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Scientists and wildlife managers implementing adaptive monitoring and management schemes, are tasked with providing predictions of population responses to harvest and environmental changes. Such predictions are useful not only to forecast direct effects of climate, productivity, land use, or habitat degradation, but also changes in the food web, such as expanding/�increasing species that are predators, prey, and competitors of populations of concern. Explicit consideration of food webs and their dynamics in more complex models could provide better predictions of future changes, and allow us to better assess the influence of management actions. Here, we present our perspective on what we have learned from conducting a number of case studies using such a food web approach with a focus on climate and harvest impacts and their implications for management. We found empirical support for many of our hypothesized food web effects, and were able in some cases to obtain short-term forecasts with slightly lower prediction error using models that account for food web dynamics compared with simpler models. Predictions are the foundation of adaptive management because they allow quantitative assessment of the effects of management actions; however, evaluating predictions requires adequate and high-quality monitoring data. Results from our case studies show that a combination of long-term monitoring and different types of study designs coupled with models of adequate complexity are likely required to better understand populations’ responses to environmental changes and harvest, as well as the consequences for food webs.

The impact of climate change on the dynamics of populations has been well documented and is widespread. However, weather variability influences populations both directly and indirectly, and is mediated by species interactions. This complexity may impede proper climate impact assessments. Hence, predicting the consequences of climate change may require including processes that occur both with time lags and across trophic levels. Based on our current understanding of the mechanisms linking local climate and trophic interactions in tundra ecosystems, we used a state-space formulation of a mediation model that allowed for assessing the relative contribution of direct and indirect environmental (weather and trophic) effects on reindeer Rangifer tarandus reproductive success. Our study showed that the mediator effect of body condition caused delayed but predictable effects of weather, plant productivity, and reindeer densities on reproductive success. Furthermore, these predictors also affected reproductive success directly and with the same sign, suggesting that direct and indirect effects pulled in the same direction with respect to their combined total effect on reproductive success. Hence, poor weather conditions not only affect calf production negatively the same year, but also increase the likelihood of poor reproductive success the subsequent year. The results support the expectation that calf slaughter mass (as a proxy for herd body condition) is an important indicator of the state of reindeer herds with respect to their production potential and resilience to weather events and climate change. Finally, the model framework employed in the present study can be further developed as a potential vehicle for near-term forecasting, and thereby constitutes a useful tool for adaptive management.