Integrating individual-based modelling and multilayer networks to advance landscape connectivity analyses

Connected landscapes are fundamental for species persistence under global change, as recognised by the International Convention on Biological Diversity. Connectivity enables species range shifts in response to climate change and maintains gene flow across fragmented populations, enhancing adaptive potential and long-term persistence.Network theory offers powerful approaches to analyse landscape connectivity and prioritise conservation interventions, but current approaches to inform landscape connectivity networks face important limitations. Methods such as least-cost path analysis and circuit theory often lack ecological realism, neglecting dispersal behaviour and population dynamics, thus failing to represent . Moreover, most frameworks assume static landscapes, overlooking natural and human-driven change. These limitations risk misrepresenting connectivity and under- or over-estimating habitat availability and isolation, which highlights the need for approaches that integrate both ecological realism and temporal dynamics.Individual-based models (IBMs) offer a promising, yet unutilized, way to inform landscape networks by simulating dispersal and demography with biological realism. Their process-based nature generates temporal outputs of functional connectivity, which can be used to inform multi-layer networks. The network representation offers the generality, scalability and comparability of connectivity analysis, which IBMs lack. While multi-layer networks show promise for representing spatio-temporal connectivity, incorporating landscape heterogeneity (space) and dynamics (time) into the network representation of the connected landscapes, their application in landscape ecology is in its infancy.We developed a workflow that integrates the individual-based modelling platform RangeShifter with a multi-layer network theory framework for spatio-temporal connectivity analysis. RangeShifter integrates complex population dynamics and dispersal behaviours, includes explicit genetics, and simulates scenarios on spatially landscapes. Using multi-layer networks, the framework captures functional connectivity of one or multiple species across dynamic landscapes and enables connectivity analyses with diverse network metrics. We demonstrated the potential of this framework by comparing the effectiveness of alternative conservation actions, including ones derived from our framework based on different multi-layer connectivity metrics, in facilitating range expansion and patch occupancy for virtual species.By linking IBMs with spatio-temporal network analyses, this workflow provides a tool to advance connectivity research for conservation planning in an era of rapid environmental change.