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Questions: Which factors affect the diversity and species composition of tropical secondary rain forests in a region with little information regarding their contribution to global biodiversity? Can older secondary forests approach the diversity and composition of mature forests following 100 yr of pasture use?Location: Tropical secondary rain forest, northeast Australia. Methods:We identified trees, shrubs and vines ≥2.5 cm DBH in a chronosequence comprising 33 sites, aged 3-60 yr since the formation of closed canopy (9-69 yr since pasture abandonment) and compared them with eight sites in nearby mature forest remnants.Results: Species richness and community composition were strongly influenced by secondary forest age but did not attain values of mature forest. Sites in close proximity to mature forests had higher plant richness, whereas low soil fertility appeared to depress species recruitment. Thus, multiple factors operated in secondary forest community assembly. Unusual tree community patterns that suggest accelerated or slowed successional trajectories were observed at several sites.Conclusions: Secondary forests in our study region contained important plant diversity for conservation, particularly in older sites, however, even the oldest secondary forests (60 yr) did not converge with the species composition and diversity of mature forests. The protection of mature forest tracts and remnants must be a priority if we are to maintain high levels of plant diversity in tropical landscapes, conserve rare species and facilitate the recruitment of plant species in recovering forests.

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Predicting the impact of climate change on species is often done using species distribution models, but these can be problematic in topographically diverse environments. For species relying on particular moisture gradients, such as Australian rainforest frogs, accurate predictions of moisture availability are crucial. We found that while temperature gradients can be more accurately modeled with highresolution digital elevation models, moisture availability can be inaccurately represented by climate layers. Standard distribution models are also limited in their ability to account for other factors influencing habitat suitability, such as competitor species or disease. Expert knowledge can be useful for bridging these gaps.

The need to proactively manage landscapes and species to aid their adaptation to climate change is widely acknowledged. Current approaches to prioritizing investment in species conservation generally rely on correlative models, which predict the likely fate of species under different climate change scenarios. Yet, while model statistics can be improved by refining modeling techniques, gaps remain in understanding the relationship between model performance and ecological reality. To investigate this, we compared standard correlative species distribution models to highly accurate, fine‐scale, distribution models. We critically assessed the ecological realism of each species’ model, using expert knowledge of the geography and habitat in the study area and the biology of the study species. Using interactive software and an iterative vetting with experts, we identified seven general principles that explain why the distribution modeling under‐ or overestimated habitat suitability, under both current and predicted future climates. Importantly, we found that, while temperature estimates can be dramatically improved through better climate downscaling, many models still inaccurately reflected moisture availability. Furthermore, the correlative models did not account for biotic factors, such as disease or competitor species, and were unable to account for the likely presence of micro refugia. Under‐performing current models resulted in widely divergent future projections of species’ distributions. Expert vetting identified regions that were likely to contain micro refugia, even where the fine‐scale future projections of species distributions predicted population losses. Based on the results, we identify four priority conservation actions required for more effective climate change adaptation responses. This approach to improving the ecological realism of correlative models to understand climate change impacts on species can be applied broadly to improve the evidence base underpinning management responses.