ORIGINAL RESEARCH
published: 02 June 2022
doi: 10.3389/fcosc.2022.870041
Climate Change Reveals
Contractions and Expansions in the
Distribution of Suitable Habitats for
the Neglected Crop Wild Relatives of
the Genus Vigna (Savi) in Benin
Leonard Manda 1,2*, Rodrigue Idohou 3,4 , Achille Ephrem Assogbadjo 1 and
Clement Agbangla 5
1
Laboratoire d’Écologie appliquée (LEA), Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Godomey,
Benin, 2 Biological Sciences Department, Mzuzu University, Mzuzu, Malawi, 3 Ecole de Gestion et de Production Végétale et
Semencière (EGPVS), Université Nationale d’Agriculture, Kétou, Benin, 4 Laboratoire de Biomathématiques et d’Estimations
Forestières (LABEF), Faculté des Sciences Agronomiques, Université d’Abomey-Calavi, Cotonou, Benin, 5 Laboratoire de
Génétique Moléculaire et d’Analyse des Génomes (LGMAG), Faculty of Sciences and Techniques, Université
d’Abomey-Calavi, Cotonou, Benin
Edited by:
Jacopo Calevo,
University of Naples Federico II, Italy
Reviewed by:
Gabriele Casazza,
University of Genoa, Italy
Joana Magos Brehm,
University of Birmingham,
United Kingdom
*Correspondence:
Leonard Manda
lmanda8@gmail.com
Specialty section:
This article was submitted to
Plant Conservation,
a section of the journal
Frontiers in Conservation Science
Received: 05 February 2022
Accepted: 25 April 2022
Published: 02 June 2022
Citation:
Manda L, Idohou R, Assogbadjo AE
and Agbangla C (2022) Climate
Change Reveals Contractions and
Expansions in the Distribution of
Suitable Habitats for the Neglected
Crop Wild Relatives of the Genus
Vigna (Savi) in Benin.
Front. Conserv. Sci. 3:870041.
doi: 10.3389/fcosc.2022.870041
Sustainable conservation of crop wild relatives is one of the pathways to securing global
food security amid climate change threats to biodiversity. However, their conservation
is partly limited by spatio-temporal distribution knowledge gaps mostly because they
are not morphologically charismatic species to attract conservation attention. Therefore,
to contribute to the conservation planning of crop wild relatives, this study assessed
the present-day distribution and predicted the potential effect of climate change on
the distribution of 15 Vigna crop wild relative taxa in Benin under two future climate
change scenarios (RCP 4.5 and RCP 8.5) at the 2055-time horizon. MaxEnt model,
species occurrence records, and a combination of climate- and soil-related variables
were used. The model performed well (AUC, mean = 0.957; TSS, mean = 0.774).
The model showed that (i) precipitation of the driest quarter and isothermality were the
dominant environmental variables influencing the distribution of the 15 wild Vigna species
in Benin; (ii) about half of the total land area of Benin was potentially a suitable habitat
of the studied species under the present climate; (iii) nearly one-third of the species may
shift their potentially suitable habitat ranges northwards and about half of the species
may lose their suitable habitats by 5 to 40% by 2055 due to climate change; and (iv)
the existing protected area network in Benin was ineffective in conserving wild Vigna
under the current or future climatic conditions, as it covered only about 10% of the
total potentially suitable habitat of the studied species. The study concludes that climate
change will have both negative and positive effects on the habitat suitability distribution of
Vigna crop wild relatives in Benin such that the use of the existing protected areas alone
may not be the only best option to conserve the wild Vigna diversity. Integrating multiple in
situ and ex situ conservation approaches taking into account “other effective area-based
conservation measures” is recommended. This study provides a crucial step towards the
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development of sustainable conservation strategies for Vigna crop wild relatives in Benin
and West Africa.
Keywords: crop wild relatives, conservation biases, climate change, in situ conservation, biodiversity conservation
INTRODUCTION
of CWRs in crop improvement are widely reported (Hajjar and
Hodgkin, 2007; Dempewolf et al., 2017), the annual contribution
of their use to the global economy is estimated between USD
120–186 (PricewaterhouseCooper, 2013; Tyack et al., 2020), and
their importance is well recognised in the global business and
political agenda (e.g., CBD, 2005, 2010; FAO, 2009). Besides,
Vigna CWRs are potential candidates for neo-domestication
(Tomooka et al., 2014), and as with other CWRs, they could
also be commercialised (Abdelghany et al., 2021). As such, the
conservation and sustainable use of Vigna CWR diversity is not
only pertinent but urgent for securing global food, economy,
and other ecosystem services in a warming world (Dempewolf
et al., 2014; Fitzgerald et al., 2019; Zimmerer et al., 2019),
thus potentially contributing to the attainment of the United
Nations Development Goals (e.g., SDG 2- End hunger; SDG 13Resilience and adaptation to climate change) (United Nations,
2015).
However, as with other CWRs, conservation planning of
Vigna CWR taxa is partly limited by fundamental knowledge
gaps in their distributions under the changing climate (Khoury
et al., 2020). This is mostly because CWRs get relatively
less conservation attention since they are not morphologically
charismatic species (Maxted et al., 2016; Adamo et al., 2021,
but see Veríssimo et al., 2017). Meanwhile, they are increasingly
threatened by climate change in their natural environments
(Jarvis et al., 2008; Vincent et al., 2019; Goettsch et al., 2021). For
instance, Jarvis et al. (2008) predicted that 16–22% of Vigna CWR
species risk extinction globally by the year 2055 and the majority
of the species may lose over 50% of their climatically suitable
habitats. The authors further observed differential responses to
different environmental factors in Vigna CWR species. On the
other hand, Vincent et al. (2019) identified 150 potential sites for
the conservation of 66% of the studied 1,261 CWRs that included
selected Vigna species in light of future climate change (2060–
2089), and a few areas around West, Central and East Africa
were among the sites earmarked. However, findings obtained at
the global scale are less likely to inform conservation decisionmaking at a local scale (Phillips et al., 2017; Stephan et al.,
2020). Moreover, the use of only the optimistic Representative
Concentration Pathway (RCP 4.5) by Vincent et al. (2019) might
have overestimated the potentially severe impacts of climate
change (Scridel et al., 2021), especially for areas where climate
change impacts are predicted to become more severe such as
West Africa (IPCC, 2021). Therefore, understanding how Vigna
CWR taxa would respond to future climate change effects in
Benin would help in planning for their adaptive management
approaches (Iriondo et al., 2021).
To preserve CWR genetic diversity and ensure they meet
future food security needs under the changing climate, in
situ conservation of CWR diversity has long been considered
the best conservation option (Maxted and Kell, 2009; Maxted
et al., 2011; Bellon et al., 2017; FAO, 2017). In this regard, the
Conserving biodiversity under the changing climate is one of the
greatest challenges of our time, with recent studies predicting
expansions or contractions of suitable habitats for many taxa
(Aguirre-Gutiérrez et al., 2017; Phillips et al., 2017; Ratnayake
et al., 2021; Zuza et al., 2021; Hoveka et al., 2022), shifts in
phenology (Lima et al., 2021) and unprecedented biodiversity loss
(Bellard et al., 2012; Habibullah et al., 2022). It is predicted that
most plants may lose over half of their suitable habitats if the
global surface temperatures are to rise by 3◦ C by 2100 (Warren
et al., 2018). Yet, global surface temperatures are predicted to
increase by up to 5.7◦ C by the end of this Century under the
business as usual scenario (IPCC, 2021). Therefore, to optimise
conservation and sustain ecosystem services that biodiversity
brings to people (Jaradat, 2015; Zimmerer et al., 2019), it is
important to understand the effects of climate change on the
habitat suitability distribution of species and identify those that
are vulnerable and require urgent conservation attention (Pacifici
et al., 2015; Phillips et al., 2017). Species distribution models
(SDMs), also known as ecological niche models (ENMs) or
habitat suitability models (HDMs) (Elith and Graham, 2009)
have been used in this regard across a range of taxa including crop
wild relatives (CWRs) (Guisan et al., 2013; Phillips et al., 2017;
Vincent et al., 2019; Ratnayake et al., 2021). These are numerical
tools that correlate known occurrences with environmental
variables to explain and predict a species’ potential range (Barlow
et al., 2021).
Crop wild relatives (CWRs) are wild plant species that are
genetically closely related to domesticated plants including their
progenitors (Maxted et al., 2006). The genus Vigna Savi (Family
Fabaceae) is a tropical and subtropical taxon, comprising nearly
105 species from which only nine have been domesticated (Somta
et al., 2019; Catarino et al., 2021). The potential contribution
of Vigna CWR species to the global food security improvement
as well as human and ecological health is well-documented
(Maxted et al., 2004; Tomooka et al., 2014; Harouna et al.,
2018; Takahashi and Tomooka, 2020; van Zonneveld et al.,
2020; Catarino et al., 2021). Like other CWRs, Vigna CWR taxa
have high genetic diversity since they have not gone through
domestication bottlenecks and artificial selection (Dempewolf
et al., 2017; Zhang et al., 2017; Bohra et al., 2021), and harbour
various genes responsible for environmental stress adaptation
(Takahashi et al., 2016; van Zonneveld et al., 2020). These traits
may be used in the development of more productive, nutritious,
and resilient Vigna crop varieties (Castañeda-Álvarez et al., 2016)
against the background of the cascading impacts of climate
change on the productivity of domesticated species (Dempewolf
et al., 2017; Ortiz-Bobea et al., 2021), the projected risks on
the future food systems (Müller and Robertson, 2014), and the
increasing concerns over food and nutrition insecurity (Godfray,
2014; Willett et al., 2019). Moreover, the actual and potential uses
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increasing urbanisation and land-use change (Guidigan et al.,
2019).
Benin is divided into three contrasting climatic zones
(Adomou et al., 2006): Guinean (in the south), Sudanian (in the
north), and Sudano-Guinean (a transition zone in the centre).
Basically, the Guinean zone is characterised by a sub-humid
climate, bimodal rainfall (April–June & September–November)
averaging 1,200 mm/year, a temperature range of 18–33◦ C,
relative humidity ranging between 30 and 98%, ferrallitic and
hydromorphic soils without concretions, and woodlands and
fallows as the dominant habitats and/or vegetation types. While,
sub-humid climate, unimodal rainfall (May–October) ranging
between 900 and 1,100 mm/year, an annual temperature range of
20–36◦ C, relative humidity of 31–98%, ferruginous to somewhat
tropical ferruginous and ferrallitic soils on crystalline basement
characterise the Sudano-Guinean zone. This zone is dominated
by a mosaic of savanna woodlands, dense shrub and tree forests,
and gallery forests. On the other hand, the Sudanian zone
is characterised by Sudano-Sahelian climate, unimodal rainfall
(700–900 mm/year), a relatively wide temperature range (17–
42◦ C), and a wide relative humidity (18–99%). Furthermore, this
zone is dominated by ferruginous soils on crystalline basement
and mostly dry shrubby forests and savannas (Adomou et al.,
2006; Assogbadjo et al., 2011; Hounkpatin et al., 2022). Like in
most parts of the sub-Saharan Africa, the soils in Benin are mostly
sandy and infertile (Hounkpatin et al., 2022).
establishment of genetic reserves within or on the fringes of the
existing protected areas (PAs) has been strongly recommended
(Maxted and Kell, 2009; Maxted et al., 2016; FAO, 2017). But,
the effectiveness of PAs in conserving CWRs has been put into
question, considering that PAs were established for charismatic
species (Maxted et al., 2016), and are static establishments
while species are shifting due to climate change (Thomas and
Gillingham, 2015; Heywood, 2019). With regard to the use of PAs,
Maxted et al. (2004) and Moray et al. (2014) showed that most
African Vigna CWR species could be effectively conserved in the
existing PAs in Africa. But, the extent to which such PAs would
become potential refugia for Vigna CWR diversity under the
changing climate (Jarvis et al., 2008) was not addressed. Further,
as part of the initial steps towards the development of sustainable
conservation measures for CWRs in the region, prioritisation of
CWRs has been done in Benin (Idohou et al., 2013) and West
Africa (Nduche et al., 2021), but studies on the effects of climate
change on CWRs are nebulous in the literature for the region.
To date, many studies that have been conducted in this respect
have mostly been on non-CWR tree species (e.g., Favi et al., 2021;
Lompo et al., 2021; Salako et al., 2021; Assogba et al., 2022),
palms (Idohou et al., 2017a; Salako et al., 2019), and woody lianas
(Vihotogbé et al., 2021).
This study intended to address these gaps and contribute to
the existing efforts in the conservation of CWRs in West Africa
and the global network of genetic reserves (Maxted and Kell,
2009). It was aimed at assessing the present-day distribution
and forecasting the potential effect of climate change on the
distribution of 15 Vigna CWR taxa in Benin under two future
climate change scenarios (RCP 4.5 and RCP 8.5) at the 2055time horizon. Three objectives were formulated: (1) to identify
environmental factors influencing the distribution of habitats
suitable for 15 Vigna CWR taxa in Benin; (2) to map the presentday habitat suitability distribution of 15 wild Vigna taxa and
project their future distribution under two climate scenarios
(RCP 4.5 and RCP 8.5) in the 2055-time horizon; and (3) to
prioritise in situ conservation sites for Vigna CWR taxa in Benin
and evaluate the effectiveness of PAs in conserving the studied
taxa. The study hypothesised that (1) different Vigna CWR taxa
would respond differentially to a different suite of environmental
variables (Jarvis et al., 2008); (2) there would be an increase in
the northern habitat (Lenoir et al., 2020); and (3) the existing PA
network in Benin will be less effective in conserving Vigna CWR
diversity than non-PAs.
Study Species
Vigna CWRs are herbaceous, annual or perennial, climbing,
scrambling or prostrate plants. Perennial species generally have
large, woody rootstocks which often die back in cooler months
only to grow again in warm weather or following burning
e.g., Vigna frutescens A. Rich (Maxted et al., 2004). Their sizes
generally range from <1 m for species like Vigna laurentii De
Wild. to over 7 m e.g., Vigna racemosa (G. Don) Hutch. & Dalziel.
Their stems may be glabrous or with various levels of pubescence.
They are found in a wide range of habitats such as savannas,
grasslands, open woodlands, and shrublands usually at low
altitudes, with some species such as Vigna luteola (Jacq.) Benth.
and V. laurentii often associated with wet areas. Most Vigna CWR
taxa in Benin flower and reproduce between August/September
and December. Like other leguminous plants, wild Vigna species
have a ballochory (explosive dehiscence) seed dispersal as their
primary seed dispersal mechanism (Lush et al., 1980; TrzeciakLimeira et al., 2013); thus, they are likely to be mostly shortdistance dispersed plants, probably dispersing their seeds over
a 1–5 m radius (Vittoz and Engler, 2007; Parker et al., 2021). A
few species like V. luteola are known to be dispersed over long
distances by sea-drifting (Miryeganeh et al., 2014). While those
that are used as forage such as V. racemosa and Vigna reticulata
Hook. f. (Catarino et al., 2021), are likely to be dispersed over
long distances by secondary agents such as herbivores (RamírezRodríguez et al., 2021; Wang and Hou, 2021). Vigna CWR taxa
favour warm temperatures, but higher temperatures (>36◦ C) are
detrimental to their metabolic processes such as photosynthesis
(Farooq et al., 2017).
MATERIALS AND METHODS
Study Area
Benin is located in West Africa between 6◦ 25′ N to 12◦ 30′ N and
0◦ 45′ E to 4◦ 00′ E. Generally, Benin is a low lying country, with
altitudes varying from sea level to 400 m a.s.l., although it can go
up to 650 m in the northwest of the country, where temperatures
can also be exceptionally high (35–40◦ C) (Adomou et al., 2006).
Agriculture is the main source of livelihood in rural areas.
Like most developing countries, Benin is experiencing rapid
population growth, with the recent human population estimated
at 12.5 million people (United Nations, 2021) and associated with
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Twenty-one bioclimatic variables for the present and future
scenarios were downloaded from the AfriClim database version
3.0 at a spatial resolution of 30 arc seconds (∼1 km2 ) (https://
www.york.ac.uk/environment/research/kite/resources/) (Platts
et al., 2015) [accessed on March 17, 2020]. The database spans
10 general circulation models (GCMs), downscaled using
five bias-corrected regional climate models (RCMs) and four
contemporary baselines, under two representative concentration
pathways of the IPCC-AR5 (RCP4.5 and RCP8.5) (Platts et al.,
2015). For the current conditions, the WorldClim v1.4 option
with the baseline year of 1975 (1950–2000) was used from the
database. For the future climatic conditions—horizon 2055
(2041–2070), the AfriClim Ensembles 3.0 with the WorldClim
as the baseline was used, and two scenarios, RCP 4.5 (optimistic
scenario) and RCP 8.5 (pessimistic scenario) (Platts et al., 2015)
were considered. The two scenarios seem to be plausible for
Africa (Platts et al., 2015), and have been widely used in SDM
studies across Africa (Lompo et al., 2021; Zuza et al., 2021;
Assogba et al., 2022). The mid-Century horizon (the 2050s) was
chosen to align with the United Nations framework of global
challenges in agriculture and food security (Zuza et al., 2021),
which also dovetails well with the Agenda 2063 for Africa.
Soil data were obtained at 250 m resolution from the Africa
Soil Profiles Database (http://www.isric.org) (Hengl et al., 2017).
Eleven sets of bio-physiochemical soil characteristics were
downloaded. These were bulk density (t/m3 ), soil organic carbon
(g/kg), pH in water, clay content (%), sand content (%), silt
content (%), cation exchange capacity (cmolc/kg), exchangeable
acidity (cmolc/kg), exchangeable Ca (cmolc/kg), exchangeable
K (cmolc /kg), and exchangeable Mg (cmolc/kg). These soil
variables have been used in previous studies in Benin (e.g.,
Idohou et al., 2017b; Vihotogbé et al., 2021). Since soil-plant
interactions seem to be critical within the 0–16 cm soil depth
(Goebes et al., 2019) and given that Vigna species are herbaceous
small-statured plants, only a maximum of three soil depth
horizons (0–5, 5–15, and 15–30 cm) were considered for this
study. A total of 30 soil layers were thus downloaded. The soil
data were resampled at 1 km resolution to match the resolution
of the bioclimatic variables.
Currently, Benin has about 31 Vigna CWR taxa that have
been described (Supplementary Table 1), but this study used
only 15 of these; the rest were left out as they had ≤10 occurrence
records (Yesuf et al., 2021). The full list of the studied taxa
were Vigna comosa Baker, Vigna filicaulis Hepper, V. frutescens,
Vigna gracilis (Guill. & Perr.) Hook. f., Vigna heterophylla A.
Rich., V. laurentii, Vigna longifolia (Benth.) Verdc., V. luteola,
Vigna multinervis Hutch. & Dalziel, Vigna nigritia Hook. f.,
Vigna oblongifolia A. Rich., V. racemosa, V. reticulata, Vigna
unguiculata subsp. baoulensis (A. Chev.) Pasquet, and Vigna
unguiculata var. spontanea (Schweinf.) Pasquet. Of these 15 taxa,
only three (V. heterophylla, V. unguiculata subsp. baoulensis, and
V. unguiculata var. spontanea) were at the time of this study not
yet assessed using the IUCN Red List Categories and Criteria
(Supplementary Table 1). The spatial distributions of the 15
studied taxa are presented in Figure 1.
Occurrence Records and Processing
This study is a follow-up on the national inventory and
prioritisation of CWRs for Benin (Idohou et al., 2013). The
Global Biodiversity Information Facility (GBIF, www.gbif.org)
was the main source of species occurrence records (1,952)
[accessed on July 14, 2020]. The RAINBIO (https://gdauby.
github.io/rainbio) [accessed on July 16, 2020], and the Genesys
Global Portal on Plant Genetic Resources (https://www.genesyspgr.org) [accessed on July 15, 2020], provided additional records,
128 and 24, respectively.
In addition, random field visits were made to verify location
data related to the distribution of some species and partly
minimise the sampling bias in the database (Meng et al.,
2021). To ensure data quality, visual inspection was used to
identify outliers and these were clipped out. Records collected
earlier than 1990 were removed from the data set to reduce
the effects of temporary bias (Idohou et al., 2017b). To
reduce clumping bias, duplicate records were removed from
the data set, and occurrence records were spatially thinned
to a geographic distance of 1 × 1 km2 (Idohou et al.,
2017b) using Environmental Niche Modelling (ENM) tools
(www.ENMTools.com) (Warren et al., 2010) performed in QGIS
version 3.8.1 (QGIS Project https://qgis.org). Spatial thinning
not only reduces model overfitting but also improves the
performance of models better than background manipulation
(Ratnayake et al., 2021). The Flora of Benin (Akoegninou et al.,
2006) and experts were consulted to verify the adequacy of the
observed range distribution of the target species.
Model Calibration and Evaluation
Maximum Entropy (Maxent ver. 3.4.1) algorithm (Phillips
et al., 2006) was used. As with other correlative models, the
MaxEnt procedure establishes the relationship between species
occurrence records at sites and the environmental variables
and/or spatial characteristics of such sites (Phillips et al., 2006;
Elith et al., 2011). Despite its limitations (Lissovsky and Dudov,
2021), MaxEnt is among the many SDM tools (see Elith and
Graham, 2009) with increasing use in conservation-oriented
studies owing to its high predictive accuracy, stability and
reliability even with presence-only data and small data sets
(Elith et al., 2011; Phillips et al., 2017; Vincent et al., 2019;
Çoban et al., 2020; Mponya et al., 2021; Ratnayake et al., 2021).
Moreover, it produces spatially open habitat suitability maps
and evaluates the significance level of individual environmental
variables using the built-in Jackknife test (Çoban et al., 2020),
which were among the core objectives of the current study.
Environmental Data and Processing
The study used a combination of bioclimatic and soil
variables. Bioclimatic data represent annual trends in climate
conditions, seasonality and climate extremes, which may impact
reproduction and survival of species over broad extents (AguirreGutiérrez et al., 2017; Idohou et al., 2017a). On the other
hand, soil variables may directly constrain the establishment and
development of species (Aguirre-Gutiérrez et al., 2017), and their
incorporation in SDM, especially at the local scale, which was
the case in the current study, appears to improve the predictive
capacity of models (Hageer et al., 2017; Zuquim et al., 2020).
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FIGURE 1 | Spatial distribution maps of the modelled 15 Vigna CWR taxa in Benin.
Dealing with variable collinearity in SDM is evolving just
like the algorithms themselves, with the use of the Pearson’s
correlation test in ENMTools (Warren et al., 2010) and priori
selection of variables based on the ecological system (Scridel et al.,
2021) as some of the common approaches to reducing potential
multicollinearity. However, Feng et al. (2019) showed that the
exclusion of highly correlated variables does not significantly
influence model performance, especially those built by MaxEnt,
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as the algorithm accounts for redundancy in variables. According
to Mod et al. (2016), neglecting eco-physiological meaningful
predictors could result in incomplete niche quantifications,
thereby limiting the predictive power of SDMs. The authors thus
suggested that the selection of climatic-related variables should
be determined by the environmental conditions of the study site
and the requirements of the target species. Therefore, this study
followed the approach used by Singh et al. (2021) of eliminating
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Benin and converted to Ascii format using SDM Tools for use
in modelling.
variables that consistently contributed less or nothing to the
model for three successive runs. Firstly, two variables, minimum
temperature of the coldest month (BIO 6), and mean temperature
of the coldest quarter (BIO 11), were deemed unsuitable and
were thus removed based on expert knowledge (Vihotogbé et al.,
2021). Thereafter, all the remaining 49 variables were used in
the model pre-assessment (but see Dormann et al., 2013) from
which variables that contributed <5 % to each of the model
explanatory power after every run for three successive runs were
excluded following a Jackknife test (Singh et al., 2021). Finally,
five variables that contributed the most to each of the 15 models
were retained for modelling (Table 1).
Since the area included in the background can influence
MaxEnt model fit (Merow et al., 2013; Pang et al., 2021), and
given that models are more reliable when built at a larger scale
(Barve et al., 2011), as this reduces the risk of niche truncations
(Pang et al., 2021), species occurrence records were thus first
filtered for West Africa, and models trained by projecting the
present-day variables over West Africa. To further improve
the model performance, the maximum number of iterations in
MaxEnt was adjusted to 5,000, the cross-validation method was
used, and the number of replications was increased to 15 (Abrha
et al., 2018). Increasing the number of iterations and replications
provides, respectively, ample time for the model to converge
and run multiple times to develop superlative averaged results
(Merow et al., 2013; Abrha et al., 2018). The “clogclog” was used
as an output format (Favi et al., 2021; Scridel et al., 2021), since it
appears to provide the model output with a stronger theoretical
justification than the logistic transform (Qu et al., 2018) and
seems to give realistic binary predictions of species distributions
(Scridel et al., 2021). The jackknife test was used to determine the
variable contribution to the models.
The model performance was assessed through two widely
used metrics, the threshold-independent Area Under the Curve
(AUC) of the Receiver Operating Characteristic (ROC) curve
and the threshold-dependent True Skill Statistic (TSS) (Fielding
and Bell, 1997; Allouche et al., 2006; Favi et al., 2021; Ratnayake
et al., 2021). The AUC measures the model’s ability to distinguish
between random and background points (AUC = 0.5) with
values ranging from 0 to 1 (Fielding and Bell, 1997; Favi et al.,
2021). AUC values closer to 1 indicate good-performance models,
and a stronger correlation between predictor variables and the
distribution of the target species (Fielding and Bell, 1997). As
such, models were considered acceptable if 0.7 ≤ AUC < 0.8,
good if 0.8 ≤ AUC < 0.9 and excellent if AUC ≥ 0.9 (Ratnayake
et al., 2021). The TSS is a measure of accuracy i.e., the capacity
of the model to detect the true presence (sensitivity) and true
absences (specificity), expressed as the sensitivity plus specificity1 (Allouche et al., 2006). Its values range from −1 to +1, and
like AUC, TSS values closer to 1 indicate good-performance
models (Allouche et al., 2006). Therefore, models were described
as poor (TSS<0.4), acceptable (0.4 ≤ TSS < 0.8), and very good
(TSS > 0.8) (Ratnayake et al., 2021). Response curves were used
to further evaluate and quantify the biological plausibility of
the models since they show the predicted relative occurrence
rate (ROR) of species against the value of a predictor variable
(Merow et al., 2013). The variables were finally clipped to
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Modelling the Current and Future Habitat
Suitability Distribution, and Range
Changes
MaxEnt was used to predict the habitat suitability distribution
for each of the 15 species under the current and future
climate change scenarios using 1,432 occurrence records (range:
14 to 333) and a combination of bioclimatic and edaphic
variables as inputs (Table 1). The same settings as in the
model calibration were used. Habitat suitability maps were
developed following Ramírez-Rodríguez et al. (2021) with
slight modifications. Briefly, output rasters from MaxEnt were
converted into binary layers and exported to QGIS to develop
habitat maps. Binarization of continuous habitats, in spite of its
shortcomings (Santini et al., 2021), is still one of the popular
approaches for delimiting habitats in SDM, quantifying range
changes and building species richness overtime in conservation
studies (Politi et al., 2021; Ramírez-Rodríguez et al., 2021; Singh
et al., 2021; Vihotogbé et al., 2021; Lima et al., 2022). It is
believed that binairization avoids the effects of model over-fitting
(Vihotogbé et al., 2021), and that it makes interpretation of
distribution maps much easier compared with the more liberal
interpretation of a continuous habitat output (Singh et al., 2021).
Using the 10th percentile presence threshold to separate suitable
from unsuitable habitats Ramírez-Rodríguez et al. (2021), all
values above and below this threshold were considered suitable
and unsuitable habitats, respectively. According to Politi et al.
(2021), compared with other percentile thresholds, the 10th
percentile maximises the correct prediction of the percentage of
presences and absences, thereby providing conservative species
distributional ranges.
Subsequently, to calculate range changes between the current
and future habitat distributions, binary-thresholded future
rasters were subtracted from the current rasters (RamírezRodríguez et al., 2021) using the range shift tool in the SDM
Toolbox. This tool classifies output layers into four classes:
expansion in range (absence in current, presence in future),
no occupancy (absence in both current and future), occupancy
or stable (presence in current and future) and contraction in
range (presence in current, absence in future). Accordingly, three
range changes in habitat suitability (stability, expansion and
contraction) were mapped and areal extents were calculated.
Prioritising Areas for in situ Conservation
and Effectiveness of the PA Network
To prioritise areas for in situ conservation, binary raster layers
for the range change of all the 15 species and each of the three
distributions (current, 2055 RCP 4.5 and 2055 RCP 8.5) were
summed up to show overlaps of potential habitat distributions
(Ramírez-Rodríguez et al., 2021). Each suitable distribution was
thereafter reclassified into three classes (low suitability, moderate
suitability, and high suitability) using the SDM Toolbox. Finally,
these were overlaid onto the PA network of Benin to further
estimate the extent to which the PAs would conserve the 15 Vigna
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TABLE 1 | Occurrence records and environmental variables used for modelling the 15 Vigna CWR taxa in Benin.
Species
Occurrence records
Environmental variables used in modelling
Vigna comosa
17
BIO 7
LLDS
BIO 1
BIO 4
Vigna filicaulis
147
BIO 10
BIO 2
PHIHOX_T_M_sd2
EACKCL_T_M_sd2
BIO 12
Vigna frutescens
60
BIO 17
BIO 15
BIO 10
BIO 2
BIO 3
Vigna gracilis
179
BIO 12
BIO 14
BIO 2
BIO 17
BIO 7
Vigna heterophylla
176
BIO 3
BIO 2
BIO 5
BIO 17
BIO 4
Vigna laurentii
44
SNDPPT_T_M_sd1
BIO 14
BIO 12
BIO 2
EMGX_T_M_xd2
Vigna longifolia
31
BIO 1
BIO 3
EACKCL_T_M_sd2
BIO 17
BIO 12
Vigna luteola
77
BIO 7
BIO 17
EXKX_T_M_xd1
BIO 12
BIO 14
Vigna multinervis
45
ORCDRC_T_M_sd1
BIO 14
BIO4
BIO 3
BIO 1
Vigna nigritia
72
BIO 12
BIO 17
BIO 2
SNDPPT_T_M_sd1
EMGX_T_M_xd2
Vigna oblongifolia
38
EMGX_T_M_xd2
BIO 17
BIO 1
SNDPPT_T_M_sd1
BIO 12
Vigna racemosa
333
BIO 3
BIO 2
EACKCL_T_M_sd3
BIO 17
BIO 5
Vigna reticulata
166
BIO 4
BIO 17
EACKCL_T_M_sd3
BIO 3
BIO 5
BIO 12
Vigna unguiculata subsp. baoulensis
33
BIO 3
BIO 17
BIO 1
EACKCL_T_M_sd2
BIO 14
Vigna unguiculata var. spontanea
14
BIO 1
BIO 3
EACKCL_T_M_sd3
BIO 14
SNDPPT_T_M_sd2
Variable key: BIO 1, mean annual temperature (◦ C); BIO 2, mean diurnal range in temperature (◦ C); BIO 3, Isothermality; BIO 4, temperature seasonality (◦ C); BIO 5, max temperature
warmest month (◦ C); BIO 7, annual temperature range (◦ C); BIO 10, mean temperature of the warmest month (◦ C); BIO 12, mean annual precipitation (mm); BIO 14, precipitation of
the driest month (mm); BIO 15, precipitation seasonality (mm); BIO 17, precipitation of the driest quarter (mm); EACKCL_T_M_sd2, exchangeable acidity (KCl) for 10 cm depth (0.05–
0.15 m horizon) (cmol/kg); EACKCL_T_M_sd3, exchangeable acidity (KCl) for 22.5 cm depth (0.15–0.30 m horizon) (cmol/kg); EMGX_T_M_xd2, exchangeable Mg for 20–50 cm depth
(0.20–0.50 m horizon) (cmol/kg); EXKX_T_M_xd1, exchangeable K for 0–20 cm depth (0–0.20 m horizon) (cmol/kg); LLDS, length of the longest season (months); ORCDRC_T_M_sd1,
soil organic carbon for 2.5 cm depth (0–0.03 m horizon) (g/kg); PHIHOX_T_M_sd2, soil pH in H2 O for 10 cm depth (0.05–0.15 m horizon); SNDPPT_T_M_sd1, soil texture fraction sand
(%) for 2.5 cm depth (0–0.05 m horizon); SNDPPT_T_M_sd2, soil texture fraction sand (%) for 10 cm depth (0.05–0.15 m horizon).
CWR taxa. The area of each distribution was calculated using the
extract by mask tool in the SDM Toolbox.
V. luteola, V. nigritia, V. oblongifolia, V. racemosa, V. reticulata,
and V. unguiculata subsp. baoulensis). This was closely followed
by isothermality (seven models: V. heterophylla, V. longifolia,
V. multinervis, V. racemosa, V. reticulata, V. unguiculata subsp.
baoulensis, and V. unguiculata var. spontanea); whilst mean
diurnal range in temperature predicted five models (V. filicaulis,
V. frutescens, V. heterophylla, V. laurentii and V. racemosa).
Also worth noting was the influence of soil variables on some
models. For instance, exchangeable acidity (KCl) for 10 cm
depth (EACKCL_T_M _sd2) was important for V. filicaulis and
V. unguiculata subsp. baoulensis,; while soil texture fraction
sand for 2.5 cm depth (SNDPPT_T_M_sd1), was among the key
variables for V. laurentii, V. nigritia and V. oblongifolia. Another
important result was the influence of the length of the driest
month (LLDS) on V. comosa, where it was the top-most predictor
variable (53.7 %).
However, the Jackknife test of variable importance (not
included here) flagged the mean diurnal range in temperature
as containing the most important information by itself and,
therefore, explaining the gain in six models (V. filicaulis,
V. frutescens, V. heterophylla, V. laurentii, V. oblongifolia,
and V. racemosa). This was followed by isothermality for
four models (V. longifolia, V. multinervis, V. unguiculata
subsp. Baoulensis, and V. unguiculata var. spontanea)
and precipitation of the driest quarter for three models
(V. gracilis, V. luteola, and V. nigritia). On the other hand,
isothermality appeared to have contained the most unique
information that could not be found in all other variables
for nine models (V. frutescens, V. heterophylla, V. longifolia,
V. multinervis, V. oblongifolia, V. racemosa, V. reticulata,
RESULTS
Model Accuracy and Performance
The AUC and TSS values ranged from 0.914 to 0.997 (median =
0.956; mean = 0.957) and 0.617 to 0.876 (median = 0.771; mean
= 0.774), respectively (Figure 2). Likewise, the ROC curves were
away from the random distribution (not shown here), and there
were lower differences between AUC values for training and test
samples for the model corresponding to all the 15 taxa (range
= 0.001–0.025; mean = 0.008), indicating good performance
and high accuracy of the model for generalisation. The models
were thus considered excellent and highly informative to describe
the distribution patterns in the 15 modelled Vigna CWR species
in Benin.
Key Predictor Variables of the 15 Vigna
CWR Taxa in Benin
The modelled species demonstrated differential responses to
different environmental variables. Based on the frequency of the
power of predictive contribution of each variable across all the
15 species, three variables, precipitation of the driest quarter
(BIO 17), isothermality (BIO 3), and mean diurnal range in
temperature (BIO 2), in that order, were found to contribute
the most in explaining the majority of the models (Table 2).
For instance, precipitation of the driest quarter was consistently
found among the dominant three variables for 10 of the 15
models (V. frutescens, V. gracilis, V. heterophylla, V. longifolia,
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FIGURE 2 | Graph of the AUC and TSS values of the modelled 15 Vigna CWR taxa in Benin.
TABLE 2 | The top three dominant environmental factors affecting the distribution of 15 Vigna CWR taxa in Benin.
Species
Dominant environmental variables (% contribution in parentheses)
Vigna comosa
LLDS (53.7)
BIO 4 (19.1)
BIO 7 (11.2)
Vigna filicaulis
BIO 2 (45)
EACKCL_T_M_sd2 (14.7)
BIO 10 (13.6)
Vigna frutescens
BIO 15 (31.4)
BIO 2 (24.2)
BIO 17 (21.2)
Vigna gracilis
BIO 14 (39.4)
BIO 17 (25.2)
BIO 12 (15.6)
Vigna heterophylla
BIO 2 (35.7)
BIO 17 (26.6)
BIO 3 (15)
Vigna laurentii
BIO 14 (38.3)
BIO 2 (19)
SNDPPT_T_M_sd1 (16.8)
Vigna longifolia
BIO 3 (33.3)
BIO 17 (30)
BIO 1 (15.8)
Vigna luteola
BIO 17 (40.3)
BIO 12 (20.8)
BIO 7 (14.2)
Vigna multinervis
BIO 14 (46.3)
BIO 3 (27.6)
ORCDRC_T_M_sd1 (12.4)
Vigna nigritia
BIO 17 (48.5)
SNDPPT_T_M_sd1 (15.2)
BIO 12 (14)
Vigna oblongifolia
BIO 17 (45)
SNDPPT_T_M_sd1 (20.1)
EMGX_T_M_sd2 (18.8)
Vigna racemosa
BIO 2 (26.7)
BIO 17 (21)
BIO 3 (18.2)
Vigna reticulata
BIO 17 (35.6)
BIO 3 (24.4)
BIO 4 (19.5)
Vigna unguiculata subsp. baoulensis
BIO 17 (55.4)
EACKCL_T_M_sd2 (18.3)
BIO 3 (13.1)
Vigna unguiculata var. spontanea
BIO 3 (46.1)
BIO 14 (24.3)
BIO 1 (19.3)
isothermality (BIO 3), with an average range of 64.3 to 80.4;
and mean diurnal range in temperature (BIO 2), with an average
range of 7.4–12.2◦ C. For the edaphic-related factors, suitable
habitats were likely to be found in sites having a wide range of
sandy soils, with an average soil texture fraction sand for 2.5 cm
depth (SNDPPT_T_M_sd1) ranging between 17 and 90%, and
somewhat acid soils, with an average exchangeable acidity (KCl)
for 10 cm depth (EACKCL_T_M_sd2) below 0.6 cmolc/kg.
V. unguiculata subsp. Baoulensis, and V. unguiculata
var. spontanea).
Suitable ranges of the three most important environmental
variables for each model are represented by their respective
response curves (Supplementary Figure 3). Generally, suitable
habitats for the majority of the models were suggested highly
likely to be in areas characterised by precipitation of the
driest quarter (BIO 17) ranging between 5.5 and 133.5 mm;
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Predicted Habitat Suitability Distribution
Range of the 15 Vigna CWR Taxa Under
Current and Future Climate
V. filicaulis registered a small retraction under RCP 8.5 compared
with RCP 4.5.
Priority Conservation Areas for the 15
Vigna CWR Taxa and Effectiveness of the
PA Network
Habitat Suitability Distribution Range Under the
Current Climate
Nearly half of the land surface area in Benin was predicted
to be presently a suitable habitat for the 15 Vigna CWR taxa,
although uneven patterns were observed, with some species
showing striking gaps (patches), while others were narrowly or
widely distributed (Figure 3). For instance, V. racemosa was
predicted to have the largest potentially suitable area (103, 817.6
km2 ), representing nearly 88.20% of the total surface area of
Benin. This was closely followed by V. heterophylla (91, 664
km2 ), representing about 77.88% of the land area. The habitats
of these two models were predicted to be mostly in the Sudanian
and Sudano-Guinean Zones. On the contrary, V. laurentii had
the least potentially suitable habitat distribution range (1,911
km2 ) and was closely followed by V. oblongifolia (3,735 km2 ),
representing ∼1.62 and 3.17% of the total land, respectively. Both
of these species were predicted to be localised at the southern tip
of the Guinean Zone (Figure 3).
Figure 5 represents the results from the predicted potential
distribution of the suitable habitat richness of the 15 modelled
taxa under both the current and future climatic conditions,
taking into account the PA network in Benin. Under the current
conditions, the largest portion of the highly suitable area was
predicted to be in the Sudano-Guinean Zone, with a few patches
in the Sudanian and Guinean Zones. The current potential
suitable habitat accounted for 39,647.88 km2 , representing
∼33.69% of the land surface area of Benin. Out of this area, an
estimated 3,931.72 km2 (9.92%) fell in the existing PA network.
As for the future climate change scenarios, only about
27,107.87 km2 , representing about 23.03% of the land surface
area was predicted potentially suitable under the moderate
emission scenario (RCP 4.5). This represented a substantial loss
(∼31.63%) relative to the present conditions, mostly in the
northerly located patches. Out of this suitable area, 2,718.18
km2 (10.03% of the land surface area), was predicted to fall
in the existing PA network. A somewhat different situation
was projected under the severe climate change scenario (RCP
8.5). Here, the suitable habitat appeared to expand northwards,
while the southern portion became less favourable. An estimated
48,933.01 km2 (∼41.57%) of the total land surface area was
predicted to become potentially highly suitable, representing an
increase of about 23.42% relative to the present conditions. From
this area, about 5,322.57 km2 (∼10.88%) of the land surface area
of Benin, was predicted to fall under the existing PA network.
Habitat Suitability Distribution Range Under Future
Climate
Climate change was predicted to positively or negatively affect
the future ranges of habitat suitability distribution of the 15
Vigna CWR taxa (Figure 3) with their per cent changes shown
in Figure 4. Nearly half of the species were predicted to
potentially lose their suitable habitats by 5–36% under moderate
conditions (RCP 4.5) and 8–40% under severe conditions (RCP
8.5). A substantial contraction (35.57–39.53%) was registered
for V. multinervis, with zero expansion under both climate
change scenarios. Interestingly, a zero contraction was forecast
for V. frutescens under both scenarios. On the other hand, five
models including V. heterophylla, V. laurentii, and V. oblongifolia
had insignificant retraction (<1.5%) in their habitat distribution
ranges under RCP 4.5. The same trend was observed for these
models under RCP 8.5, thus making V. frutescens, V. heterophylla,
V. laurentii, V. nigritia, V. racemosa, and V. reticulata among
the models whose potentially suitable habitats were predicted to
remain the most stable under future climatic conditions. It was
further observed that about one-third of the models including
V. comosa and V. longifolia tended to expand their suitable areas
towards the north of Benin i.e., higher latitude and altitude, while
another one third including V. frutescens and V. unguiculata var.
spontanea showed a tendency to expand to the other directions.
As was expected, when the two future climate change
scenarios were compared, slightly higher contractions were
predicted under severe climatic conditions (RCP 8.5: mean
= 10.87%; median = 2.4%) than under moderate conditions
(RCP 4.5: mean = 6.94%; median = 4.73%), although, this
was also accompanied by a slightly higher expansion under
RCP 8.5 (mean = 14.96%; median = 6.57 %). The models
of V. unguiculata subsp. baoulensis and V. unguiculata var.
spontanea showed the most marked changes in contractions
between the two scenarios. On the contrary, the model of
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DISCUSSION
Key Environmental Factors Affecting the
Distribution of the 15 Vigna CWR Taxa in
Benin
The results from this study showed variations in taxa response
to different environmental factors, thus supporting the first
hypothesis that different Vigna CWR species would respond
differentially to a different suite of environmental variables.
Jarvis et al. (2008) reported a similar tendency among Vigna
species. Several other studies have also reported similar generic
tendencies such as in Cucurbita in Mexico (Lira et al., 2009),
Piper and Oryza in Sri Lanka (Ratnayake et al., 2021), Vaccinium
in the Netherlands (van Treuren et al., 2020) and Adansonia in
Madagascar (Tagliari et al., 2021). Differential response of species
to environmental factors underscores the need for speciesspecific studies on climate change effects (Jarvis et al., 2008).
While climate-related factors are generally the key
environmental factors influencing species distribution (Jarvis
et al., 2008; Amissah et al., 2014; Lompo et al., 2021), several
studies have also shown that habitat distribution of species,
especially at a local scale, is influenced by a combination of
climatic and edaphic factors (Hageer et al., 2017; Idohou et al.,
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FIGURE 3 | Continued
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FIGURE 3 | (A–C) Predicted distribution maps of the 15 Vigna CWR taxa in Benin under the current and future climates.
The importance of precipitation of the driest quarter (BIO
17) to Vigna species as found in this study may relate to
their reproductive fitness and adaptation to arid- and semi-arid
areas. Given that reproduction and maturity in the modelled
species largely coincide with the driest quarter, precipitation
during this period might be important for gamete formation
and viability, pod-set, and pod-filling (Nadeem et al., 2019). It
may also play a role in seed dormancy (Smýkal et al., 2014).
Precipitation of the driest quarter was also found to be one
of the most important predictor variables for four legume
species (Adenocarpus mannii, Afzelia bella, Afzelia bipindensis,
and Baphia nitida) from the Nigeria—Cameroon border in
West Africa (Salako et al., 2021) and for A. digitata in Benin
(Assogba et al., 2022). Except for species that largely thrive in wet
areas including V. laurentii, V. luteola, and V. multinervis, most
Vigna CWR species are renowned for their persistence in arid
areas, partly owing to their absorptive root systems or tuberous
rootstocks (Maxted et al., 2004; Iseki et al., 2018). This might
explain why the majority of species in this study demonstrated
less demand for heavy precipitation. In Tibet in China, Xin
et al. (2021) attributed the absence of excessive demand for
precipitation in Sophora moorcroftiana and the distribution of
2017b; Zuquim et al., 2020; Assogba et al., 2022; Liu et al.,
2022). The results from this study were consistent with these
observations. The influence of edaphic factors appears to be
particularly critical for narrowly distributed or understorey
species (e.g., Hageer et al., 2017; Idohou et al., 2017b; Wang
et al., 2019; Roe, 2020), and may be related to specialised habitat
requirements by species that constrain their distribution (Corlett
and Tomlinson, 2020). For instance, Idohou et al. (2017b)
observed that the potential cultivable areas for the relatively
localised and understorey palms such as Raphia hookeri and
R. vinifera in Benin were more characterised by soil factors
compared with overstorey palm species. Similarly, soil factors
were the most discriminating factors in the distribution of an
endemic orchid Spiranthes parksii (Navasota ladies’ tresses) in the
USA (Wang et al., 2019). Indeed, disregarding edaphic factors
in SDM may overestimate future habitat adaptability of many
plant species (Bertrand et al., 2012; Zuquim et al., 2020, but see
Feng et al., 2020). Soil texture, for instance, is important for plant
root development, especially for the relatively high-biomass
rooted plants like most Vigna CWR species (Iseki et al., 2018),
while exchangeable acidity (KCL) is crucial for soil nutrition and
texture balance (Liebenberg et al., 2020).
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FIGURE 4 | Graph of per cent changes in the suitable habitat distribution ranges of the 15 Vigna CWR taxa in Benin under current and future climates.
However, it is also becoming apparent that some species
may shift their distributions towards other directions (Tagliari
et al., 2021; Balima et al., 2022). This was also the case
in this study with some models like those of V. frutescens,
V. heterophylla, V. racemosa, V. reticulata, and V. unguiculata
var. spontanea that appeared to shift their suitable habitats
eastwards (Figure 3).
The predicted patterns in the expansions and contractions in
the current study might relate to an increase in the number of
suitable habitat patches and a reduction in the sizes of those
patches, respectively, as was reported by Jarvis et al. (2008).
The discrepancies between this study and Jarvis et al. (2008),
including the higher contraction rate of over 50% as observed by
Jarvis et al. (2008), might be due to the differences in the sources
of environmental data, types of species studied, the scale of the
study, and the methods used. For example, Jarvis et al. (2008) did
not incorporate edaphic variables in their study. Bertrand et al.
(2012) suggested that edaphic factors may increase the tolerance
of a species in confronting climate constraints, which could have
been the case in the current study.
Climatic models have predicted that global climate change will
lead to increased night temperatures and prolonged droughts,
with West Africa being one of the most affected regions
(IPCC, 2021). As a result, lower isothermality values and
reduced precipitation of the driest quarter around the 2055s
are anticipated than currently observed (Platts et al., 2015). The
predicted contractions and expansions in the suitable habitats in
the mid-Century observed in the studied species might partly
reflect these changes.
The suggested minimum negative effects of climate change
on the habitat suitability distribution of the seven species
this species in drought-prone areas to its strong absorptive
root system.
On the other hand, large isothermality against the affinity
for low temperatures as found in this study may, according to
Zhang et al. (2018), suggest that the species use the relatively high
temperatures during the day for photosynthesis while reserving
energy at night through decreased respiration when temperatures
are relatively low. Isothermality was suggested to be the second
most important predictor factor for barbed goatgrass (Aegilops
triuncialis) in Iran (Mousavi Kouhi and Erfanian, 2020), and its
importance in shaping the distribution of plant taxa in the tropics
has been widely reported (Amissah et al., 2014; Xin et al., 2021;
Zuza et al., 2021).
These results provide an understanding of the key
environmental factors that are shaping the distribution of
wild Vigna species in Benin, and how changes in these factors
as a result of future climate might affect the distribution of the
studied taxa. This knowledge is critical in effective planning for
adaptive management approaches of wild Vigna taxa in the face
of climate change (Iriondo et al., 2021).
Habitat Suitability Distribution Patterns in
the 15 Vigna CWR Taxa
Several studies have demonstrated that climate change is causing
species to shift their climatically suitable habitats towards higher
latitudes and elevations (Aguirre-Gutiérrez et al., 2017; Lenoir
et al., 2020). The observed northerly expansion in suitable
habitats found in this study was thus consistent with these
global findings. This partly confirmed the second hypothesis
that there would be an increase in the northern habitat.
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FIGURE 5 | Predicted hotspot distribution maps of potentially suitable habitats of the 15 Vigna CWR taxa in Benin under the current and future climates.
may aid in the promotion of cultivation of populations of some
taxa to enhance their direct use by local communities so as to
diversify the quantity and quality of the food basket, while acting
as an incentive for the conservation of wild populations.
(V. frutescens, V. heterophylla, V. laurentii, V. nigritia,
V. oblongifolia, V. racemosa, and V. reticulata) (Figures 3, 4)
may be explained by several factors. First, the stability in the
key predictor variables, for species like V. frutescens which
appeared to be spatially confined to the north of Benin (Figure 1).
Second, the relatively higher habitat heterogeneity that may
provide a wider range of microhabitat options (Jarvis et al.,
2008; Foden et al., 2019) for species such as V. heterophylla,
V. racemosa, and V. reticulata. Indeed, these three species were
among the most spatially distributed, spanning the different ecogeographical zones of Benin (Figure 1) and were predicted to
have comparably wider habitat distributions under the present
and future conditions (Figure 3). According to Hirst et al. (2017),
common taxa as these within a clade are expected to perform
relatively well across a wider range of novel environmental
conditions than their rarer relatives. Lastly, it might be due the
constraining influence of edaphic factors (Bertrand et al., 2012),
especially on the narrowly-distributed species like V. laurentii,
V. nigritia, and V. oblongifolia. These three species were among
the least spatially distributed (Figure 1) and appear to have
specialised habitats, with a predilection for marshy or seasonally
inundated areas that have poor soils (Supplementary Table 1).
The habitat distribution maps generated in this study provide
insights into potentially suitable habitats for the 15 Vigna taxa
which may help in planning for taxa-targeted conservation
measures including in situ and ex situ approaches. Further, they
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Priority Conservation Areas for the 15
Vigna CWR Taxa and Effectiveness of PAs
The area that was predicted to remain stable under future
climatic conditions may be considered for conservation (Iriondo
et al., 2021), since stability suggests the existence of favourable
climatic conditions that may provide refugia for genetic diversity
of species (Cobben et al., 2011). Given that the creation of
new PAs solely for the conservation of CWRs may have huge
cost implications (Maxted et al., 2016), and may also escalate
the existing human-conservation conflicts over land (Tranquilli
et al., 2014), the existing PA network together with its bordering
landscape in the predicted stable sites is thus recommended for
the conservation of Vigna species.
The suggested PAs in this regard include (not in the order of
importance) Oueme Superior, Wari-Maro, Mont Kouffe, Agoua,
Savalou, Oueme Boukou, and Dogo Forests [in the SudanoGuinean Zone]; Pendjari National Park [Sudanian Zone]; and
Lama, Ahozou (Pahou) and Drabo Gbo Forests [Guinean
Zone]. However, it has been suggested that aligning these
sites with hotspots of other plant species would help optimise
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Manda et al.
Although MaxEnt has proved to be a robust SDM tool in
delineating habitat suitability maps for many taxa under the
changing climate (e.g., Aguirre-Gutiérrez et al., 2017; Phillips
et al., 2017; Ratnayake et al., 2021), the use of a single SDM
algorithm does not provide for comparisons, and therefore
accuracy in predictions may be put into question (Zuza et al.,
2021). Future studies may consider a combination of models.
Another weakness of this study could be the use of publicly
available occurrence data from the herbarium and/or online
databases. It has been posited that such data may not randomly
sample the true occurrences of species since such data is often
biassed towards easily accessible areas such as roads and human
settlements (Barlow et al., 2021). According to Barlow et al.
(2021), such a spatial bias may present an over-representation
of environmental conditions associated with regions of higher
sampling effort. While this shortcoming is recognised, it should
also be pointed out that scientific evidence is growing indicating
that CWRs are often associated with such anthropogenically
disturbed areas (Jarvis et al., 2015; Iriondo et al., 2021). As such,
given that sites that have been accessible to species for a long time
are ideal for species distribution modelling (Barve et al., 2011),
it might as well be argued that data from such disturbed sites
may provide a true reflection of the potential habitat distribution
ranges of these taxa.
Nevertheless, this study has identified potential areas for
prioritising conservation efforts for the 15 Vigna taxa in Benin.
Further, it has raised a red flag for species that may need
more attention, considering the predicted vulnerability of
their potentially suitable habitats. The taxa include V. comosa,
V. filicaulis, V. multinervis, V. unguiculata subsp. baoulensis,
and V. unguiculata var. spontanea. Likewise, close attention
ought to be paid to the predicted locally narrowly-distributed
species, V. laurentii and V. oblongifolia. Although the suitable
habitats for these two species were suggested to be negligibly
affected by climate change, the species may fail to move
and thus become predisposed to intense anthropogenic
pressure (Qu et al., 2018; Corlett and Tomlinson, 2020; Spicer
et al., 2021). Especially worrying about the two models of
V. laurentii and V. oblongifolia is that their distribution ranges
are localised at the southern tip of Benin, where land-use
change is the greatest due to increasing urbanisation and
agriculture (Guidigan et al., 2019). Moreover, V. laurentii
is classified as Endangered on the IUCN Red List of species
with a declining population (McFarlane and Maxted, 2019).
Therefore, population monitoring of the seven species ought
to be considered to inform the development of appropriate
area-and species-based measures. Equally important are
complimentary ex situ collections for long-term conservation of
these taxa.
conservation resources (Maxted et al., 2016; Vincent et al.,
2022). In this respect, the relative high species richness and
endemism in Pendjari National Park (Akoegninou et al., 2006;
Neuenschwander et al., 2011) and its high legal protection
status (Neuenschwander et al., 2011) would probably make
conservation efforts within and around Pendjari National Park
more capturing, less expensive, and practically manageable.
For effective conservation efforts in the suggested
conservation sites, the following are recommended:
species population monitoring, floristic inventories, habitat
characterisation, genetic diversity, and ethnobotanical studies
(Iriondo et al., 2008; Jarvis et al., 2015; FAO, 2017). Equally
important are studies on the reproductive ecology, seedling
recruitment, seed longevity, dispersal mechanisms, and
responses to abiotic and biotic stresses of Vigna CWR species.
Given that nearly 40 of the estimated 63 Africa Vigna species
(54 out of an estimated 105 Vigna spp. globally) have presently
been assessed using the IUCN Red List Categories and Criteria
(https://www.iucnredlist.org) (accessed 02 June 2021), a risk
assessment of the remaining 23 African Vigna spp. (51 spp.
globally) is also recommended.
The favourable conditions in disturbed areas outside PAs
where most CWRs have often persisted for long periods (Jarvis
et al., 2015), coupled with slight increases in potentially suitable
habitats in non-PAs (Figure 3) appeared to have rendered the PA
network cover a comparably small habitat suitability area for the
modelled species. This finding supported the third hypothesis
that the existing PA network in Benin would be less effective in
conserving Vigna CWR diversity than the non-PAs. This poses
a threat to the conservation of these species, considering the
increasing anthropogenic pressure outside the PAs (Guidigan
et al., 2019), which, if taken into account, would substantially
reduce the predicted suitable habitat (Riordan and Rundel, 2014).
These results also corroborate previous reports of increased
mismatches between climatically suitable habitats for CWRs and
existing PAs, such as in Ethiopia (Davis et al., 2019), Mexico (Lira
et al., 2009), the Netherlands (Aguirre-Gutiérrez et al., 2017),
and Sri Lanka (Ratnayake et al., 2021). These results, therefore,
buttress the calls for effective conservation of CWRs both within
and outside PAs, using an integration of multiple approaches
(Riordan and Nabhan, 2019; Iriondo et al., 2021) that takes
into account “other effective area-based conservation measures”
(OECMs) (IUCN, 2019), since PAs may not be the only best
option (Goettsch et al., 2021). To achieve this, and for long-term
monitoring and active management that involves participation of
various stakeholders including local communities, a wide range
of guidelines have long been made available (e.g., Iriondo et al.,
2008, 2021; Maxted and Kell, 2009; Hunter and Heywood, 2011;
FAO, 2017; IUCN, 2019).
The main caveat of this study is that the models represent
only potentially suitable habitats for the modelled species.
Distribution, access and persistence may be controlled by many
other factors including anthropogenic such as land use, biotic
interactions such as pollination, parasitism and diseases, and
dispersal (Feng et al., 2020; Spicer et al., 2021). This study did not
consider these factors, and, therefore, caution should be exercised
when interpreting these results.
Frontiers in Conservation Science | www.frontiersin.org
CONCLUSIONS
Understanding the habitat suitability distribution range of a
species under the changing climate is crucial for its effective
conservation planning. Using MaxEnt, occurrence records of 15
Vigna CWR taxa and a combination of climatic and edaphic
factors in Benin for the 2055-time horizon, this study for the first
14
June 2022 | Volume 3 | Article 870041
Climate Change and Wild Vigna
Manda et al.
time evaluated the effects of climate change on the distribution of
multiple Vigna CWR taxa at a local scale. The model showed that
climatic factors that shape the distribution of species are likely
to change with future climate, consequently resulting in negative
or positive changes in the distribution ranges of potentially
suitable habitats of the species. The study concludes that in situ
conservation of CWRs using the existing PA network alone may
not be the only best option. Therefore, to effectively conserve
Vigna CWR diversity, an integration of multiple in situ and
ex situ conservation approaches (Iriondo et al., 2021) taking
into account “other effective area-based conservation measures”
(OECMs) (IUCN, 2019; Iriondo et al., 2021) is recommended to
inform appropriate area-based and species-based conservation
actions (Heywood, 2019). This study provides a crucial step
towards the development of sustainable conservation strategies
for Vigna CWRs in Benin and West Africa. It also provides
a stepping stone for generating hypotheses about mechanistic
links between Vigna CWR taxa and their environment (Kearney,
2006).
ran the models,
the authors read
for publication.
FUNDING
This study was supported by the Regional Universities
Forum for Capacity Building in Agriculture (RUFORUM)
through the Intra-Africa—Partnership for Training Regional
academic exchange for enhanced skills in fragile ecosystems
management in Africa (REFORM) scholarship (2018-2022).
RI acknowledges support from the Rufford Foundation
(Grant 31042-D) which provided a foundation for the
current project.
ACKNOWLEDGMENTS
The authors are grateful to the funders for providing a
Ph.D., scholarship to LM. Messrs. Gafarou Agounde and
Medard Kafoutchoni for helping with model running and
cartographic work. The invaluable comments made by reviewers
on the draft manuscript. Otherwise, omissions and errors are
our responsibility.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included
in the article/Supplementary Material. Further inquiries can be
directed to the corresponding author.
SUPPLEMENTARY MATERIAL
AUTHOR CONTRIBUTIONS
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fcosc.
2022.870041/full#supplementary-material
AEA and RI conceived the idea and together with
CA supervised the work. LM collected the data,
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