Mining is a vital part of the global, and many national, economies. Mining also has the potential to drive extensive land cover change, including deforestation, with impacts reaching far from the ...mine itself. Understanding the amount of deforestation associated with mining is important for conservationists, governments, mining companies, and consumers, yet accurate quantification is rare. We applied statistical matching, a quasi-experimental methodology, along with Bayesian hierarchical generalized linear models to assess the impact on deforestation of new mining developments in Zambia from 2000 to present. Zambia is a globally significant producer of minerals and mining contributes ~ 10% of its gross domestic product and ~ 77% of its exports. Despite extensive deforestation in mining impacted land, we found no evidence that any of the 22 mines we analysed increased deforestation compared with matched control sites. The extent forest lost was therefore no different than would likely have happened without the mines being present due to other drivers of deforestation in Zambia. This suggests previous assessments based on correlative methodologies may overestimate the deforestation impact of mining. However, mining can have a range of impacts on society, biodiversity, and the local environment that are not captured by our analysis.
The World Bank is the single largest source of development finance, with wide‐reaching influence. The Bank's safeguards aim to minimize the negative impacts of the projects it funds. These policies ...have recently been updated in a new Environmental and Social Framework. For conservation, the key changes include a mechanism for the use of biodiversity offsets and borrowers’ own frameworks to manage impacts. Concerns have been raised that these changes may weaken protections as there is substantial flexibility about when offsets or borrowers’ frameworks can be used, and uncertainty around the efficacy of offsets. The project‐by‐project nature of these mechanisms and the lack of clear criteria may also hinder future efforts to hold the Bank to account. Concerns about these changes were raised by conservation organizations during the consultation process, but the framework's formulation does not fully reflect recommendations made. Although elements of the new policy have the potential to benefit conservation, the flexibility presents a risk to biodiversity. It is vital for conservation organizations to engage effectively to ensure that any negative impacts arising do not go unchallenged.
•We examine the locations of potentially harmful development activities funded by the World Bank.•We found significant positive relationships with three conservation metrics.•For two metrics this ...relationship was true even after including socio-economic confounders.•Within countries we found limited evidence highly diverse areas are avoided.•Protected areas had a significant negative relationship both globally and within most countries.
For many countries in the global south the World Bank is a key funder of development. A subset of the activities it funds have the potential to cause harm to biodiversity. Currently, however, little is known about the spatial coincidence of Bank-funded projects and important areas for biodiversity. Using a dataset of World Bank projects funded between 1995 and 2014, we examine the relationship between potentially harmful project activities and the ranges of globally threatened birds, mammals, and amphibians, Key Biodiversity Areas, protected areas, and biodiversity hotspots. We find that 5 by 5 km cells containing a project activity are more likely to contain a Key Biodiversity Area, or a biodiversity hotspot, and have on average greater richness of globally threatened species, than those without. This relationship was statistically significant even after considering human population and country-level socio-economic effects except in the case of Key Biodiversity Areas. We also found limited evidence that activities are systematically placed within countries to avoid the ranges of threatened species or Key Biodiversity Areas. By contrast, we found a negative relationship between project activities and protected areas globally and within most countries, which may be evidence that potentially harmful activities are placed to avoid protected areas. Our findings raise questions about whether the Banks environmental safeguards have adequately translated into avoidance of highly diverse areas. Given the size of the World Bank’s lending portfolio and its role in setting industry best practice our results are concerning for conservation efforts.
Objectives
The morphological characteristics of the thumb are of particular interest due to its fundamental role in enhanced manipulation. Despite its possible importance regarding this issue, the ...body of the first metacarcapal (MC1) has not been fully characterized using morphometrics. This could provide further insights into its anatomy, as well as its relationship with manipulative capabilities. Hence, this study quantifies the shape of the MC1's body in the extant Homininae and some fossil hominins to provide a better characterization of its morphology.
Materials and methods
The sample includes MC1s of modern humans (n = 42), gorillas (n = 27), and chimpanzees (n = 30), as well as Homo neanderthalensis, Homo naledi, and Australopithecus sediba. 3D geometric morphometrics were used to quantify the shape of MC1's body.
Results
The results show a clear distinction among the three extant genera. H. neanderthalensis mostly falls within the modern human range of variation. H. naledi varies slightly from modern humans, although also showing some unique trait combination, whereas A. sediba varies to an even greater extent. When classified using a discriminant analysis, the three fossils are categorized within the Homo group.
Conclusion
The modern human MC1 is characterized by a distinct suite of traits, not present to the same extent in the great apes, that are consistent with an ability to use forceful precision grip. This morphology was also found to align very closely with that of H. neanderthalensis. H. naledi shows a number of human‐like adaptations, while A. sediba presents a mix of both derived and more primitive traits.
Principal component (PC) analysis of the shape data: the (a) three main axes of morphological variation are displayed (ellipses represent 95% confidence intervals, red spheres: fossils, orange spheres: Homo sapiens, green spheres: Pan troglodytes; golden spheres: Gorilla gorilla, golden cubes: Gorilla beringei); Violin plots of the PCs scores of the analyzed sample are shown for (b) PC1, (c) PC2, and (d) PC3 (fossil values are displayed as red triangles). The white dot in the middle is the median value, while the thick black bar in the center represents the interquartile range. The thin black line extended from it corresponds to the upper (maximum) and lower (minimum) adjacent values in the data. The distribution shape of the data for each one of the three PCs is represented by a kernel density plots that were rotated and placed on each side of each one of the boxplots. To visualize shape differences warped models representing the shape changes along the first three PCs were plotted alongside the violin plots (dorsal views). The models closest to the mean shape was to match the multivariate mean using the thin plate spline method. Then, the obtained average model was warped to display the variation along the three plotted PC axes (mag = 1).
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