Species distributional or trait data based on range map (extent-of-occurrence) or atlas survey data often display spatial autocorrelation, i.e. locations close to each other exhibit more similar values than those further apart. If this pattern remains present in the residuals of a statistical model based on such data, one of the key assumptions of standard statistical analyses, that residuals are independent and identically distributed (i.i.d), is violated. The violation of the assumption of i.i.d. residuals may bias parameter estimates and can increase type I error rates (falsely rejecting the null hypothesis of no effect). While this is increasingly recognised by researchers analysing species distribution data, there is, to our knowledge, no comprehensive overview of the many available spatial statistical methods to take spatial autocorrelation into account in tests of statistical significance. Here, we describe six different statistical approaches to infer correlates of species' distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations. A comprehensive comparison of the relative merits of these methods is beyond the scope of this paper. To demonstrate each method's implementation, however, we undertook preliminary tests based on simulated data. These preliminary tests verified that most of the spatial modeling techniques we examined showed good type I error control and precise parameter estimates, at least when confronted with simplistic simulated data containing

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We examine the advantages and disadvantages of a methodological framework designed to analyze the poorly understood relationships between the ecosystem properties of large portions of land, and their capacities (stocks) to provide goods and services (flows). These capacities (stocks) are referred to as landscape functions. The core of our assessment is a set of expert-and literaturedriven binary links, expressing whether specific land uses or other environmental properties have a supportive or neutral role for given landscape functions. The binary links were applied to the environmental properties of 581 administrative units of Europe with widely differing environmental conditions and this resulted in a spatially explicit landscape function assessment. To check under what circumstances the binary links are able to replace complex interrelations, we compared the landscape function maps with independently generated continent-wide assessments (maps of ecosystem services or environmental parameters/ indicators). This rigorous testing revealed that for 9 out of 15 functions the straightforward binary links work satisfactorily and generate plausible geographical patterns. This conclusion holds primarily for production functions. The sensitivity of the nine landscape functions to changes in land use was assessed with four land use scenarios (IPCC SRES). It was found that most European regions maintain their capacity to provide the selected services under any of the four scenarios, although in some cases at other locations within the region. At the proposed continental scale, the selected input parameters are thus valid proxies which can be used to assess the mid-term potential of landscapes to provide goods and services.

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Long-term societal trends which include decreasing population in structurally poorer regions and changes in agricultural policies have been leading to land abandonment in various regions of Europe. One of the consequences of this development includes spontaneous forest regeneration of formerly open-land habitats with likely significant effects on plant and animal diversity. We assess potential effects of agricultural decline in Switzerland (41,000 km 2) and potential impacts on the spatial distribution of seven open-land species (insects, reptile, birds) under land-use change scenarios: (1) a businessas-usual scenario that extrapolates trends observed during the last 15 years into the future, (2) a liberalisation scenario with limited regulation, and (3) a lowered agricultural production scenario fostering conservation. All scenarios were developed in collaboration with socioeconomists. Results show that spontaneous reforestation is potentially minor in the lowlands since combinations of socioeconomic (better accessibility), topographic (less steep slopes), and climatic factors (longer growing seasons) favour agricultural use and make land abandonment less likely. Land abandonment, spontaneous reforestation, and subsequent loss of open-land, however, are potentially pronounced in mountainous areas except where tourism is a major source of income. Here, socioeconomic and natural conditions for cultivation are more difficult, leading to higher abandonment and thus reforestation likelihood. Evaluations for openland species core habitats indicate pronounced spatial segregation of expected landscape change. Habitat losses (up to 59%) are observed throughout the country, particularly at high elevation sites in the Northern Alps. Habitat gains under the lowered agricultural production scenario range between 12 and 41% and are primarily observed for the Plateau and the Northern Alps. Keywords Agricultural decline Á Habitat suitability maps Á Species habitat distribution modelling Á Scenarios of land use change Á Switzerland

Many landscape genetic studies promise results that can be applied in conservation management. However, only few landscape genetic studies have been used by practitioners. Here, we identified scientific topics in landscape genetics that need to be addressed before results can more successfully be applied in conservation management. For each topic, weaknesses of common practice in landscape genetic analysis are described by presenting examples from current studies and further recommendations for improvements are outlined. First, we suggest matching the extent of the study area with those of conservation management units and the study species' dispersal potential when designing landscape genetic studies. Second, the quality of the underlying statistical models should be optimised, and models should include variables that are useful for management implementation. Third, to further improve the applicability of landscape genetic studies, thresholds for landscape effects on gene flow should be identified. Fourth, landscape genetic models could be used for the development of conservation planning tools, which ideally also incorporate the above described thresholds.Fifth and as discussed in earlier studies, the use of multiple species and replication at the landscape scale is recommended. Although it appears that only few landscape genetic studies have been applied in practical management until now, examples presented in this article show that landscape genetic methods can provide important information to formulate concrete management implications. Thus, addressing the above-mentioned scientific topics in landscape genetic studies would enhance the benefits of their results for practitioners.

Artificial light at night (ALAN) is closely associated with modern societies and is rapidly increasing worldwide. A dynamically growing body of literature shows that ALAN poses a serious threat to all levels of biodiversity—from genes to ecosystems. Many “unknowns” remain to be addressed however, before we fully understand the impact of ALAN on biodiversity and can design effective mitigation measures. Here, we distilled the findings of a workshop on the effects of ALAN on biodiversity at the first World Biodiversity Forum in Davos attended by several major research groups in the field from across the globe. We argue that 11 pressing research questions have to be answered to find ways to reduce the impact of ALAN on biodiversity. The questions address fundamental knowledge gaps, ranging from basic challenges on how to standardize light measurements, through the multi-level impacts on biodiversity, to opportunities and challenges for more sustainable use.