The Arctic tundra biome is undergoing rapid shrub expansion (“shrubification”) in response to anthropogenic climate change. During the previous ~2.6 million years, glacial cycles caused substantial shifts in Arctic vegetation, leading to changes in species’ distributions, abundance, and connectivity, which have left lasting impacts on the genetic structure of modern populations. Examining how shrubs responded to past climate change using genetic data can inform the ecological and evolutionary consequences of shrub expansion today. Here we test scenarios of Quaternary population history of dwarf birch species (Betula nana L. and Betula Glandulosa Michx.) using SNP markers obtained from RAD sequencing and approximate Bayesian computation. We compare the timings of population events with ice sheet reconstructions and other paleoenvironmental information to untangle the impacts of alternating cold and warm periods on the phylogeography of dwarf birch. Our best supported model suggested that the species diverged in the Mid-Pleistocene Transition as glaciations intensified, and ice sheets expanded. We found support for a complex history of inter- and intraspecific divergences and gene flow, with secondary contact occurring during periods of both expanding and retreating ice sheets. Our spatiotemporal analysis suggests that the modern genetic structure of dwarf birch was shaped by transitions in climate between glacials and interglacials, with ice sheets acting alternatively as a barrier or an enabler of population mixing. Tundra shrubs may have had more nuanced responses to past climatic changes than phylogeographic analyses have often suggested, with implications for future eco-evolutionary responses to anthropogenic climate change.

High resolution monitoring is fundamental to understand and predict the dynamics of ecological communities in an era of global change and biodiversity declines. While real-time and fully automated monitoring of the abiotic components of ecosystems has been possible for some time, monitoring the biotic components at different organizational scales, e.g. from individual behaviours and traits to the abundance and distribution of species, is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which are able to extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological populations and communities via such technologies is still in its infancy, being primarily achieved at low spatiotemporal resolutions within specific stages of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify, and count multiple species, and even record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology and conservation: the ability to rapidly generate high resolution, multidimensional, and critically, standardized data across complex ecologies.