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Aim: The increasing availability of remote sensing (RS) products from airborne laser scanning (ALS) surveys, synthetic aperture radar acquisitions and multispectral satellite imagery provides unprecedented opportunities for describing the physical structure and seasonal changes of vegetation. However, the added value of these RS products for predicting species distributions and animal habitats beyond land cover maps remains little explored. Here, we aim to assess how metrics derived from different types of high‐resolution (10 m) RS products predict the habitat suitability of wetland birds. Location: North‐eastern part of the Netherlands. Methods: We built species distribution models (SDMs) with occurrence observations from territory mapping of two selected wetland bird species (great reed warbler and Savi's warbler) and metrics from a Dutch land cover map, country‐wide ALS and Sentinel‐1 and Sentinel‐2 RS products. We then compared model performance, relative variable importance and response curves of the SDMs to assess the contribution and ecological relevance of each RS product and metric. Results: Our results showed that ALS and Sentinel metrics improve SDMs with only land cover metrics by 11% and 10% of the Area Under Curve (AUC) for the great reed warbler and the Savi's warbler respectively. Assessments of feature importance revealed that all types of RS products contributed substantially to predicting the habitat suitability of these wetland birds, but that the most important variables vary among species. Main conclusions: Our study demonstrates that metrics from different high‐resolution RS products capture complementary ecological information on animal habitats, including aspects such as the proportional cover of habitat types, vegetation density and the horizontal variability of vegetation height. Land cover maps with detailed spatial and thematic information can already achieve high model accuracies, but adding metrics derived from ALS point clouds and Sentinel imagery further improve model accuracy and enhance the understanding of animal–habitat relationships. [ABSTRACT FROM AUTHOR]

Aim: Changes in climate and land use practices have been found to affect animal populations in different parts of the world. These studies have typically been conducted during the breeding season, whereas the non-breeding season (hereafter 'winter') has received much less attention. Changes in regional winter abundances could be caused by changes in overall population sizes and/or redistribution of populations. We tested these mechanisms for terrestrial winter bird population changes in Northern Europe and explored the role of climate change and species habitat preference. Location: The Netherlands, Denmark, Sweden, Finland. Methods: We used winter bird counts from four countries conducted annually between 15 December and 20 January in 1980/1981-2013/2014. We report national population trends for 50 species for which a trend could be calculated in at least three of the countries. We analysed country-specific population growth rates in relation to species' climatic summer and winter niches, habitat preference and migratory behaviour. Results: Species breeding in colder (typically northern) areas showed more negative winter population trends than species breeding in warmer areas. Regional winter population trends were negatively correlated with characteristics of their winter climatic niche: populations in the colder part of their winter distribution increased in abundance, whereas populations in the warmer part of their winter distribution decreased. Woodland species tended to do better than farmland species. Migratory behaviour did not explain variation in population trends. Main conclusions: The generally decreasing winter population trends of colddwelling breeding species probably reflect the general decline in population sizes of these species. In contrast, increasing winter population trends for populations in the colder parts of the winter distribution indicate a redistribution of wintering individuals towards the north-east. Both these patterns are likely caused by climate change. [ABSTRACT FROM AUTHOR]