Stanley Cramp

Bio: Stanley Cramp is an academic researcher. The author has contributed to research in topics: Western Palaearctic & North Atlantic oscillation. The author has an hindex of 12, co-authored 12 publications receiving 5781 citations.

Handbook of the birds of Europe, the Middle East and North Africa

Abstract: 1983 — Handbook of the Birds of Europe, the Middle East and North Africa. The Birds of Western Palearctic, vol. 3 — Oxford University Press, Oxford, 913 pp. Handbook of the Birds of Europe, the Middle East and North Africa: The Birds of the Western Palearctic by CRAMP, Stanley et al. (eds) and a great selection. AERC TAC Checklist of bird taxa occurring in Western Palearctic region, 15th Draft. Available at: Handbook of the birds of Europe, the Middle East and Africa. The birds of the North Atlantic Oscillation and timing of spring migration in birds.

Handbook of the Birds of Europe, the Middle East and North Africa; the Birds of the Western Palearctic

Abstract: 1983 — Handbook of the Birds of Europe, the Middle East and North Africa. The Birds of Western Palearctic, vol. 3 — Oxford University Press, Oxford, 913 pp. Handbook of the Birds of Europe, the Middle East and North Africa: The Birds of the Western Palearctic by CRAMP, Stanley et al. (eds) and a great selection. AERC TAC Checklist of bird taxa occurring in Western Palearctic region, 15th Draft. Available at: Handbook of the birds of Europe, the Middle East and Africa. The birds of the North Atlantic Oscillation and timing of spring migration in birds.

Optimal Bird Migration: The Relative Importance of Time, Energy, and Safety

Abstract: “Optimization is the process of minimizing costs or maximizing benefits, or obtaining the best possible compromise between the two. Evolution by natural selection is a process of optimization” (R. McNeill Alexander 1982).

An Update of Wallace’s Zoogeographic Regions of the World

Abstract: Modern attempts to produce biogeographic maps focus on the distribution of species, and the maps are typically drawn without phylogenetic considerations. Here, we generate a global map of zoogeographic regions by combining data on the distributions and phylogenetic relationships of 21,037 species of amphibians, birds, and mammals. We identify 20 distinct zoogeographic regions, which are grouped into 11 larger realms. We document the lack of support for several regions previously defined based on distributional data and show that spatial turnover in the phylogenetic composition of vertebrate assemblages is higher in the Southern than in the Northern Hemisphere. We further show that the integration of phylogenetic information provides valuable insight on historical relationships among regions, permitting the identification of evolutionarily unique regions of the world.

Spatial autocorrelation and red herrings in geographical ecology

Abstract: Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates 'red herrings', such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro-scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least-squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north-south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non-detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de-emphasized predictors with strong autocorrelation and long-distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.

Developing indicators for European birds

Abstract: The global pledge to deliver ‘a significant reduction in the current rate of biodiversity loss by 2010’ is echoed in a number of regional and national level targets. There is broad consensus, however, that in the absence of conservation action, biodiversity will continue to be lost at a rate unprecedented in the recent era. Remarkably, we lack a basic system to measure progress towards these targets and, in particular, we lack standard measures of biodiversity and procedures to construct and assess summary statistics. Here, we develop a simple classification of biodiversity indicators to assist their development and clarify purpose. We use European birds, as example taxa, to show how robust indicators can be constructed and how they can be interpreted. We have developed statistical methods to calculate supranational, multi-species indices using population data from national annual breeding bird surveys in Europe. Skilled volunteers using standardized field methods undertake data collection where methods and survey designs differ slightly across countries. Survey plots tend to be widely distributed at a national level, covering many bird species and habitats with reasonable representation. National species' indices are calculated using log-linear regression, which allows for plot turnover. Supranational species' indices are constructed by combining the national species' indices weighted by national population sizes of each species. Supranational, multi-species indicators are calculated by averaging the resulting indices. We show that common farmland birds in Europe have declined steeply over the last two decades, whereas woodland birds have not. Evidence elsewhere shows that the main driver of farmland bird declines is increased agricultural intensification. We argue that the farmland bird indicator is a useful surrogate for trends in other elements of biodiversity in this habitat.