Interbank borrowing and lending between financially constrained banks

Some stylized facts about transactions among banks are not easily reconciled with coinsurance of short-term liquidity risks. In our model, interbank markets play a different role. We argue that lending to another bank can reduce a bank’s overall portfolio risk through diversification. If insolvency is costly, this diversification improves the interbank lender's funding liquidity, boosting credit supply to nonbanks. However, diversification comes at an endogenous cost that depends on bank-specific factors of interbank borrower and lender. The model provides a framework for understanding the importance of interbank lending for aggregate credit supply and the stability of banking systems. The model’s predictions are consistent with evidence documented in the literature that other theories cannot consistently explain

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas