Latitudinal Gradients in Climatic Niche Evolution
by
Adam Matthew Lawson
A thesis submitted in conformity with the requirements for the degree of Master of Science
Department of Ecology and Evolutionary Biology University of Toronto
© Copyright by Adam Matthew Lawson 2014
ii
Latitudinal Gradients in Climatic Niche Evolution
Adam Matthew Lawson
Master of Science
Department of Ecology and Evolutionary Biology
University of Toronto
2014
Abstract
Either tropical niche divergence or tropical niche conservatism could drive the latitudinal
diversity gradient. Greater niche divergence in the tropics could accelerate reproductive isolation
leading to more rapid species formation. Alternatively, latitudinal asymmetry in niche
conservatism, whereby tropical species are more conserved than high latitude species, could
promote more dispersal in to than out of the tropics, leading to greater tropical richness. Here I
test whether rates of climatic niche evolution vary across the latitudinal gradient for 164 closely
related pairs of species. Using the evolutionary ages at which sister species diverge, and the
niche divergence between them, I applied Brownian motion models to test whether rates of
climatic niche evolution varied with latitude. My results indicate that climatic niche conservatism
is strongest in the tropics. This suggests that the latitudinal diversity gradient is driven by the
inability of tropical to adapt to temperate climates and colonize non-tropical latitudes.
iii
Acknowledgments
I would like to thank Jason Weir for all of his help and guidance during my time as a graduate
student under his supervision.
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Table of Contents
Chapter 1
Latitudinal Gradients in Climatic Niche Evolution
pg. 1 - 12
1 Introduction: pg. 1 - 3
2 Methods: pg. 4 - 7
2.1 Data Collection: pg. 4 - 5
2.2 Data Analysis Evolutionary Rate: pg. 5 - 7
2.3 Data Analysis Climatic Specialization: pg. 7
3 Results: pg. 7 - 8
4 Discussion: pg. 9 - 12
Figures and Tables: pg. 13 - 27
References Cited: pg. 28 - 32
v
List of Tables
Table 1 pg. 13
vi
List of Figures
Figure 1 pg. 14
Figure 2 pg. 15
Figure 3 pg. 16
Figure 4 pg. 17 - 27
1
Chapter 1
Latitudinal Gradients in Climatic Niche Evolution
1 Introduction
The existence of the latitudinal diversity gradient is widely accepted, but its cause remains
unknown despite decades of analysis (Mittlebach et al. 2007). Hypotheses for the diversity
gradient generally focus on evolutionary or ecological explanations (Mittlebach et al. 2007).
Evolutionary explanations suggest that diversification rates are higher in the tropics, or that the
tropics have had more time in which diversification could occur (Fischer, 1960). Ecological
explanations often focus on the ecological niche, and factors that might allow for greater number
of niches, and thus species, in the tropics (Wiens and Donoghue 2004, Wiens et al. 2009).
Evolutionary and ecological explanations are not mutually exclusive. For example, the ecological
niche of species may evolve more rapidly at certain latitudes which may influence the latitudinal
diversity gradient. Both high tropical rates of evolution (tropical niche divergence) and low
tropical rates of evolution (tropical niche conservatism) have been proposed to act as drivers of
high tropical species richness (Kozak and Wiens, 2012). Niche divergence results in ecological
differentiation between closely related species through evolutionary time, allowing them to
exploit novel regions of ecological space (Schluter, 2000). Faster divergence in the tropics could
promote greater utilization of available niche space resulting in fast rates of speciation. In
contrast, niche conservatism describes a pattern of stasis in the ecological niche through
evolutionary time (Wiens and Graham 2005). If tropical species are highly conserved in their
niche through time, they might have difficulty adapting to freezing temperatures of temperate
latitudes, and thus exhibit poor reduced dispersal out of the tropics. Whether tropical niche
divergence or tropical niche conservatism drives the diversity gradient is unknown and
disentangling the contributions of each remains a challenge.
Niche divergence between species can promote both local and regional species richness.
At regional scales, strong ecological divergent selection drives diversifying populations into
different niches, where they evolve towards different ecological optima (Slabbekoorn and Smith
2001). This divergent evolution may result in rapid development of reproductive isolation
2
between populations that have adapted to different ecological conditions which, in turn, could
cause a fast rate of speciation (Rundle and Nosil 2005; Price 2008). For example, birds adapting
to different habitat types sing at different pitches (Weir et al. 2012), and these differences in song
can lead to reproductive isolation. Ecological divergence can also result in selection against
maladapted hybrids, resulting in post-zygotic isolation (Schulter 2009). At the local scale,
ecological niche divergence may allow for more rapid attainment of sympatry between newly
formed species (i.e. Weir & Price 2011), allowing for stable coexistence. If rates of ecological
niche divergence were to differ latitudinally, they could provide an explanation for the origin of
the latitudinal richness gradient. For example, more rapid ecological divergence in the tropics
could accelerate the attainment of reproductive isolation and sympatric coexistence, leading to
high tropical species richness (Mittlebach et al. 2007).
Niche conservatism could also promote elevated tropical species richness (Mittlebach et
al. 2007). Strong niche conservatism hinders dispersal into regions with novel climates. Recent
studies suggest an asymmetry in dispersal ability with high latitude lineages more readily
colonizing the tropics than the reverse (Smith and Klicka 2010; Smith et al. 2012; Weir et al
2009). Such asymmetrical dispersal could be driven by a number of factors including strong
asymmetries in niche conservatism. As an example, physiological intolerance to temperate
climates may prevent tropical species from expanding into temperate latitudes (Sunday et al
2012; Smith et al. 2012). In contrast, high latitude species are more likely to be adapted to a wide
range of climatic conditions, and as a result may more easily colonize the tropics (Addo-Bediako
et al. 2000; Smith et al. 2012). All else being equal (i.e. diversification rates, etc.) this greater
ease of colonization of the tropics would result in a latitudinal diversity gradient.
Climate is an easily quantifiable aspect of the ecological niche that varies between
geographic localities. The climatic niche is defined as the set of climatic conditions (eg.
temperature, precipitation, seasonality) a species experiences across its natural geographic range
(Hutchinson 1957). During the diversification process there appears to be extensive opportunities
for climatic niche divergence across latitude. For example, both equatorial and boreal latitudes
consist of many habitat types ranging from rainforest to alpine grassland, all of which differ in
climate (Olsen et al. 2001). Despite this, available climatic niche space may not be utilized
equally across latitude during diversification. As an example, generalists have been found to
3
occupy more climatic niche space than specialists (Devictor et al. 2010, Peers et al. 2012). If the
proportion of specialist to generalist clades varies between tropical and extra-tropical regions,
then a latitudinal gradient of climatic niche divergence may result.
Rates of climatic niche evolution have not previously been quantified across latitude.
Previous authors have instead, focused on climatic niche overlap between closely related species.
These studies have produced contradictory results (Hua and Wiens 2010) with reduced niche
overlap occurring in the tropics (Plethodontid salamanders: Kozak & Wiens 2007), at high
latitudes (various high elevation vertebrate groups; Cadena et al 2011), or with no latitudinal
differences detected (frogs; Hua and Wiens 2010). What is now needed are studies that quantify
rates of climatic niche evolution across latitudinal gradients. To do this, the amount of niche
divergence between species must be corrected for the age at which pairs of species diverged from
a common ancestor.
Here I test whether rates of climatic niche evolution vary across the latitudinal gradient
for closely related pairs of New World bird and mammal species. I quantify climatic niche
evolution as a function of the amount of climatic divergence between sister pairs and their time
to most recent common ancestry. Brownian motion and Ornsetin-Ulhenbeck models are used to
test whether rates of climatic niche evolution vary as a function of latitude. In addition, the
climatic ranges of species are calculated, in order to determine whether tropical species are more
climatically specialized than temperate species. If rates of climatic niche divergence are found to
be greatest in the tropics, insipient tropical species would likely become reproductively isolated
rapidly, due to adaptation to different ecological conditions. Under this scenario the tropics
would act as a speciation pump, resulting in high tropical species richness. Alternatively, if rates
of climatic niche evolution are found to be slowest in the tropics, tropical species would likely be
highly conserved in their climatic niche. This strong niche conservatism may cause tropical
species to be intolerant to freezing temperature disallowing colonization into extra tropical
regions. Under this alternative scenario, the inability for tropical species to disperse outside of
the tropics would result in high tropical species richness and drive the latitudinal gradient of
species diversity.
4
2 Methods
2.1 Data Collection
Published molecular phylogenies were used to obtain 111 avian, and 53 mammalian New
World sister pairs. Sister pairs included both sister species and phylogroup splits within species
(sister pairs were never phylogenetically nested within other sister pairs, and are each statistically
independent contrasts). For each sister species, GTR- distances were calculated in PAUP
4.0b10 (Swofford 2002) from cytochrome b sequences obtained from Genbank (see Figure 4 for
accession numbers). Avian and mammalian sister pairs were included if cytochrome b sequences
(of at least 500 base pairs in length) were available for both members of a sister pair, and if
GTR- distances exceed 0.75 percent divergence. Sister species with GTR- distances less than
0.75 percent divergence were excluded because the stochastic nature of mutation along a short
DNA sequence renders time estimates highly inaccurate for young sister species. In order to
determine the relative age of each sister pair, relaxed clock phylogenies were created separately
for birds and mammals in BEAST version1.7.5 (Drummond et al. 2012) using cytochrome B
sequences, and a lognormal relaxed clock (using a Yule speciation prior) with a GTR- model.
Tree topologies were fixed (using Baker (2004) for higher level relationships in birds and
Bininda-Emonds (2007) in mammals, and using a large number of published molecular
phylogenies for relationships between species and genera) and BEAST was used to estimate
branch lengths only. BEAST phylogenies were not time calibrated, and node ages represented
relative rather than absolute time. For both birds and mammals, Bayesian analyses were run for
20 million generations and trees were sampled every 1000 generations, following a burn-in of 10
million generations. Maximum clade credibility trees with median node heights were generated
in TreeAnnotator v1.7.5 (Drummond et al. 2012) (Figure 4). The relative sister pair ages were
obtained from branch lengths along this tree All latitudinal values were obtained from range
maps located on Natureserve (birds: Ridgley et al. 2003; mammals: Patterson et al. 2007, IUCN
2012). Midpoint latitude was used to quantify the latitudinal position of a sister pair. Midpoint
latitude of a sister pair was calculated as the mean absolute midpoint latitude of each sister’s
breeding range. Sister pairs were excluded if the midpoint latitudes of both sisters in a pair
differed by more than 25°, and if the combined latitudinal distribution of both species in a pair
was greater than 45° absolute latitude. This exclusion removed sister pairs with very wide
latitudinal distributions.
5
For each species, climatic data was obtained for each georeferenced coordinate of known
occurrence. An alternative approach would systematically sample across a species range map
(e.g. Botero et al 2013), but published range maps, especially from the tropics, are highly
inaccurate (Jetz et al. 2008). In total ~ 275,000 locality records (latitude and longitude) were
obtained from ORNIS 2 (http://ornis2.ornisnet.org/), MaNIS (http://manisnet.org/) and the
Global Biodiversity Information Facility (GBIF) (http://data.gbif.org/welcome.htm). On a
species level, individual coordinates were excluded if they did not occur within a species range,
as specified by Natureserve range maps. With the exception of species known from less than 5
localities, species (and the resulting sister pair) were excluded if less than 5 unique locality
records were obtained. Climatic niche was calculated for only allopatric and parapatric sister
pairs, because sympatric sister pairs occupy the same geographic range and experience similar
climates.
Climatic information was obtained from WorldClim (http://www.worldclim.org/), a
database that integrates climatic data from a global distribution of weather stations (Hijmans et
al. 2005). Climatic data was obtained separately for each coordinate using a global resolution of
30 arc seconds (~ 1km2). I chose 48 variables to quantify the overall climatic conditions a species
encounters. These included 36 temperature variables (maximum, minimum, and mean for each
month of the year) and 12 precipitation variables (mean for each month of the year). Because
seasonality is not synchronous in different geographic regions, the monthly maximum,
minimum, and mean temperatures and precipitations were each sorted from highest to lowest
values for each locality. This reduces overestimation of climatic divergence between sister pairs
with asynchronous seasonality.
2.2 Data Analysis: Evolutionary Rate
To quantify climatic niche, principal component analyses (PCA hereafter) were
performed on all log transformed climatic variables for all sample localities in a species range.
Separate PCA analyses were performed on birds and mammals. For each of the first three
principal components (PC’s), the midpoint value along that PC was calculated for each species. I
used midpoints rather than means to represent the climatic niche of each species, because means
6
are more heavily skewed by densely surveyed regions within a species range. Euclidean distance
between the midpoints of species within a sister pair were used as a measure of climatic
divergence between sister pairs.
Both Brownian motion (BM) and Ornstein-Uhlenbeck (OU) models were used to
compare evolutionary rates of climatic niche across the latitudinal gradient. The BM model
estimates a single parameter, the evolutionary rate, β, and assumes no limit on trait divergence
(i.e. climatic niche divergence) through time. The OU model adds an additional parameter, α,
which acts as a constraint on trait divergence so that trait divergence cannot increase indefinitely
through time. Under the OU model, α represents a “pull” towards an intermediate trait value
between each sister pair (assumed to be the ancestral trait value). This prevents strong trait
divergence away from the ancestral trait value and acts as an evolutionary constraint . An OU
model is appropriate when trait space is finite as is expected to be true for climatic variables. At
high values of α (i.e. a high constraint), evolutionary divergence becomes difficult. As α
approaches 0, the OU model collapses to the simpler BM model in which evolution is not
constrained. To determine if latitude drives rates of climatic niche evolution, I use maximum
likelihood (MLE) to fit of a BM and OU models in which a single rate (and constraint for OU) of
evolution was estimated for all sister pairs, to models in which evolutionary rate (and constraint
for OU) was allowed to vary linearly with latitude or to change after a latitudinal breakpoint. If
the climatic dataset best supports a model where evolutionary rate strongly increases or decreases
across latitude there would be verification of for tropical niche conservatism and tropical niche
divergence, respectively. All MLE analyses were performed in R using the package EvoRAG
(Weir 2014). The likelihood functions for the single rate, linear and breakpoint models are
formulated here:
( ) ∏
√ (
) ,
(BM null model) = ,
(BM model with latitude) = ( ),
(BM two rate model) = ( ( ) ( ))
(OU model) = ( α)(1-exp(α )),
7
(OU mode with latitude) = (( ) ( ))(1-exp(α )),
(OU two rate model) = ( α)(1-exp(α )), where ( )
( ) ( ) ( )
where D is the Euclidean distance between species pairs, T is the relative age of each sister pair,
bβ and bα are the slope, and cβ, and cα are the intercept parameters describing the linear change
of β and α respectively as a function of Latitude (Lat). Model fit was determined using Akaike
Information Criterion (AICc). The best fit model is considered to be the one with the lowest
AICc.
Maximum likelihood analyses were performed on an additional dataset to determine
whether avian results were an artifact of high latitude migration. The dataset used only
temperate, northern hemisphere sister pairs from the original avian dataset and used climatic data
from the months of April to September (a rough estimation of the high latitude breeding season).
2.3 Data Analysis: Climatic Specialization
To determine whether tropical species are more climatically specialized than temperate
species, the climatic ranges of each species were calculated for PC1 to PC3. Specialized species
are expected to have lower climatic ranges than generalist species. Pagel’s lambda (Pagel 1999)
was used to determine if the climatic ranges of species increased with latitude, while correcting
for phylogenetic relatedness between species. The phylogenies in Figure 4 were used for
phylogenetic regression, and Pagel’s lambda was performed using the APE package (Paradis et
al. 2004) in R.
3 Results
The first three PCs explained 94 and 96 percent of the variance in birds (PC1 61%, PC2
20% PC3 13%) and mammals (PC1 71%, PC2 20% PC3 5%), respectively. Rates of climatic
niche evolution were estimated separately for each of the first three PC’s. All three PCs represent
nearly identical aspects of climate in both birds and mammals (fig. 1a, 1b). PC1 represents
8
temperature, PC2 represents precipitation and PC3 represents seasonality of both temperature
and precipitation (fig. 1).
Euclidean distances are shown in figure 2 for PCs that best support a model where
climatic niche evolution changes across latitude. For the avian dataset, maximum likelihood
results for PC1, PC2, and PC3 supported models in which rates of climatic niche divergence
increased with latitude (Table 1a). PC1 best supported an OU linear model where alpha
decreased across latitude (α at 0° latitude = 1.73, α at 60° latitude = 1.13) and beta increased
across latitude (β at 0° latitude = 29.57, β at 60° latitude = 144.54) (Fig 2a). PC1 did not provide
significantly less support for the OU two rate model (AICc = 1.45). Here, both alpha and beta
increased at 39° latitude. The best supported model for PC2 was an OU linear two rate model,
where the breakpoint was calculated at 5 degrees latitude. Because this breakpoint does not
represent a division between tropical and temperate regions, PC2 warrants no further analysis.
For PC3, the best supported model was the OU linear model. Here, evolution rate increased
across latitude (β at 0° latitude = 0.66, β at 60° latitude = 9.92) while evolutionary constraint
decreased across latitude (α at 0° latitude = 0.77, α at 60° latitude = 0.23) (Fig 2b).
For mammals, PC3 best supported a model where latitude increased linearly, while PC1
and PC2 best supported OU constant (null) models. The best fit model for PC3 was a BM
constant two rate model, with a breakpoint calculated at 24 degrees latitude (β at 0-23° latitude =
0.27, β at 24-60° latitude = 0.80) (Fig 2c).
For the migration control test (North American temperate bird data for summer months
only) PC1 supported a model in which climatic niche divergence increases across latitude while
PC2 and PC3 supported null models where climatic niche divergence did not change latitudinally
(Table 1c). The best fit model for PC1 was a BM constant two rate model, with a breakpoint
calculated at 23 degrees latitude (β at 0-23° latitude = 3.25, β at 24-60° latitude =26.16).
Climatic range size variation across latitude is displayed in Figure 3. The climatic ranges
of mammals increased significantly across latitude within PC1 (slope = 0.021, p = 0.004), but the
result was not significant for PC2 (slope = 0.010, p = 0.130) or PC3 (slope = 0.015, p = 0.477).
For birds, the climatic ranges of PC1 (slope = 0.004, p < 0.001) and PC3 (slope = 0.015, p <
9
0.001) increased significantly across latitude while the climatic ranges within PC2 (slope =
0.006, p = 0.081) did not increase significantly across latitude (Figure 3b).
4 Discussion
My results indicate faster evolutionary divergence in climatic niche at high latitudes for
temperature (PC1) in birds and seasonality (PC3) in birds and mammals. In contrast, no climatic
axes of variation supported faster evolutionary divergence in the tropics. These results reject the
role of climatic niche divergence as a driver of high tropical species richness, and instead support
the role of niche conservatism in the tropics as a potential driver of the latitudinal diversity
gradient.
Strong climatic niche divergence in temperate species could be driven by extensive
climatic fluctuations at high latitudes that occurred during the Plio-Pleistocene glacial cycles
(Weir & Schluter 2004) and earlier periods. In contrast, the tropics have experienced less severe
climate fluctuations (Bush et al. 1990, Colinvaux et al. 1996). The comparatively greater stability
of paleoclimates in the tropics may have allowed for a high degree of specialization in climate,
with tropical species occupying narrow temperature ranges, and exhibiting reduced seasonality
(Janzen 1967). The reduced climatic ranges exhibited by tropical birds (for temperature and
seasonality) and mammals (temperature) support these predictions. Ecological specialization
reduces evolutionary divergence of climatic niche by limiting a species’ ability to readily adapt
to novel climatic conditions (Futuyma and Moreno 1988). For example, climatically specialized
tropical species may experience difficulty adapting physiologically to freezing conditions, and
are thereby limited in their ability to colonize temperate latitudes (Smith and Klicka 2010;
Hawkins et al. 2006). In contrast, increased seasonality at high latitudes promotes more climatic
niche generalization (Deutsch et al. 2008). This generalization allows high latitude species to
more easily adapt to tropical climates, increasing the probability of their colonization of tropical
regions. All else being equal, the resulting asymmetry in colonization ability between tropical
and temperate regions promotes the buildup of high tropical species richness. The tropical
conservatism hypothesis (Wiens & Donoghue 2004; Wiens et al. 2006; Mittlebach et al 2007)
posits that clades preferentially originate in the tropics and only rarely disperse out of the tropics
10
due to physiological limitations to freezing temperatures. The results of this study are consistent
with this hypothesis, but do not require that clades preferentially originate in the tropics. Rather,
the greater ease with which high latitude species colonize the tropics is sufficient to generate and
maintain a latitudinal diversity gradient.
The results of this study are highly congruent with New World avian colonization
patterns that occurred before the completion of the Central American Landbridge between North
and South America (Weir et al. 2009, Smith and Klicka 2010). During this time, many Nearctic
species were able to colonize South America, but relatively few South American species were
able to colonize North America (Weir et al. 2009; Smith and Klicka 2010) demonstrating an
asymmetry in dispersal ability. My finding of a latitudinal asymmetry in climatic niche
conservatism may be the cause of this differential dispersal ability between Nearctic and South
American derived Neotropical taxa. Completion of the landbridge precipitated a wave of avian
and mammalian dispersalists which easily colonized tropical regions of North America from
South America (Weir et al. 2009). However, this wave of invading species were generally unable
to colonize beyond the northern extent of tropical forest in central Mexico, suggesting that
tropical niche conservatism limited their dispersal out of the tropics. South American derived
mammal groups that colonized tropical regions of North America likewise seldom extended their
range north of the tropics.
Niche divergence can accelerate reproductive isolation leading to ecological speciation,
whereby extrinsic postzygotic isolation causes hybrids with intermediate ecological preferences
to be selected against (Rundle and Nosil 2005; Schluter 2009). My results indicate that climatic
niche is more divergent at high latitudes, suggesting that ecological speciation occurs faster
there. These results are consistent with previous estimates of accelerated speciation (Weir &
Schluter 2007) and with increased opportunity for incipient speciation (i.e. elevated subspecies
richness) in birds and mammals at high latitudes (Botero et al 2014). This poleward increase in
speciation cannot explain the latitudinal diversity gradient and suggests that other evolutionary
factors such as extinction and dispersal are the key drivers (e.g. Roy & Goldberg 2007;
Mittlebach et al. 2007). Previous studies have estimated extinction in birds, mammals (Weir &
Schluter 2007) and marine bivalves (Jablonsky et al. 2006) to be elevated at high latitudes. In
combination with my study, these results suggest that rates of speciation are elevated at high
11
latitudes, but that low temperate species richness is maintained due to accelerated extinction at
high latitudes and elevated rates of immigration into the tropics.
This study is not the first to discover a positive relationship between latitude and factors
that promote reproductive isolation. Recent research suggests the presence of a latitudinal
gradient in the evolution of sexually selected traits (e.g. length and syllable diversity of avian
song, plumage colouration), where sister pair divergence between such traits increases with
latitude (Martin et al. 2010; Weir and Wheatcroft 2011; Weir et al. 2012). Greater divergence in
climatic niche at high latitudes may have driven the latitudinal increase in divergence of sexually
selected characters. Under climatic niche divergence, populations may evolve towards different
optima that select for dissimilar sexual signals (Baldassarre et al. 2013). Both ecological and
sexually selected traits support a latitudinal pattern where divergent selection is strongest at high
latitudes. Additionally, these studies provide examples of both prezygotic (colour and song
divergence) and postzygotic (niche divergence) isolation occurring most rapidly towards the
poles.
In this study, I analyzed evolution of the realized climatic niche (the climatic conditions
that are present within a species’ geographic range) and not the fundamental climate niche (the
climatic tolerances of a species) (Hutchinson 1957). It is the realized climatic niche that exerts
environmental selection on a species. Given that tropical climate has been relatively stable over
long periods of time, the selection exerted by the realized niche should result in physiological
adaptation and specialization. If tropical species are more specialized, as many studies suggest
(Deutsch et al. 2008; Addo-Bediako et al. 2000), then I predict that the discrepancy between their
fundamental and realized niches will be less extensive in the tropics than at high latitudes. If true,
then the latitudinal asymmetry in niche conservatism should apply to the fundamental niche as
well.
This study is not without limitation. First, most sister pairs in my dataset originated
during the Pliocene and Pleistocene, suggesting that my results may be heavily influenced by
glacial cycles that occurred at this time. Whether my results apply to earlier time periods is
unknown. Second, I follow previous authors (e.g. Botero et al. 2014, Cadena et al. 2012) in
estimating climatic niche within the breeding range of each species, despite the fact that many
12
high latitude avian species are migratory. To determine the sensitivity of the results to migration,
I restricted the avian dataset to Nearctic species using climatic data only from the breeding
period when all Nearctic species are present. The results for this analysis for temperature (PC1)
showed the same pattern of increased divergence with increasing latitude (PC3, seasonality was
not analyzed because I restricted this analysis to spring and summer months), suggesting that my
results are not likely to be invalidated by migration.
I find that tropical species are more specialized in their climatic niche than temperate
species and that rates of climatic niche evolution are greater at high latitudes than in the tropics.
My results suggest faster divergent selection at high latitudes, which could drive faster ecological
speciation where species richness is lowest. This result is inconsistent with the model that views
the tropics as a species pump. Instead, my data show latitudinal asymmetries in niche
conservatism and degree of climatic specialization, which should promote differences in
dispersal ability between tropical and temperate latitudes. What are now needed are direct
estimates of dispersal differences between tropical and temperate regions.
13
Table 1. Likelihoods and support for Brownian Motion and Ornstein-Ulhenbeck models of
climatic niche evolution for Avian (a) and Mammalian (b) and non-migratory Avian (c) sister
pairs. ΔAICc scores (AICc for each model – smallest AICc score) and Akaike Weights (wAIC)
are used as metrics of model support. The best-fit model has the smallest ΔAICc value of 0
(bold). Akaike weights indicate the probability of fit for each model. N indicates the number of
parameters in each model
1a)
1b)
1c)
Migration Test
PC1 PC2 PC3
MODEL N LogLikelihood ΔAICc wAIC LogLikelihood ΔAICc wAIC LogLikelihood ΔAICc wAIC
BM Constant 1 -58.212 6.615 0.020 -38.838 2.592 0.107 -20.305 1.095 0.266
BM Two Rate 3 -52.905 0.000 0.535 -36.371 1.658 0.171 -19.982 4.449 0.050
BM Linear 2 -56.658 5.506 0.034 -37.958 2.833 0.095 -20.273 3.031 0.101
OU Constant 2 -55.231 2.653 0.142 -36.542 0.000 0.392 -18.757 0.000 0.461
OU Linear 4 -53.370 2.930 0.124 -36.367 3.649 0.063 -18.610 3.704 0.072
OU Two Rate 5 -52.210 2.610 0.145 -34.356 1.658 0.171 -18.061 4.449 0.050
Aves
PC1
PC2
PC3
MODEL N LogLikelihood ΔAICc wAIC LogLikelihood ΔAICc wAIC LogLikelihood ΔAICc wAIC
BM Constant 1 -266.339 47.244 0.000 -207.357 26.380 0.000 -143.643 51.338 0.000
BM Two Rate 3 -245.875 10.316 0.004 -204.138 23.942 0.000 -119.550 7.152 0.026
BM Linear 2 -252.120 20.806 0.000 -204.411 22.488 0.000 -121.291 8.634 0.012
OU Constant 2 -249.737 16.041 0.000 -195.687 5.040 0.065 -131.769 29.590 0.000
OU Two Rate 5 -239.443 1.453 0.324 -190.167 0.000 0.809 -117.432 6.916 0.029
OU Linear 4 -239.717 0.000 0.671 -193.030 3.727 0.126 -114.974 0.000 0.932
Mammals
PC1 PC2 PC3
MODEL N LogLikelihood ΔAICc wAIC LogLikelihood ΔAICc wAIC LogLikelihood ΔAICc wAIC
BM Constant 1 -109.913 22.782 0.000 -87.315 15.220 0.000 -62.704 2.408 0.111
BM Two Rate 3 -107.283 21.522 0.000 -85.599 15.789 0.000 -59.500 0.000 0.371
BM Linear 2 -107.153 19.263 0.000 -86.297 15.184 0.000 -62.001 3.001 0.083
OU Constant 2 -97.522 0.000 0.660 -78.705 0.000 0.648 -61.552 2.103 0.130
OU Two Rate 5 -95.707 2.370 0.202 -77.208 3.006 0.144 -57.817 0.633 0.270
OU Linear 4 -97.081 3.118 0.139 -77.844 2.278 0.270 -60.868 4.736 0.035
14
(B) Mammalian Species
(A) Avian Species
-0.4 -0.2 0 0.2 0.4
T_min_1
T_min_5
T_min_9
T_max_1
T_max_5
T_max_9
T_mean_1
T_mean_5
T_mean_9
Precip_1
Precip_5
Precip_9
PC1
-0.4 -0.2 0 0.2 0.4
T_min_1
T_min_5
T_min_9
T_max_1
T_max_5
T_max_9
T_mean_1
T_mean_5
T_mean_9
Precip_1
Precip_5
Precip_9
PC2
-0.4 -0.2 0 0.2 0.4
T_min_1
T_min_5
T_min_9
T_max_1
T_max_5
T_max_9
T_mean_1
T_mean_5
T_mean_9
Precip_1
Precip_5
Precip_9
PC3
Figure 1. Variable loadings patterns for the first three principal components of climatic niche measurements
extracted from the correlation matrix. Tmin represents minimum temperate for all months of the year, Tmax
represents maximum temperature for all months of the year, Tmean represents mean temperature for all months of
the year and Precip represent mean precipitation for all months of the year. Because climatic data was sorted,
numerical vales do not represent months in annual order.
T m
in
1
4
8
1
2
T m
ax
4
8
1
2
T
min
4
8
12
Pre
cip
4
8
1
2
T m
in
1
4
8
1
2
T m
ax
4
8
1
2
T m
in
4
8
1
2
P
reci
p
4
8
1
2
T m
in
1
4
8
1
2
T
max
4
8
12
T
min
4
8
12
Pre
cip
4
8
1
2
T m
in
1
4
8
1
2
T m
ax
4
8
1
2 T
mea
n
4
8
12
Pre
cip
4
8
1
2
T m
in
1
4
8
1
2
T m
ax
4
8
12
T m
ean
4
8
12
Pre
cip
4
8
1
2
T m
in
1
4
8
1
2 T
max
4
8
12
T m
ean
4
8
12
Pre
cip
4
8
1
2
15
ai) aii) aiii)
bi) bii) biii)
ci) cii) ciii)
Figure 2. Climatic divergence through time (branch length) for (a) PC1 Aves, (b) PC3 Aves and (c) PC3
Mammalia. PCs that best supported a model in which climatic divergence changes across latitude are
displayed. Branch lengths represent relative time and are in units of expected mutations per 100 base pairs
per million years. Graphs labeled (i) represent climatic divergence for tropical sister pairs (those with an
absolute midpoint latitude between 0° and 23°) and graphs labeled (ii) represent climatic divergence for
temperate sister pairs (those with an absolute midpoint latitude between 23° and 57° for birds and 23° and
65° for mammals). Graphs labeled (iii) demonstrate how climatic niche evolution is expected to change
after 3 branch length units of time across the latitudinal gradient based on maximum likelihood estimates
of evolution rate (β) and constrain (α) at each latitude under the best supported model.
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10
Cli
ma
tic
Div
erg
ence
Branch Length
0
2
4
6
8
10
12
14
16
0 2 4 6 8 10
Cli
ma
tic
Div
erg
ence
Branch Length
0
1
2
3
4
5
6
0 2 4 6 8 10
Cli
ma
tic
Div
erg
ence
Branch Length
0
1
2
3
4
5
6
0 2 4 6 8 10
Cli
ma
tic
Div
erg
ence
Branch Length
0
1
2
3
4
5
6
7
8
0 5 10 15 20
Cli
ma
tic
Div
erg
ence
Branch Length
0
1
2
3
4
5
6
7
8
0 5 10 15 20
Cli
ma
tic
Div
erg
ence
Branch Length
0
1
2
3
4
5
6
7
0 10 20 30 40 50 60 70
Exp
ecte
d C
lim
ati
c
Div
erg
ence
Absolute Latitude
0
0.5
1
1.5
2
2.5
3
0 10 20 30 40 50 60 70
Exp
ecte
d C
lim
ati
c
Div
erg
ence
Absolute Latitude
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70
Exp
ecte
d C
lim
ati
c
Div
erg
ence
Absolute Latitude
Tropical (0° to 23°) Temperate (23°+)
16
ai) aii) aiii)
bi) bii) biii)
Figure 3. The climatic range sizes of species and their corresponding midpoint latitudes for ai) mammals
PC1, aii) mammals PC2, aiii) mammals PC3, bi) birds PC1, bii) birds PC2 and biii) birds PC3. Black lines
indicate phylogenetically corrected regression lines (using Pagel’ lambda).
Absolute Latitude
Cli
mati
c R
an
ge
Absolute Latitude
Cli
mati
c R
an
ge
Absolute Latitude
Cli
mati
c R
an
ge
Absolute Latitude
Cli
mati
c R
an
ge
Absolute Latitude
Cli
mati
c R
an
ge
Cli
mati
c R
an
ge
Absolute Latitude
17
A)
18
19
Figure 4A Mammalian maximum clades credibility phylogenies created in BEAST v1.7.5.
Branches tips are labelled with the name of each species and their corresponding Genbank
Ascension number.
20
B)
21
22
23
24
25
26
27
Figure 4B Avian maximum clades credibility phylogenies created in BEAST v1.7.5.
Branches tips are labelled with the name of each species.
28
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