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The demand for wood in Europe is expected to increase in the coming decades. However, any theoretical maximum supply will be affected by sustainability constraints, the motivations of forest owners and regional factors, such as incentives, species and assortments. However, the influence of these factors on supply is changeable. In this study, we quantify what might be realistically available as additional wood supply from currently existing European forests, based on a combination of results of the forest resource model EFISCEN-Space and a literature review of national supply projections. Wood mobilization scenarios for 10 representative Model Regions in Europe that assume forest owners and managers in the simulated regions will adapt their behaviour to alternative behaviour as recorded from other regions were projected with the EFISCEN-Space model. The realistic additional potential based on the literature review is 90 million m3 yr−1. This potential should be attainable within 10–20 years. However, the simulations in the Model Regions found potentials to be lower in 7 out of 10 cases as compared with the country they are located in. On average, the model regions reached less than half of the potential as compared with the literature review. This suggests that the realistic additional potential at the European scale may well be lower if all mobilization barriers are taken into account in more detail, but also highlights the uncertainty surrounding these estimates. We conclude from the analyses that although there are large differences in potential between regions and the analysis method employed, there are no ‘hotspots’ where a large pool of accessible wood can be quickly mobilized using existing infrastructure for nearby industries. An increase in harvest would therefore only be possible with a large effort that spans the whole chain, from forest owners’ behaviour to capacity building, financial incentives and matching resources to harvesting capacity. The additionally available wood can most likely only be mobilized against higher marginal costs and will thus only become available in times of higher stumpage prices. The largest potential lies in privately owned forests which often have a fragmented ownership but will most likely be able to supply more wood, though mostly from deciduous species. In the long term (more than 20 years), additional wood, compared with the amounts we found for short term, can only be made available through investments in afforestation, forest restoration, improved forest management and more efficient use of raw material and recycled material.

The use of georeferenced information on the presence of a species to predict its distribution across a geographic area is one of the most common tools in management and conservation. The collection of high-quality presence-absence data through structured surveys is, however, expensive, and managers usually have more abundant low-quality presence-only data collected by citizen scientists, opportunistic observations, and culling returns for game species. Integrated Species Distribution Models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually less abundant and more spatially restricted presence-absence data, with the lower quality, unstructured, but usually more extensive and abundant presence-only data. Joint-likelihood ISDMs can be run in a Bayesian context using INLA (Integrated Nested Laplace Approximation) methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. These models, however, have only been applied to simulated data so far. Here, for the first time, we apply this approach to empirical data, using presence-absence and presence-only data for the three main deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km2 resolution across the island. Models’ predictions were associated to spatial estimate of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we validated the three species-specific models using independent deer hunting returns. Our work clearly demonstrates the applicability of spatially-explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unused and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.