Addressing risks to biodiversity arising from a changing climate: The need for ecosystem restoration in the Tana River Basin, Kenya

However, as the area projected to contain refugia decreases with greater levels of warming, so does the restoration potential. Areas not considered to be refugia, by definition, are expected to become climatically unsuitable for at least 25% of current species. Therefore, many of the species present in the area prior to conversion to agriculture may be unable to tolerate the new climatic conditions if higher levels of warming are realised. The projected reduction in plant species richness may undermine efforts towards achieving these restoration targets. The Kenya Forestry Research Institute [86] compiled a list of suitable tree species for afforesta- tion or reforestation projects. However, many of these species have been projected to see a reduction in climate suitability with higher temperatures [60]. Some species identified as less sensitive to climate changes are the neem tree (Azadirachta indica), sycamore fig (Ficus syco- morus) and wild date palm (Phoenix reclinata). Future research into additional tree species PLOS ONE Risks to Kenya’s biodiversity from climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0254879 July 21, 2021 17 / 25

with less sensitivity to the changing climate which can be used in ecosystem restoration proj- ects is vital. In terms of restoration, there are still gaps in knowledge of how to effectively restore most types of ecosystems [87]. Some researchers have shown the importance of intact faunal communities, particularly those that disperse seeds, for the successful restoration of for- est areas [88]. Implications for tourism These changes to the biodiversity of the Tana River Basin will have implications for tourism. Kenya is known internationally as a hotspot of wildlife tourism, with the majority of tourists visiting to watch wildlife. Nearly 10% of Kenya’s GDP comes from tourism. Most visitors focus on the more well-known PAs in the south of the country. Visitor numbers in selected PAs in South Africa, Namibia and Botswana were higher in areas where there was high preda- tor species richness and a presence of locally rare ungulate species [89]. A high diversity of large mammals was also found to be important in attracting high numbers of wildlife tourists. The PAs within the Tana River Basin generally saw a decrease in suitability for many of the predator species examined here, such as the wild dog, cheetah and African lion. Similarly, a reduction in the number of species in the PAs with climate change could impact tourist num- bers. This could be particularly significant for the Tsavo East National Park, which could expe- rience reductions in the number of species present in the future. Other than the Masai Mara,

the Tsavo ecosystem is the most popular with tourists [77]. In order to maintain high visitor numbers, additional conservation spending may be necessary. Limitations Although this analysis provides important information about the impacts of climate change and development plans on the terrestrial biodiversity of the Tana River Basin, a number of caveats need to be considered. There are several relevant factors that have not been included in this analysis due to the spatial scale used in the Wallace Initiative database [47]. These factors include the potential spread of disease pathogens and pests, interactions between species (food availability, predator-prey relationships and competitive interactions) [90], the effects of extreme climatic events [91] and the direct biotic effects of increases in CO2 concentrations on plants [92]. In addition, many species, especially narrow-ranged endemic species, are not included in the Wallace Initiative database. Narrow-ranged species are generally sensitive to climate change [93] so many of the endemic species not included in this analysis will also be vulnerable to the effects of global temperature rise. Species with fewer than 10 data points (occupied grid cells) were excluded from the Wallace Initiative database in order to maintain a robust analysis [47]. Despite these limitations, the Wallace Initiative fulfils many of the criteria recently set out as contributing to best practice in species distribution modelling [94]. GBIF data combines many datasets worldwide and the data was further cleaned (checked for locational consistency

and outliers) before use. Commission errors were accounted for by clipping species to their biogeographic zones and omission errors were taken into account by generous buffers around species distributions. Furthermore, uncertainty is taken into account by considering a range of GCMs and dispersal rates. There are some general limitations with species distribution models which should also be considered. In some cases, there is not sufficient data available to fully inform the model as to the true distribution of a species [95, 96]. Datasets used to drive the SDM are often biased because of an unequal sampling effort across the study area [97]. SDMs can be overfitted, which can lead to flawed outputs by limiting the model’s capacity to generalise. During the PLOS ONE Risks to Kenya’s biodiversity from climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0254879 July 21, 2021 18 / 25

development of the Wallace Initiative, a reduced set of variables was used to minimize poten- tial autocorrelation. SDMs cannot include and account for all biotic and abiotic factors. Even though various uncertainties exist, SDMs are extremely useful for examining the future impacts of climate change on species. This knowledge is fundamental for policymakers and conservation planners. For this analysis, it was assumed that all of the species that had suitable climate space within the PAs with higher temperatures are protected in these spaces and occur in viable popula- tions. Although this might not be the case, this assumption allows for the identification of spe- cies that require more future conservation attention (i.e. those not occurring at all within the PAs). Furthermore, some PAs extend beyond the basin. For instance, the Tsavo East National Park and it’s connected Tsavo West and Amboseli National Parks. The greater Tsavo ecosys- tem may contain refugia even though the part of the Tsavo East National Park located within the Tana River Basin may not. Even if this is the case, it further demonstrates the importance of allowing species to disperse across the landscape. Conclusions This study showed significant implications of projected climate change on the terrestrial biodiversity of the Tana River Basin. When these changes in species distributions are con- sidered alongside development plans, it becomes clear that trade-offs between development and biodiversity protection may occur in the future. Various adaptation options for biodi-

versity have been proposed, including expanding protected areas to better conserve pro- jected climate refugia. Restoring lands that are projected to be refugia for plants but have already been converted to agriculture could help conserve biodiversity, facilitate species movements and contribute to Kenya’s ecosystem restoration targets. By understanding these potential conflicts between biodiversity and development, decision makers can ensure the basin is managed in a way as to ensure effective future biodiversity protection, successful ecosystem restoration and sustainable socio-economic development within this important area of Kenya. Supporting information S1 Table. Protected Areas (PAs) within the Tana River Basin. Data on the locations of pro- tected areas were obtained from the World Database on Protected Areas (WDPA). This table only includes the PAs considered in this study. (DOCX) S1 Fig. Locations of projected refugia for amphibians with 2˚C and 4.5˚C warming. A refu- gium is identified in a grid cell only if at least 11 of the 21 GCMs agree in projecting its exis- tence. (TIF) S2 Fig. Locations of projected refugia for birds with 2˚C and 4.5˚C warming. A refugium is identified in a grid cell only if at least 11 of the 21 GCMs agree in projecting its existence. (TIF) S3 Fig. Locations of projected refugia for mammals with 2˚C and 4.5˚C warming. A refu- gium is identified in a grid cell only if at least 11 of the 21 GCMs agree in projecting its exis- tence. (TIF) PLOS ONE Risks to Kenya’s biodiversity from climate change

PLOS ONE | https://doi.org/10.1371/journal.pone.0254879 July 21, 2021 19 / 25

S4 Fig. Locations of projected refugia for plants with 2˚C and 4.5˚C warming. A refugium is identified in a grid cell only if at least 11 of the 21 GCMs agree in projecting its existence. (TIF) S5 Fig. Locations of projected refugia for reptiles with 2˚C and 4.5˚C warming. A refugium is identified in a grid cell only if at least 11 of the 21 GCMs agree in projecting its existence. (TIF) S6 Fig. Locations of projected refugia for all five taxa with 2˚C and 4.5˚C warming. A refu- gium is identified in a grid cell only if at least 11 of the 21 GCMs agree in projecting its exis- tence. (TIF) S7 Fig. GCM agreement that protected areas contain refugia with different levels of warm- ing. These results are based on a no dispersal scenario. The five classes of species are amphibi- ans, birds, mammals, plants and reptiles. (TIF) S8 Fig. Key features of Kenya’s National Spatial Plan affecting the Tana River Basin. Key features of the National Spatial Plan (Government of Kenya, 2017) within the Tana River Basin boundary were digitised using GIS. (TIF) S9 Fig. Importance of facilitating dispersal for birds with 2˚C warming. The first column (left) shows species richness remaining when species are not able to shift their ranges, the cen- tre column shows the scenario where species are able to disperse, and the final column (right) shows the difference between the first two. The top panels show the 10th percentile, the middle shows the 50th percentile and the bottom panel shows the 90th percentile. (TIF)

S10 Fig. Importance of facilitating dispersal for birds with 4.5˚C warming. The first column (left) shows species richness remaining when species are not able to shift their ranges, the cen- tre column shows the scenario where species are able to disperse, and the final column (right) shows the difference between the first two. The top panels show the 10th percentile, the middle shows the 50th percentile and the bottom panel shows the 90th percentile. (TIF) S11 Fig. Importance of facilitating dispersal for mammals with 2˚C warming. The first col- umn (left) shows species richness remaining when species are not able to shift their ranges, the centre column shows the scenario where species are able to disperse, and the final column (right) shows the difference between the first two. The top panels show the 10th percentile, the middle shows the 50th percentile and the bottom panel shows the 90th percentile. (TIF) S12 Fig. Importance of facilitating dispersal for mammals with 4.5˚C warming. The first column (left) shows species richness remaining when species are not able to shift their ranges, the centre column shows the scenario where species are able to disperse, and the final column (right) shows the difference between the first two. The top panels show the 10th percentile, the middle shows the 50th percentile and the bottom panel shows the 90th percentile. (TIF) PLOS ONE Risks to Kenya’s biodiversity from climate change PLOS ONE | https://doi.org/10.1371/journal.pone.0254879 July 21, 2021 20 / 25

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