistic, as it fails to account for the essential time lag between case confirmation to death, and reporting delays. These limitations often lead to biased CFR estimates, particularly in the early stages of outbreaks. This study introduces a novel approach—the distributed-delay method that, like the direct method, utilizes publicly available aggregate time-series data on cases and deaths. It estimates CFR by flexibly incorporating a case-to-death time distribution without requiring a priori assumptions on distribution parameters. Using a fitting approach to forecast case fatalities based on known or assumed case-to-death time distributions, the method consistently recovers true CFR much earlier than the direct method under various simulation settings. These settings reflect variability in disease severity, uncertainties in case-to-death time parameters, and limited knowledge of case-to-death time distributions. It outperforms other methods such as Baud’s, which assumes a non-zero constant case-to-death time, and the Generalized Baud’s method, which allows for a direct comparison with our new approach. While evaluations based on empirical data are challenging, our conclusions are supported by CFR estimates obtained using empirical COVID-19 data from 34 countries. As an added value, this analysis also demonstrates a significant negative association between eventual CFR and the expected case-to-death time within the context of COVID-19 data. Our study highlights the complexities of inferring real-time CFR from aggregate time-series case and death data, highlighting that refining this method can lead to accurate real-time CFR estimations for actual outbreaks.

The Asia-Pacific Region (APR) encompasses a vast geographical area rich in marine biodiversity that plays critical roles in global ecological stability and climate regulation, but it also faces daunting challenges in maintaining these roles under global change. Environmental dynamics in the APR manifest regularly over a range of timescales, including storms, earthquakes, floods, and extreme heat events. Further, coastal and marine ecosystems, including extensive commercial fisheries and coral reefs, are under threat from intense resource extraction and increasingly frequent marine heatwaves. Knowledge gaps for understanding these complex systems are aggravated by substantial barriers to cross-national efforts caused by the region’s vast diversity of cultures, languages, socioeconomics, politics, and management practices. Effective management of marine resources in the APR will necessitate multidisciplinary research based on continuous, region-wide observations supported by robust collaborations. In 2023, we gathered APR researchers across disciplines to discuss these issues and find solutions during a thematic seminar and workshop program at Tohoku University in Japan. Based on the results of this program, we present a review of the current state of APR marine ecosystems, raise key questions addressable through multidisciplinary approaches, and identify future priorities for the region. We conclude that sustaining biodiversity, ecosystem functions, and climate resilience in the APR will depend on stronger interdisciplinary collaboration, better integration of biological and geophysical data, and broader access to marine observations. These efforts are both urgent and essential for supporting better science-based policy decisions to address the escalating effects of global change on marine systems across the region.

What drives the emergence of new species has fascinated biologists since Darwin. Reproductive barriers to gene flow are a key step in the formation of species, and recent advances have shed new light on how these are established. Genetic, genomic, and comparative techniques, together with improved theoretical frameworks, are increasing our understanding of the underlying mechanisms. They are also helping us forecast speciation and reveal the impact of human activity.

In this paper, we present a telegraph diffusion model with variable exponents for image despeckling. Moving beyond the traditional assumption of a constant exponent in the telegraph diffusion framework, we explore three distinct variable exponents for edge detection. All of these depend on the gray level of the image or its gradient. We rigorously prove the existence and uniqueness of weak solutions of our model in a functional setting and perform numerical experiments to assess how well it can despeckle noisy gray-level images. We consider both a range of natural images contaminated by varying degrees of artificial speckle noise and synthetic aperture radar (SAR) images. We finally compare our method with the nonlocal speckle removal technique and find that our model outperforms the latter at speckle elimination and edge preservation.

Cope’s rule posits that evolution gradually increases the body size in lineages. Over the last decades, two schools of thought have fueled a debate on the applicability of Cope’s rule by reporting empirical evidence, respectively, for and against Cope’s rule. The apparent contradictions thus documented highlight the need for a comprehensive process-based synthesis through which both positions of this debate can be understood and reconciled. Here, we use a process-based community-evolution model to investigate the eco-evolutionary emergence of Cope’s rule. We report three characteristic macroevolutionary patterns, of which only two are consistent with Cope’s rule. First, we find that Cope’s rule applies when species interactions solely depend on relative differences in body size and the risk of lineage extinction is low. Second, in environments with higher risk of lineage extinction, the recurrent evolutionary elimination of top predators induces cyclic evolution toward larger body sizes, according to a macroevolutionary pattern we call the recurrent Cope’s rule. Third, when interactions between species are determined not only by their body sizes but also by their ecological niches, the recurrent Cope’s rule may get inverted, leading to cyclic evolution toward smaller body sizes. This recurrent inverse Cope’s rule is characterized by highly dynamic community evolution, involving the diversification of species with large body sizes and the extinction of species with small body sizes. To our knowledge, these results provide the first theoretical foundation for reconciling the contrasting empirical evidence reported on body-size evolution.

The distribution of fitness effects (DFE) of new mutations has been of interest to evolutionary biologists since the concept of mutations arose. Modern population genomic data enable us to quantify the DFE empirically, but few studies have examined how data processing, sample size and cryptic population structure might affect the accuracy of DFE inference. We used simulated and empirical data (from Arabidopsis lyrata) to show the effects of missing data filtering, sample size, number of single nucleotide polymorphisms (SNPs) and population structure on the accuracy and variance of DFE estimates. Our analyses focus on three filtering methods—downsampling, imputation and subsampling—with sample sizes of 4–100 individuals. We show that (1) the choice of missing-data treatment directly affects the estimated DFE, with downsampling performing better than imputation and subsampling; (2) the estimated DFE is less reliable in small samples (<8 individuals), and becomes unpredictable with too few SNPs (<5000, the sum of 0- and 4-fold SNPs); and (3) population structure may skew the inferred DFE towards more strongly deleterious mutations. We suggest that future studies should consider downsampling for small data sets, and use samples larger than 4 (ideally larger than 8) individuals, with more than 5000 SNPs in order to improve the robustness of DFE inference and enable comparative analyses.

Multicellularity has evolved several independent times over the past hundreds of millions of years and given rise to a wide diversity of complex life. Recent studies have found that large differences in the fundamental structure of early multicellular life cycles can affect fitness and influence multicellular adaptation. Yet, there is an underlying assumption that at some scale or categorization multicellular life cycles are similar in terms of their adaptive potential. Here, we consider this possibility by exploring adaptation in a class of simple multicellular life cycles of filamentous organisms that only differ in one respect, how many daughter filaments are produced. We use mathematical models and evolutionary simulations to show that despite the similarities, qualitatively different mutations fix. In particular, we find that mutations with a tradeoff between cell growth and group survival, i.e. "selfish" or "altruistic" traits, spread differently. Specifically, altruistic mutations more readily spread in life cycles that produce few daughters while in life cycles producing many daughters either type of mutation can spread depending on the environment. Our results show that subtle changes in multicellular life cycles can fundamentally alter adaptation.

At the outset of an epidemic, available case data typically underestimate the total number of infections due to insufficient testing, potentially hampering public responses. Here, we present a method for statistically estimating the true number of cases with confidence intervals from the reported number of deaths and estimates of the infection fatality ratio; assuming that the time from infection to death follows a known distribution. While the method is applicable to any epidemic with a significant mortality rate, we exemplify the method by applying it to COVID-19. Our findings indicate that the number of unreported COVID-19 infections in March 2020 was likely to be at least one order of magnitude higher than the reported cases, with the degree of underestimation among the countries considered being particularly high in the United Kingdom.

Estimates of tree heartwood and sapwood profiles are important in the pulp industry and for dynamic vegetation models, in which they determine tree biomechanical stability and hydraulic conductivity. Several phenomenological models of stem profiles have been developed for this purpose, based on assumptions on how tree crown and foliage distributions change over time. Here, we derive estimates of tree profiles by synthesizing a simple pipe model theory of plant form with a recently developed theory of branch thinning that from simple assumptions quantifies discarded branches and leaves. This allows us to develop a new trunk model of tree profiles from breast height up to the top of the tree. We postulate that leaves that are currently on the tree are connected by sapwood pipes, while pipes that previously connected discarded leaves or branches form the heartwood. By assuming that a fixed fraction of all pipes remain on the trunk after a branching event, as the trunk is traversed from the root system to the tips, this allows us to quantify trunk heartwood and sapwood profiles. We test the trunk model performance on empirical data from five tree species across three continents. We find that the trunk model accurately describes heartwood and sapwood profiles of all tested tree species (calibration; R2: 84-99%). Furthermore, once calibrated to a tree species, the trunk model predicts heartwood and sapwood profiles of conspecific trees in similar growing environments based only on the age and height of a tree (cross-validation/prediction; R2: 68-98%). The fewer and often contrasting parameters needed for the trunk model make it a potentially useful complementary tool for biologists and foresters.