Strong context-dependence in the relative importance of climate and
habitat on macro-moth community changes in Finland
Abstract
Evaluating the relative impacts of land use and climate change on
community change is challenging – and their impact may be
contextdependent. Here, we use long-term nocturnal macro-moth community
data to evaluate the relative impacts of changing habitats vs. changing
climates on community composition and diversity of moths in different
landscape settings and for moth species associated with different
traits. We used Hierarchical Modelling of Species Communities to
pinpoint moth species’ responses to climate and habitat composition in
109 sites across Finland. To characterise context-dependence, we
extended this framework with conditional variance partitioning analysis.
We used the model predictions to evaluate the relative effect of drivers
on community diversity across Finland. The landscape context (i.e. the
habitat composition around the site and its changes) emerged as the
dominant driver of macro-moth communities. At the site level, where
forests or shrub-like vegetation dominates, variation in species
occurrence was mostly explained by local habitat conditions. In
heterogeneous and water-dominated habitats, both habitat and climate
variability contributed equally to patterns in species occurrence. At
the species level, macro-moth responses to drivers of change varied
according to their host plant affinity but independently of their
wingspan. Climate and habitat changes can thus contribute congruently or
unequally to community change, depending on the habitat. At the
community level, traits also give insights into trends in and temporal
variability of biogeographic patterns. Our results underpin the
importance of land-use change as a key driver of community change –
even among heatsensitive ectotherms. We also demonstrate that the
sensitivity of local communities to climate and land use change varies
among habitat profiles. Overall, our results highlight the importance of
accounting for local conditions to understand and predict community
patterns under global change.