An alternative simplified version of the VECEA potential natural vegetation map for eastern Africa

<p><strong>Summary</strong><br>Spatial data layer containing the potential natural vegetation map of eastern Africa and documentation describing the methodology used to create this map.</p> <p><strong>Introduction</strong><br>This data set consists of a spatial data layer (raster layer at geotif format) describing the potential natural vegetation of eastern Africa (Kenya, Uganda, Tanzania, Rwanda, Malawi, Zambia). The map is based on the potential natural vegetation (PNV) map for eastern Africa developed by the VECEA (Vegetation and Climate change in East Africa) project, which was also the one who created the present map. This map is based on historical vegetation maps, developed during the 50s to 70s of the twenties' century. It is available on http://vegetationmap4africa.org, including a detailed documentation of the individual vegetation types and a description of the methodology used to create the map.</p> <p>The original PNV map can be used e.g., as baseline in assessments of vegetation change, reforestation options and conservation assessments. One problem for regional analysis is that not all PNVs were consistently mapped across the region. A number of the vegetation types were mapped in one country individually, while mapped as part of a more aggregated vegetation type in other countries. This was done to maintain the maximum level of information available. However, this may be sub-optimal for regional level assessments.</p> <p>Most locations in the region are classified as one unique PNV. However, there are also a number of areas where the vegetation was classified as a compound vegetation type (i.e., consisting of two or more vegetation types. In these cases the existing historical maps, documentation and available expert knowledge was not conclusive as to the exact nature of the potential natural vegetation in that area. For estimations of e.g., area statistics one would ideally be able to classify each location as a unique PNV.</p> <p><strong>Objective</strong><br>The objective of the work presented in this manuscript was to harmonize the vegetation classification across the different countries in the eastern Africa region. Furthermore, to be able to estimate area statistics for the different PNVs, we also split compound vegetation types into unique PNVs. The latter was done using environmental distribution modelling based on the distribution of the PNVs in the original map.</p> <p><strong>File set</strong><br>The file set consists of a manuscript detailing the methods used to create the revised and a simplified potential natural vegetation map and the data layer created (a raster file in geotif format). If you use the data, we are requesting you to notify us of the intended use (http://vegetationmap4africa.org/6_Contact_us/Contact_form.html)</p> <p> </p

Classification of potential natural vegetations into three categories of conservation risk according to the criteria presented in the current paper (A) and according to the criteria of Hoekstra et al. [55] (B).

<p>C1 and H1 are based on all protected areas; C2 and H2 are based on the PA1 protected areas only.</p><p>A) Critically endangered (CR) = PNVs with a conservation risk index (CRI) > 10 and human influence (HI) > 50; Endangered (EN) = PNVs with a CRI > 4 and HI > 40; Vulnerable (VU) = PNVs with a CRI > 2 and HI > 20. B) As A, but with CRI threshold values of 25, 10 and 2, respectively. Potential natural vegetation (PNV) codes are provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121444#pone.0121444.t001" target="_blank">Table 1</a>.</p><p>Classification of potential natural vegetations into three categories of conservation risk according to the criteria presented in the current paper (A) and according to the criteria of Hoekstra et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121444#pone.0121444.ref055" target="_blank">55</a>] (B).</p

Geographic coverage of the potential natural vegetations.

<p>A) The percent area protected of potential natural vegetation types by the protected areas network (GC). B) As A, but only considering the more strictly protected PAs of IUCN class Ib-IV.</p

Relationship between geographic coverage and environmental bias in the protected areas network.

<p>A) Scatterplot of the percent area protected (GC) and environmental bias (EB) per potential natural vegetation (PNV). The EB was computed as the absolute difference in the median of the MES1 for the protected areas and the whole PNV, divided by the median absolute deviation of MES1 in the PNV (see text for details). The PNVs are grouped in three classes with small (green), intermediate (blue) and large (red) EB values. Open green circles indicate that the EB does not significantly deviate from 0 (Mann–Whitney with Bonferroni adjustment, two-tailed p>0.05). B) As A, but the GC and EB values given for the PA 1 protected areas only.</p

A map of the human influence in eastern Africa.

<p>A) Map of the distribution of the human influence index (HI), and B) map of the average human influence (HI_pnv) by potential natural vegetation type (PNV).</p

Environmental Gap Analysis to Prioritize Conservation Efforts in Eastern Africa

<div><p>Countries in eastern Africa have set aside significant proportions of their land for protection. But are these areas representative of the diverse range of species and habitats found in the region? And do conservation efforts include areas where the state of biodiversity is likely to deteriorate without further interventions? Various studies have addressed these questions at global and continental scales. However, meaningful conservation decisions are required at finer geographical scales. To operate more effectively at the national level, finer scale baseline data on species and on higher levels of biological organization such as the eco-regions are required, among other factors. Here we adopted a recently developed high-resolution potential natural vegetation (PNV) map for eastern Africa as a baseline to more effectively identify conservation priorities. We examined how well different potential natural vegetations (PNVs) are represented in the protected area (PA) network of eastern Africa and used a multivariate environmental similarity index to evaluate biases in PA versus PNV coverage. We additionally overlaid data of anthropogenic factors that potentially influence the natural vegetation to assess the level of threat to different PNVs. Our results indicate substantial differences in the conservation status of PNVs. In addition, particular PNVs in which biodiversity protection and ecological functions are at risk due to human influences are revealed. The data and approach presented here provide a step forward in developing more transparent and better informed translation from global priorities to regional or national implementation in eastern Africa, and are valid for other geographic regions.</p></div

Conservation risk for potential natural vegetations.

<p>A) Scatterplot of the average human influence (HI_pnv) and the percent area protected (GC) for PNVs. We defined the CRI (conservation risk index) as the ratio between the HI_pnv and the GC. PNVs with a HI_pvn > 50 and a CRI > 10 were classified as critically endangered; PNVs with a HI_pnv > 40 and CRI > 4 as endangered and PVNs with a HI_pnv > 20 and CRI > 2 as vulnerable. All other PNVs were classified as low risk. Regression statistics: R² = 0.35, p < 0.01. B) As A, but with the GC for the PA1 protected areas only. Regression statistics: R² = 0.14, p = 0.02.</p

Crisis potential natural vegetations overlaid with global priority areas for conservation.

<p>The crisis potential natural vegetations (PNVs) are categorized as critically endangered (CR), endangered (EN) and vulnerable (VU). The map is overlaid with the: A) WWF’s global 200 terrestrial ecoregions map [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121444#pone.0121444.ref048" target="_blank">48</a>]; B) the Centres of Plant Diversity map [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121444#pone.0121444.ref089" target="_blank">89</a>]; C) the Crisis Ecoregions map [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121444#pone.0121444.ref055" target="_blank">55</a>]; D) the conservation priorities for Sub-Saharan Africa map [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121444#pone.0121444.ref090" target="_blank">90</a>]; E) the priorities for conservation intervention in Africa map [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121444#pone.0121444.ref041" target="_blank">41</a>]; and F) the "Biodiversity Hotspots", Conservation International 2011 map [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121444#pone.0121444.ref029" target="_blank">29</a>]. Where relevant, level of priority (1 = highest) is indicated by hatching pattern.</p

Map of the multivariate environmental similarity (MES2) of the protected areas.

<p>A) It combines maps of the 50 PNVs showing how similar environmental conditions in each raster cell are to those in the PA1 + PA2 areas. B) As A, but for PA1 areas only. C) Locations of PA1 and PA2 areas.</p

Framework to compute the human influence index.

<p>Framework to compute the human influence index.</p