Received: 18 May 2020
|
Revised: 24 October 2020
|
Accepted: 29 October 2020
DOI: 10.1111/jbi.14030
RE SE ARCH PAPER
Departing from an ideal: An asymmetric, bimodal and nonEquatorial latitudinal gradient of marine diversity in Western
Atlantic burrowing shrimps (Decapoda: Axiidea and Gebiidea)
Patricio Hernáez1
| Phillip B. Fenberg2
1
Centro de Estudios Marinos y Limnológicos,
Facultad de Ciencias, Universidad de
Tarapacá – UTA, Arica, Chile
2
| Marcelo M. Rivadeneira3,4,5
Abstract
Aim: Despite the generality of the latitudinal gradient of species diversity (LDG) phe-
School of Ocean and Earth Science,
National Oceanography Centre, University
of Southampton, Southampton, UK
nomenon, there is growing evidence showing deviations from an idealized pattern,
3
underlying causes remain little studied. We here evaluate the existence of departures
Centro de Estudios Avanzados en Zonas
Áridas (CEAZA), Coquimbo, Chile
4
Departamento de Biología Marina, Facultad
de Ciencias del Mar, Universidad Católica del
Norte, Coquimbo, Chile
5
Departamento Biología, Universidad de La
Serena, La Serena, Chile
Correspondence
Marcelo M. Rivadeneira, Centro de Estudios
Avanzados en Zonas Áridas (CEAZA), Av.
Bernardo Ossandón 877, C.P. 1781681,
Coquimbo, Chile.
Email: marcelo.rivadeneira@ceaza.cl
Funding information
Fundação de Amparo à Pesquisa do Estado
de São Paulo, Grant/Award Number:
2015/09020-0; ANID/FONDECYT, Grant/
Award Number: 1200843
Handling Editor: Werner Ulrich
that is, a single peak of species richness symmetrically centred in the Equator, but the
from the idealized LDG in a group of marine crustaceans and the explanatory role of
environmental variables.
Location: Coastal shelf (<200 m depth) along the Western Atlantic coast (46°N–47°S).
Taxon: Burrowing shrimps (Decapoda: Axiidea and Gebiidea).
Methods: We assessed the shape of the LDG in 100 burrowing shrimp species using
the reported latitudinal ranges of distribution. Species richness was calculated in
one-degree latitude bands using a range-through approach. The shape of the LDG
was statistically evaluated in terms of latitudinal symmetry, number of modes and
location. We evaluated the importance of 10 environmental variables (proxies of different hypothesis categories) predicting the LDG using a random forest model.
Results: Burrowing shrimps exhibit an increase in diversity towards the tropics, but
departures to the idealized LDG were evident. The LDG is asymmetric between
hemispheres and bimodal (two peaks within the tropics), and this trend cannot be
explained in terms of sampling artifacts. A random forest model explains 92% of species richness, but the only significant variables were bottom seawater temperature,
bottom seawater salinity, bottom seawater temperature range, and tidal range. These
predictors were nonlinearly related to species richness, whereas only bottom seawater temperature and bottom seawater salinity showed a significant phylogenetic
signal.
Main conclusions: The LDG of burrowing shrimps is driven by ecophysiological
restrictions of species, reflecting the role of evolutionary (i.e. ‘time for species accumulation’ and/or ‘diversification dynamics’) and ecological processes (i.e. ‘ecological limits’). Departures from the idealized LDG could be explained by the nonlinear
responses of species richness to environmental conditions, the spatial structure of
Journal of Biogeography. 2020;00:1–12.
wileyonlinelibrary.com/journal/jbi © 2020 John Wiley & Sons Ltd
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HERNÁEZ et al.
these environmental conditions, and a varying degree of phylogenetic conservatism
of key ecophysiological constraints.
KEYWORDS
Amazon/Orinoco Rivers, Caribbean Sea, Crustacea, diversity gradients, fossorial shrimps,
random forest, Thalassinidea
1 | I NTRO D U C TI O N
environmental variables (i.e. linear vs. non-linear) and the combined
effect of these predictors.
The latitudinal gradient of species diversity (LDG), posing that
The LDG in marine crustaceans has been studied for more than
species richness increases from the poles towards the tropics, is
70 years (Pianka, 1966; Thorson, 1957). A recent synthesis encom-
perhaps the most investigated pattern in biogeography and macro-
passing 20 studies and seven crustacean orders, revealed a strong
ecology (Hillebrand, 2004a; Pianka, 1966; Willig et al., 2003). Marine
effect size of the species richness-latitude correlation, supporting
organisms are no exception to this generality, as demonstrated by
the existence of a ‘idealized’ LDG in marine crustaceans (Rivadeneira
several meta-analyses (Hillebrand, 2004b; Kinlock et al., 2018;
& Poore, 2020). In contrast, studies analysing causes of the LDG in
Menegotto et al., 2019). In recent years, however, the ‘idealized
crustaceans are scant. Most have considered only ecological expla-
LDG’ in marine systems (i.e. a single peak of species richness, sym-
nations, based on correlations between species richness and proxies
metrically centred around the Equator) has been challenged by more
of environmental energy. Temperature seems to be the most import-
detailed analysis of the shape of the pattern (Brayard et al., 2005;
ant predictor of species richness (Astorga et al., 2003; Macpherson,
Rivadeneira & Poore, 2020). Firstly, studies have reported asym-
2002; Rombouts et al., 2009; Levinton & Mackie, 2013). While sea
metric latitudinal trends (i.e. LDG does not follow the same pattern
temperature could explain increasing species richness towards lower
across hemispheres; Chown et al., 2004; Rex et al., 1993; Valdovinos
latitudes, it alone cannot easily explain deviations from the ideal-
et al., 2003). Secondly, recent studies have documented the exis-
ized LDG (Powell et al., 2012). A recent analysis found that, after
tence of a bimodal LDG, where peaks in species richness may occur
accounting for latitudinal differences in sampling effort, the LDG of
inside or outside the tropics (Chaudhary et al., 2016; Rutherford
five major crustacean orders are bimodal (with modes located inside
et al., 1999; Saeedi et al., 2017; Worm et al., 2005). Thirdly, several
or outside the tropics; Rivadeneira & Poore, 2020). However, the
studies have documented that the peak in species richness is slightly
coarse resolution of analyses (10° bands) across the global ocean
shifted from the geographic Equator and it is placed c. 10°N (Núñez-
precluded detailed analyses of the role of environmental factors
Flores et al., 2019; Powell et al., 2012).
shaping the deviations of the LDG.
The underlying causes of the LDG have been extensively debated
Here we put these ideas to the test using burrowing shrimps of
in the literature, and the plethora of explanations formulated can be
the infraorders Axiidea and Gebiidea (formerly treated together as
broadly classified into three categories (i.e. ‘ecological limits’, ‘diver-
Thalassinidea) distributed along the Western Atlantic (WA). Given
sification dynamics’, and ‘time for species accumulation’; Mittelbach
its prominent north–south orientation, and continuous extension
et al., 2007; Pontarp et al., 2019). At first sight, explanations for the
crossing both hemispheres, the WA coast represents an excellent
deviations from the idealized LDG could be due to sampling deficien-
study system to evaluate patterns and processes related to the
cies within tropical zones (Boltovskoy & Correa, 2017; Menegotto
LDG. We used burrowing shrimps as study model, a diverse group
& Rangel, 2018), yet other explanations (e.g. the presence of the
that represents two clades of marine decapods with a body form
inter-tropical convergence zone, habitat availability, mid-domain
completely adapted for a fossorial lifestyle and that are an import-
effects and thermal constraints of species) may also be operating
ant benthic component of sandy or muddy intertidal and shallow
(Powell et al., 2012). Nevertheless, the same hypothesis categories
subtidal habitats worldwide (Hernáez, 2018a). These organisms
(and their specific hypotheses and proxies) proposed to explain the
are known for constructing burrows of different shapes and depths
LDG could also be applied to investigate its departures, by relaxing
(Griffis & Suchanek, 1991) and for playing an important role in shap-
some of the assumptions. For instance, a nonlinear hump-shaped re-
ing the community structure (Pillay, 2019). Bioturbation produced
lationship between species richness and temperature may lead to a
by burrowing shrimps, that is, the activity of water and sediment
bimodal LDG (Brayard et al., 2005; Rutherford et al., 1999; Saeedi
expulsion from the galleries, contributes to the suspension of or-
et al., 2017). Inter-hemispheric asymmetries in the LDG have also
ganic matter, nitrogen fixation and the increases of food availability
been attributed to geographic differences in present-day factors
among the trophic levels (Pillay & Branch, 2011). In this study, we
(Chown et al., 2004). For instance, the continental area and the
are particularly interested in the following goals: (a) to evaluate the
width of the continental shelf are much larger in the northern than in
existence of departures to the idealized LDG, and (b) to assess the
the southern hemisphere (Powell et al., 2012). Therefore, the shape
role of environmental variables—coarse proxies of the different hy-
of the LDG may depend on the spatial structure of environmental
pothesis categories—for predicting the LDG. We show that diversity
predictors, the functional relationship between species richness and
increases towards the tropics, yet the overall LDG is asymmetric,
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HERNÁEZ et al.
3
F I G U R E 1 Latitudinal diversity gradient of burrowing shrimps along the Western Atlantic coast. Also shown is the bioregionalization of
burrowing shrimps based on the Infomap algorithm. Only four well-represented bioregions (i.e. represented in more than three latitudinal
bin) are shown. Map in Mollweide projection
bimodal within the tropics, and not centred around the Equator.
fieldwork carried out from 2007 to the present along the coast
Moreover, we showed that these trends could be closely explained
of Central America and South America (Hernáez, 2014, 2018b;
by the spatial interplay of a small subset of environmental factors.
Hernáez et al., 2012, 2015, 2020; Hernaez et al., 2020; Hernáez &
Our results demonstrate that the same conceptual framework used
Vargas, 2013; Hernáez & Wehrtmann, 2007). The taxonomic status
to study the canonical LDG phenomenon could be also used to ex-
of each species was validated using the World Register of Marine
plore departures from the idealized trend.
Species. The reported latitudinal ranges are provided in the Dataset
S1 in the Supporting information.
2 | M ATE R I A L S A N D M E TH O DS
2.1 | Database
2.2 | Shape of the LDG
Species richness was estimated in one-degree latitudinal bins,
We gathered a database documenting the northernmost and south-
assuming a range through approach as commonly used in ma-
ernmost limit of the distribution of 100 burrowing shrimp species
rine macroecological studies (Roy et al., 1998; Tomašových
inhabiting the coastal shelf (<200 m depth) along the WA coast,
et al., 2016). This approach assumes that the sampling effort is
from Canada (46°N) to Patagonia (47°S). This represents c. 15% and
spatially homogenous, which is often not the case. Although we
83% of all valid species of Axiidea and Gebiidea described for the
lack estimates of local sampling effort across the study area,
world and western Atlantic, respectively (World Register of Marine
such bias should produce specific trends different than reported
Species, www.marin​espec​ies.org, accessed on July 17th 2020). The
here. For instance, sampling effort in global databases such as
information was collected from an exhaustive literature review and
the Ocean Biogeographic Information System is strongly biased
Niche conservatism.
Climatic stability (decades to millennia).
Climatic stability (millennia to million of years).
Area: diversification.
Area: carrying capacity.
More specialization.
h
g
More individuals: carrying capacity.
e
i
f,g
Mean primary productivity
b
b
f,h
Total suspended material
Shelf area
b
b
pH
Seasonal coexistence.
d
c
b
f
f
Wave height
Temperature-dependent speciation.
a
f,g
Coastal length
i
f
Tidal range
b
b
e
c,d
b
Time for spp
accumulation
Bottom water temperature
range (BWTR)
a
Diversification
dynamics
b
Ecological
limits
Mean bottom water salinity
(BWS)
Mean bottom water
temperature (BWT)
Predictor
Hypothesis category
4.169
2.380
5.375
2.574
1.312
2.078
1.477
2.679
7.580
7.252
VIF
3.309
3.731
3.214
3.181
2.904
3.089
2.423
2.357
1.762
1.364
Min depth of
tree
2.641
3.000
4.845
5.330
9.228
9.420
11.249
18.883
26.998
102.112
MSE
Relative importance in random forest model
94.7
91.3
94.3
94.2
92.8
93.7
98.1
97.1
98.8
99.1
% sign.
Trees
1
1
1
1
1
1
<0.00001
<0.00001
<0.00001
<0.00001
p-value
0.415
—
0.437
0.448
0.421
—
0.385
0.482
0.517
0.695
K
0.421
—
0.335
0.315
0.654
—
0.842
0.189
0.040
0.003
p-value
Phylogenetic signal
TA B L E 1 Environmental predictors of the latitudinal gradient of diversity of burrowing shrimps along the Western Atlantic coast. These predictors are proxies of different hypotheses
grouped into three categories (sensu Pontarp et al., 2019). Also shown are their level of collinearity (variance inflation factor, VIF), variable importance according three measures (mean
minimal depth, mean increase of mean squared error and percentage of trees in which a split on target predictor occurs) in the random forest analysis and their phylogenetic signal (Blomberg's
K). Significant values (p < 0.05) highlighted in bold
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HERNÁEZ et al.
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HERNÁEZ et al.
towards mid-latitudes (especially in the northern hemisphere), and
5
2.3 | Predictors of the LDG
the tropics tend to be undersampled (Menegotto & Rangel, 2018).
Because tropical areas are represented mostly by developing coun-
We evaluated the importance of 10 environmental variables pre-
tries (Sachs, 2001), sampling effort might be considerably lower
dicting species richness (Table 1). The data were obtained from
in these areas (Amano & Sutherland, 2013) thus leading to spuri-
BioOracle 2.0 (Assis et al., 2018), GMED (http://gmed.auckl​
ous extra-tropical peaks of diversity (Menegotto & Rangel, 2018);
and.ac.nz; 0.05° resolution each), and Aquamaps database v2.0
this is not our case (e.g. peaks are intra-tropical, see Figure 1).
(Kaschner et al., 2008; 0.5° resolution). Values were averaged over
That said, we explored the potential impact of different types of
one-degree latitudinal bins, using only pixels on the coastal shelf
sampling biases on the shape of the LDG. Analyses were carried
(<200 m depth). The information of environmental variables is
out for: (a) higher taxonomic levels (genus and families), at which
provided in the Supporting information (Dataset S2). These vari-
sampling biases are expected to be much lower than species; (b)
ables represent proxies of the different hypothesis categories of-
excluding singletons (i.e. species documented in a single locality,
fered to explain the LDG (Pontarp et al., 2019). A same proxy may
or with a known distribution smaller than one degree of latitude);
be related to more than one category; for instance, shelf area may
(c) using subsets of species according to their year of taxonomic
reflect the importance of ‘ecological limitation’ (e.g. ‘more individu-
description (i.e. before 1900, and 1980). If the LDG is independent
als or more area’), or ‘diversification dynamics (e.g. diversification
of the level of taxonomic completeness, which may not necessar-
rates). In order to test the relevance of the niche conservatism hy-
ily be true (Edie et al., 2017), then the shape of the LDG should be
pothesis (see Pontarp et al., 2019), we estimated the phylogenetic
insensitive to the discovery of new species.
signal of each one of the eight environmental variables linked to
We evaluated three aspects of the shape of the LDG, namely:
the species’ ecophysiological tolerances. To do this, we used the
(a) symmetry, (b) unimodality and (c) equatorial centring. Departures
median of the environmental trait across the entire latitudinal dis-
from an inter-hemispheric symmetry were evaluated using the sym-
tribution of each species. Since we lack a molecular phylogeny for
metry test proposed by Miao et al. (2006), implemented in the li-
the group, we used the taxonomic structure as a coarse proxy of
brary ‘lawstat’ in R (Gastwirth et al., 2019). The unimodality of the
phylogenetic relatedness among species as this still may retain
LDG was statistically evaluated using a Gaussian finite mixture mod-
enough evolutionary history in order to carry out comparative phy-
elling (GMM). The GMM evaluates whether a mixture of several dis-
logenetic analyses (Soul & Friedman, 2015). We used Blomberg's
tributions combine to create an observed frequency distribution. A
K (Blomberg et al., 2003) to measure the degree of phylogenetic
Bayesian approach was used to estimate the optimum number of
signal, where larger values of K indicate a strong phylogenetic con-
clusters or distributions, from one to nine, where larger Bayesian
servatism of the trait. Analyses were carried out using the libraries
Information Criterion (BIC) values indicate stronger support for the
‘ape’ (Paradis et al., 2004), 'palaeotree' (Bapst, 2012) and ‘picante’
model and number of clusters (Fraley & Raftery, 2003). Then the
(Kembel et al., 2010) in R.
mean value for each cluster was estimated, with bootstrapped 95%
The effect of these predictors shaping the LDG was evalu-
confidence intervals. Analyses were carried out using the library
ated using a random forest regression. Although analyses of the
‘mclust’ in R (Scrucca et al., 2016). We evaluated whether the boot-
LDG often relies on linear or multivariate regressions, or general-
strapped confidence interval (CI) of all clusters contained the 0° lat-
ized linear models using a multi-model average approach (Kreft &
itude to test departures from Equatorial centring.
Jetz, 2007; Tittensor et al., 2010), the random forest—a machine
We tested whether multiple peaks of species richness occur
learning method—offers the advantage of making no assumptions
within or among different bioregions. We used a network-theory
on the error structure, is able to deal with classification and regres-
approach to estimate the number and position of bioregions. Unlike
sion problems, has few tuning hyperparameters and is robust to
classic classification methods, the network-based approach maxi-
overfitting (Breiman, 2001; Liaw & Wiener, 2002). Random forests
mizes the use of information, and has proven to be especially useful
outperforms other modelling approaches including regression tree
for the detection of biogeographic modules (i.e. bioregions) through
analysis, bagging trees, multivariate regression and multivariate re-
different algorithms (Bloomfield et al., 2018). Recent studies demon-
gression splines (Oliveira et al., 2012; Prasad et al., 2006). We ran
strate a better performance of the several network methods over
the random forest using the default parameters, that is, mtry = p/3
classic hierarchical classification analyses to explore biogeographic
(where p is the number of predictors per tree) and node size = 5,
patterns (Bloomfield et al., 2018; Vilhena & Antonelli, 2015). We
but we grew 10,000 trees to ensure the stability of the estimates of
analysed the modularity (Q) of the bipartite network (i.e. presence–
variable importance. Hyperparameter tuning, in fact, may increase
absence species-latitudinal bin matrix) using the Infomap algorithm
the performance of random forest (Probst et al., 2019). Variable im-
(Edler et al., 2016; Rosvall & Bergstrom, 2008), and its significance
portance was evaluated using mean minimal depth, mean increase of
was evaluated using 10,000 null matrices preserving the degree
mean squared error and percentage of trees in which a split on target
distribution (i.e. conserving the original distribution of edges per
predictor occurs. P-values were estimated from a one-sided bino-
species). Only modules represented by at least three latitudinal bins
mial test. Analyses were carried out in the libraries ‘randomForest’
were considered as bioregions. Analyses were carried out using the
(Liaw & Wiener, 2002) and ‘randomForestExplainer’ (Paluszynska
library ‘igraph’ (Csardi & Nepusz, 2006) in R.
et al., 2019) in R. Partial dependence plots were used to inspect the
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HERNÁEZ et al.
conditional shape of the predicted species richness versus selected
analyses at higher taxonomic levels, excluding singletons, or control-
predictors, using the library ‘pdp’ (Greenwell, 2017) in R.
ling for the year of taxonomic description did not alter these results
Since collinearity can severely mask the relative importance of
(Figure S1 in the Supporting Information).
predictors (Strobl et al., 2008), we carried out a variance inflation
The random forest model had a very high predictive accuracy
factor (VIF) to select only variables with a VIF < 10. VIF was es-
(pseudo-r2 = 0.92) and showed that only four variables were signifi-
timated using the library ‘usdm’ in R (Naimi et al., 2014). Since all
cant (Table 1). Although variable importance varied according to the
variables showed a low VIF (Table 1), they were all retained in further
criteria used, in general, mean bottom water temperature (BWT) was
analyses. Although random forest is robust to non-independence of
the most important variable followed by bottom water temperature
residuals, we also carried out an analysis including latitude and longi-
range (BWTR), mean bottom water salinity (BWS) and tidal range
tude as co-variables in the model. This approach is analogous to the
(Table 1). The four environmental predictors showed different spa-
use of spatial eigenvectors (Diniz-Filho & Bini, 2005). We inspected
tial characteristics. All predictors were multimodal (Figure S2) and
the residuals of the model to explore the presence of unexplained
only BWT and BWS were symmetrical (p = 0.91 and 0.80, respec-
factors using a spatial autocorrelogram (with 1,000 runs) analysis in
tively). Only BWT and BWTR had one their modes centred around
the library ‘ncf’ in R (Bjornstad, 2019).
the Equatorial line (Figure S2). The random forest model including
only these four variables still had a high accuracy (pseudo-r2 = 0.90).
Partial dependence plots show a positive growing response of spe-
3 | R E S U LT S
cies richness to BWT (Figure 4a), with a sudden increase of c. 23°C
and then a stabilization above 25°C. BWS also had a nonlinear re-
The species richness of burrowing shrimps increases from the tem-
sponse, with richness increasing at higher salinities (35 PSS), but
perate regions towards the tropical region, revealing a significant
then declining slightly at maximum salinity values (Figure 4b). On the
negative correlation between species richness and absolute latitude
contrary, the partial response of richness to BWTR shows a nega-
(Pearson's r = −0.82, p < 0.0001, N = 93) across both hemispheres
tive trend, falling abruptly at values higher than c. 10°C (Figure 3c).
(Figure 1). The LDG was significantly asymmetric between hemi-
The partial response due to the tidal range shows a nonlinear trend,
spheres (p < 0.0001). The GMM showed that the optimal number
with maximum values of species richness found at lower tidal ranges
of clusters describing the LDG were two (Figure 2a); the first and
(<0.3 m), and minimum at intermediate levels (0.3–1 m; Figure 4d).
major peak located around the Caribbean Sea (c. 10–17°N), and
Blomberg's K was significant for BWT (K = 0.70, p = 0.003) and
the secondary and minor peak situated at the northernmost state
BWS (K = 0.52, p = 0.04); all other predictors showed non-significant
of the northeastern region of Brazil (i.e. State of Maranhão, 2–3°S),
K values (Table 1).
Despite residuals of the random forest models (with all variables,
from the mouth of the Amazon River to the point where the South
and with only significant variables) showing a significant autocor-
Equatorial Current reaches the coast of Brazil (Figures 1 and 2b). The
relation at intermediate spatial scales (c. 3,500 km, Figure S3), ac-
modularity of the biogeographic network was significant (Q = 0.15,
counting for the spatial autocorrelation in the random forest model
p < 0.0001) and defined four major bioregions (Figure 1). Both
(Figure S3) did not alter the responses of species richness to envi-
peaks of species richness fall within the Tropical Atlantic bioregion
ronmental predictors (Figure 4). The main peak of species richness
(Figure 1), and their mean locations were statistically different from
around the Caribbean Sea is associated with high BWT, low BWTR,
the null expectation of the Equatorial line (0°, Figure 2c). Repeating
low tidal range and high BWS (Figure 4). The tropical drop in species
(b)
V
●
●
(c)
Overall
Individual clusters
−11200
●
0.6
Frequency
●
0.4
Density
●
0.015
●
●
0.010
●
●
0.020
●
●
●
0.8
●
−11180
●
2
4
6
Clusters
8
0.0
●
0.2
0.005
●
0.000
−11160
EV
−11220
−11240
BIC
●
0.025
●
1.0
(a)
−11140
−11120
with a broad dip of richness between 0 and 10°N, that is, the area
−40 −20
0
20
Latitude (º)
40
−20
−10
0
10
Latitude (º)
20
F I G U R E 2 Bimodality of the latitudinal
diversity gradient of burrowing shrimps.
(a) Optimum number of clusters detected
by a Gaussian finite mixture model,
assuming equal (E) or variable (V) variance
among clusters. (b) Density probability of
latitudinal gradient of species richness,
showing the decomposed probabilities for
each detected cluster. (c) Mean location
(° latitude) associated to each cluster
showing the 95% confidence intervals
(10,000 bootstrapped values), not
including the 0° in both cases. Note the
(c) has a different scale in abscissa
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HERNÁEZ et al.
Observed
Predicted
●
●
●● ●
●●
●
●
●
●
●●
●●
●●
● ●
●●● ●
●● ●
●●
●●● ●●
●●
●
●
●
●
●●●●●●●
●
●
●
●
●●
●
●
●
●
0
(a)
●
of burrowing shrimps occurs at 10°N, thus corroborating the aforementioned study. This bimodal LDG has also been noticed in other
marine crustaceans, where one of the two modes is located outside
the tropics (Chaudhary et al., 2016; Dworschak, 2000; Levinton &
Mackie, 2013). Our analyses, nevertheless, point out the existence
LDG bimodality could be attributed to sampling insufficiency in the
tropics (Dworschak, 2005; Fernandez & Marques, 2017; Menegotto
(b)
10
15
BWTR (ºC)
& Rangel, 2018), our analyses suggest that the intra-tropical bimodality is not attributable to sampling biases or taxonomic completeness, being a real biological pattern for WA burrowing shrimps.
4.2 | Factors shaping the LDG
5
10 15 20 25
The high accuracy of the random forest model indicates that the
(c)
Mean bottom water salinity (BWS)
Tidal range
0.5
1
1.5
tidal range (m)
33 34 35 36 37 38 5
BWT (ºC)
of intra-tropical bimodality. While the phenomenon of extra-tropical
●
●
● ●●●
● ●
●●● ●
●●
●
●
●
●●●●●
● ●●
●●●●●●●●●●●
Mean bottom water temperature (BWT)
Bottom water temperature range (BWTR)
BWS (PSS)
taxa is shifted from the 0° line. We have found that maximum richness
2
20
10
Species richness
30
●
7
LDG, and its departures from the idealized trend, can be largely explained by the combined effect of a few environmental variables.
These variables likely reflect the importance of ecophysiological
restrictions shaping the LDG operating at both evolutionary and
ecological time-scales. Interestingly, bimodality cannot be reproduced by isolated factors, but by the combination of several factors.
For instance, although sea temperature was the most important
−50 −40 −30 −20 −10
0
10
20
30
40
50
Latitude (º)
F I G U R E 3 Environmental predictors of the latitudinal diversity
gradient of burrowing shrimps along the Western Atlantic coast. (a)
Observed and predicted species richness by a random forest model
including only significant variables (see Table 1), (b) latitudinal
variation of mean bottom water temperature and bottom water
temperature range and (c) latitudinal variation of mean bottom
water salinity, and tidal range
variable explaining species richness, along with previous analyses
(Astorga et al., 2003; Levinton & Mackie, 2013; Macpherson 2002;
Rombouts et al., 2009), it alone cannot create the bimodal LDG
(Powell et al., 2012). Previous studies indicate a nonlinear relationship between species richness and temperature, showing either a
saturation trend at higher temperatures (Saeedi et al., 2017), or a
hump-shaped pattern, that is, declining species richness at higher
temperatures (Brayard et al., 2005). However, the partial dependence plot of predicted species richness versus BWT only shows a
soft bound at higher temperatures (Figure 4a). We propose that the
interplay of predictor variables is what leads to the observed devia-
richness in the northern South American coast is associated with a
tions from the idealized LDG. In fact, all four main predictors had
high BWT, low BWTR, low BWS and high tidal range (Figure 4).
complex spatial patterns (Figure S2); all of them were multimodal,
some of them were symmetric and some of those modes fall within
4 | D I S CU S S I O N
4.1 | Shape of the LDG
the Equatorial line.
The main peak in diversity, in the Caribbean Sea, is specifically
associated to high and stable thermal conditions, combined with
an elevated salinity and low tidal ranges. These factors reflect a
likely ecophysiological control on species distribution. Speed of
Our analyses indicate that burrowing shrimps increase in species
evolution directly driven by temperature has been noted as one
richness towards the tropics, but also reveal deviations from an ide-
of the main factors explaining the greatest species richness in
alized pattern: the LDG is asymmetric and bimodal, with peaks of
certain animal groups of the tropical regions (Rohde, 1999; Willig
species richness located outside the equatorial line (yet within the
et al., 2003). In particular, temperature can modify bioturbation
tropics).
rates of burrowing shrimps, provoking a significant reduction of
Asymmetries in the LDG have long been recognized (Chown
sediment expulsion from burrows at low temperatures compared
et al., 2004; Rex et al., 1993; Valdovinos et al., 2003). For instance,
with high temperatures, and consequently endangering the sur-
Powell et al. (2012) evaluated the LDG in living and fossil biotas dis-
vival of these organisms (Berkenbusch & Rowden, 1999; Rowden
tributed along the Pacific and Atlantic coast, concluding that—beyond
et al., 1998). On the other hand, several studies have demon-
possible sampling artifacts—the peak of diversity among studies and
strated that changes in water salinity are strongest at the equator,
8
|
HERNÁEZ et al.
16
15
14
16
12
13
12
5
10
15
20
25
33
BWT (ºC)
34
35
36
37
BWS (PSS)
(d)
15.5
(c)
14
14.5
15
with spatial autocorrelation control
without spatial autocorrelation control
13.5
13
Predicted species richness
F I G U R E 4 Partial dependence plot
showing the relationship between
predicted species richness of burrowing
shrimps and (a) mean bottom water
temperature, (b) mean bottom water
salinity, (c) bottom water temperature
range, and (d) tidal range. Rugs are
shown along the x-axis to visualize the
distribution of data points
(b)
8
Predicted species richness
(a)
5
10
15
0.5
BWTR (ºC)
1.0
1.5
2.0
Tidal range (m)
where the progressive increment of rainfall during the wet season
Our correlational approach also allowed us to explore the impor-
causes a sustained reduction of salinity in marine waters (Amador
tance of the three categories of explanations proposed for the LDG
et al., 2006; Cortés, 1996). While adult burrowing shrimps are eu-
phenomenon (Table 1). For instance, the phylogenetic signal detected
ryhalines, exhibiting a high tolerance to salinity fluctuations, the
for the BWT and BWS suggests strong ecophysiological niche con-
mortality of larval phases is increased at lower salinities (Faleiro
servatism, providing support for the ‘time for the diversification hy-
et al., 2012; Paula et al., 2001).
potheses’ (assuming that clades originated first at low latitudes). BWT
While it is true that our approach just considered latitudinal
may also reflect the importance of ‘diversification dynamics hypoth-
variation, the longitudinal information, that is, the shelf area,
eses’, as shown in other crustaceans (Rivadeneira & Poore, 2020;
was not a significant predictor of the diversity of WA burrowing
Yasuhara et al., 2009). The Caribbean Sea is a hotspot of diversity of
shrimps. Indeed, although shelf area is much larger around the
many marine organisms (Bowen et al., 2013; Kerswell, 2006; Roberts
Gulf of Mexico and northern South America (Ludt & Rocha, 2015),
et al., 2002; Tittensor et al., 2010) that could be linked to strong di-
the peaks of diversity were not associated with these areas.
versification dynamics (Bowen et al., 2013; Rocha et al., 2008). The
Nevertheless, our approach does not allow us to differentiate
insufficient fossil record, represented mostly in Callianassidae (Hyžný
patterns and processes occurring within the Caribbean basin, as
& Klompmaker, 2015), but unavailable in other families of burrowing
multiple ecoregions with large differences in species composition
shrimps and the controversial phylogenetic information about these
are being combined (Williams et al., 2015). The secondary peak in
organisms (Poore et al., 2014, 2019; Sakai, 2014) precludes, for the
diversity occurs in northern Brazil, and its conditions differ from
moment, a more formal evaluation of the importance of evolutionary
the Caribbean Sea, having even higher salinities and intermedi-
drivers on LDG. In addition, the other key predictors such as BWTR
ate to high tidal ranges. The LDG declines across c. 2,800 km in
and tidal range showed no phylogenetic signal, and they might reflect
the northern portion of South America. This fall in diversity is at-
the importance of the ‘ecological limits’ hypotheses, that is, climatic
tributed to maximum tidal ranges and low salinity. The reduction of
stability and local carrying capacity. However, other factors or vari-
salinity is due to the freshwater discharges of the Amazon/Orinoco
ables not considered in this study may be also relevant. In fact, the
Rivers, ranked as top one and three in freshwater discharge to the
residuals of the random forest models exhibit significant spatial auto-
ocean (Kang et al., 2013). This creates a large area of suboptimal
correlations with characteristic scales of c. 3,500 km, suggesting that
osmotic conditions for many species, especially during the larval
processes operating at those scales may be also relevant for the LDG.
phase (Anger, 2001). Previous studies have shown the importance
For instance, habitat availability, not considered in our study, is a top
of Amazon River discharges on spatial patterns on larval dispersal
variable explaining the LDG and bioregionalization of rocky intertidal
and spatial distribution of marine invertebrate populations (Goes
gastropods along the eastern Pacific (Fenberg & Rivadeneira, 2019).
et al., 2014). Interestingly, freshwater discharge also increases the
Beach morphodynamics (i.e. beach slope and grain size) are also im-
total suspended matter, but it had no effect on species richness
portant predictors of macrobenthic species richness in sandy beaches
as burrowing shrimps are adapted to highly hypoxic sediments
(Barboza & Defeo, 2015; Defeo & McLachlan, 2013) and should be
(Atkinson & Taylor, 2005).
considered by future studies.
|
HERNÁEZ et al.
4.3 | Concluding remarks
Our study shows that deviations from the idealized LDG do not necessarily refute the generalized increase in diversity towards the tropics. In fact, the effect size of the strength of the LDG (i.e. Fisher's
transformation of the correlation between species richness and absolute latitude) was −1.17, stronger than the mean effect size estimated for other marine datasets (Hillebrand, 2004a, 2004b; Kinlock
et al., 2018; Menegotto et al., 2019; Rivadeneira & Poore, 2020).
Further comparative studies are needed in order to make firmer
conclusions regarding the generality of the departures from the idealized LDG. In fact, these departures may not be exclusive to marine
systems, being also present in terrestrial taxa as suspected by previous studies (Brayard et al., 2005). However, given the fact that many
(if not most) environmental variables are not unimodal, symmetrical and centred around the Equator (e.g. Chown et al., 2004; Powell
et al., 2012), we may anticipate that new studies may confirm our
findings. Our analysis confirms the need for more refined methods
to characterize the causes of the spatial variability of LDG patterns.
AC K N OW L E D G E M E N T S
We appreciate the constructive and useful comments and suggestions made by Werner Ulrich, Matthew Powell and one anonymous
reviewer. We are grateful to ‘Fundação de Amparo à Pesquisa do
Estado de São Paulo' (FAPESP) for providing funding in the form of
a postdoctoral research fellowship for PH (#2015/09020-0). Also,
the first author is grateful to his friend Marcelo Pinheiro for provide
working space at the Grupo de Pesquisa em Biologia de Crustáceos
(Crusta), Universidade Estadual Paulista (UNESP). The research of
MMR was funded by project ANID/FONDECYT #1200843. MMR
thanks the continuous support and cogent critics of Benjamin
Rivadeneira and Adele Bruna-Bello. No permits were needed to
carry out this study.
DATA AVA I L A B I L I T Y S TAT E M E N T
All data related to this article are included in the Supporting
Information openly available in the Dryad Repository https://datad​
ryad.org/stash/​datas​et/doi:10.5061/dryad.31zcr​jdh9.
ORCID
Patricio Hernáez
Phillip B. Fenberg
https://orcid.org/0000-0002-3785-2050
https://orcid.org/0000-0003-4474-176X
Marcelo M. Rivadeneira
https://orcid.
org/0000-0002-1681-416X
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B I O S K E TC H
Patricio Hernáez, Philip B. Fenberg and Marcelo Rivadeneira are
interested in the evolutionary ecology and autoecology of marine
organisms and their biogeography. They have used crustaceans,
among other invertebrates, as model organisms to study latitudinal changes in species richness and reproductive biology. Their
research includes seasonal and spatial patterns of reproduction,
functional biogeography and population dynamics.
Authors Contributions: P.H. and M.M.R. conceived the idea for
this study; P.H. and M.M.R. collected the data; M.M.R. analysed
the data; P.H., P.B.F. and M.M.R. wrote the paper.
S U P P O R T I N G I N FO R M AT I O N
Additional supporting information may be found online in the
Supporting Information section.
How to cite this article: Hernáez P, Fenberg PB, Rivadeneira
MM. Departing from an ideal: An asymmetric, bimodal and
non-Equatorial latitudinal gradient of marine diversity in
Western Atlantic burrowing shrimps (Decapoda: Axiidea and
Gebiidea). J Biogeogr. 2020;00:1–12. https://doi.org/10.1111/
jbi.14030