Summary Predicting and managing species’ responses to climate change is one of the most significant challenges of our time. Tools are needed to address problems associated with novel climatic conditions, biotic interactions and greater climate velocities. We present a spatially explicit moving‐habitat model (MHM) and demonstrate its versatility in tackling critical questions in climate change research, including dispersal in multiple spatial dimensions, population stage structure, interspecific interactions, asymmetric range shifts, Allee effects and the presence of infectious diseases. The model utilizes integrodifference equations to track changes in population density over time in a habitat that is moving. The model is quite flexible and can accommodate variation in demography, dispersal patterns, biotic interaction and stochasticity in the velocity of climate change. The methods provide a general mechanistic understanding of the underlying ecological processes that drive a system. Field data can be readily incorporated into the model to give insight into specific populations of interest and inform management decisions. Synthesis. Moving‐habitat models unite ecological theory, data‐centred modelling and conservation decision support under a single framework. Their ability to generate testable hypotheses, incorporate data and inform best management practices proves that these models provide a valuable framework for climate change biologists.
White pine blister rust (WPBR, Cronartium ribicola) is a fungal pathogen and a threat to whitebark pines (Pinus albicaulis). It has a complex life cycle that requires two hosts, a white pine and an alternate host, typically a currant or gooseberry (Ribes spp.). WPBR is transmitted between hosts by means of two types of airborne spores whose average dispersal distances differ by several orders of magnitude. In this paper, we introduce a discrete-time model based on the life cycle of WPBR. We then extend this model to include a continuous spatial domain, disease-induced mortality in the pines, and a latency period. After each extension, we find the pathogen's asymptotic speed of invasion analytically using exponential transforms and the method of steepest descent. Our results show that invasion speeds are strongly reduced by the latency period in the pine host. In addition, these speeds are highly dependent on the carrying capacity and infectiousness of each host type. If these parameters are sufficiently small, high mortality in pines may stop the spread of WPBR completely.
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