Language diversity is under threat, with between a third to a half of all languages considered endangered, and predicted rates of loss equivalent to one language per month for the rest of the century. Rather than reviewing the extensive body of linguistic research on endangered languages, this review focuses specifically on the interdisciplinary transfer of methods developed in conservation biology, macroecology and macroevolution to the study of language endangerment and loss. While the causes of language endangerment and loss are different to those for species, studying patterns of diversity of species and languages involves similar analytical challenges, associated with testing hypotheses and identifying causal relationships. Solutions developed in biology can be adapted to illuminate patterns in language endangerment, such as statistical methods that explicitly model phylogenetic nonindependence, spatial autocorrelation and covariation between variables, which may otherwise derail the search for meaningful predictors of language endangerment. However, other tools from conservation biology may be much less use in understanding or predicting language endangerment, such as metrics based on International Union for Conservation of Nature (IUCN) criteria, population viability analysis or niche modelling. This review highlights both the similarities and the differences in approaches to understanding the concurrent crises in loss of both linguistic diversity and biodiversity.

Self-exciting point processes have been proposed as models for the location of criminal events in space and time. Here we consider the case where the triggering function is isotropic and takes a non-parametric form that is determined from data. We pay special attention to normalisation issues and to the choice of spatial distance measure, thereby extending the current methodology. After validating these ideas on synthetic data, we perform inference and prediction tests on public domain burglary data from Chicago. We show that the algorithmic advances that we propose lead to improved predictive accuracy.

In this paper, we assess whether or not organic agriculture has a positive impact on local economies. We first identify organic agriculture hotspots (clusters of counties with positively correlated high numbers of organic operations) using spatial statistics. Then, we estimate a treatment effects model that classifies a county's membership in an organic hotspot as an endogenous treatment variable. By modeling what a hotspot county's economic indicators would have been had the county not been part of a hotspot, this model captures the effect of being in a hotspot on a county's economic indicators. We perform the same analysis for general agricultural farm hotspots to confirm that the benefits associated with organic production hotspots are, in fact, due to the organic component. Our results show that organic hotspot membership leads to a lower county-level poverty rate and a higher median household income. A similar result is not found when investigating the impact of general agriculture hotspots. On the other hand, our result is robust to alternative hotspot definitions based on type of organic operations to alternative methods of estimating average treatment effects on the treated. These results provide strong motivation for considering hotspots of organic handling operations, which refers to middlemen such as processors, wholesalers and brokers, and hotspots of organic production to be local economic development tools, and may be of interest to policymakers whose objective is to promote rural development. Our results may incentivize policymakers to specifically focus on organic development, rather than the more general development of agriculture, as a means to promote economic growth in rural areas, and may further point them in the direction of not only encouraging the presence of organic operations, but of fostering the development of clusters or hotspots of these operations.

This study is based on crime mapping and crime analysis of property crimes in Jodhpur. The property crimes which were selected were house breaking, auto thefts and chain snatching. Data from police stations were used to generate the maps to locate hot spots of crimes. The profile of these hot spots was analyzed through observations supplemented with interviews of police officers and public 100 cases of house breaking and 100 cases of auto thefts were further analyzed to understand the contexts which lead to these crimes. These contexts are in consonance with situational crime prevention theories. This study may help to understand the environmental factors which may be responsible for certain places becoming hot spot areas of property crimes in Jodhpur.

Although Brazil is the centre with the third highest diversity of Cactaceae, the distribution of species at the local scale in Brazil remains largely unknown. This article describes the distribution patterns of the Cactaceae communities in the protected area of the Contendas do Sincorá National Forest (CSNF) in a semiarid region in the north-east of Brazil. Trails were distributed between the altitudes of 290 and 596 m, along which 91 plots (1.8 ha) were established for quantitative surveys. The floristic data were also recorded separately on these trails. With the floristic survey, 18 taxa were recorded, and the quantitative surveys within the plots identified 10 species, represented by 1135 individuals. The distribution patterns of the species of Cactaceae were intermittent, which provided a likely explanation for the differences between the floristic survey and the species composition of the plots. The largest fraction of the floristic variation was not explained by environment or space, and this may be related to climatic alterations in the past, evolutionary processes, natural and anthropic disturbances and other unmeasured environmental variables. The recorded endemic, vulnerable and endangered species of the caatingas of Bahia indicate that broader protection areas for the Cactaceae species in the region of the CSNF are necessary.

Potassium–argon (K–Ar) ages on whole rock samples have been measured on lavas from the subaerial Samoa Islands, which form a broadly linear volcanic chain that extends from the ESE to the WNW for about 360 km. The Manu'a Islands near the southeast limit of the chain exhibit youthful ages, with most <0.4 Ma, in keeping with the geological observations. Tutuila consists of several volcanoes, and previous work yielded a mean K–Ar age of 1.26 ± 0.15 Ma for the shield-building volcanism. Upolu, to the WNW of Tutuila, gives a mean age of 2.15 ± 0.35 Ma for the shield-building phase, represented by the Fagaloa Volcanics, with much of the island covered by significantly younger volcanic rocks. Savai'i, further to the WNW, is dominated by youthful volcanism, extending into historic times. In a restricted area, adjacent to the NE coast of Savai'i, previously thought to have volcanic rocks correlating with the Fagaloa Volcanics of Upolu, the ages are much younger than those on Upolu, lying between 0.32 and 0.42 Ma. Considering only the subaerial volcanism from Ta'u to Upolu, but also including Vailulu'u, the volcanism has migrated in a systematic ESE direction at 130 ± 8 mm a−1 over 300 km in the last 2.2 Ma. This rate is nearly twice that obtained from GPS measurements of Pacific Plate motion of 72 mm a−1 at N64°W in this area. However, if the much older age of shield-building volcanism from the submarine foundations of Savai'i is included, the regression yields a volcanic migration rate of 72 ± 14 mm a−1, in keeping with the measured GPS rate and consistent with a hotspot origin for the island chain. This suggests that the volcanic migration rates determined from the age of subaerial volcanism can be considerably overestimated, and this is now evident in other Pacific Ocean island chains. Clearly, the ages of the main shield-building volcanism from subaerial volcanism are minima, and if the older submarine lavas can be measured, these may yield a migration rate more in keeping with current plate motions.

This study used Monte Carlo methods to generate simulations that considered the effort distribution to determine the locations of significant aggregations of cetacean sightings inside the northern Pelagos Sanctuary (north-western Mediterranean Sea). For three years, monitoring has been conducted from five motor vessels covering about 30,050 km. The most frequently encountered species were the striped dolphin Stenella coeruleoalba representing 64.7% of all sightings, the fin whale Balaenoptera physalus (17.0%), Cuvier's beaked whale Ziphius cavirostris (9.9%), Risso's dolphin Grampus griseus (4.3%) and the sperm whale Physeter catodon (2.2%). Sightings' positions and effort coverage were distributed over a grid of 5′ longitude and 5′ latitude. Spatial concentrations of sightings were analysed according to the distribution of effort to identify ‘hotspots’ (locations where the species occurred at a significantly greater frequency than expected), and ‘coldspots’ (locations with a significantly lower frequency than expected). Most fin whale hotspots (14) were located on the bathyal plain between 2000 and 2500 m, four hotspots were around the 1000 m isobaths, and one is located close to the seamount off Genoa. Fin whale coldspots were mainly along the coast. Striped dolphin hotspots were widely distributed over two main areas, in waters with depths between 2000 and 2500 m and at the continental slope; coldspots for this species were also mainly along the coast in the northern part of the study area. Many hotspots were found in the Genoa Canyon, and hotspots of striped dolphins, Cuvier's beaked whales, sperm whales and Risso's dolphins overlapped in this region. Some of the hotspots of Cuvier's beaked whales were identified at the seamount in the study area, where no other species was sighted frequently. Risso's dolphin hotspots were mainly near the 1000 m isobath. For sperm whales, several hotspots were identified: three associated with steep slope features (such as canyons or the continental slope), and one was in the centre of the flat area of Pelagos where the depth is 2500 m. This study highlights the ecological importance of particular locations inside the Pelagos Sanctuary—locations that should be protected from anthropogenic degradations for marine mammal conservation.

The protected area network of Africa has grown from nothing to over 2 million km2 in the past 110 years. This network covers parts of all biomes and priority areas for biodiversity conservation but protected area gaps remain, as identified at the 5th World Parks Congress in 2003. Forest reserves, managed by Forest Departments, are typically excluded from global protected area lists, but in Africa they are found in 23 countries and cover at least 549,788 km2, adding 25% to the conservation estate. Forest reserves protect 5.3% (2,027 km2) of the dry forest habitats, 5% (165,285 km2) of lowland and montane moist forests, 2.6% (364,354 km2) of savannah woodlands, 1.8% (10,561 km2) of flooded grasslands, and 1.65% (1,177 km2) of mangroves. Forest reserves also protect parts of three conservation schemes: 6.5% (61,630 km2) of BirdLife's Endemic Bird Areas, 3.4% (147,718 km2) of Conservation International's Hotpots and 3.4% (346,864 km2) of WWF's Global 200 Ecoregions. Several of the global protected area gaps identified in Africa are also covered by forest reserves, in the Eastern Arc Mountains, Eastern African coastal forests, Kenyan Highlands, Cameroon-Nigerian Mountains, West African Forests and mountain areas of Malawi, Zimbabwe, Mozambique and Zambia. Some African forest reserves have a legally defined role in biodiversity conservation and are strictly protected; they thus fit criteria for protected areas. Working with forest departments in individual countries may help develop a more comprehensive protected area network without creating additional new reserves.

Madagascar is a critical priority for international conservation efforts, based on almost unparalleled levels of endemism, species diversity, and human threat. Field research conducted over the past two decades has greatly expanded knowledge of the island's biota, making it difficult for any single research team to maintain up to date estimates of biodiversity and endemism across taxonomic groups. The recent publication of The Natural History of Madagascar, a synthesis by nearly 300 contributors from a wide range of disciplines, provides the means to update these estimates. Here, we summarize biodiversity information presented in the volume by providing revised estimates of species richness, endemism, and state of knowledge of a wide variety of taxonomic groups occurring on the island.