Cholera in Haiti and other Caribbean regions, 19th century.

Medical journals and other sources do not show evidence that cholera occurred in Haiti before 2010, despite the devastating effect of this disease in the Caribbean region in the 19th century. Cholera occurred in Cuba in 1833-1834; in Jamaica, Cuba, Puerto Rico, St. Thomas, St. Lucia, St. Kitts, Nevis, Trinidad, the Bahamas, St. Vincent, Granada, Anguilla, St. John, Tortola, the Turks and Caicos, the Grenadines (Carriacou and Petite Martinique), and possibly Antigua in 1850-1856; and in Guadeloupe, Cuba, St. Thomas, the Dominican Republic, Dominica, Martinique, and Marie Galante in 1865-1872. Conditions associated with slavery and colonial military control were absent in independent Haiti. Clustered populations, regular influx of new persons, and close quarters of barracks living contributed to spread of cholera in other Caribbean locations. We provide historical accounts of the presence and spread of cholera epidemics in Caribbean islands.

Mainland size variation informs predictive models of exceptional insular body size change in rodents.

The tendency for island populations of mammalian taxa to diverge in body size from their mainland counterparts consistently in particular directions is both impressive for its regularity and, especially among rodents, troublesome for its exceptions. However, previous studies have largely ignored mainland body size variation, treating size differences of any magnitude as equally noteworthy. Here, we use distributions of mainland population body sizes to identify island populations as 'extremely' big or small, and we compare traits of extreme populations and their islands with those of island populations more typical in body size. We find that although insular rodents vary in the directions of body size change, 'extreme' populations tend towards gigantism. With classification tree methods, we develop a predictive model, which points to resource limitations as major drivers in the few cases of insular dwarfism. Highly successful in classifying our dataset, our model also successfully predicts change in untested cases.