Good knowledge on how increasing urbanization affects biodiversity is essential in order to preserve biodiversity in urban green spaces. We examined how urban development affects species richness and total abundance of butterflies as well as the occurrence and abundance of individual species within the Helsinki metropolitan area in Northern Europe. Repeated butterfly counts in 167 separate 1-km-long transects within Helsinki covered the entire urbanization gradient, quantified by human population density and the proportion of built-up area (within a 50-m buffer surrounding each butterfly transect). We found consistently negative effects of both human population density and built-up area on all studied butterfly variables, though butterflies responded markedly more negatively to increasing human population density than to built-up area. Responses in butterfly species richness and total abundance showed higher variability in relation to proportion of built-up area than to human density, especially in areas of high human density. Increasing human density negatively affected both the abundance and the occurrence of 47% of the 19 most abundant species, whereas, for the proportion of built-up area, the corresponding percentages were 32% and 32%, respectively. Species with high habitat specificity and low mobility showed higher sensitivity to urbanization (especially high human population density) than habitat generalists and mobile species that dominated the urban butterfly communities. Our results suggest that human population density provides a better indicator of urbanization effects on butterflies compared to the proportion of built-up area. The generality of this finding should be verified in other contexts and taxonomic groups.

Aim: Species distribution models (SDMs) are widely used to make predictions on how species distributions may change as a response to climatic change. To assess the reliability of those predictions, they need to be critically validated with respect to what they are used for. While ecologists are typically interested in how and where distributions will change, we argue that SDMs have seldom been evaluated in terms of their capacity to predict such change. Instead, typical retrospective validation methods estimate model's ability to predict to only one static time in future. Here, we apply two validation methods, one that predicts and evaluates a static pattern, while the other measures change and compare their estimates of predictive performance. Location: Fennoscandia.Methods: We applied a joint SDM to model the distributions of 120 bird species in four model validation settings. We trained models with a dataset from 1975 to 1999 and predicted species' future occurrence and abundance in two ways: for one static time period (2013- 2016, "static validation') and for a change between two time periods (difference between 1996- 1999 and 2013- 2016, "change validation'). We then measured predictive performance using correlation between predicted and observed values. We also related predictive performance to species traits. Results: Even though static validation method evaluated predictive performance as good, change method indicated very poor performance. Predictive performance was not strongly related to any trait.Main Conclusions: Static validation method might overestimate predictive performance by not revealing the model's inability to predict change events. If species' distributions remain mostly stable, then even an unfit model can predict the near future well due to temporal autocorrelation. We urge caution when working with forecasts of changes in spatial patterns of species occupancy or abundance, even for SDMs that are based on time series datasets unless they are critically validated for forecasting such change.