The accelerating rates of international trade, travel, and transport in the latter half of the twentieth century have led to the progressive mixing of biota from across the world and the number of species introduced to new regions continues to increase. The importance of biogeographic, climatic, economic, and demographic factors as drivers of this trend is increasingly being realized but as yet there is no consensus regarding their relative importance. Whereas little may be done to mitigate the effects of geography and climate on invasions, a wider range of options may exist to moderate the impacts of economic and demographic drivers. Here we use the most recent data available from Europe to partition between macroecological, economic, and demographic variables the variation in alien species richness of bryophytes, fungi, vascular plants, terrestrial insects, aquatic invertebrates, fish, amphibians, reptiles, birds, and mammals. Only national wealth and human population density were statistically significant predictors in the majority of models when analyzed jointly with climate, geography, and land cover. The economic and demographic variables reflect the intensity of human activities and integrate the effect of factors that directly determine the outcome of invasion such as propagule pressure, pathways of introduction, eutrophication, and the intensity of anthropogenic disturbance. The strong influence of economic and demographic variables on the levels of invasion by alien species demonstrates that future solutions to the problem of biological invasions at a national scale lie in mitigating the negative environmental consequences of human activities that generate wealth and by promoting more sustainable population growth.climate | economy | exotic plants and animals | geography | prediction

Riparian ecosystems support mosaics of terrestrial and aquatic plant species that enhance regional biodiversity and provide important ecosystem services to humans. Species composition and the distribution of functional traits – traits that define species in terms of their ecological roles – within riparian plant communities are rapidly changing in response to various global change drivers. Here, we present a conceptual framework illustrating how changes in dependent wildlife communities and ecosystem processes can be predicted by examining shifts in riparian plant functional trait diversity and redundancy (overlap). Three widespread examples of altered riparian plant composition are: shifts in the dominance of deciduous and coniferous species; increases in drought‐tolerant species; and the increasing global distribution of plantation and crop species. Changes in the diversity and distribution of critical plant functional traits influence terrestrial and aquatic food webs, organic matter production and processing, nutrient cycling, water quality, and water availability. Effective conservation efforts and riparian ecosystems management require matching of plant functional trait diversity and redundancy with tolerance to environmental changes in all biomes.

Predicting biodiversity responses to climate change remains a difficult challenge, especially in climatically complex regions where precipitation is a limiting factor. Though statistical climatic envelope models are frequently used to project future scenarios for species distributions under climate change, these models are rarely tested using empirical data. We used long-term data on bird distributions and abundance covering five states in the western US and in the Canadian province of British Columbia to test the capacity of statistical models to predict temporal changes in bird populations over a 32-year period. Using boosted regression trees, we built presence-absence and abundance models that related the presence and abundance of 132 bird species to spatial variation in climatic conditions. Presence/ absence models built using 1970-1974 data forecast the distributions of the majority of species in the later time period, 1998-2002 (mean AUC = 0.79 AE 0.01). Hindcast models performed equivalently (mean AUC = 0.82 AE 0.01). Correlations between observed and predicted abundances were also statistically significant for most species (forecast mean Spearman 0 s q = 0.34 AE 0.02, hindcast = 0.39 AE 0.02). The most stringent test is to test predicted changes in geographic patterns through time. Observed changes in abundance patterns were significantly positively correlated with those predicted for 59% of species (mean Spearman 0 s q = 0.28 AE 0.02, across all species). Three precipitation variables (for the wettest month, breeding season, and driest month) and minimum temperature of the coldest month were the most important predictors of bird distributions and abundances in this region, and hence of abundance changes through time. Our results suggest that models describing associations between climatic variables and abundance patterns can predict changes through time for some species, and that changes in precipitation and winter temperature appear to have already driven shifts in the geographic patterns of abundance of bird populations in western North America.

Both human-related and natural factors can affect the establishment and distribution of exotic species. Understanding the relative role of the different factors has important scientific and applied implications. Here, we examined the relative effect of human-related and natural factors in determining the richness of exotic bird species established across Europe. Using hierarchical partitioning, which controls for covariation among factors, we show that the most important factor is the human-related community-level propagule pressure (the number of exotic species introduced), which is often not included in invasion studies due to the lack of information for this early stage in the invasion process. Another, though less important, factor was the human footprint (an index that includes human population size, land use and infrastructure). Biotic and abiotic factors of the environment were of minor importance in shaping the number of established birds when tested at a European extent using 50!50 km 2 grid squares. We provide, to our knowledge, the first map of the distribution of exotic bird richness in Europe. The richest hotspot of established exotic birds is located in southeastern England, followed by areas in Belgium and The Netherlands. Community-level propagule pressure remains the major factor shaping the distribution of exotic birds also when tested for the UK separately. Thus, studies examining the patterns of establishment should aim at collecting the crucial and hard-to-find information on community-level propagule pressure or develop reliable surrogates for estimating this factor. Allowing future introductions of exotic birds into Europe should be reconsidered carefully, as the number of introduced species is basically the main factor that determines the number established.

Aim Assessing the influence of land cover in species distribution modelling is limited by the availability of fine-resolution land-cover data appropriate for most species responses. Remote-sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote-sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon.Location Central Coast Range, Cascade and Klamath Mountains Oregon, USA.Methods Information on bird observations was collected from 4598 fixedradius point counts. Reflectance data were obtained using 30-m resolution Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used boosted regression tree (BRT) models to analyse relationships between distributions of birds and reflectance values and evaluated prediction performance of the models using area under the receiver operating characteristic curve (AUC) values.Results Prediction success of models using all reflectance values was high (mean AUC = 0.79 AE 0.10 SD). Further, model performance using individual reflectance bands exceeded those that used only Normalized Difference Vegetation Index (NDVI). The relative influence of band 4 predictors was highest, indicating the importance of variables associated with vegetation biomass and photosynthetic activity. Across spatial scales, the average influence of predictors at the 2000 m scale was greatest.Main Conclusions We demonstrate that unclassified remote-sensing imagery can be used to produce species distribution models with high prediction success. Our study is the first to identify general patterns in the usefulness of spectral reflectances for species distribution modelling of multiple species. We conclude that raw Landsat Thematic Mapper data will be particularly useful in species distribution models when high-resolution predictions are required, including habitat change detection studies, identification of fine-scale biodiversity hotspots and reserve design.