Since the establishment of perovskite solar cells (PSCs), there has been an intense search for alternative materials to replace lead and improve their stability toward moisture and light. As single-metal perovskite structures have yielded unsatisfactory performances, an alternative is the use of double perovskites that incorporate a combination of metals. To this day, only a handful of these compounds have been synthesized, but most of them have indirect bandgaps and/or do not have bandgaps energies well-suited for photovoltaic applications. Here we report the synthesis and characterization of a unique mixed metal ⟨111⟩-oriented layered perovskite, CsCuSbCl (1), that incorporates Cu and Sb into layers that are three octahedra thick (n = 3). In addition to being made of abundant and nontoxic elements, we show that this material behaves as a semiconductor with a direct bandgap of 1.0 eV and its conductivity is 1 order of magnitude greater than that of MAPbI (MA = methylammonium). Furthermore, 1 has high photo- and thermal-stability and is tolerant to humidity. We conclude that 1 is a promising material for photovoltaic applications and represents a new type of layered perovskite structure that incorporates metals in 2+ and 3+ oxidation states, thus significantly widening the possible combinations of metals to replace lead in PSCs.

Summary 1.Many species are adversely affected by human activities at large spatial scales and their conservation requires detailed information on distributions. Intensive ground surveys cannot keep pace with the rate of land-use change over large areas and new methods are needed for regional-scale mapping. 2. We present predictive models for great bustards in central Spain based on readily available advanced very high resolution radiometer (AVHRR) satellite imagery combined with mapped features in the form of geographic information system (GIS) data layers. As AVHRR imagery is coarse-grained, we used a 12-month time series to improve the definition of habitat types. The GIS data comprised measures of proximity to features likely to cause disturbance and a digital terrain model to allow for preference for certain topographies. 3. We used logistic regression to model the above data, including an autologistic term to account for spatial autocorrelation. The results from models were combined using Bayesian integration, and model performance was assessed using receiver operating characteristics plots. 4. Sites occupied by bustards had significantly lower densities of roads, buildings, railways and rivers than randomly selected survey points. Bustards also occurred within a narrower range of elevations and at locations with significantly less variable terrain. 5. Logistic regression analysis showed that roads, buildings, rivers and terrain all contributed significantly to the difference between occupied and random sites. The Bayesian integrated probability model showed an excellent agreement with the original census data and predicted suitable areas not presently occupied. 6. The great bustard's distribution is highly fragmented and vacant habitat patches may occur for a variety of reasons, including the species' very strong fidelity to traditional sites through conspecific attraction. This may limit recolonization of previously occupied sites. 7. We conclude that AVHRR satellite imagery and GIS data sets have potential to map distributions at large spatial scales and could be applied to other species. While models based on imagery alone can provide accurate predictions of bustard habitats at some spatial scales, terrain and human influence are also significant predictors and are needed for finer scale modelling.

Summary1. Predictive models of species' distributions are used increasingly in ecological studies investigating features as varied as biodiversity, habitat selection and interspecific competition. In a pilot study, we based a successful model for the great bustard Otis tarda on advanced very high resolution radiometer (AVHRR) satellite data, which offer attractive predictor variables because of the global coverage, high temporal frequency of overpasses and low cost. We wished to assess whether the approach could be applied at very large spatial scales, and whether the coarse resolution of the imagery (1 km 2 ) would limit application to those bird species with large home ranges or to simple recognition of broad habitat types. 2. We modelled the distributions of three agricultural steppe birds over the whole of Spain using a common set of predictor variables, including AVHRR imagery. The species, great bustard, little bustard Tetrax tetrax and calandra lark Melanocorhypha calandra , have similar habitat requirements but differently sized home ranges, and are all species of conservation concern. Good models would reveal differences in distribution between the species and have high predictive power despite the large geographical extent covered. 3. Generalized additive models (GAMs) were built with the presence-absence of the species as the response variable. Individual species' responses to the habitat variables were identified using partial fits and compared with each other. We found that this modelling framework could successfully distinguish the habitats selected by the three species, while the response curves indicated how the habitats differed. Model fits and cross-validations assessed using receiver operating characteristic (ROC) plots showed the models to be successful and robust. 4. We overlaid the predictive maps to identify key areas for agricultural steppe birds in Spain and compared these with the present network of protected sites in two sample regions. In Castilla León the provision of protected sites appears appropriate, but in Castilla La Mancha large areas of apparently suitable habitat have no protection. 5. These results confirm that large-scale models are able to increase our understanding of species' ecology and provide data for conservation planning. AVHRR imagery, in combination with other variables, has sufficient resolution to model a range of bird species, and GAMs have the flexibility to model subtle species-habitat responses.

Habitat loss and deterioration represent the main threats to wildlife species, and are closely linked to the expansion of roads and human settlements. Unfortunately, large-scale effects of these structures remain generally overlooked. Here, we analyzed the European transportation infrastructure network and found that 50% of the continent is within 1.5 km of transportation infrastructure. We present a method for assessing the impacts from infrastructure on wildlife, based on functional response curves describing density reductions in birds and mammals (e.g., road-effect zones), and apply it to Spain as a case study. The imprint of infrastructure extends over most of the country (55.5% in the case of birds and 97.9% for mammals), with moderate declines predicted for birds (22.6% of individuals) and severe declines predicted for mammals (46.6%). Despite certain limitations, we suggest the approach proposed is widely applicable to the evaluation of effects of planned infrastructure developments under multiple scenarios, and propose an internationally coordinated strategy to update and improve it in the future.anthropogenic development | birds | Europe | mammals | road-effect zone