Introduction

Understanding spatial and temporal population trends and the drivers behind them is a key aim in population ecology (Turchin 2003). Such knowledge is also necessary when planning actions to mitigate the pervasive effects of habitat loss, fragmentation and climate change (Pressey et al. 2007). In fragmented landscapes, large-scale population trends often result from metapopulation dynamics, through the local processes of colonisation and extinction (Hanski 1999). Theoretical studies suggest that metapopulation viability strongly depends on landscape features and processes such as the availability, size and longevity of suitable habitat patches (e.g. old stands or appropriate tree structures in forest landscapes), spatial connectivity, patterns of patch destruction and creation, and interactions between these (Johst et al. 2011).
Habitat patches naturally appear and disappear through succession and disturbance, but in production landscapes these processes are largely replaced by management and conservation actions (Kuuluvainen 2009). A continuous local supply of new resource units is critical for the persistence of species that are confined to ephemeral resource units such as living or dead trees. These species need to balance the local extinctions (stochastic or resulting from resource unit disappearance) with local colonisations of new resource units. These units need to have high enough density in space and frequency through time to allow regional persistence (Gourbiere and Gourbiere 2002, Snäll et al. 2003).
It is important that forecasts of the long-term effects of management and conservation actions on species populations are realistic and accurate, because today’s decisions may give rise to adverse or unexpected consequences that may be difficult to overturn (Guisan et al. 2013). To parameterise models of spatially realistic metapopulation dynamics (Hanski 1999) to be used as a basis of forecasts, one should ideally have collected data repeatedly on the size and distribution of all habitat patches and local populations, and information about the dispersal rate and range of the species (Higgins and Cain 2002). Such data are usually lacking and thus other solutions must be sought.
A common method to predict species responses to future environmental changes is to use Species Distribution Models (SDMs) fit to a single (static) snapshot of presence/absence data across the landscape (Elith and Leathwick 2009). These include occupancy models which we evaluate for projection herein. SDMs associate the spatial pattern of a species’ occurrence across a subset of the populations in the landscape with habitat and climate data. Such models fitted to snapshot pattern data, however, assume that the current occurrence pattern of the species is at metapopulation equilibrium with its environment. Violations of this assumption can produce biased results as at disequilibrium, occupancy-environment relationship are expected to vary over time and space (Yackulic et al. 2015). For species with high colonization-extinction dynamics, e.g. many mammals, birds and insects, the species distribution pattern can indeed be assumed to much depend on the current landscape structure (Ovaskainen and Hanski 2002). If the landscape structure changes, e.g. due to management operations, the species distribution will promptly adjust to the new structure. For such species, SDMs may produce reliable projections of future population trends. In contrast, for sessile species with slow colonization-extinction dynamics, such as probably many fungi and plants, the distribution patterns may not reflect the present spatial structure of the landscape (Ovaskainen and Hanski 2002). With changing landscape structure, the species distribution patterns will reflect the past rather than the current landscape structure (Snäll et al. 2004, Paltto et al. 2006). Thus for sessile species, a SDM may be inappropriate for predictive modelling, e.g. resulting in overly optimistic projections in situations where the area and connectivity of the habitat have decreased over time.
When data are available over multiple time points it is preferable to acknowledge the temporal change and model the processes which generated the patterns (Gimenez et al. 2014), for instance using what we refer here to as colonisation-extinction models (aka dynamic occupancy models, occupancy dynamics models or multiseason occupancy models) (MacKenzie et al. 2003). Under models for colonisation-extinction dynamics, the past landscape structure becomes less influential, because colonisation events that take place between the two surveys reflect the current locations of the dispersal sources. Especially for species with slow colonization-extinction dynamics, SDMs based on occupancy–environment relationships can be expected to produce biased future occupancy patterns (Ovaskainen and Hanski 2002), and it should be better to base predictions on models that incorporate both rates of local colonization and extinction and their dependence on environmental conditions (Yackulic et al. 2015). Projections of future population development have focused on changes in the distribution patterns (del Rosario Avalos and Hernandez 2015), while estimates of the future summed occupancies or population sizes have to date received little attention.
A major issue in predictive ecology is the scale at which ecological processes should be considered (Chave 2013, Evans et al. 2013, Mouquet et al. 2015). Predictions made from models fit to data at different spatial modelling scales can lead to drastically different conclusions (León-Cortés et al. 1999). When modelling is performed at too large a spatial modelling scale, local heterogeneities in resource quality and quantity relevant for the species in question will go undetected (Mouquet et al. 2015). SDM model performance has been shown to depend on the chosen grain size, especially for systems that can be relatively accurately modelled, but the direction and strength of this effect depends strongly on the type of species (Guisan et al. 2007).
In studies of species that are restricted to a particular resource unit in the habitat patch, such as living trees or deadwood, field surveys often involve a trade-off between resource resolution, i.e. the minimum size or the types of the resource unit to be included (e.g. minimum deadwood diameter), and the survey area covered (Zotz and Bader 2011). If small or particular kinds of resource units are abundant, including them in the survey may make it difficult to attain a survey design that would cover the within-habitat heterogeneity and give information about the occupancy-environment relationship that is general for the focal species and habitat type. It is justifiable to exclude the small resource units from the survey if they are seldom used by the species and if they therefore do not significantly influence its population dynamics (Zotz and Bader 2011, Loos et al. 2015).
There were four aims with our study. The first aim was to test for differences in the future occupancies of (1) species with different landscape-scale occupancy when using occupancy versus colonisation-extinction models. The occupancy models are based on data from one point in time while the colonisation-extinction models are based on data from two points in time. As data suitable for occupancy models are available and frequently used for many species and many geographical areas, it is important to find out how the trends and magnitudes of change that occupancy models reveal differ from the ones revealed by colonisation-extinction models for which data are currently scarce. Colonisation-extinction models are expected to be more realistic for predicting changes as they focus on rate of changes (of occupancy). We hypothesise that the difference in the projected future occupancy between occupancy and colonisation-extinction models is greater for a species with lower landscape-scale occupancy because rare species can be expected to have slower colonization-extinction rates and therefore track changes in forest landscapes with a greater delay than common species. We further test for differences in projected future occupancies between modelling the data at (2) three different spatial modelling scales (cell, plot or patch), and (3) two resource-unit resolutions (two different minimum diameters for deadwood to be included) to find out how scale and resolution influence predictions of future population trends. Inferences were made based on projections of occupancy of two model species in forest production land and in land set aside from production across the whole boreal zone of Sweden. The projections were obtained through stochastic simulations using the occupancy and colonization-extinction models fitted at different spatial scales and resource-unit resolutions. Building the models was part of our fourth aim, specifically (4) to test which local and regional environmental variables explain the occupancy and colonization-extinction dynamics at different spatial scales and resource-unit resolutions.