Royal Society Open Science最新文献

Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations and when estimating uncertainty in model predictions. However, methods for doing this can be computationally expensive, particularly when the number of unknown model parameters is large. The aim of this study is to develop and test an efficient profile likelihood-based method, which takes advantage of the structure of the mathematical model being used. We do this by identifying specific parameters that affect model output in a known way, such as a linear scaling. We illustrate the method by applying it to three toy models from different areas of the life sciences: (i) a predator-prey model from ecology; (ii) a compartment-based epidemic model from health sciences; and (iii) an advection-diffusion reaction model describing the transport of dissolved solutes from environmental science. We show that the new method produces results of comparable accuracy to existing profile likelihood methods but with substantially fewer evaluations of the forward model. We conclude that our method could provide a much more efficient approach to parameter inference for models where a structured approach is feasible. Computer code to apply the new method to user-supplied models and data is provided via a publicly accessible repository.

Here, in the first of two investigations, we evaluate and extend the analyses of the Randomised Badger Culling Trial (RBCT) to estimate the effectiveness of proactive badger culling for reducing incidence of tuberculosis (TB) in cattle within culling areas. Using previously reviewed, publicly available data, alongside frequentist and Bayesian approaches, we re-estimate culling effects for confirmed incidence of herd breakdowns (TB incidents in cattle) within proactive culling areas. We appraise the varying assumptions and statistical structures of individual models to determine model appropriateness. Our re-evaluation of frequentist models provides results consistent with peer-reviewed analyses of RBCT data, due to the consistency of beneficial effects across three analysis periods. Furthermore, well-fitting Bayesian models with weakly informative prior distribution assumptions produce high probabilities (91.2%-99.5%) of beneficial effects of proactive culling on confirmed herd breakdowns within culling areas in the period from the initial culls (between 1998 and 2002) until 2005. Similarly high probabilities of beneficial effects were observed post-trial (from 1 year after last culls until March 2013). Thus, irrespective of statistical approach or study period, we estimate substantial beneficial effects of proactive culling within culling areas, consistent with separate, existing, peer-reviewed analyses of the RBCT data.

Recent accumulation of evidence across taxa indicates that the ecological impacts of invasive alien species are predictable from their functional response (FR; e.g. the maximum feeding rate) and functional response ratio (FRR; the FR attack rate divided by handling time). Here, we experimentally derive these metrics to predict the ecological impacts of both juvenile and adult lionfish (Pterois volitans), one of the world's most damaging invaders, across representative and likely future prey types. Potentially prey-population destabilizing Type II FRs were exhibited by both life stages of lionfish towards four prey species: Artemia salina, Gammarus oceanicus, Palaemonetes varians and Nephrops norvegicus. FR magnitudes revealed ontogenetic shifts in lionfish impacts where juvenile lionfish displayed similar if not higher consumption rates than adult lionfish towards prey, apart from N. norvegicus, where adult consumption rate was considerably higher. Additionally, lionfish FRR values were very substantially higher than mean FRR values across known damaging invasive taxa. Thus, both life stages of lionfish are predicted to contribute to differing but high ecological impacts across prey communities, including commercially important species. With lionfish invasion ranges currently expanding across multiple regions globally, efforts to reduce lionfish numbers and population size structure, with provision of prey refugia through habitat complexity, might curtail their impacts. Nevertheless, the present study indicates that management programmes to support early detection and complete eradication of lionfish individuals when discovered in new regions are advised.

Several animal species use tools for foraging; however, very few manufacture and/or modify those tools. Humpback whales, which manufacture bubble-net tools while foraging, are among these rare species. Using animal-borne tag and unoccupied aerial system technologies, we examine bubble-nets manufactured by solitary humpback whales (Megaptera novaeangliae) in Southeast Alaska while feeding on krill. We demonstrate that the nets consist of internally tangential rings and suggest that whales actively control the number of rings in a net, net size and depth and the horizontal spacing between neighbouring bubbles. We argue that whales regulate these net structural elements to increase per-lunge prey intake by, on average, sevenfold. We measured breath rate and swimming and lunge kinematics to show that the resulting increase in prey density does not increase energetic expenditure. Our results provide a novel insight into how bubble-net tools manufactured by solitary foraging humpback whales act to increase foraging efficiency.

Although it is well known that humans substantially altered the Malagasy ecosystems, the timing of the human arrival as well as the extension of their environmental impact is yet not well understood. This research aims to study the influence of early human impact and climate change on rainforests and wildlife in northern Madagascar during the past millennia. Results obtained from the lake sediment in a montane environment showed significant changes in vegetation within the lake catchment associated with a major drought that started approximately 1100 years ago. Human impact, revealed by fires, began at roughly the same time and occurred outside the lake catchment. Although this does not dismiss the impacts that humans had at a regional scale, this result demonstrates that the late Holocene natural drought also significantly impacted the ecosystems independently of anthropogenic activities. At a regional scale, a review of species demographic history revealed a substantial number of population bottlenecks during the last millennia, probably resulting from this combination of human-related impact and natural climate changes. This research highlights the importance of a multi-site and multi-proxy comparison for deciphering the nature and succession of environmental changes.

Eukaryotes have evolved to dominate the biosphere today, accounting for most documented living species and the vast majority of the Earth's biomass. Consequently, understanding how these biologically complex organisms initially diversified in the Proterozoic Eon over 539 million years ago is a foundational question in evolutionary biology. Over the last 70 years, palaeontologists have sought to document the rise of eukaryotes with fossil evidence. However, the delicate and microscopic nature of their sub-cellular features affords early eukaryotes diminished preservation potential. Chemical biomarker signatures of eukaryotes and the genetics of living eukaryotes have emerged as complementary tools for reconstructing eukaryote ancestry. In this review, we argue that exceptionally preserved Proterozoic microfossils are critical to interpreting these complementary tools, providing crucial calibrations to molecular clocks and testing hypotheses of palaeoecology. We highlight recent research on their preservation and biomolecular composition that offers new ways to enhance their utility.

Porous alkali-activated materials are synthetic aluminosilicates that should be often produced as granules for practical applications. In the present study, municipal solid waste incineration fly ash with ~1.2 wt% of metallic aluminium was used as a novel blowing agent for metakaolin (their ratio ranged from 0% to 100%) with an aqueous sodium silicate solution as the alkali-activator and granulation fluid in high-shear granulation. The compressive strength of all granules was sufficient (≥2 MPa). Water absorption indicated an increase in porosity as the fly ash content increased. However, X-ray microtomography imaging showed no clear correlation between the fly ash content and porosity. The granules exceeded the leaching limits for earth construction materials for antimony, vanadium, chloride and sulphate. Of those, antimony, chloride and sulphate could be controlled by decreasing the ash content, but the source of vanadium was identified as metakaolin. The increase in the fly ash content decreased the cation exchange capacity of the granules. In conclusion, the recommended fly ash content is equivalent to 0.3 wt% of Al⁰ and the developed granules could be best suited as light-weight artificial aggregates in concrete where the additional binder would provide stabilization to decrease the leaching.

Aspirations for artificial intelligence (AI) as a catalyst for scientific discovery are growing. High-profile successes deploying AI in domains such as protein folding have highlighted AI's potential to unlock new frontiers of scientific knowledge. However, the pathway from AI innovation to deployment in research is not linear. Those seeking to drive a new wave of scientific progress through the application of AI require a diffusion engine that can enhance AI adoption across disciplines. Lessons from previous waves of technology change, experiences of deploying AI in real-world contexts and an emerging research agenda from the AI for science community suggest a framework for accelerating AI adoption. This framework requires action to build supply chains of ideas between disciplines; rapidly transfer technological capabilities through open research; create AI tools that empower researchers; and embed effective data stewardship. Together, these interventions can cultivate an environment of open data science that deliver the benefits of AI across the sciences.

In the second investigation in a pair of analyses which re-evaluates the Randomised Badger Culling Trial (RBCT), we estimate the effects of proactive badger culling on the incidence of tuberculosis (TB) in cattle populations in unculled neighbouring areas. Throughout peer-reviewed analyses of the RBCT, proactive culling was estimated to have detrimental effects on the incidence of herd breakdowns (i.e. TB incidents) in neighbouring areas. Using previously published, publicly available data, we appraise a variety of frequentist and Bayesian models as we estimate the effects of proactive culling on confirmed herd breakdowns in unculled neighbouring areas. For the during trial period from the initial culls until 4 September 2005, we estimate consistently high probabilities that proactive culling had adverse effects on confirmed herd breakdowns in unculled neighbouring areas, thus supporting the theory of heightened risk of TB for the neighbouring cattle populations. Negligible culling effects are estimated in the post-trial period across the statistical approaches and imply unsustained long-term effects for unculled neighbouring areas. Therefore, when considered alongside estimated beneficial effects within proactive culling areas, these conflicting adverse side effects render proactive culling complex, and thus, decision making regarding potential culling strategies should include (i) ecological, geographical and scientific considerations and (ii) cost-benefit analyses.

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyse a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behaviour to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.