This study explores Mpox transmission dynamics using a mathematical and data-driven epidemiological model that incorporates two viral strains, Clade I and Clade II. The model includes transmission pathways between humans and mammals and divides the human population into susceptible, exposed, infectious, hospitalized, and recovered groups. Weekly data from the WHO for Spain, Italy, Nigeria, and the DRC from 2022 to 2024 are used for model validation via non-linear least-squares fitting, with model performance assessed by Root Mean Squared Error (RMSE). We conduct time-series analysis to detect trends and anomalies in Mpox cases, with scenario simulations examining strain-specific transmission and the basic reproduction number (${\mathcal{R}}_0)$. The mathematical model fit is compared with two statistical model fits to emphasize the importance of developing a model that incorporates Mpox strain. Mathematical analysis confirms the model’s key properties, including positivity, boundedness, and equilibrium stability. Results underscore the importance of strain-specific dynamics and varying infection proportions for ${\mathcal{R}}_0$. This study combines mathematical rigor with empirical data to provide valuable insights into Mpox transmission and offers a framework for understanding multi-strain pathogens in diverse populations. Results from the simulation indicate that an increase in the effective contact rate leads to the dominance of the prevalent Mpox Clades in each country. Based on these findings, we recommend the implementation of strategies aimed at reducing the effective contact rate to control the spread of the virus strains.

Specialisation enhances the efficiency of plant–pollinator networks through the exchange of conspecific pollen transfer for floral resources. Floral resources form the currency of plant–pollinator interactions, but the understanding of how floral resources affect the structure of plant–pollinator networks remains modest. Previous theory predicts that optimally foraging animal species will specialise to improve resource acquisition under high resource availability. Although floral resource availability depends on both the plant production and animal consumption of the resources, previous work has assumed that production and availability are equivalent. This potentially may have led to erroneous inferences on the effect of resource availability on specialisation. We develop a mutualistic Lotka–Volterra consumer‐resource model to investigate the influence of floral resource availability on plant–pollinator network structure. The model incorporates animal adaptive foraging behaviour, floral resource dynamics, and density‐dependent dynamics. Specialisation, nestedness and modularity of simulated networks generated from the model under a wide range of parameters were explained using the generalised linear model. We found that the distinction between floral resource dynamics and plant density dynamics was necessary for partial specialisation of plant–pollinator networks. This is because floral resource dynamics constrained animal preference due to its depletion by animal species. Floral resource abundance had a positive effect on network specialisation, but animal density had a negative effect on network specialisation. Floral resource dynamics thus play key roles in the structure of plant–pollinator networks, distinctive from plant species density dynamics.

The negative impact of alien species is recognised as a major threat to biodiversity. An impact indicator, a measurable proxy of the status of alien species impacts over space and time, is essential for informing biological invasion policies and management strategies. We introduce an open-source workflow for computing impact indicators of alien species, combining occurrence data from the Global Biodiversity Information Facility (GBIF) with assessments of Environmental Impact Classification for Alien Taxa (EICAT). To implement the workflow, we developed an R package, impIndicator, which allows users to compute and visualise impact indicators for individual species and sites, as well as regional impact indicators. This tool can support ecological research and management by providing standardised insights into where alien species pose the greatest threats and whether current interventions are effectively reducing them. Such information is directly relevant to policy frameworks, including Target 6 of the Convention on Biological Diversity’s Kunming–Montreal Global Biodiversity Framework and the Sustainable Development Goals. We demonstrate the workflow using Acacia species in the Iberian Peninsula, South Africa and California, showcasing the spatiotemporal dynamics of their impacts and highlighting sites with higher impact risk. The resulting impact indicators were sensitive to variation in sampling effort and the completeness of the underlying occurrence data, highlighting the importance of well-sampled and systematically curated biodiversity datasets for robust impact assessment.

The negative impact of alien species is recognised as a major threat to biodiversity. However, there is a lack of evidence-based impact indicators that adhere to the Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles and employ standardised workflows. As a result, the impacts of alien species cannot be systematically estimated over time, posing a major hurdle for policymaking. We introduce an open-source workflow for computing impact indicators of alien species, combining occurrence data from the Global Biodiversity Information Facility (GBIF) with assessments of Environmental Impact Classification for Alien Taxa (EICAT). To operationalise the workflow, we developed an R package, impIndicator , which allows users to compute and visualise impact indicators for individual species and sites, as well as derive a regional estimate of alien species impacts. This tool can support ecological research and management by providing timely, accurate insights into where alien species pose the greatest threats and whether current interventions are effectively reducing them. Such information is directly relevant to policy frameworks, including Target 6 of the Convention on Biological Diversity’s Kunming–Montreal Global Biodiversity Framework and the Sustainable Development Goals. We demonstrate the workflow using Acacia species in the Iberian Peninsula, South Africa and California, showcasing the spatiotemporal dynamics of their impacts and highlighting sites with higher impact risk. The results were sensitive to variation in sampling effort in the underlying occurrence data, indicating that impact indicators depend strongly on data completeness. This highlights the importance of well-sampled and systematically curated occurrence datasets for computing robust impact indicators.