Implications of market integration for cardiovascular and metabolic health among an indigenous Amazonian Ecuadorian population

BackgroundMarket integration (MI), the suite of social and cultural changes that occur with economic development, has been associated with negative health outcomes such as cardiovascular disease; however, key questions remain about how this transition manifests at the local level.AimThe present paper investigates the effects of MI on health among Shuar, an indigenous lowland Ecuadorian population, with the goal of better understanding the mechanisms responsible for this health transition.Subjects and methodsThis study examines associations between measures of MI and several dimensions of cardiovascular and metabolic health (fasting glucose, lipids [LDL, HDL and total cholesterol; triglycerides] and blood pressure) among 348 adults.ResultsOverall, Shuar males and females have relatively favourable cardiovascular and metabolic health. Shuar who live closer to town have higher total (p < 0.001) and HDL cholesterol (p < 0.001), while Shuar in more remote regions have higher diastolic blood pressure (p = 0.007). HDL cholesterol is positively associated with consumption of market foods (r = 0.140; p = 0.045) and ownership of consumer products (r = 0.184; p = 0.029).ConclusionsThis study provides evidence that MI among Shuar is not a uniformly negative process but instead produces complex cardiovascular and metabolic health outcomes

Identifying Corridors among Large Protected Areas in the United States.

Conservation scientists emphasize the importance of maintaining a connected network of protected areas to prevent ecosystems and populations from becoming isolated, reduce the risk of extinction, and ultimately sustain biodiversity. Keeping protected areas connected in a network is increasingly recognized as a conservation priority in the current era of rapid climate change. Models that identify suitable linkages between core areas have been used to prioritize potentially important corridors for maintaining functional connectivity. Here, we identify the most "natural" (i.e., least human-modified) corridors between large protected areas in the contiguous Unites States. We aggregated results from multiple connectivity models to develop a composite map of corridors reflecting agreement of models run under different assumptions about how human modification of land may influence connectivity. To identify which land units are most important for sustaining structural connectivity, we used the composite map of corridors to evaluate connectivity priorities in two ways: (1) among land units outside of our pool of large core protected areas and (2) among units administratively protected as Inventoried Roadless (IRAs) or Wilderness Study Areas (WSAs). Corridor values varied substantially among classes of "unprotected" non-core land units, and land units of high connectivity value and priority represent diverse ownerships and existing levels of protections. We provide a ranking of IRAs and WSAs that should be prioritized for additional protection to maintain minimal human modification. Our results provide a coarse-scale assessment of connectivity priorities for maintaining a connected network of protected areas

Linear (top) and non-linear (bottom) versions of resistance surfaces based on wildness (left) and human modification (right) used to model connectivity between large protected areas.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154223#pone.0154223.s001" target="_blank">S1 Fig</a> for distribution of resistance surfaces in relation to original indices.</p

Map of the contiguous United States showing large core protected areas and Protected Areas Database of the U.S. units not included in our definition of core protected areas.

<p>Map of the contiguous United States showing large core protected areas and Protected Areas Database of the U.S. units not included in our definition of core protected areas.</p

Composite corridor value between large protected core areas, which was calculated by summing and reclassifying normalized least-cost corridors shown in Fig 3.

<p>Black polygons are large core protected areas used in our analysis. Raster data are available as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154223#pone.0154223.s006" target="_blank">S1 File</a>.</p

Normalized least cost corridors (after removing the linkages >300 km shown in S3 Fig and the 10% worst linkages shown in S4 Fig) from Linkage Mapper connectivity models based on linear (left) and non-linear (right) versions of resistance surfaces using wildness (top) and human modification (bottom).

<p>Normalized least cost corridors (after removing the linkages >300 km shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154223#pone.0154223.s003" target="_blank">S3 Fig</a> and the 10% worst linkages shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154223#pone.0154223.s004" target="_blank">S4 Fig</a>) from Linkage Mapper connectivity models based on linear (left) and non-linear (right) versions of resistance surfaces using wildness (top) and human modification (bottom).</p

Mean composite corridor values (classified into deciles) among non-core Protected Area Database land units (A) and federal inventoried roadless and wilderness study areas (B).

<p>Roadless and wilderness study areas are shown in greater detail for the Northern Rockies and Southern Appalachian Mountains.</p