Environmental variation predicts patterns of phenotypic and genomic
variation in an African tropical forest frog
Running title: Mapping environmentally-associated variation in a
tropical forest frog
Courtney A. Miller1*, Geraud Canis Tasse
Taboue2,3, Eric B. Fokam2, Katy
Morgan1, Ying Zhen4, Ryan J.
Harrigan4, Vinh Le Underwood4,
Kristen Ruegg4, Paul R. Sesink
Clee5, Stephan Ntie6, Patrick
Mickala6, Jean Francois Mboumba6,
Trevon Fuller4, Breda M. Zimkus7,
Thomas B. Smith4,8, Nicola M.
Anthony1
1. Department of Biological Sciences, University of New Orleans, New
Orleans, LA, USA
2. Department of Zoology and Animal Physiology, University of Buea,
Buea, Cameroon
3. Institute of Geological and Mining Research, Yaoundé, Cameroon
4. Institute of Environment and Sustainability, University of
California, Los Angeles, CA, USA
5. Department of Biology, Drexel University, Philadelphia, Pennsylvania,
USA
6. Department of Biology, University of Science and Technology of
Masuku, Franceville, Gabon
7. Museum of Comparative Zoology, Harvard University, Cambridge, MA, USA
8. Department of Ecology and Evolutionary Biology, University of
California, Los Angeles, CA, USA
ABSTRACT
Central African rainforests are predicted to be disproportionately
affected by future climate change. How species will cope with these
changes is unclear, but rapid environmental changes will likely impose
strong selection pressures. Here we examined environmental drivers of
phenotypic and genomic variation in the central African puddle frog
(Phrynobatrachus auritus ) to identify areas of elevated
environmentally-associated turnover where populations may have the
greatest capacity to adapt. We also compared current and future climate
models to pinpoint areas of high genomic vulnerability where allele
frequencies will have to shift the most in order to keep pace with
future climate change. Analyses of body size, relative leg length, and
head shape suggest that seasonal aspects of temperature and
precipitation significantly influence phenotypic variation, whereas
geographic distance and precipitation seasonality are the most important
drivers of SNP allele frequency variation. However, neither landscape
barriers nor the effects of past Pleistocene refugia had any influence
on genomic differentiation. Most phenotypic and genomic differentiation
coincided with key ecological gradients across the forest-savanna
ecotone, montane areas and a coastal to interior rainfall gradient.
Areas of greatest vulnerability were found in the lower Sanaga basin and
southeastern region of Cameroon. In contrast with past conservation
efforts that have focused on hotspots of species richness or endemism,
our findings highlight the importance of preserving environmentally
heterogeneous landscapes to preserve putatively adaptive variation and
ongoing evolutionary processes in the face of climate change.
KEYWORDS: central Africa, amphibians, RAD-seq, environmental gradients,
genomic vulnerability, climate change
1. INTRODUCTION
The tropical forests of the Congo Basin and Gulf of Guinea represent one
of the most biologically diverse regions in the world. This region ranks
third in plant, mammal, bird, and amphibian species richness after the
Amazon and New Guinea (Mittermeier et al., 2003). With respect to
amphibians, the Cameroonian highlands are recognized as one of the
world’s most important biodiversity hotspots (Amiet, 2008; Gvoždík et
al., 2020; Herrmann et al., 2005). Several hypotheses have been advanced
to explain the high biodiversity in this region. Previous
phylogeographic studies have shown that past Pleistocene refugia shaped
population structure in several central African rainforest species,
providing support for their role as potential engines of diversification
(Anthony et al., 2007; Born et al., 2011; Murienne et al., 2013; Nicolas
et al., 2011; Plana, 2004; Quérouil et al., 2003). Alternatively, the
riverine barrier hypothesis has argued that rivers could have led to the
isolation and diversification of tropical forest species (Colyn et al.,
1991; Wallace, 1854). Support for this hypothesis has been found in
primates (Anthony et al., 2007; Mitchell et al., 2015; Telfer et al.,
2003), birds (Aleixo, 2004) and rodents (Nicolas et al., 2011). In
contrast, far less attention has been paid to the impact of historical
barriers to gene flow or the effects of environmental variation (i.e.
precipitation, temperature, vegetation density, etc.) on patterns of
amphibian diversification in this region.
Several general hypotheses have been advanced to explain body size
variation in amphibians. First, intra-specific amphibian body size has
been shown to increase with decreasing temperatures, consistent with the
heat balance hypothesis which predicts a negative relationship between
body size and measures of energy availability (Boaratti & Da Silva,
2015; Ficetola et al., 2010; Rivas et al., 2018), although this has been
found to not always be the case (Goldberg et al., 2018; Guo et al.,
2019). Second, several studies have shown that larger bodied animals are
found in drier areas (Goldberg et al., 2018; Guo et al., 2019;
Olalla-Tárraga et al., 2009) providing support for the water
availability hypothesis which predicts that larger bodied animals are
less prone to desiccation due to reduced surface-to-volume ratio. In
contrast, the converse water availability hypothesis argues that body
size is positively related to water availability such that larger
animals will be found in areas with greater precipitation (Zug et al.,
2001). Finally, the primary productivity hypothesis posits that larger
body sizes will be found in areas with greater resource availability
(Olalla-Tárraga & Rodríguez, 2007). In support of this hypothesis,
Ficetola et al. (2010) found that body size increased in areas of higher
primary productivity suggesting that less productive areas may have
insufficient food supplies to support larger body sizes. However, aside
from a study by (Charles et al. (2018) on the African leaf folding frogAfrixalus paradorsalis , few studies have addressed morphological
variation in central African tropical frogs.
Environmental variation can act as a strong agent of diversifying
selection, particularly in areas of high environmental heterogeneity
(Endler, 1973), such as that observed across ecotones (Freedman et al.,
2010; Smith et al., 1997; Termignoni-García et al., 2017) or across
different levels of elevation (Thomassen et al., 2011). In central
Africa, environmental variation has been shown to explain patterns of
genetic differentiation in olive sunbirds (Smith et al., 2011), little
greenbuls (Smith et al., 1997; Zhen et al., 2017), skinks (Freedman et
al., 2010), chimpanzees (Mitchell et al., 2015), forest antelope (Ntie
et al., 2017), soft-furred mice (Morgan et al., 2020), and reed frogs
(Bell et al., 2017). These heterogeneous environments may capture
ecological and evolutionary processes that are fundamental to
maintaining and generating biological diversity (Moritz et al., 2000).
One major challenge is being able to effectively partition the effects
of isolation by environment (IBE) from other potential drivers of
population differentiation, namely: isolation by distance (IBD) and
isolation by resistance due to landscape barriers (IBB). Advances in
landscape genomics can be used to simultaneously assess the relative
importance of competing ecological and historical drivers on genomic
differentiation (Manthey & Moyle, 2015; Termignoni-García et al.,
2017). Specifically, Fitzpatrick and Keller (2015) have shown that
Generalized Dissimilarity Modelling (GDM; Ferrier et al., 2007) and
Gradient Forests (GF; Ellis et al., 2012) can be powerful tools for
analyzing gene–environment associations at the landscape level. Under a
model of IBE, genetic differentiation increases with environmental
differences, independent of geographic distance (Shafer & Wolf, 2013;
Wang & Bradburd, 2014). In contrast, under a model of IBD, genetic
differentiation is predicted to increase as a function of geographic
distance whereas IBB is driven by landscape barriers to animal or plant
dispersal (Balkenhol et al., 2017; Cushman & Schwartz, 2006).
Resistance distances due to barriers between populations can be based on
landscape features that may influence gene flow, such as habitat type
(Tucker et al., 2017), physical barriers such as rivers (Mitchell et
al., 2015) or the effects of past refugia (Ntie et al., 2017), which
hereafter will be referred to as isolation by Pleistocene refugia (IBP).
In many of these cases, circuit theory is used to incorporate IBE and
IBB into models of population connectivity and identify which variables
are the most important predictors of gene flow (McRae & Beier, 2007).
Central Africa faces a variety of threats from human activities and is
especially vulnerable to climate change (Abernethy et al., 2013; James
et al., 2013; Laporte et al., 2007; Oates et al., 2004). Temperatures
are expected to rise along with potential shifts in rainfall patterns,
including more intense dry seasons that could result in forest retreat
(James et al., 2013). Species in this region, if they are to survive,
would therefore be forced to respond to climate change either through
dispersal, evolutionary adaptation or phenotypic plasticity (Davis et
al., 2005; Holt, 1990). Given the threat that climate change poses to
many species, there is now an increasing need to identify current and
historical drivers of evolutionary diversification and recognize key
areas for future conservation where species capacity to adapt is
greatest (Anthony et al., 2015). Mapping landscape-level predictions of
environmentally-associated genomic variation under both current and
projected future environments can shed light on both the ability of
populations to persist in their current state as well as their future
capacity to respond to change through evolutionary adaptation
(Gunderson, 2000; Sgro et al., 2010; Thrush et al., 2009). In this
regard, the “genomic vulnerability” (Bay et al., 2018; Ruegg et al.,
2018) has been used as a measure of the degree of “mismatch”, or
“offset”, between current and future projections of
environmentally-associated genomic variation and can be used as a proxy
for population vulnerability to environmental change (Ruegg et al.,
2018). Genomic vulnerability estimates how strongly allele frequencies
would have to change to keep track with the environmental changes
predicted to occur at a certain location. Thus, the locations with the
greatest vulnerability are those with the strongest predicted changes in
allele frequencies, since these are the places where adaptation will
have to keep up with environmental change the most.
In the present study, we used a combination of statistical methods to
determine the potential drivers of phenotypic and genomic
diversification in the widespread puddle frog, Phrynobatrachus
auritus across its range in the west Central African countries of
Cameroon, Equatorial Guinea, and Gabon. We used geospatial modeling to
map patterns of genomic turnover (i.e. the change in allele frequencies
with geographic distance) and predict areas of elevated genomic
vulnerability. P. auritus serves as an ideal model for examining
the effects of environmental heterogeneity because it occurs in a
variety of forest types (Zimkus & Schick, 2010)and occupies a wide
range of environmental conditions. Findings from these methods were then
used to address the following hypotheses: 1) Temperature and
precipitation are the most important drivers of morphological
differentiation 2) IBE will influence genomic differentiation more than
IBB or IBP 3) Areas of greatest environmentally-associated genomic
turnover are associated with strong environmental gradients across the
landscape 4) Patterns of environmentally-associated genomic variation
reflect those observed for phenotypic variation 5) Genomic vulnerability
will be highest in areas expected to undergo the greatest environmental
change.
2. MATERIALS AND METHODS