DOI: 10.5061/dryad.w3r228151
title: Data from: Maps and additional figures for climate change refugia hotspots for priority species: A case study in East Africa

description: Natural resource managers and policymakers need actionable climate data to
 guide conservation decisions. Conserving climate change refugia, areas
 relatively buffered from contemporary climate change, is increasingly
 considered an effective strategy for adaptation. Despite tropical species
 facing heightened vulnerability to climate change, the tropics remain
 underserved in climate adaptation research. We coproduced the first
 comprehensive assessment of climate change refugia across Tanzania with
 Tanzanian partners through extensive consultation, in-person
 conversations, and field visits to priority ecosystems, ensuring our
 analysis addressed local conservation needs and decision-making contexts.
 We developed species distribution models for 33 terrestrial animal species
 using maximum entropy and boosted regression tree algorithms. We projected
 future suitable habitats for SSP126 and SSP585, for 2011-2040 and
 2071-2100, using GFDL Earth System and the U.K. Earth System models. More
 than half under SSP126 and 79% of the focal species under SSP585 lost
 their suitable habitat by 2100. Serengeti National Park, Northern
 Highlands Forest Reserve, and the Eastern Arc Mountains emerged as key
 climate change refugia, while other protected areas, including Kigosi and
 Ugalla River National Parks, had no climate change refugia. This
 assessment provides actionable insights for Tanzania’s conservation
 prioritization while identifying critical research gaps in western and
 montane ecosystems.
description: # Data from: Maps and additional figures for climate change refugia
 hotspots for priority species: A case study in East Africa Dataset DOI:
 [10.5061/dryad.w3r228151](https://doi.org/10.5061/dryad.w3r228151) ##
 Description of the data and file structure #### **Species information** We
 acquired species occurrence data (Table S2) from the Global Biodiversity
 Information Facility (GBIF, [www.gbif.org](http://www.gbif.org); 2024) by
 searching for occurrences using the scientific name and crosschecking
 historical and alternative names against Mammal Species of the World, 3rd
 edition (Wilson & Reeder, 2005) and the Integrated Taxonomic
 Information System ([https://www.itis.gov/](https://www.itis.gov/)).
 TAWIRI contributed a dataset of mammal occurrences from their previous
 work, including data from camera traps and opportunistic encounters. Our
 analysis included 20,265 occurrence records across study species. (more
 details here SampleTable_ITIS_Habitat.csv) Column header description:
 IUCN_Common_name: The commonly used name of the species as listed by the
 IUCN (e.g., Cheetah, Lion). Species: The scientific binomial name of the
 species (genus and species), e.g., Acinonyx jubatus. ITIS taxonomy: The
 taxonomic name from ITIS (Integrated Taxonomic Information System),
 confirming species identification. Gbif Data: Indicates if occurrence data
 for this species is available from GBIF (marked with an "x").
 TAWIRI Data: Indicates if occurrence data is available from TAWIRI (marked
 with an "x"). Sample_size_cleaned: The total cleaned sample size
 or number of data records available for this species. TAWIRI_SampleSize:
 Number of sample records specifically from TAWIRI. GBIF_DIO: Citation or
 source of the GBIF data download, including date and DOI link. Taxa_1:
 Broad taxonomic group the species belongs to (e.g., Mammal). Taxa_2: More
 specific ecological or dietary classification (e.g., Carnivore, Herbivore,
 Primate). Family: Taxonomic family of the species (e.g., Felidae,
 Bovidae). Habitat_1: Primary or general habitat classification (e.g.,
 Savanna, Forest). IUCN_Habitat_and_ecology_notes: Detailed habitat
 description and ecological notes from the IUCN Red List, describing
 environments where the species occurs. Range_Map_Citation: Full citation
 for the IUCN Red List species assessment, providing the official source
 for range and ecological information, often including a DOI link.
  **Climate data**
 Dossier_DD_1_Climate_Historical_Future_PAs_AllBios_SSP126_and_SSP585_20Oct2025_Complete.pdf We obtained climate data from CHELSA version V2.1 (Climatologies at High resolution for the Earth’s Land Surface Areas), a downscaled model output temperature and precipitation estimates at a horizontal resolution of 30 arcsec for the present and future time periods under CMIP6 (Karger et al., 2017) (Table S3).  We selected two atmospheric-ocean global circulation models: the U.K. Earth System Model (UKESM1-0-LL), which has higher climate sensitivity, indicating greater temperature response to CO~2~ changes and thus stronger climate change impacts (than other General Circulation Model (GCM) in the CHELSA dataset ( Sellar et al., 2019; Hausfather et al., 2022); and the GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1), which has lower climate sensitivity (Dunne et al., 2020). We chose to bracket future climate change by selecting two future scenarios of climate change: SSP126, a low emissions pathway that projects future warming below 2°C and peak emissions before 2050; and SSP585, a high emissions pathway with the highest warming and continuous emission growth for mid-century 2040 and late-century 2100 (IPCC, 2023) (Figure 1, Figure S4 and DD1). **Environmental data** We selected 9 environmental variables known to influence target species distributions: crop probability (Pittman et al., 2010), soil type (FAO/UNESCO, 2003), contrast (a measure of habitat heterogeneity) (Tuanmu et al., 2015), stratified forest cover (Tyukavina et al., 2015), human impact (JRC & CIESIN, 2021), population density (CIESIN, 2018), terrain ruggedness (Sappington et al., 2007), slope (U.S. Geological Survey, 1996), and aspect transformed to “northness” and “eastness’ (U.S. Geological Survey, 1996) (Table S3). We converted categorical environmental variables to factors. **Modeling methods** Since the data lacked true absences, background sites were created using each species’ study area (Barbet-Massin et al., 2012; Liu et al., 2019). We randomly sampled 10,000 background sites from this study area and extracted environmental and climate data (Barbet-Massin et al., 2012; Liu et al., 2019). We employed both Maximum Entropy (MaxEnt) and Boosted Regression Tree (BRT) modeling through the ‘enmSdmX’ package (Smith et al., 2023). Both models were trained using occurrence data and background sites as the response variable and the selected variables as predictors. We modeled current habitat suitability by projecting MaxEnt and BRT models using CHELSA climate data for the baseline period (1981-2010). For future projections, we implemented these models under two contrasting emissions scenarios (SSP126 and SSP585) for both mid-century (2011-2040) and late-century (2071-2100) time periods, using climate projections from the GFDL Earth System Model Version 4.1 and U.K. Earth System Model. These projections provided habitat suitability estimates across all combinations of scenarios, timeframes, and climate models. Current values of non-climate predictors were used as projections were not available. We established thresholds using the Maximum Sum of Sensitivity and Specificity (Max SSS) method to convert continuous suitability predictions into binary presence-absence maps, as recommended by Liu et al. (2013). **Climate change refugia identification** * TIFs_Individual_species_in_situ_and_ex_situ_climate_change_refugia.zip * TIFs_Individual_species_in_situ_climate_change_refugia_model_agreement.zip * Dossier_DD_2__Individual_Species_In_situ__and_Ex_situ__Climate_Change_Refugia_n_33_20Oct2025_Complete.pdf * Dossier_DD_3_Species_In_situ_Climate_Change_Refugia_Model_Agreement_map_n_33_20Oct2025_Complete.pdf We identified two types of climate change refugia, *in situ* climate change refugia (*in situ* CCR) are restricted to locations that remain suitable for a species and *ex situ* climate change refugia (*ex situ* CCR) are found in previously unsuitable locations (Figure 2 B and DD 2). Climate change refugia were calculated by overlaying current and future binary habitat suitability maps, where cells were classified as *in situ* CCR if they had suitable habitat in the current and future time periods and as *ex situ* CCR if they were not suitable habitat in current but contained suitable habitat in future. We also calculated loss of suitable habitat, cells that contained suitable habitat in the current but not future time period.   To compare habitat changes across climate change refugia scenarios and time periods, we calculated the percent of current suitable habitat projected to be lost under future climate conditions by dividing the number of cells classified as habitat loss by the total number of current suitable habitat cells (Table 3). We calculated the percent of current suitable habitat in *in situ* CCR and *ex situ* CCR using the same method (Table 3). TIFs_Multi-species_in_situ_climate_change_refugia_hotspots_for_33_species.zip  We created individual *in situ* CCR maps for each unique combination of species, time period, General Circulation Model (GCM), Shared Socioeconomic Pathway (SSP), and statistical model, resulting in 528 distinct maps (16 variations/species for 33 species)(Figure 2 A). To assess the robustness of refugia predictions, we developed time period and SSP *in situ* CCR maps for each species by combining individual *in situ* CCR maps across modeling approaches. These composite maps integrated predictions from both GCMs (GFDL-ESM4 and UKESM1-0-LL) and statistical models (BRT and MaxEnt) for each time period (2011-2040 or 2071-2100) and scenario (SSP 126 or SSP 585). Cells were classified as refugia only when identified consistently across all four model combinations, generating 132 consolidated maps (4 maps/species). To identify areas of conservation significance across multiple species, we developed multi-species Climate Change Refugia “Hotspot” maps by aggregating the Year and SSP *in situ* climate change refugia maps across all 33 species. The resulting maps quantify the number of species for which each cell represents *in situ* CCR, with values ranging from 0 (no species have *in situ* CCR) to 33 (all study species have *in situ* CCR). ## Code/software You can view the raster files in R using library terra and the rast function. ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: | Comman name | Scientifc name | Source | | :----------------------------------- | :------------------------- | :---------------------------------------------------------------------------------------------------------------------------- | | IUCN\_Common\_name  | Species | GBIF\_DIO | | Cheetah | Acinonyx jubatus | GBIF.org (16 April 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.qazh4q](https://doi.org/10.15468/dl.qazh4q) | | Lion | Panthera leo | GBIF.org (21 April 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.xck4jz](https://doi.org/10.15468/dl.xck4jz) | | Spotted hyena | Crocuta crocuta | GBIF.org (21 April 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.ssvsf9](https://doi.org/10.15468/dl.ssvsf9) | | African wild dog | Lycaon pictus | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.vjk85u](https://doi.org/10.15468/dl.vjk85u) | | Giraffe | Giraffa camelopardalis | GBIF.org (13 December 2023) GBIF Occurrence Download [https://doi.org/10.15468/dl.c8veb6](https://doi.org/10.15468/dl.c8veb6) | | Impala | Aepyceros melampus | GBIF.org (08 January 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.qfdrut](https://doi.org/10.15468/dl.qfdrut) | | African buffalo | Syncerus caffer | GBIF.org (21 April 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.6sfxv5](https://doi.org/10.15468/dl.6sfxv5) | | Elephant | Loxodonta africana | GBIF.org (21 April 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.ey9buz](https://doi.org/10.15468/dl.ey9buz) | | Olive baboon | Papio anubis | GBIF.org (10 January 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.2r4h7u](https://doi.org/10.15468/dl.2r4h7u) | | Blue monkey | Cercopithecus mitis | GBIF.org (10 January 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.357e83](https://doi.org/10.15468/dl.357e83) | | Angolan colobus | Colobus angolensis | GBIF.org (01 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.r4cawm](https://doi.org/10.15468/dl.r4cawm) | | African straw-coloured fruit-bat | Eidolon helvum | GBIF.org (01 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.kutxtw](https://doi.org/10.15468/dl.kutxtw) | | Guttural toad | Sclerophrys gutturalis | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.f5ewzh](https://doi.org/10.15468/dl.f5ewzh) | | Dune squeaker | Arthroleptis stenodactylus | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.c3vew7](https://doi.org/10.15468/dl.c3vew7) | | Nile monitor | Varanus niloticus | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.wbvn66](https://doi.org/10.15468/dl.wbvn66) | | Common African flap-necked chameleon | Chamaeleo dilepis | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.yy5q9f](https://doi.org/10.15468/dl.yy5q9f) | | Mwanza flat-headed rock agama | Agama mwanzae  | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.nr3uf5](https://doi.org/10.15468/dl.nr3uf5) | | Striped skink | Trachylepis striata | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.ap844k](https://doi.org/10.15468/dl.ap844k) | | Puff adder | Bitis arietans | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.rzj7u7](https://doi.org/10.15468/dl.rzj7u7) | | Leopard tortoise | Stigmochelys pardalis | GBIF.org (09 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.evf8z6](https://doi.org/10.15468/dl.evf8z6) | | Hamerkop | Scopus umbretta | GBIF.org (01 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.d5r5ux](https://doi.org/10.15468/dl.d5r5ux) | | African wood-owl | Strix woodfordii | GBIF.org (01 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.5wfymm](https://doi.org/10.15468/dl.5wfymm) | | Silvery-cheeked hornbill | Bycanistes brevis | GBIF.org (01 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.8s3797](https://doi.org/10.15468/dl.8s3797) | | Evergreen-forest warbler | Bradypterus lopezi | GBIF.org (01 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.2r2ha3](https://doi.org/10.15468/dl.2r2ha3) | | Palm-nut vulture | Gypohierax angolensis | GBIF.org (10 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.tjvuvt](https://doi.org/10.15468/dl.tjvuvt) | | Livingstone's turaco | Tauraco livingstonii | GBIF.org (01 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.9r3cwk](https://doi.org/10.15468/dl.9r3cwk) | | Common ostrich | Struthio camelus | GBIF.org (10 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.mrze9z](https://doi.org/10.15468/dl.mrze9z) | | African fish-eagle | Haliaeetus vocifer | GBIF.org (13 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.2ptyu4](https://doi.org/10.15468/dl.2ptyu4) | | White-backed vulture | Gyps africanus | GBIF.org (13 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.wj5k4b](https://doi.org/10.15468/dl.wj5k4b) | | Secretarybird | Sagittarius serpentarius | GBIF.org (13 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.7mb66k](https://doi.org/10.15468/dl.7mb66k) | | White-headed buffalo-weaver | Dinemellia dinemelli | GBIF.org (13 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.nezdj9](https://doi.org/10.15468/dl.nezdj9) | | Red-necked francolin | Pternistis afer | GBIF.org (14 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.6rqs8t](https://doi.org/10.15468/dl.6rqs8t) | | Yellow-necked spurfowl | Pternistis leucoscepus | GBIF.org (13 May 2024) GBIF Occurrence Download [https://doi.org/10.15468/dl.f9apya](https://doi.org/10.15468/dl.f9apya) | 1\. Aspect "northness" * Description: The compass direction or azimuth that a terrain surface faces, derived from a digital elevation map. Aspect was calculated using Arc GIS Pro 3.1.1. As aspect is a circular variable and to contrast northern versus southern exposition, it was transformed using sine "northness" (1 = north, -1 = south). * Spatial precision: 1 km * Hypothesized ecological role: Influences solar radiation and microclimate effects. 2\. Aspect "eastness" * Description: The compass direction or azimuth that a terrain surface faces, derived from a digital elevation map. Aspect was calculated using Arc GIS Pro 3.1.1. To contrast eastern versus western exposition, it was transformed using cosine "eastness" (1 = east, -1 = west). * Spatial precision: 1 km * Hypothesized ecological role: Influences solar radiation and microclimate effects. 3\. Ruggedness * Description: Estimate of evenness and steepness of terrain derived from a digital elevation map using Arc GIS Pro 3.1.1. * Spatial precision: 1 km * Hypothesized ecological role: Estimates traversal difficulty, affecting species movement, habitat suitability, and human development patterns. 4\. Slope * Description: Percent change in elevation over a certain distance derived from a digital elevation map (GTOPO30) using Arc GIS Pro 3.1.1. * Spatial precision: 1 km * Hypothesized ecological role: Influences species movement, precipitation dynamics, vegetation communities, soil development, and erosion. 5\. Crop probability * Description: Probability that a pixel is production cropland; values range from 1 to 100. * Spatial precision: 1 km * Hypothesized ecological role: Indicates human disturbance, simplified habitat structure, altered disturbance regimes, and edge effects. 6\. Soil * Description: Digital Soil Map of the World from the FAO and UNESCO. * Spatial precision: 1 km * Hypothesized ecological role: Affects primary productivity, water dynamics, and habitat structure. 7\. Contrast * Description: Measures heterogeneity by exponentially weighted differences in Enhanced Vegetation Index (EVI) between adjacent pixels. * Spatial precision: 1 km * Hypothesized ecological role: Spatial variation in biotic and abiotic conditions influences species and community diversity, disturbance, edge effects, and species movement. 8\. Pan Tropical Forest Strata * Description: Forest strata classification (1 = low cover, 2 = medium short, 3 = medium tall, 4 = dense short, 5 = dense short intact, 6 = dense tall, 7 = dense tall intact). A measure of landcover. * Spatial precision: 1 km * Hypothesized ecological role: Influences species movement, connectivity, microclimate effects, and habitat complexity. 9\. Human impact * Description: Population (POP), Built-Up Estimates (BUILT), and Degree of Urbanization Settlement Model Grid (SMOD), v1 (2014). * Spatial precision: 1 km * Hypothesized ecological role: Reflects habitat conversion and fragmentation, edge effects, connectivity, altered disturbance regimes, competition with livestock or invasive species, hunting pressure. 10\. Population Density * Description: UN WPP-Adjusted Population Density, v4.11 (2015). * Spatial precision: 1 km * Hypothesized ecological role: Habitat conversion and fragmentation, edge effects, connectivity, altered disturbance regimes, competition with livestock and invasive species. 11\. Elevation * Description: Global digital elevation model (DEM) with a horizontal grid spacing of 30 arc seconds (~1 km). * Spatial precision: 1 km * Hypothesized ecological role: Influences temperature gradients, precipitation patterns, growing season length, solar radiation, and microclimate effects. 12\. bio1: Mean annual air temperature * Description: Mean annual daily air temperature averaged over one year (°C). * Spatial precision: 1 km * Hypothesized ecological role: Thermal stress and stress on vegetation. 13\. bio2: Mean diurnal air temperature range * Description: Mean diurnal range of temperatures averaged over one year (°C). * Spatial precision: 1 km * Hypothesized ecological role: Average daily thermal experience. 14\. bio3: Isothermality * Description: Ratio of diurnal variation to annual variation in temperatures (°C). * Spatial precision**:** 1 km * Hypothesized ecological role: Thermal variation experience over the year. 15\. bio4: Temperature seasonality * Description: Standard deviation of the monthly mean temperatures (°C/100). * Spatial precision: 1 km * Hypothesized ecological role: Seasonal thermal variation experience. 16\. bio5: Mean daily maximum air temperature of the warmest month * Description: Highest monthly daily mean maximum temperature (°C). * Spatial precision: 1 km * Hypothesized ecological role: Thermal stress and stress on vegetation. 17\. bio6: Mean daily minimum air temperature of the coldest month * Description: Lowest monthly daily mean minimum temperature (°C). * Spatial precision: 1 km * Hypothesized ecological role: Thermal stress, stress on vegetation, thresholds for overwintering insects. 18\. bio7: Annual range of air temperature * Description: Difference between the maximum temperature of the warmest month and the minimum of the coldest month (°C). * Spatial precision: 1 km * Hypothesized ecological role: Range of thermal experience. 19\. bio8: Mean daily mean air temperatures of the wettest quarter * Description: Average temperature of the wettest quarter (°C). * Spatial precision: 1 km * Hypothesized ecological role: Thermal stress and stress on vegetation. 20\. bio9: Mean daily mean air temperatures of the driest quarter * Description: Average temperature of the driest quarter (°C). * Spatial precision: 1 km * Hypothesized ecological role: Thermal stress and stress on vegetation. 21\. bio10: Mean daily mean air temperatures of the warmest quarter * Description: Average temperature of the warmest quarter (°C). * Spatial precision: 1 km * Hypothesized ecological role: Thermal stress and stress on vegetation. 22\. bio11: Mean daily mean air temperatures of the coldest quarter * Description: Average temperature of the coldest quarter (°C). * Spatial precision: 1 km * Hypothesized ecological role: Thermal stress, stress on vegetation, thresholds for overwintering insects. 23\. bio12: Annual precipitation amount * Description: Total precipitation over one year (kg m⁻² year⁻¹). * Spatial precision: 1 km * Hypothesized ecological role: Forage, prey, and water availability. 24\. bio13: Precipitation amount of the wettest month * Description: Precipitation of the wettest month (kg m⁻² month⁻¹). * Spatial precision: 1 km * Hypothesized ecological role: Forage, prey, and water availability. 25\. bio14: Precipitation amount of the driest month * Description: Precipitation of the driest month (kg m⁻² month⁻¹). * Spatial precision: 1 km * Hypothesized ecological role: Forage, prey, and water availability. 26\. bio15: Precipitation seasonality * Description: Coefficient of variation of monthly precipitation estimates expressed as a percentage of the annual mean (kg m⁻²). * Spatial precision: 1 km * Hypothesized ecological role: Forage, prey, and water availability. 27\. bio16: Mean monthly precipitation amount of the wettest quarter * Description: Average precipitation of the wettest quarter (kg m⁻² month⁻¹). * Spatial precision: 1 km * Hypothesized ecological role**:** Forage, prey, and water availability. 28\. bio17: Mean monthly precipitation amount of the driest quarter * Description: Average precipitation of the driest quarter (kg m⁻² month⁻¹). * Spatial precision: 1 km * Hypothesized ecological role: Forage, prey, and water availability. 29\. bio18: Mean monthly precipitation amount of the warmest quarter * Description: Average precipitation of the warmest quarter (kg m⁻² month⁻¹). * Spatial precision: 1 km * Hypothesized ecological role: Forage, prey, and water availability. 30\. bio19: Mean monthly precipitation amount of the coldest quarter * Description: Average precipitation of the coldest quarter (kg m⁻² month⁻¹). * Spatial precision: 1 km * Hypothesized ecological role: Forage, prey, and water availability.