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The vast boreal biome plays an important role in the global carbon cycle but is experiencing particularly rapid climate warming, threatening the integrity of valued ecosystems and their component species. We developed a framework and taxonomy to identify climate‐change refugia potential in the North American boreal region, summarizing current knowledge regarding mechanisms, geographic distribution, and landscape indicators. While “terrain‐mediated” refugia will mostly be limited to coastal and mountain regions, the ecological inertia (resistance to external fluctuations) contained in some boreal ecosystems may provide more extensive buffering against climate change, resulting in “ecosystem‐protected” refugia. A notable example is boreal peatlands, which can retain high surface soil moisture and water tables even in the face of drought. Refugia from wildfire are also especially important in the boreal region, which is characterized by active disturbance regimes. Our framework will help identify areas of high refugia potential, and inform ecosystem management and conservation planning in light of climate change.

demoniche is a freely available R-package which simulates stochastic population dynamics in multiple populations of a species. A demographic model projects population sizes utilizing several transition matrices that can represent impacts on species growth. Th e demoniche model off ers options for setting demographic stochasticity, carrying capacity, and dispersal. Th e demographic projection in each population is linked to spatially-explicit niche values, which aff ect the species growth. With the demoniche package it is possible to compare the infl uence of scenarios of environmental changes on future population sizes, extinction probabilities, and range shifts of species.Studying processes and systems on multiple scales, and how these systems interact, is essential to understanding the ecology of environmental change. Simultaneously, it is diffi cult to carry out multi-scale, interlinked experiments, so the simulation of ecological systems is an increasingly necessary complement to the experimental approach. We also used models to guide responses to present and future threats to biodiversity in a world of increasing human and environmental changes (Brook et al. 2008, Pereira et al. 2010. In part, these objectives can be accomplished by coupling models that characterize diff erent processes at diff erent scales. Th ese are called coupled or hybrid models (Jongejans et al. 2008, Th uiller et al. 2008, Franklin 2010, Gallien et al. 2010 and are promising approaches to improve understanding of the interlinked and hierarchical processes governing distributions and abundances in space and time.Here, we introduce the R-package demoniche software (R Development Core Team) that enables the creation of coupled models of species distributions. In short, it creates demographic models with multiple populations connected by dispersal and with diff erent habitat suitability. Th e package couples detailed models of a species population growth to large-scale predictions about future environmental conditions, modelling both intrinsic and extrinsic eff ects on species persistence. By changing parameter values between simulations and comparing results, we can also estimate model and species sensitivity, and guide future data collection. demoniche can also be used to develop hypotheses concerning habitat confi guration, species persistence, and range shifts. Model algorithm Demographic model in each populationTh e inner model in demoniche is a demographic model, employing transition matrices to project population growth (Leslie 1945, Lefkovitch 1965, Caswell 2001, Morris and Doak 2002. Th e matrix rows and columns represent distinct life stages or sizes of an organism. Th e matrix diagonal represents the probability of surviving within the same stage, and off -diagonal elements represent probabilities of growing or receding to another stage. Multiplying a matrix by a population vector representing current population structure gives the number of individuals one time step later. Structured matrix models diff er from unstructured po...