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There is good evidence that the results of a restoration program depend largely on the landscape context such as habitat cover, connectivity and isolation. Such evidence, however, is not coherently presented in the scientific literature. This review aims to provide an overview of how landscape ecology has been used in restoration projects in the last 15 years. We found only 54 empirical restoration studies published in international journals from 1997 to 2011 that used a landscape approach, mostly published between 2009 and 2011. The majority of the studies were carried out in temperate zones and forests, with habitat loss and fragmentation as the major disturbance factor (77%). Biotic manipulation was the most common management protocol (39%), followed by abiotic manipulation, land abandonment, and control of disturbance sources. Most of the studies (84%) demonstrate that the landscape context plays an important role in restoration processes. Particularly, a positive influence of the landscape context on restoration effectiveness was observed for restored areas in close proximity to neighboring patches and in landscapes with high habitat cover. However, we found that the effect of landscape characteristics on restoration outcomes may vary with species characteristics, and differ according to the population or community parameters (e.g. abundance, richness, composition) considered. In addition, different landscape aspects mediated the effects of restoration on biological communities, and thus there is not a unique set of landscape indices that can be universally used for restoration planning and monitoring. Although the literature has important gaps, since most studies are restricted to few habitat and disturbance types and consider only a limited set of landscape attributes, our findings demonstrate that landscape characteristics can be as important as local factors in influencing restoration outcomes and should be incorporated in restoration projects and programmes. By considering a wide range of landscape resilience and disturbance condition since the beginning of future restoration plans, we expect that the main gaps of knowledge identified here can be filled in the near future, helping then to reveal a more general pattern relating landscape structure to restoration outcomes.

Forests are the largest terrestrial biomass pool, with over half of this biomass stored in the highly productive tropical lowland forests. The future evolution of forest biomass depends critically on the response of tree longevity and growth rates to future climate. We present an analysis of the variation in tree longevity and growth rate using tree-ring data of 3,343 populations and 438 tree species and assess how climate controls growth and tree longevity across world biomes. Tropical trees grow, on average, two times faster compared to trees from temperate and boreal biomes and live significantly shorter, on average (186 ± 138 y compared to 322 ± 201 y outside the tropics). At the global scale, growth rates and longevity covary strongly with temperature. Within the warm tropical lowlands, where broadleaf species dominate the vegetation, we find consistent decreases in tree longevity with increasing aridity, as well as a pronounced reduction in longevity above mean annual temperatures of 25.4 °C. These independent effects of temperature and water availability on tree longevity in the tropics are consistent with theoretical predictions of increases in evaporative demands at the leaf level under a warmer and drier climate and could explain observed increases in tree mortality in tropical forests, including the Amazon, and shifts in forest composition in western Africa. Our results suggest that conditions supporting only lower tree longevity in the tropical lowlands are likely to expand under future drier and especially warmer climates.

Biological data are often intrinsically hierarchical (e.g., species from different genera, plants within different mountain regions), which made mixed‐effects models a common analysis tool in ecology and evolution because they can account for the non‐independence. Many questions around their practical applications are solved but one is still debated: Should we treat a grouping variable with a low number of levels as a random or fixed effect? In such situations, the variance estimate of the random effect can be imprecise, but it is unknown if this affects statistical power and type I error rates of the fixed effects of interest. Here, we analyzed the consequences of treating a grouping variable with 2–8 levels as fixed or random effect in correctly specified and alternative models (under‐ or overparametrized models). We calculated type I error rates and statistical power for all‐model specifications and quantified the influences of study design on these quantities. We found no influence of model choice on type I error rate and power on the population‐level effect (slope) for random intercept‐only models. However, with varying intercepts and slopes in the data‐generating process, using a random slope and intercept model, and switching to a fixed‐effects model, in case of a singular fit, avoids overconfidence in the results. Additionally, the number and difference between levels strongly influences power and type I error. We conclude that inferring the correct random‐effect structure is of great importance to obtain correct type I error rates. We encourage to start with a mixed‐effects model independent of the number of levels in the grouping variable and switch to a fixed‐effects model only in case of a singular fit. With these recommendations, we allow for more informative choices about study design and data analysis and make ecological inference with mixed‐effects models more robust for small number of levels.