The ecological processes that create spatial patterns have been examined by direct measurement and through measurement of patterns resulting from experimental manipulations. But in many situations, creating experiments and direct measurement of spatial processes can be difficult or impossible. Here, we identify and define a rapidly emerging alternative approach, which we formalize as “space as a surrogate” for unmeasured processes, that is used to maximize inference about ecological processes through the analysis of spatial patterns or spatial residuals alone. This approach requires three elements to be successful: a priori hypotheses, ecological theory and/or knowledge, and precise spatial analysis. We offer new insights into a long‐standing debate about process–pattern links in ecology and highlight six recent studies that have successfully examined spatial patterns to understand a diverse array of processes: competition in forest‐stand dynamics, dispersal of freshwater fish, movement of American marten, invasion mechanisms of exotic trees, dynamics of natural disturbances, and tropical‐plant diversity. Key benefits of using space as a surrogate can be found where experimental manipulation or direct measurements are difficult or expensive to obtain or not possible. We note that, even where experiments can be performed, this procedure may aid in measuring the in situ importance of the processes uncovered through experiments.

Summary: 1. Wolves Canis lupus recently recolonized the Western Alps through dispersal from the Italian Apennines, representing one of several worldwide examples of large carnivores increasing in highly human‐dominated landscapes. Understanding and predicting expansion of this population is important for conservation because of its direct impact on livestock and its high level of societal opposition. 2. We built a predictive, spatially explicit, individual‐based model to examine wolf population expansion in this fragmented landscape, and livestock depredation risk. We developed the model based on known demographic processes, social structure, behaviour and habitat selection of wolves collected during a 10‐year intensive field study of this wolf population. 3. During model validation, our model accurately described the recolonization process within the Italian Alps, correctly predicting wolf pack locations, pack numbers and wolf population size, between 1999 and 2008. 4. We then projected packs and dispersers over the entire Italian Alps for 2013, 2018 and 2023. We predicted 25 packs (95% CI: 19–32) in 2013, 36 (23–47) in 2018 and 49 (29–68) in 2023. The South‐Western Alps were the main source for wolves repopulating the Alps from 1999 to 2008. The source area for further successful dispersers will probably shift to the North‐Western Alps after 2008, but the large lakes in the Central Alps will probably act as a spatial barrier slowing the wolf expansion. 5. Using the pack presence forecasts, we estimated spatially explicit wolf depredation risk on livestock, allowing tailored local and regional management actions. 6. Synthesis and applications. Our predictive model is novel because we follow the spatio‐temporal dynamics of packs, not just population size, which have substantially different requirements and impacts on wolf–human conflicts than wandering dispersers. Our approach enables prioritization of management efforts, including minimizing livestock depredations, identifying important corridors and barriers, and locating future source populations for successful wolf recolonization of the Alps.

Summary: Well‐informed management of harvested species requires understanding how changing ecological conditions affect demography and population dynamics, information that is lacking for many species. We have limited understanding of the relative influence of carnivores, harvest, weather and forage availability on elkCervus elaphusdemography, despite the ecological and economic importance of this species. We assessed adult female survival, a key vital rate for population dynamics, from 2746 radio‐collared elk in 45 populations across western North America that experience wide variation in carnivore assemblage, harvest, weather and habitat conditions. Proportional hazard analysis revealed that ‘baseline’ (i.e. not related to human factors) mortality was higher with very high winter precipitation, particularly in populations sympatric with wolvesCanis lupus. Mortality may increase via nutritional stress and heightened vulnerability to predation in snowy winters. Baseline mortality was unrelated to pumaPuma concolorpresence, forest cover or summer forage productivity. Cause‐specific mortality analyses showed that wolves and all carnivore species combined had additive effects on baseline elk mortality, but only reduced survival by <2%. When human factors were included, ‘total’ adult mortality was solely related to harvest; the influence of native carnivores was compensatory. Annual total mortality rates were lowest in populations sympatric with both pumas and wolves because managers reduced female harvest in areas with abundant or diverse carnivores. Mortality from native carnivores peaked in late winter and early spring, while harvest‐induced mortality peaked in autumn. The strong peak in harvest‐induced mortality during the autumn hunting season decreased as the number of native carnivore species increased. Synthesis and applications. Elevated baseline adult female elk mortality from wolves in years with high winter precipitation could affect elk abundance as winters across the westernUSbecome drier and wolves recolonize portions of the region. In the absence of human harvest, wolves had additive, although limited, effects on mortality. However, human harvest, and its apparent use by managers to offset predation, primarily controls overall variation in adult female mortality. Altering harvest quotas is thus a strong tool for offsetting impacts of carnivore recolonization and shifting weather patterns on elk across western North America.