The Effect of the Secret Ballot on Party System Fragmentation: A Test of Three Competing Arguments

Few studies have examined the impact that the adoption of the secret ballot had on party system fragmentation outside the United States. This article tests arguments maintaining that the adoption of the secret ballot had a negative, positive or nil effect on party system fragmentation. Using time-series cross-sectional data from
several countries adopting the secret ballot around the turn of the twentieth century, the results demonstrate that the adoption of the secret ballot did not hinder – though did not favour, either – the development of multiparty systems.

New age estimates and climatostratigraphic correlations for the Borrobol and Penifiler Tephras: evidence from Abernethy Forest, Scotland

The emerging tephrostratigraphy of NW Europe spanning the last termination (ca. 15–9 ka) provides the potential for synchronizing marine, ice-core and terrestrial records, but is currently compromised by stratigraphic complications, geochemical ambiguity and imprecise age estimates for some layers. Here we present new tephrostratigraphic, radiocarbon and chironomid-based
palaeotemperature data from Abernethy Forest, Scotland, that refine the ages and stratigraphic positions of the Borrobol and Penifiler tephras. The Borrobol Tephra (14.14–13.95 cal ka BP) was deposited in a relatively warm period equated with Greenland Interstadial sub-stage GI-1e. The younger Penifiler Tephra (14.09–13.65 cal ka BP) is closely associated with a cold oscillation equated with GI-
1d. We also present evidence for a previously undescribed tephra layer that has a major-element chemical signature identical to the Vedde Ash. It is associated with the warming trend at the end of the Younger Dryas, and dates between 11.79 and 11.20 cal ka BP.

The organizational ecology of ethnic cleavages: The nonlinear effects of ethnic diversity on party system fragmentation

The conventional wisdom regarding party system fragmentation assumes that the effects of electoral systems and social cleavages are linear. However, recent work applying organizational ecology theories to the study of party systems has challenged the degree to which electoral system effects are linear. This paper applies such concepts to the study of social cleavages. Drawing from theories of organizational ecology and the experience of many ethnically diverse African party systems, I argue that the effects of ethnic diversity are nonlinear, with party system fragmentation increasing until reaching moderate levels of diversity before declining as diversity reaches extreme values. Examining this argument cross-nationally, the results show that accounting for nonlinearity in ethnic diversity effects significantly improves model fit.

Spatio-temporal Change in Crowned (Propithecus coronatus) and Decken’s Sifaka (Propithecus deckenii) Habitat in the Mahavavy-Kinkony Wetland Complex, Madagascar.

The crowned sifaka (Propithecus coronatus) and Decken’s sifaka (Propithecus deckenii) are Endangered lemurs endemic to west and central Madagascar. Both have suffered habitat loss and fragmentation throughout their ranges. The goal
of this study, conducted in the Mahavavy-Kinkony Wetland Complex (MKWC) in northwestern Madagascar, was to assess the effects of historical change in the species’ habitats, and to model the potential impact of further land-use change on their habitats. The IDRISI Andes Geographical Information System and image-processing software was used for satellite-image classifiation, and the Land Change Modeler was used to compare the natural habitat of the species from 1973 to 2005, and to predict available habitat for 2050. We analyzed two forests in the MKWC occupied by P. coronatus (Antsilaiza and Anjohibe), and three forests occupied by P. deckenii (Tsiombikibo, Marofandroboka and Andohaomby). The two forests occupied by P. coronatus contracted during the period 1949–1973, but then expanded to exceed their 1949 area by 28% in 2005. However, the land change model predicted that they will contract again to match their 1949 area by 2050, and will again lose their corridor connection, meaning that the conservation gains for this species in the complex are at risk of being reversed. The three forests occupied by P. deckenii have declined in area steadily since 1949, losing 20% of their original area by 2005, and are predicted to lose a further 15% of their original area by 2050. Both species are therefore at risk of becoming even more threatened if land-use change continues within the complex. Improved conservation of the remaining forest is recommended to avoid further loss, as well as ecological restoration and reforestation to promote connectivity between the forests. A new strategy for controlling agriculture and forest use is required in order to avoid further destruction of the forest.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.