Nymphaea) in Africa Indicates Varying Suitable Habitats and Distribution in Climate Change

Aquatic Botany 173 (2021) 103416

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Aquatic Botany

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The past, current, and future distribution modeling of four water lilies (Nymphaea) in Africa indicates varying suitable habitats and distribution in climate change

John M. Nzei a,b,c, Boniface K. Ngarega a,b, Virginia M. Mwanzia a,b, Paul M. Musili d, Qing-Feng Wang b,e, Jin-Ming Chen a,b,* a Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China b Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, 430074, China c University of Chinese Academy of Sciences, Beijing, 100049, China d East Africa Herbarium, National Museums of Kenya, P.O. Box 451660-0100, Nairobi, Kenya e Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan, 430074, China

ARTICLE INFO ABSTRACT

Keywords: Mapping and modeling suitable habitat and distribution of aquatic species is important to help assess the impact Nymphaeaa of factors such as climate change in affecting the shift, decline, or expansion of species habitat ranges. In Africa, Climate change the distribution of water lily (Nymphaea) species is geographically varied and the habitats suitable for individual Species distribution species are prone to effects of global warming, though only limited conservation measures have been taken to Temperature and precipitation date in aquatic environments. In this study, four widely distributed water lily species (N. nouchali, N. micrantha, Africa N. lotus, and N. heudelotii) were modeled using MaxEnt which highlighted the individual species’ suitable cli­ matic distribution. The current distribution indicates a partial distribution of N. nouchali in West Africa unlike N. micrantha, N. lotus, and, N. heudelotii. Nymphaea lotus displays wider distribution in West, East, and parts of South African countries including their coastlines compared to all other species. Nymphaea nouchali indicates dense distribution in countries South of Africa while N. micrantha and N. heudelotii in West African countries. Greater habitat changes were noticed between the future and the past projection due to limited range expansion in 2.6, 4.5, and 8.5 (2050) Representative Concentration Pathways (RCPs) in almost all species. The species’ suitable habitat distribution was mainly influenced by nine variables, mostly the temperature and precipitation variables. This study provides projections of future climatic scenarios potentially influencing the distribution of Nymphaea species in Africa, which may be useful for the ongoing conservation and management of these plants especially in areas loosing suitable climatic conditions.

1. Introduction 2016). This rate of decline has been linked to human over exploitation of resources through agriculture, water extraction, flow regulation, the Aquatic and wetland ecosystems have become a center for major introduction of new species, and ecosystem pollution. Ultimately this activities that influence species suitable habitat areas and the natural has translated to the decline in abundance and distribution range of population. The prioritization of the ecosystems for economic impor­ freshwater species and thus a steep rise of global biodiversity threats tance such as in agriculture and domestication of some freshwater spe­ (Dudgeon et al., 2006), and the most influencing factor is climate cies in different parts of the world for their economic value has increased change, and specifically global warming, which has raised the global downstream chances for invasive species distribution from water flow surface temperature to approximately 0.6 ℃ over the past century and flooding( Wu and Ding, 2019). It is estimated that approximately 81 (IPCC, 2001) and by 2100 it is projected to increase by 4.3 ± 0.7 ℃ % of the world’s freshwater ecosystems have been lost between (IPCC et al., 2013). 1970–2012 at a rate of 3.9 % per year (The World-Wide Fund for Nature, Although species have evolved and adapted in their habitat areas in

* Corresponding author at: Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China. E-mail address: [email protected] (J.-M. Chen). https://doi.org/10.1016/j.aquabot.2021.103416 Received 18 December 2020; Received in revised form 4 June 2021; Accepted 8 June 2021 Available online 12 June 2021 0304-3770/© 2021 Elsevier B.V. All rights reserved. J.M. Nzei et al. Aquatic Botany 173 (2021) 103416 response to the changing climate, the effects are likely to accelerate in RCP 2.6 (stringent mitigation scenario), RCP 4.5 (intermediate scenario) the future causing substantial effects to species adaptation and habitat and RCP 8.5 (high greenhouse gas emission) scenarios of the Interna­ distribution, causing biological inversions and unidirectional movement tional Panel of Climate Change (IPCC5) of the fifthreport (IPCC 2014), of the species depending on the species community (Kelly and Goulden, with the aim of (i) determining the potential contribution of climatic 2008). This is without the exception of inland aquatic systems (Dudgeon variables in the distribution of Nymphaea species, (ii) determining et al., 2006), consisting of the freshwater ecosystems that holds suitable climatic habitat and (iii) suitability changes from the current approximately 1% of the Earth’s surface and 10 % of the current distribution. documented species (Strayer and Dudgeon, 2010). Moreover, with the simulated increase of temperature, extinction risk will rise to 8.5 % 2. Materials and methods when the earth warms to 3 ℃ thus losing one in every six species (Urban, 2015). Therefore, the consequences of climate change will not be limited 2.1. Occurrence records to individual species life history patterns and distribution ranges, but there will be an increased loss of biodiversity, habitat fragmentation, The species distribution modeling focused on all the available and change of spatial patterns of plant species (Warren et al., 2018). occurrence records obtained from the Global Biodiversity Information For that reason, the use of climate change models provides insight Facility (GBIF, http://www.gbif.org/), RAINBIO database (Dauby et al., into species potential biogeographic distribution habitats under threat 2016), published work of Kennedy et al. (2015), and fieldworkvisits to (ranges under contraction) and possible conservation areas (stable Africa (July 2018- May 2019) (Table S1). In this study, the occurrence ranges and ranges under expansion) (Chen and Peterson Townsend, records for Nymphaea nouchali subspecies caerulea (Savigny) were 2002). This information plays a crucial role in species monitoring and included within the parent species N. nouchali (Burm.f.). Hereafter all proper conservation, especially on threatened habitat ranges. Also, they these records are referred to as Nymphaea nouchali. This is because the contribute to the decisions made by policy makers and conservation majority of the occurrences were obtained from GBIF with the infra­ biologists concerning species management and protection (Hendry species having the same geographical extent. Also, some occurrences et al., 2010). Further, it is important in planning for today’s species shared similar coordinates an indication that different collectors might niche conservation and projection of their future distribution under have confused the identificationif not found at the same location. Since global warming (Wan et al., 2020). we could not ascertain with confidence such discrepancies, we consid­ Nymphaea is a cosmopolitan genus with species inhabiting shallow ered the two species together. Duplicate occurrence points were then and freshwater ecosystems in tropical temperate areas. The species are removed and projected in a google distribution map for each species to considered valuable because of varied factors: (i) they are considered as correct any possible geographical error by manually discarding the valuable ornamental plants because of their diversity in flower colors points with obvious geocoding errors and those that cannot be geore­ and ability to survive indoor conditions (Chen et al., 2017), (ii) they ferenced to the nearby wetland or freshwater ecosystem. Country cen­ produce valuable chemicals in medicine and cosmetology such as fla­ troids were also removed to avoid sample bias. Then the occurrence vonoids, alkaloids and tannins used in the treatment of diabetes, liver points were rarefied to a distance of not less than 10 km between each inflammations and urinary infections and many more complications other using SDMTools in ArcGis (Jackson et al., 2000) to minimize (Archana and Ashwani, 2016), and (iii) they are used in water purifi­ autocorrelation (Fig. 1). A total of 398 geographic points representing cation from heavy metal and soap contamination (Tani et al., 2006). occurrences for N. nouchali Burm.f., 442 for N. lotus L., 137 for In Africa, water lily (Nymphaea) species in the subgenus Brachyceras N. micrantha Guill. & Perr., and 23 for N. heudelotii Planch. was obtained and Lotos appear to have a wider distribution than other species in the indicating a variable distribution within the African continent genus (Lohne¨ et al., 2008). Although some parts of the continent are (Table S1). poorly sampled (Murphy et al., 2019), the species are geographically variable probably driven by climate change. Currently, the majority of 2.2. Climatic variables water lilies are not vulnerable or endangered. On the International Union or Conservation of Nature (IUCN) red-list, the profiledspecies are We assessed water lily species distribution using climatic variables approximately ten. Three are data deficient( N. divaricate, N. stuhlmannii, from the Community Climate System Model (CCSM4) from the Coupled and N. sulphurea), one is on the verge of extinction from the world Model Intercomparison Project (CMIP5) (Gent et al., 2011). The vari­ (N. thermarum), and one is vulnerable (N. alba) in Africa. Most Nymphaea ables include the seasonal variations of uncorrelated average layers and species occurring in Africa are designated by the IUCN as being of least because of their suitability, they have been used in species distribution concern, although their detailed population status often remains un­ and ecological niche modeling (Thomas et al., 2004). The climatic sce­ known and their distribution in some regions is certainly under-sampled narios are thought to depend on greenhouse emissions responsible for (Murphy et al., 2019). All too often there are no active conservation climate change (Moss et al., 2010). This makes them favorable for the measures for these species in Africa, except for species occurring in past, current, and future climatic predictions. The average climatic Ramsar sites and a few well protected nature reserve areas such as variables for 2041–2060, are represented as 2050 projection, while Kasanka National Park in Zambia. Improved understanding of the dy­ variables for 2061–2080, are represented as 2070 projections (CCSM4, namics of the response of Nymphaea species to climate change is hence accessed 13 May 2020). In this study, they were obtained from World­ important for the conservation and management of their populations Clim (v 1.4) database (http://www.worldclim.org) (Hijmans et al., within the native ranges of the species (e.g., Daoud-Bouattour et al., 2005). The variables included temperature layers (bio_1 – bio_11) and 2011). More so in this era when non-native species are introduced for precipitation (bio_12 – bio_19) obtained at a spatial resolution of 2.5 economic value or colonize new areas due to climate change. arcsec (Table S2). Assuming climate change restricts species distribution, and climatic variables are the primary driver towards species range, the projection 2.3. Ecological niche modeling and evaluation model uses species occurrence points to attest their reliability in pre­ dicting species distribution under climate change (Baker et al., 2000). The maximum entropy method (MaxEnt 3.4.1) was used for the Therefore, to understand the contribution of climate change for the past, species distribution modeling (Phillips et al., 2006; Phillips and Dudík, present, and future distribution of the Nymphaea species in Africa, the 2008). This popular correlative modeling technique performs better projections were done in Last Glacial Maxima (LGM; 22,000 bp), compared to other techniques (Elith et al., 2006). Besides, it can perform Mid-Holocene (MH; 6000 bp), the present (1970–2000) and the future in only presence data, as opposed to masked data (presence-absence (2050, 2080) using three representative concentration pathways (RCPs): data) (Trethowan et al., 2011), and can also perform well regardless of a

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Fig. 1. Species occurrence localities used for the modeling of the four water lily. (Nymphaea) species. species’ geographical extent (Radosavljevic and Anderson, 2014). In this classes of features (L, LQ, H, LQH, LQHP, LQHPT; L = linear, study, highly correlated bioclimatic layers were eliminated using vari­ Q = quadratic, H = hinge, P = product, and T = threshold). The class ance inflationfactors (VIF) in R software to avoid collinearity and model with the lowest delta AICc (Akaike information criterion) was preferred. overfitting( Merow et al., 2013). Finally, nine bioclimatic variables with Using the MaxEnt algorithm, 75 % of the occurrence points were used as VIF values below 10 were retained, considering that MaxEnt can predictive training samples, and 25 % as test points for performance perform to a degree of collinearity (Elith et al., 2011) (Table 1). testing (Suarez-Seoane´ et al., 2008). The run was performed as a sub­ Using ENMeval package in R software (version 3.6.3, R Core Team, sample with 10 replicates for data efficiency and model uncertainty 2017) regularization multiplier was chosen among the six different evaluation (Merow et al., 2013). To avoid under and over prediction of relationships within the model, the maximum number of interactions (background points) was set to 10000, convergence threshold at Table 1 0.00001, and 5000 maximum iterations for adequate predictions Nine climatic variables retained after eliminating the correlated ones using (Phillips et al., 2006). For samples less than 25, leave one out approach variance inflation factors. was employed (Pearson and Dawson, 2003). The maps representing Variable Bioclimatic variable Code VIF suitable climatic distribution ranges among the Nymphaea species were number produced using the 10th percentile training presence threshold (Phillips

1 Mean diurnal range D Mean of monthly bio_2 1.993033 and Dudík, 2008). (max temp -min temp) The model evaluation was performed using each species range of 2 Isothermality (bio2/bio7) (*100) bio_3 2.484002 occurrence to predict their potential climatic range in Africa using the 3 Mean temperature of wettest quarter bio_8 1.444701 area under the curve (AUC) in MaxEnt (Mason and Graham, 2002) and a 4 Mean temperature of the driest quarter bio_9 1.333522 plot of sensitivity against specificity (Phillips et al., 2006), which at 5 Precipitation of wettest month bio_13 4.424525 6 Precipitation of driest month bio_14 2.060646 random the probability of a cell presence have a higher value than the 7 Precipitation seasonality (coefficient of bio_15 2.134044 pseudo absence cell (Elith et al., 2006). These measures help identify the variation) most important variables contributing to the distribution of the modeled 8 Precipitation of warmest quarter bio_18 4.030031 species. 9 Precipitation of coldest quarter bio_19 2.225751

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The model is considered to perform well in the assessment of the descriptive (Zhao et al., 2018). species suitable climatic distribution considering the model revealed high reliability from the range of the AUC values (Merow et al., 2013) 2.4. Habitat suitability and change in suitable ranges whereby, the AUC values range from 0 to 1. The AUC value above 0.5 indicate better performance than random estimation (Phillips and Elith, The ASCII MaxEnt output files for the past, current, and future pro­ 2010), AUC value above 0.7 indicates a fair model (Merow et al., 2013), jections were converted to a raster float file. Then using the Zonal Sta­ and the closer the AUC value to 1 the model is evaluated as sensitive and tistics extension in Spatial Analyst tools in ArcGIS we calculated suitable

Fig. 2. Estimates of the relative contributions in percentage of the environmenta variables using MaxEnt models, fittedto the current and projected to the past and future. (a) represents the current contribution of the climatic variables to the distribution, (b) Mid-Holocene (MH; 6000 Bp), (c) Last Glacial Maxima (LGM; Bp), (d) RCP 2.6 (2050), (e) RCP 2.6 (2070), (f) RCP 4.5 (2050), (g) RCP 4.5 (2070), (h) RCP 8.5 (2050), and RCP 8.5 (2070). The legend represents all species in each graph.

4 J.M. Nzei et al. Aquatic Botany 173 (2021) 103416 climatic ranges for each species for the current distribution (O’Donnell value of 2.5. The current mean AUC value for N. nouchali was 0.907, et al., 2012). The prediction of suitability changes was then carried by N. micrantha (0. 928), N. lotus (0. 852), and N. heudelotii (0. 934). The comparing the current distribution map and the maps representing the mean AUC values of all the projections and scenarios ranged between past and the future projection using binary distribution changes in 0.859 in the current projection of N. lotus and 0.934 for the current SDMTools implemented in ArcGis 10.1 (Jackson et al., 2000). projection of N. heudelotii, respectively. The models were considered to perform well in the assessment of the suitable climatic habitats for the 3. Results distribution of Nymphaea species considering that an AUC value of 0.5 is an indicator of a model performing no better than random, while AUC 3.1. Climatic variable contribution values closer to 1.0 indicate better performance. The mean training and test AUC signify suitable selection of predictive variables for the Nym­ After excluding the collinear variables, the correlation coefficients phaea species modeling (Table 2). The slight differences between the ranged between 0.01 and 0.72, minimum in bio_8 and bio_13 while training and test AUC signify slight over fit but the standard deviation maximum in bio_13 and bio_18. The nine climatic variables (Table 1) suggests near approximation to the probability distribution. were chosen as the biologically meaningful layers for this group of species and their contribution is represented in Fig. 2. The precipitation 3.3. Distribution of suitable habitat of the wettest month (bio_13) had the highest contribution to the dis­ tribution of all the species in all scenarios, followed by the mean tem­ The current suitable climatic habitat for the four Nymphaea species is perature of the driest quarter (bio_9) and isothermity (bio_3) for illustrated in Fig. 4. Nymphaea lotus had the highest potential suitable N. nouchali, N. micrantha, and N. lotus. In addition, the distribution habitat of 7,510,832.45 Km Sq followed by N. nouchali (6,795,526.41 model for N. nouchali was also largely contributed by the precipitation of Km Sq), N. heudelotii (5,484,218.68 Km Sq), and N. micrantha the driest month (bio_14) and precipitation of the warmest quarter (4,899,147.87 Km Sq) respectively. The potential climatic suitability for (bio_18) but restrained by the mean temperature of the wettest quarter the four Nymphaea species varies geographically among the species. (bio_8) and precipitation seasonality (bio_15). The distribution model Nymphaea nouchali suitable ranges are densely predicted in East Africa, for N. micrantha was greatly contributed by bio_15 in all scenarios, bio_8, countries in southern parts of Africa including Madagascar, while and bio_18 in LGM and RCP 8.5 (2070) scenarios. The model distribu­ partially in West Africa countries (Fig. 4.a). Nymphaea micrantha suit­ tion is also constrained from the less contribution of bio_8 in MH, RCPs able ranges are densely projected in West Africa, along the tropical rain 2.6, 4.5, and 8.5 (2050) scenarios. The distribution model for N. lotus forest, and sparsely in parts of East Africa, Angola, and Madagascar was highly contributed by bio_2 and bio_8 in all scenarios in addition to (Fig. 4.b). Nymphaea lotus is widely projected in Africa with highly bio_3, bio_9, and bio_13. The precipitation of the warmest quarter suitable climatic ranges on West Africa, East Africa, countries below East (bio_18) contributed largely for LGM and RCP 8.5(2070) scenarios while Africa and Madagascar (Fig. 4.c). Lastly, potentially suitable climatic bio_14, bio_15, and bio_19 contributed slightly lower in all scenarios. ranges for N. heudelotii are densely projected in West Africa, central Two bioclimatic variables, bio_2 and bio_13 contributed relatively Africa, some parts of East Africa, Mozambique, and Madagascar (Fig.4. higher compared to bio_18 and bio_19 for N. heudelotii distribution in all e). scenarios. While, its distribution was restrained by the less contribution of bio_3, bio_8, bio_9, bio_14, and bio_15 in all scenarios. On average across all the species, bio_13 contributed the highest followed by bio_9 3.4. Past and future distribution change and bio_18 respectively. The variables bio_14 and bio_15 approximately contributed equally in all scenarios. The climatic variable bio_19 The changes of suitable climatic ranges for the four Nymphaea spe­ contributed the least to the model distribution of the four Nymphaea cies are represented in Fig. 5 (Table S4). The range expansion (Fig. 5.a) species (Fig. 3). of N. nouchali was slightly greater than the contraction (Fig. 5.d) in the LGM and vice versa in the MH. Its future climatic suitability indicates greater contraction than expansion in all scenarios. The expansion of 3.2. Model performance potential suitable climatic habitat areas is projected to be greater in West African countries in LGM, MH, and RCP 8.5(2070) compared to The ENMeval results for the four Nymphaea species are shown in RCPs 2.6, 4.5, and 8.5 (2050) scenarios. The MH, RCPs 2.6, 4.5, and 8.5 (Table S3.a to Table S3.d). The LQH features were chosen for N. nouchali (2050) indicated persistence stable climatic habitat (Fig. 5.c), although and N. micrantha with a rm value of 1.5 and 3. The LQHPT features were slight contraction of climatic ranges is noticed in parts of East Africa chosen for N. lotus with rm value of 1 and LQ for N. heudelotii with rm such as Ethiopia, Tanzania, Guinea, and South of Africa in Angola

Fig. 3. The average percent contribution of the nine climatic variables in all scenarios among the four water lily (Nymphaea) species projection.

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Table 2 than contraction (Table S4). However, the projection of climatic suit­ The AUC values for training (75 %) and test (25 %) data for the Nymphaea ability in MH indicates reduced expansion ranges in East Africa such as species distribution suitability using the 10th percentile training presence lo­ in Sudan, Ethiopia, and Somalia compared to the LGM. Same time ex­ gistic threshold. The AUC values describe the fitnessof the model in predicting pansions are projected in Zambia, Malawi, Mozambique, and the species distribution. Madagascar in MH compared to the LGM. The habitat change is Species Scenario Logistic Training Test Standard persistent in RCP 2.6 and RCP 4.5 in both 2050 and 2070 projections Threshold AUC AUC Deviation although range expansion is lost in Congo-Brazzaville, Democratic Re­ Nymphaea current 0.2235 0.9083 0.9066 0.0125 public of Congo, and Madagascar. Stable climatic habitat is projected to nouchali increase in Sudan and Somali in RCPs 2.6 and 4.5 in both 2050 and 2070 LGM 0.2011 0.907 0.8987 0.0148 projections and greater expansions in RCP 8.5 (2070), although some MH 0.2191 0.9053 0.8974 0.0134 2.6 0.2236 0.9028 0.9043 0.0126 parts of Tanzania, Mozambique, Malawi, and Zambia are projected to (2050) experience greater contraction in RCP 8.5 (2070) (Fig. S2). The habitat 2.6 0.2073 0.9028 0.9018 0.0129 suitability for N. lotus indicates greater range contractions than expan­ (2070) sions in all scenarios (Fig. 5.d; Table S4). The LGM expansion ranges in RCPs 4.5 0.2191 0.9054 0.9054 0.013 Mauritania, Sudan, Somalia, Uganda, Congo, and Angola decrease in (2050) 4.5 0.2181 0.9049 0.8998 0.0137 MH, but expansions are noticed in Mali and Madagascar. Unlike RCP 2.6 (2070) (2050), expansions are projected in Central Africa Republic, Zambia, 8.5 0.2141 0.9013 0.9013 0.0132 and Botswana in RCP 2.6 (2070). More expansions in RCP 4.5 (2070) are (2050) noticed in Gabon which contracts in future projection and shifts between 8.5 0.2153 0.9081 0.8974 0.0134 (2070) Congo-Brazzaville and the Democratic Republic of Congo in RCP 8.5 Nymphaea current 0.2683 0.931 0.9278 0.0132 (2070). Although most parts of West Africa indicate stable climatic micrantha suitability, parts of East Africa and South African countries indicate LGM 0.3142 0.9163 0.9069 0.0144 greater contractions (yellow color) such as Somali, Zambia, and MH 0.2881 0.9309 0.9239 0.0134 Mozambique in RCP 8.5 (2070) (Fig. S3). The expansions of suitable 2.6 0.2613 0.9276 0.9267 0.013 (2050) climatic ranges for N. heudelotii are projected to be higher than 2.6 0.248 0.924 0.9265 0.012 contraction in all scenarios (Table S4). In the MH suitable climatic (2070) ranges in West Africa, East Africa, and countries below East Africa such RCPs 4.5 0.254 0.9289 0.925 0.0141 as Nigeria, Tanzania, and Mozambique experienced climatic contraction (2050) 4.5 0.252 0.9272 0.924 0.0126 compared to LGM with an exception of Ethiopia in which suitable cli­ (2070) matic habitat is projected to expand. In the RCPs 2.6, 4.5, and 8.5 (2050) 8.5 0.2854 0.9281 0.9125 0.0152 most parts of West Africa experience climatic contraction such as (2050) Nigeria while Central Africa, East Africa, and countries below experi­ 8.5 0.3058 0.9139 0.9013 0.0153 ence expansion such as Tanzania and Mozambique. In the RCP 8.5 (2070) Nymphaea current 0.2491 0.8846 0.8525 0.0145 (2070) most parts regain climatic suitability and greater expansions as lotus noticed in Madagascar, Tanzania, Ethiopia, and the border between LGM 0.2752 0.8923 0.8641 0.0136 Kenya and Sudan compared to all future projections while in Angola MH 0.3192 0.8929 0.8646 0.013 expansion range is small compared to RCP 8.5 (2050) (Fig. S4). 2.6 0.2935 0.8944 0.8636 0.0135 (2050) 2.6 0.2939 0.8934 0.8643 0.0135 4. Discussion (2070) RCPs 4.5 0.2954 0.8932 0.8664 0.0137 Ecological models are important in predicting the potential impact of (2050) 4.5 0.2933 0.8907 0.859 0.0146 climate change on species distribution as well as their response (Jackson (2070) et al., 2000). In Africa, with the availability of the Palaeo-records, the 8.5 0.287 0.8921 0.8596 0.0139 approach has been used in some studies to indicate the significance of (2050) losing suitable habitat areas which may lead to species extinction. For 8.5 0.2982 0.8935 0.8642 0.0137 example, modeling using the HadCM3 general circulation model pre­ (2070) Nymphaea current 0.2581 0.9327 0.934 0.0301 dicted the extinction of between 25 % and 42 % of sub-Saharan plant heudelotii species by 2085, from reduced suitable habitat areas (McClean et al., LGM 0.2621 0.9058 0.9208 0.0368 2005). Therefore, the present study, utilizes MaxEnt, an ecological MH 0.2415 0.9399 0.9187 0.034 habitat modeling approach, to project climatic suitable areas useful in 2.6 0.2477 0.9234 0.9142 0.0411 (2050) determining the potential spatial distribution for the Nymphaea species 2.6 0.2357 0.9225 0.8958 0.0394 in Africa and the impacts of climate change on their past and future (2070) potential habitat distribution. The modeling technique is preferred RCPs 4.5 0.2352 0.9204 0.9044 0.0453 because it avoids pseudo absence data which enhances accuracy and (2050) provides the user with an option to set the percentage of omission error 4.5 0.2562 0.9261 0.9075 0.0415 (2070) in the distribution models. 8.5 0.2412 0.9194 0.9124 0.0434 In our study, we notice that temperature and precipitation are the (2050) critical variables influencingspecies ’ suitable climatic distribution, thus 8.5 0.198 0.9116 0.8906 0.0463 conforming to studies by Parmesan (2006) and Elith and Leathwick (2070) (2009). The immense contribution of temperature variables (bio_2, bio_3, and bio_9) as the principal factor toward species distribution and compared to LGM and RCP 8.5 (2070). The suitable climatic ranges in precipitation variables (bio_14, bio_15, and bio_19) (Fig.2) as constrains Namibia are projected to expand in RCP 8.5 (2070) while the stable of species distribution are both used together to predict and understand climate in Gabon experience contraction (Fig. S1). In all the projected the most likely response of species to climate change, something sup­ scenarios N. micrantha has a greater expansion of the suitable ranges ported in Crimmins et al. (2011). These findings are more important especially for aquatic species whose habitat ranges rely on water

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Fig. 4. Current potential suitable distribution ranges for the four Nymphaea species. (a) Nymphaea nouchalli, (b) Nymphaea micrantha, (c) Nymphaea lotus, and (d) Nymphaea heudelotii. availability. an approximate annual precipitation of 800–2000 mm. This might Although the prediction of the African climate regime is complex and indicate why bio_13 was the highest contributing variable for the pro­ probably controlled by diverse dynamic causes, we try to associate the jection of suitable climatic ranges. Uniquely, the diurnal temperature current projected distribution of climatic suitability for the Nymphaea range (bio_2) was projected to have a greater influence on the distri­ species to variation in precipitation according to Nicholson (2000). The bution of N. heudelotii probably due to characteristics of the tropical distribution of N. nouchali in regions of East Africa and Southern Africa equatorial environment such as high humidity. The precipitation of the including Madagascar indicates a preference for precipitation in its driest month (bio_14) in the savannah plays a key role in replenishing habitat area (Fig. 4). These regions experience one or two rainy seasons the ecosystem with moisture especially during the dry season. Its high with an approximate annual rainfall of between 800 to 1200 mm. contribution to N. nouchali explains why the species is highly distributed Nymphaea micrantha seemed favored by one rainy season along the in the region. Lastly, bio_8 contributed highly for N. lotus distribution lower sides of the Sahara desert influenced by North East trade winds modeling possibly from the short, high, and seasonal rains experienced from low pressure in between July and August season which is appar­ in the savannah ecosystem. ently between 400–1200 mm. Nymphaea lotus was widely distributed in Compared to the LGM and MH, the projection of Nymphaea species West Africa, regions of East Africa, its coastlines, and countries below, suitable climatic distribution indicates a greater change in expansion while unfavored to Central Africa areas of Congo, areas characterized by and contraction in RCPs 2.6, 4.5, and 8.5 (2050) especially in West and equatorial climate. Apparently, its preferred areas of distribution East parts of Africa probably due to long-term habitat contraction in (though note the likely issues of under recording in parts of the study Tropical Africa (Kissling et al., 2012), which is associated with changes area, especially Democratic Republic of Congo) appeared to be associ­ of warm to cold extremes approximately 65 million years ago (Zachos ated with areas experiencing a single or two dry seasons per year, with et al., 2001). The expansions of climatically suitable ranges predicted in

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Fig. 5. Change difference in percentage (%) between the current potential distribution with the past and the future changes using binary changes in each scenario. (a) represents range expansion, (b) unsuitable range, (c) stable range, and (d) range contraction. our study (results section) provide an understanding of suitable poten­ suitability determines species suitable distribution ranges is important tial areas for the spread of the species. The projected expansion areas are to understand species response to climate change. Whereby, through also possible future restoration areas for conservation and management global warming, climate change has effects on species range distribu­ efforts (Araújo and Rahbek, 2006), especially when the species popu­ tion, ecosystem assemblances, an increase of invasive species, decline lation is decreasing at an alarming rate or becomes threatened. Besides, and extinction of species as well as their suitable habitat areas. The ef­ they are possibly the future savior for the species’ biodiversity and fects of climate change will in the future affect aquatic assemblages therefore they need special attention in terms of protection. On the other leading to consequences not limited to individual species, but the species hand, as global temperatures rise, more attention is needed to con­ community although the susceptibility of the species habitat may be tracting suitable habitat ranges than the expanding suitable ones mediated by species’ response to the effects of climate change and (named in the results section). This is because the declining suitable biological traits. Besides, species adapted to unique environments and climatic ranges may pose a future threat of habitat loss and species incapacitated to conger and expand their territories are more susceptible extinction (Urban, 2015). to declining populations and at risk of extinction. Also, the ability of a Besides, the expansion and contraction of the suitable climatic species to positively respond to the effects of climate change may ranges predicted, Nymphaea species may have received influence from translate to a shift of the species or the community to a more favorable the landscape and climate change in the LGM which contributed to the habitat. For example, in our study, N. micrantha expansion ranges in drastic change in species distribution in response to the changing Niger, Chad, Tanzania, and Mozambique are lost in RCP 8.5 (2070) but climate (Jackson et al., 2000), causing a reduction and expansions in increase in West and East Africa (Fig. S2), N. lotus experience greater geographical area change (Hewitt, 2004) as predicted in Fig. 5. The contraction than the expansion of climatic range thus increased proba­ increase in range loss than gain in the future for N. lotus (Fig. 5.d, bility of losing suitable areas such as expansion habitats in Zambia in Table S4), indicates that the effect of climate change on species distri­ RCP 8.5 (2070) but increase in Sudan (Fig. S3). bution is expected to worsen with the increase in global warming Hence, knowledge of the effects of climate change on species dis­ (Urban, 2015). While, N. heudelotii is projected to experience expansion tribution is imperative for future management and wellbeing of the (Fig. 5.a) an indication that species respond differently to the effects of species’ habitat ranges. This is also important in building ecosystem climate change which might lead to a more rapid species distribution resilience from which its habitats can recover and expand their ranges. change than in the past (Lawing and Polly, 2011). Although climate Our recommendation is to adopt a proactive strategy towards the pro­ change is not the only factor capable of contributing to species distri­ tection of species suitable habitat for survival more so the areas with bution, climatic variables have been used in the prediction of species depleting suitable climatic conditions (indicated in yellow color in the potential habitat ranges, thus useful in determining species potential maps), and lastly consider upstream and downstream habitat ranges distribution areas. For example, areas such as Zambia are projected as a alongside the protected ranges which are key in determining the survival stable range for N. nouchali, unstable for N. micrantha, and experiencing and future sustainability of aquatic species. contraction ranges for N. lotus. This could explain why in a well sampled For improved conservation amidst the effects of climate change, we Zambian river system N. nouchali samples would be more compared to recommend: (i) enhanced education and awareness to the people N. lotus (Kennedy et al., 2015). through formal education or communications; (ii) laws and policies The projection of climatic variables with an assumption that climate governing the use of natural resources in an equitable manner should be

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