A Times Bestseller Shortlisted for the Wainwright Prize for UK Nature Writing 2020 'Remarkable, and so profoundly enjoyable to read ... Its importance is huge, setting down a vital marker in the 21st century debate about how we use and abuse the land' - Joyce McMillan, Scotsman Desperate to connect with his native Galloway, Patrick Laurie plunges into work on his family farm in the hills of southwest Scotland. Investing in the oldest and most traditional breeds of Galloway cattle, the Riggit Galloway, he begins to discover how cows once shaped people, places and nature in this remote and half-hidden place. This traditional breed requires different methods of care from modern farming on an industrial, totally unnatural scale. As the cattle begin to dictate the pattern of his life, Patrick stumbles upon the passing of an ancient rural heritage. Always one of the most isolated and insular parts of the country, as the twentieth century progressed, the people of Galloway deserted the land and the moors have been transformed into commercial forest in the last thirty years. The people and the cattle have gone, and this withdrawal has shattered many centuries of tradition and custom. Much has been lost, and the new forests have driven the catastrophic decline of the much-loved curlew, a bird which features strongly in Galloway's consciousness. The links between people, cattle and wild birds become a central theme as Patrick begins to face the reality of life in a vanishing landscape.

The First Detailed Account of Statistical Analysis That Treats Models as Approximations The idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist statistics is formulated as the problem of estimating the "true but unknown" parameter value that generated the data. Forgoing any concept of truth, Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis presents statistical analysis/inference based on approximate models. Developed by the author, this approach consistently treats models as approximations to data, not to some underlying truth. The author develops a concept of approximation for probability models with applications to: Discrete data Location scale Analysis of variance (ANOVA) Nonparametric regression, image analysis, and densities Time series Model choice The book first highlights problems with concepts such as likelihood and efficiency and covers the definition of approximation and its consequences. A chapter on discrete data then presents the total variation metric as well as the Kullback-Leibler and chi-squared discrepancies as measures of fit. After focusing on outliers, the book discusses the location-scale problem, including approximation intervals, and gives a new treatment of higher-way ANOVA. The next several chapters describe novel procedures of nonparametric regression based on approximation. The final chapter assesses a range of statistical topics, from the likelihood principle to asymptotics and model choice.

The black grouse range in Britain has shrunk by 95% in the past 100 years, with 25% of that decline since 1990. Patrick Laurie's lively natural history is interwoven with his account of his on-going battle to reintroduce them on his farm in South-West Scotland.