Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions. Predicting the UnknownPredictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown.Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.The determinants of ecological predictability are, however, still insufficiently understood.Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mech...

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models.

The effectiveness of revegetation in providing habitat for fauna is expected to be determined both by within-site factors and attributes of the landscape in which a revegetation site occurs. Most studies of fauna in revegetation have been conducted in landscapes that have been extensively cleared, modified or fragmented, and in Australia, predominantly in the southern temperate zone. We investigated how within-site vegetation attributes and landscape context attributes were related to bird species richness and composition in a chronosequence of post-mining rehabilitation sites within an otherwise intact landscape in tropical northern Australia. Our working hypothesis was that bird species richness in rehabilitating sites would be positively related to site vegetation structure and landscape context including (1) proximity to woodland and (2) the proportion of woodland within a 500-m buffer of rehabilitation sites. Within each of 67 sites, we sampled vegetation once and surveyed for birds eight times over 16 months. Landscape context variables were calculated using GIS. There were large differences between bird assemblages of woodland and rehabilitation sites and between age classes of rehabilitation. Bird assemblages were strongly related to site vegetation attributes across all rehabilitation sites. Proximity to woodland was only related to bird assemblages in rehabilitation sites older than 4 years old. We conclude that the relative importance of landscape context and site variables at any point in time will be a function of how closely vegetation within the revegetation site matches the habitat resource requirements of individual species.

Tools for exploring and communicating the impact of uncertainty on spatial prediction are urgently needed, particularly when projecting species distributions to future conditions.We provide a tool for simulating uncertainty, focusing on uncertainty due to data quality. We illustrate the use of the tool using a Tasmanian endemic species as a case study. Our simulations provide probabilistic, spatially explicit illustrations of the impact of uncertainty on model projections. We also illustrate differences in model projections using six different global climate models and two contrasting emissions scenarios.Our case study results illustrate how different sources of uncertainty have different impacts on model output and how the geographic distribution of uncertainty can vary.Synthesis and applications: We provide a conceptual framework for understanding sources of uncertainty based on a review of potential sources of uncertainty in species distribution modelling; a tool for simulating uncertainty in species distribution models; and protocols for dealing with uncertainty due to climate models and emissions scenarios. Our tool provides a step forward in understanding and communicating the impacts of uncertainty on species distribution models under future climates which will be particularly helpful for informing discussions between researchers, policy makers, and conservation practitioners.

Rehabilitation of post-mining lands frequently aims to create "self-sustaining" systems. Where native vegetation is the designated post-mining land use, it is generally assumed that rehabilitation that is similar to local native ecosystems is more likely to be sustainable. I compared landscape functionality, plant community composition, and vegetation structure in (1) reference sites representing pre-mining native forest; (2) reference sites representing potential landscape analogues for the post-mining landscape; and (3) a 23-year chronosequence of post-mining rehabilitation on the Weipa bauxite plateau, Cape York Peninsula, Australia. The trends across the post-mining chronosequence indicate that vegetation growth is rapid in the first 5-8 years, and then slows with mean height approaching an asymptote after approximately 15 years. Landscape function indices showed a response that coincided with vegetation growth. Vegetation composition was significantly different from reference native forest. Most importantly, from the perspective of creating self-sustaining ecosystems, the contribution of local framework species to vegetation in rehabilitation was significantly lower than in reference native forest. I discuss the results in relation to theoretical models of succession and conclude that without management intervention, differences between post-mining rehabilitation and native forest are likely to be persistent.