The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injure or kill people. Those responsible for managing these species and mitigating conflict often lack fundamental information due to a long-standing challenge in ecology: How do we draw robust population-level inferences for elusive animals spread over immense areas? Here we showcase the application of an effective tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales that are relevant to management and conservation. We analyzed the world’s largest dataset on carnivores comprising more than 35,000 noninvasively obtained DNA samples from over 6,000 individual brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo). Our analyses took into account that not all individuals are detected and, even if detected, their fates are not always known. We show unequivocal quantitative evidence of large carnivore recovery in northern Europe, juxtaposed with the finding that humans are the single-most important factor driving the dynamics of these apex predators. We present maps and forecasts of the spatiotemporal dynamics of large carnivore populations, transcending national boundaries and management regimes.

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17 18Experimental studies have shown that many species show preferences for different climatic 19 conditions, or may die in unsuitable conditions. Climate envelope models have been used frequently 20 in recent years to predict the presence and absence of species at large spatial scales. However, many 21 authors have postulated that the distributions of species at smaller spatial scales are determined by 22 factors such as habitat availability and biotic interactions. Climatic effects are often assumed by 23 modellers to be unimportant at fine resolutions, but few studies have actually tested this. 24 25We sampled the distributions of 20 beetle species of the family Carabidae across three study sites by 26 pitfall trapping, and at the national scale from monitoring data. Statistical models were constructed 27 to determine which of two sets of environmental variables (temperature or broad habitat type) best 28 accounted for the observed data at the three sites and at the national (Great Britain) scale. 29 30High-resolution temperature variables frequently produced better models (as determined by AIC) 31 than habitat features when modelling the distributions of species at a local scale, within the three 32 study sites. Conversely, habitat was always a better predictor than temperature when describing 33 species' distributions at a coarse scale within Great Britain. Northerly species were most likely to 34 occur in cool micro-sites within the study sites, whereas southerly species were most likely to occur 35 in warm micro-sites. Effects of microclimate were not limited to species at the edges of their 36 distribution, and fine-resolution temperature surfaces should therefore ideally be utilised when 37 undertaking climate-envelope modelling. 38 39

An ability to predict the rate at which an organism spreads its range is of growing importance because the process of spread (during invasion by an exotic species) is almost identical to that occurring at the expanding range margins of a native species undergoing range shifts in response to climate change. Thus, the methods used for modelling range spread can also be employed to assess the distributional implications of climate change. Here we review the history of research on the spread of cane toads in Australia and use this case study to broadly examine the benefits and pitfalls of various modelling approaches. We show that the problems of estimating the current range, predicting the future range, and predicting the spread rate are interconnected and inform each other. Generally, we argue that correlative approaches to range-prediction are unsuitable when applied to invasive species and suggest that mechanistic methods are beginning to look promising (despite being more difficult to execute), although robust comparisons of correlative versus mechanistic predictions are lacking. Looking to the future, we argue that mechanistic models of range advance (drawing from both population ecology and environmental variation) are the approaches most likely to yield robust predictions. The complexity of these approaches coupled with the steady rise in computing power means that they have only recently become computationally tractable. Thus, we suggest that the field is only recently in a position to incorporate the complexity necessary to robustly model the rate at which species shift their range.

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