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.