Distributions of landbirds in Canadian northern forests are expected to be affected by climate change, but it remains unclear which pathways are responsible for projected climate effects. Determining whether climate change acts indirectly through changing fire regimes and/or vegetation dynamics, or directly through changes in climatic suitability may allow land managers to address negative trajectories via forest management. We used SpaDES, a novel toolkit built in R that facilitates the implementation of simulation models from different areas of knowledge to develop a simulation experiment for a study area comprising 50 million ha in the Northwest Territories, Canada. Our factorial experiment was designed to contrast climate effects pathways on 64 landbird species using climate-sensitive and non-climate sensitive models for tree growth and mortality, wildfire, and landbirds. Climate-change effects were predicted to increase suitable habitat for 73% of species, resulting in average net gain of 7.49 million ha across species. We observed higher species turnover in the northeastern, south-central (species loss), and western regions (species gain). Importantly, we found that most of the predicted differences in net area of occupancy across models were attributed to direct climate effects rather than simulated vegetation change, despite a similar relative importance of vegetation and climate variables in landbird models. Even with close to a doubling of annual area burned by 2100, and a 600 kg/ha increase in aboveground tree biomass predicted in this region, differences in landbird net occupancy across models attributed to climate-driven forest growth were very small, likely resulting from differences in the pace of vegetation and climate changes, or vegetation lags. The effect of vegetation lags (i.e., differences from climatic equilibrium) varied across species, resulting in a wide range of changes in landbird distribution, and consequently predicted occupancy, due to climate effects. These findings suggest that hybrid approaches using statistical models and landscape simulation tools could improve wildlife forecasts when future uncoupling of vegetation and climate is anticipated. This study lays some of the methodological groundwork for ecological adaptive management using the new platform SpaDES, which allows for iterative forecasting, mixing of modeling paradigms, and tightening connections between data, parameterization, and simulation.

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For species at risk, it is important that demographic models be consistent with our most recent knowledge because alternate model versions can have differing predictions for wildlife and natural resource management. To establish and maintain this consistency, we can compare predicted model values to current or past observations and demographic knowledge. When novel predictor information becomes available, testing for consistency between modeled and observed values ensures the best models are used for robust, evidence-based, wildlife management. We combine novel information on the extent of historical disturbance regimes (industrial and fire) to an existing demographic model and predict historical and projected demographics of woodland caribou (Rangifer tarandus caribou). Exploring 6 simulation experiments across 5 populations in Alberta, Canada, we identify the relative importance of industrial disturbance, fire, and population density to observed population size and growth rate. We confirm the onset of significant declines across all 5 populations began approximately 30 years ago, demonstrate these declines have been consistent, and conclude they are more likely due to industrial disturbance from the oil and gas sector within contemporary population ranges than historical fire regimes. These findings reinforce recent research on the cause of woodland caribou declines. Testing for consistency between observations and models prescribed for species recovery is paramount for assessing the cause of declines, projecting population trends, and refining recovery strategies for effective wildlife management. We provide a novel simulation method for conducting these tests.

Making predictions from ecological models—and comparing them to data—offers a coherent approach to evaluate model quality, regardless of model complexity or modelling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies, and the public has been hampered by disparate perspectives on prediction and inadequately integrated approaches. We present an updated foundation for Predictive Ecology based on seven principles applied to ecological modelling: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows that are routinely Tested (PERFICT). We outline some benefits of working with these principles: accelerating science; linking with data science; and improving science‐policy integration.

Making predictions from ecological models – and comparing these predictions to data – offers a coherent approach to objectively evaluate model quality, regardless of model complexity or modeling paradigm. To date, our ability to use predictions for developing, validating, updating, integrating and applying models across scientific disciplines while influencing management decisions, policies and the public has been hampered by disparate perspectives on prediction and inadequate integrated approaches. We present an updated foundation for Predictive Ecology that is based on 7 principles applied to ecological models: make frequent Predictions, Evaluate models, make models Reusable, Freely accessible and Interoperable, built within Continuous workflows, that are routinely Tested (PERFICT). We outline some benefits of working with these principles: 1) accelerating science; 2) bridging to data science; and 3) improving science-policy integration.