Fire's growing impacts on ecosystems: Fire has played a prominent role in the evolution of biodiversity and is a natural factor shaping many ecological communities. However, the incidence of fire has been exacerbated by human activity, and this is now affecting ecosystems and habitats that have never been fire prone or fire adapted. Kelly et al. review how such changes are already threatening species with extinction and transforming terrestrial ecosystems and discuss the trends causing changes in fire regimes. They also consider actions that could be taken by conservationists and policy-makers to help sustain biodiversity in a time of changing fire activity. Science , this issue p. eabb0355

Aim: Current interest in forecasting changes to species ranges has resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller‐scale processes such as growth, fecundity and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods.Location: Eastern North America (as an example).Methods: Our framework builds a metamodel that is constrained by the results of multiple sub‐models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence–absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America.Results: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model.Main conclusions: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off‐the‐shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi‐source and multi‐scale data into ecological decision‐making.

Network ecology is an emerging field that allows researchers to conceptualize and analyse ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the ‘ecological network dynamics framework’. This framework stresses that the interplay between species interaction networks and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links) and trade-offs among them are needed to maintain species interactions in dynamic landscapes. We conclude that to be functional, ecological networks should be scaled according to species dispersal abilities in response to landscape heterogeneity. Determining how such effective ecological networks change through space and time can help reveal their complex dynamics in a changing world.