Data from: Assessing spatio-temporal priorities for species’ recovery in broad-scale dynamic landscapes

1. As threats to biodiversity from environmental change increase,
assessing priorities for mitigation action becomes increasingly important.
However, there have been few attempts to schedule actions across broad
spatial extents that explicitly account for dynamic ecological processes
and threats. 2. We combined a dynamic occupancy model with a decision
analysis framework to spatially allocate multiple recovery actions to
maximize species’ probability of occupancy under threats posed by climate
and land-use change. We used the koala Phascolarctos cinereus across the
Australian state of New South Wales (810 000 km2) to illustrate this
approach. We considered four actions implemented on a 10 × 10 km2 grid:
reduce domestic dog attacks through dog control, reduce vehicle collisions
through fencing highways, protect habitat through land acquisitions and
restore Eucalyptus forest. We used the occupancy model to predict
ecological responses to recovery actions and simulated annealing to
identify spatio-temporal priorities for each action. We contrasted the
results against priorities generated using a traditional static
distribution model. 3. To maximize the probability of koala occupancy in
50 years’ time, with an annual budget of up to AU$20 million, investment
priorities were located in the eastern part of koala's range,
focusing on dog control with some investment in habitat protection and
restoration. With higher budgets, investment priorities shifted towards
habitat protection and restoration in the western part of the range.
However, priorities based on the static distribution model, which had a
lower predictive accuracy than the dynamic model, were different.
Regardless of budget, priorities derived from the static model were
predominantly located in the western part of koala's range, focusing
on highway fencing with some investment in dog control. 4. Synthesis and
applications. Our approach for integrating spatio-temporal dynamics into
conservation prioritization across broad spatial extents provides a
significant advance on existing approaches based on static distribution
models. The finding that the inferior static model produced different
priorities to the dynamic model cautions against the use of static models
for conservation planning in dynamic landscapes. Additionally, the
substantial changes in priorities with budget indicate that conservation
planning under dynamic landscape and climate change must carefully
consider priority actions and locations relative to the conservation
resources available.