The demography of red and grey squirrels was studied by live-trapping and radio-tagging at 14 deciduous and conifer sites in southern Britain and at eight conifer sites for one year in northern England. Densities and productivity correlated with tree seed crops for both squirrel species in deciduous and conifer habitats. Productivity was reduced by high density of full-grown squirrels relative to seed abundance. In oak±hazel woods, demography of grey squirrels correlated with abundance of acorns but not of hazel-nuts, whereas density and productivity of red squirrels correlated with hazel-nut abundance. Correlations of female density and productivity with pine-cone crops did not differ between red and grey squirrels. Predators ate many radio-tagged grey squirrels in conifers, and annual survival was only 50% compared with 80±82% for both species in other habitats. Grey squirrel populations in southern conifer sites were sustained by immigration, and at northern sites female density correlated with oak abundance within 500 m. Failure to exploit acorn crops puts red squirrels at a competitive disadvantage in deciduous woodland. Red squirrels had higher survival than grey squirrels in conifers, which may give them an advantage in that habitat, but could also have been explained by a lack of predators on their island study site.

The demography of red and grey squirrels was studied by live-trapping and radio-tagging at 14 deciduous and conifer sites in southern Britain and at eight conifer sites for one year in northern England. Densities and productivity correlated with tree seed crops for both squirrel species in deciduous and conifer habitats. Productivity was reduced by high density of full-grown squirrels relative to seed abundance. In oak±hazel woods, demography of grey squirrels correlated with abundance of acorns but not of hazel-nuts, whereas density and productivity of red squirrels correlated with hazel-nut abundance. Correlations of female density and productivity with pine-cone crops did not differ between red and grey squirrels. Predators ate many radio-tagged grey squirrels in conifers, and annual survival was only 50% compared with 80±82% for both species in other habitats. Grey squirrel populations in southern conifer sites were sustained by immigration, and at northern sites female density correlated with oak abundance within 500 m. Failure to exploit acorn crops puts red squirrels at a competitive disadvantage in deciduous woodland. Red squirrels had higher survival than grey squirrels in conifers, which may give them an advantage in that habitat, but could also have been explained by a lack of predators on their island study site.

The geographic range of red squirrels rontractrd sharply in Britain during the 1940s arid 1950s, as increasingly large areas were coloriizcd by the congcncric North American grcy squirrel. Red squirrrls remain common only on ofbhore islands, and in the large conifer forests of northern England and Scotland. 'The initial rt~placement of red squirrels was in arras dominatcd Iiy oak woodland, probably because acorn crops are exploited less rllicimtly by red squirrels than by grey squirrels. Dirt studies have shown that acorns are digested less eficicntly by the red squirrel, which occurs in conifers through most ofits Eurasian range, than by thc introduced grey squirrrl, which is primarily a native of deciduous woodland. 'l'he red squirrel will probably be replaced in deciduous and mixed woodland throughout mainland Britain, and may eventually persist only in large areas of conifers which arc far from oak trees. Thc coriservatioii of red squirrels on islands is therrfore particularly important for their survival, perhaps making it worthwhilc to crcatc rivw islarid populations where they do not at present exist.

For medical informaticians, it became more and more crucial to assess the benefits and disadvantages of AI-based solutions as promising alternatives for many traditional tools. Besides quantitative criteria such as accuracy and processing time, healthcare providers are often interested in qualitative explanations of the solutions. Explainable AI provides methods and tools, which are interpretable enough that it affords different stakeholders a qualitative understanding of its solutions. Its main purpose is to provide insights into the black-box mechanism of machine learning programs. Our goal here is to advance the problem of qualitatively assessing AI from the perspective of medical informaticians by providing insights into the central notions, namely: explainability, interpretability, understanding, trust, and confidence.

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