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.

In recent years, the increase of data availability through citizen science campaigns has raised questions on the quality of this data. Species distribution models can be severely impacted by non-random spatial distributions of records. Multiple methods exist to correct for spatial bias and most of them imply that the sampling is uneven in space and determined by the observers’ choices of where to search for observations. One common correction method is to include a covariate in the model as a proxy for sampling bias and correcting for this bias by setting this covariate equal to a common value upon prediction. However, this approach implies that each observer behaves in the same manner, which in practice may not be the case. Here, we differentiate two common observer behaviours: exploring and following. Under this paradigm, explorers seek to observe species in new places far away from other observations and away from common routes of transit. By contrast, followers search near already observed species locations and remain closer to common routes of transit. In this paper, we investiage whether the current approaches to correcting for observer bias hold under varying observer behaviours, or whether a data-driven approach based on modelled observer behaviour may lead to better predictions. To do so, we developed a new software platform, obsimulator, to simulate patterns of points driven by observer behaviour. We established two correction methods based on a bias incorporation approach using k-nearest neighbours and density calculation. Broadly, we found that the method of including a bias covariate and setting it to a common value for prediction yields the best results. We also found that the knn-based correction outperformed the density-based correction. Additionally, we provide guidance for setting model parameters based on the ratio of explorers versus followers in the observers’ cohort.