Featured article #5

sacrivzoa (view original)



Bonnet, N.1; Gao, F.2; Mendonça, M.3; Miño, C. 4; Miranda, L. 5; Muñoz, S.6; Simonov, E. 7; Soares, L.8

1 Universidade Federal do Ceará, Brazil; 2 Ocean University of China, China / Smith College, USA; 3 Universidade do Vale do Rio dos Sinos, Brazil; 4 Universidade Federal de São Carlos, Brazil; 5 Universidade de São Paulo, Brazil; 6 Universidad de Antioquía, Colombia; 7 Russian Academy of Sciences, Russia; 8 Universidade de São Paulo, Brazil.


Cryptic species are distinct but morphologically similar species that were (erroneously) classified as a single one (Pfenninger & Schwenk, 2007). They are reproductively isolated and are therefore true species under the biological species concept (Mayr, 1942); they can be millions of years old (Gómez et al., 2002), but remain morphologically indistinguishable (Westram et al., 2011). Several examples are available of such hidden diversity, and cryptic species are now more common than previously appreciated (Pfenninger and Schwenk, 2007; Westram et al., 2011). The proportion of cryptic species is almost evenly distributed among major metazoan taxa and biogeographic regions (Pfenninger & Schwenk, 2007).
Crytic species are often distinguished based on molecular data, differences in mating behavior, chemical signals, physiology, habitat preferences, etc. (Pfenninger & Schwenk, 2007; Rissler & Apodaca, 2007; McCormack et al., 2009; Westram et al., 2011). It has been proposed that cryptic species are more likely to occur in organisms that communicate reproductive signals via nonvisual means (e.g. sound, vibration, pheromones or electrical signals), because this would not require extensive morphological change (Bickford et al., 2006). An example of this is Drosophila paulistorum in which semispecies cannot be distinguished by morphology, but they have different courtship song patterns (Ritchie & Gleason, 1995).

Technical molecular advances have also allowed the identification of genetically divergent but morphologically cryptic lineages (Pfenninger & Schwenk, 2007). For instance, in the picoplanktonic algae Micromonas pusilla the ‘morphospecies’ actually contains several independent lineages that are morphologically undistinguishable. The divergence date for these cryptic species complex was estimated to be 65 Mya indicating that they are not recent species in the process of differentiation (Slapeta et al., 2006). In ascidians, the genera Pycnoclavella Garstang, 1891 and Clavelina Savigny, 1816 in the Atlanto-Mediterranean area were controversial, but a recent study with CO1 revealed the validity only of Pycnoclavella, with a great diversity within the genus and the addition of three new species. All these species were corroborated by a detailed morphological revision (Perez-Portela et al., 2007). Another example comes from a bat species, in which mtDNA suggested historically separated lineages, further hypothesized to be cryptic species (Dewey, 2006). On the other hand, STRs showed recent gene exchange between these same lineages (Lausen et al., 2008). Based on those results, the two populations were suggested to be separated in the past, and after a secondary contact, they became sympatric and interbreed (Lausen et al., 2008).

In theory, there are three processes that could explain the evolution of phenotypic conservatism in cryptic species complexes (Smith et. al, 2011): (1) neutral genetic drift: morphological similarities in historically isolated lineages are attributed to non-adaptive evolution, where alleles will be carried forward or will disappear by chance (Bostwick & Brady, 2002) and phenotypic differences will be accumulated proportionally through time (Lynch, 1990); (2) stabilizing selection: lineages exhibit long-term morphological stasis due to selection against extreme phenotypic characters, preventing divergence of form and function (Schneider & Moritz, 1999; Wiens & Graham, 2005); (3) correlated evolution: morphological traits, from multiple evolutionary origins, associated with similar ecological occupation suggest that particular habitats elicit comparable adaptive responses (Schulter & McPhail, 1993; Harmon et al., 2005). For example, in environments under harsh conditions there is a limited number of way in which an organism can adapt (Nevo, 2001). Extremophiles are expected to converge in morphology and speciate via genetic drift or through cryptic speciation mechanisms (pherormonal or behavioral differentiation) (Bickford et al., 2006).

The existence of unrecognized cryptic species can have profound consequences for interpreting the outcome of different studies (Pfenninger & Schwenk, 2007). Efficiently and accurately delimiting species is one of the most basic and important aspects of systematics because they are the fundamental unit of analysis in biogeography, ecology, evolution and conservation (Rissler & Apodaca, 2007). As cryptic species may differ in biological characteristics, species identification is fundamental to ensure the comparability between basic and applied research studies (Westram et al., 2011). Erroneously identification of cryptic species can also mislead biodiversity estimates, mask ecological interactions, and lead to inappropriate conservation and management programs (Seifert, 2009). Studies have shown, for example, that some of the so-called ‘generalist’ species are in fact cryptic complexes of specialists (Bickford et al., 2006). Furthermore, cryptic pathogens, parasites and invasive species might represent unrecognized threats to human health (Pfenninger & Schwenk, 2007; Westram et al., 2011). Understanding and quantifying biological diversity is imperative if we want to explain it and conserve it (Bickford, et al., 2006).

Different approaches are used to deal with cryptic species, as follows:

1.1 Morphology

In many cases, a complete revision of the external and internal morphology of organisms is necessary to establish diagnostic characters to properly define species. In general, organisms with similar morphology can only be differentiated by the identification of few inconspicuous characters, which may need several integrative techniques. An example is the genus Ascidia (Ascidiidae, Tunicata) in which only the species A. curvata (Traustedt, 1882) was recorded in the Brazilian coast, but a recent revision revealed significant morphological differences between samples, allowing the identification of three species: A. curvata A. tenue Monniot, 1983 and the new species A. nordestina Bonnet & Rocha, 2011 (Bonnet & Rocha, 2011). Another example is the testate amoebae whose detailed morphological analysis is essential to understand and identify both modern and fossils organisms (Porter et al., 2003).

1.2 Population genetics

The application of molecular approaches has enabled the identification of cryptic species in several taxa, most importantly those of conservation concern (Allendorf & Luikart, 2006; Wenner et al., 2012). For example, in the species complex of the Neotropical parrot Amazona farinose, mitochondrial and nuclear markers helped identifying the Central American and the South American lineages as separate species, the former one being endangered (Wenner et al., 2012). Furthermore, authors suggested that two different management units exist within the South American lineage (Wenner et al., 2012). Genetic methods that aim to assess gene flow help disentangle if distinct populations of two supposed cryptic species are still interbreeding, or not, and to what extent (Lausen et al. 2008).

1.3 Niche modeling

Ecological niche modeling uses georeferenced data from specimen records in combination with environmental data layers, using different algorithms, to identify areas of predicted presence (Soberón & Peterson, 2005; Rissler & Apodaca, 2007). These predicted regions of occurrence represent the fundamental niche, and they are presumed to contain the suite of environmental conditions necessary to maintain a viable population, without considering biotic interactions and spatial barriers (Hutchinson, 1957; Graham et al., 2004; Rissler & Apodaca, 2007). Ecological niche modeling is proved to be a powerful approach to understand how abiotic factors (e.g., temperature, salinity) impact the geographic limits of lineages and species (Graham et al., 2004; Wiens & Graham, 2005; Rissler & Apodaca, 2007). In addition, ecological niche models based on environmental data can make strong inferences when diagnosing species (Sites & Marshall, 2003; Wiens & Graham, 2005), especially cryptic ones (Rissler & Apodaca, 2007; McCormack et al., 2009).
Spatially explicit ecological data allow for large-scale tests of whether speciation is associated with niche divergence or whether closely related species tend to be similar ecologically (niche conservatism) (Rissler & Apodaca, 2007; McCormack et al., 2009). There are three main possible scenarios: (1) if a cryptic complex species is geographically separated by areas that are outside of the climatic niche envelope of all of the cryptic species, then gene flow within these species is unlikely because it would involve crossing unsuitable habitat. This pattern would support the hypothesis that the cryptic species represent distinct species (Wiens & Graham, 2005); (2) if a cryptic complex species occur under climatic conditions without overlapping, then gene flow between them may also be unlikely, and they may represent distinct species (Wiens & Graham, 2005); (3) if a cryptic complex species had similar ecological niches with no biogeographic barrier separating their distributions and high-quality habitat for them at the contact zone, then it is less likely that the cryptic species are distinct with no gene flow (Rissler & Apodaca, 2007).

1.4 Transcriptomics

A promising tool to address the differentiation among cryptic species is transcriptomics (Goetz et al., 2010). If there are actually several species within a species complex it is probable that their genetic divergence can also be reflected at the level of gene expression. Two or more species could, for instance, have diverged adaptively, altering the structure of their gene regulatory network through some few mutations in regulatory elements (Bernatchez et al., 2010). This in turn can increase adaptation of the cryptic species to its new niche. Therefore, transcriptome analyses can allow understanding to what extent cryptic species differ and what are the key genes that are involved in their ecological differentiation. Next generation sequencing can aid in transcriptome profiling providing a far more precise measurement of expression patterns than other methods (Wang et al., 2009). Analysis of gene expression in non-model species aims to identify specific patterns of heritable gene expression levels that are compatible with the action of natural selection (Romero et al., 2012).


The Sacrivzoa sp. (Fig. 1) are intertidal organisms distributed along the Brazilian Atlantic coast (Fig. 2a). They are about five centimeters long and have a direct life cycle (Sacrivzoa Team 2012a). The group Sacrivzoa was initially described as monospecific based on wing morphology analyses (Sacrivzoa Team, 2012a). However, a recent phylogeny based on nuclear and mitochondrial molecular markers suggested that there could be cryptic species in this group (Sacrivzoa Team, 2012b). This study identified a haplotype (Clade A) distributed mainly in northern Brazil, and a haplotype (Clade B) distributed in southern Brazil (Fig. 2) (Sacrivzoa Team 2012b). A very narrow contact zone was identified in Ilhabela, in the northern coast of São Paulo State (Sacrivzoa Team 2012b). The two clades probably diverged about 10 Mya (Sacrivzoa Team 2012b).Since the Sacrivzoa are endangered in some parts along its distribution range, a more integrative approach is urgently needed to correctly identify species for guiding efficient conservation actions.


Figure 1. External morphology of an adult of Sacrivzoa sp., showing main features.

In this study, we propose to test the following hypotheses: 1) The Sacrivzoa cryptic species are reproductively isolated and differ at detailed morphological characters, ecological niche, and gene regulatory networks; 2) Those differences are a result of adaptation to different ecological conditions. Our more specific objectives are to: i) carry out a detailed morphological revision of the Sacrivzoa sp. complex; ii) access whether distinct lineages of supposed cryptic species maintain gene flow by studying microsatellites’ variability and mtDNA sequences variation; iii) develop ecological niche models using georeferenced samples and environmental data layers; iv) analyze gene expression patterns and across lineages, and test whether the variation is associated with morphological and ecological or behavioral differences. We aim to provide insights into the process of speciation and adaptive ecological differentiation.


Figure 2. (a) Geographical distribution of Sacrivzoa sp. along the Brazilian Atlantic coast. Hypothetical ranges of Clade A (green) and Clade B (blue) are shown. A contact zone probably exists in Ilhabela (SP) (Sacrivzoa Team 2012b). Purple circles denote sampling points for this study. (b) Cladogram indicating evolutionary relationships among Sacrivzoa sp. (Sacrivzoa Team 2012b). Two clearly differentiated genetic clusters were identified among the morphological undistinguishable individuals, hypothesized to be cryptic species.


Previous studies have used a single approach to hypothesize cryptic species in Sacrivzoa sp. However, more often, different data can provide divergent results. Since the Sacrivzoa sp. are endangered in some parts along its distribution range, correctly identifying species is crucial for conservation purposes. Thus, a more integrative approach is needed to identify the occurrence of cryptic species.


4.1 Sampling

Collection will be carried out in three sites for each distinct haplogroup, along the range of the distribution of Sacrivzoa sp. complex (Fig. 2). Forty individuals (20 males and 20 females) will be collected from each site. At each site we will separate one male and one female for further use in transcriptomics analyses, immediately conserving them in RNAlater®. The remaining individuals will be destined to morphological and population genetic analyses. All individuals will be initially assigned to a haplogroup following Sacrivzoa team (2012b). Collection sites will be georeferenced with precision. Tissue samples will be collected clipping a 0.20 mm fragment from the tip of the tail, stored until processing at -80°C in a microtube with 95% ethanol. DNA will be extracted from tissue using a standard chloroform method (Sambrook & Russell, 2001), resuspended in buffer and stored at -20°C.

4.2 Morphological analyses

We will look for new evidences in the external and internal morphology. The following external characters will be analyzed by the stereoscopic microscopy and described in detail: the number of projections in the border of the siphons, the shape of the body, the shape and length of the tail and the pattern (concave/convex) of the lamination at the top of the arcs. The complete internal morphology will also be studied by this technique, with the aid of the dye Methylen blue. Scanning Electron microscopy and biochemical analyses will be used to investigate the scales in the tail, the toxins in the tongue, the ciliar ultrastructure and the composition of the body wall (by the Energy-Disperse X-Ray Spectroscopy). The chlorophyll present in the chloroplasts will be determined by chromatographic and spectroscopic tools.

4.3 Population genetics

One mitochondrial gene and four nuclear genes will be sequenced. Polymerase chain reaction (PCR) (10ul) will contain 0.4 0 µM of each primer, 150 µM dNTPs, 1x standard PCR buffer (including 1.5 mM MgCl2), 1unit of Taq polymerase, and 50 ng of genomic DNA. PCR conditions will consist of 5 min at 95°C; 35 cycles of 94°C for 60 s, 54°C for 60 s, and 72°C for 90 s; 5 min at 72°C. PCR products will be purified and sequenced on an ABI 3730 sequencer. Forward and reverse sequences will be edited and aligned with SeqMan II. Phase will be assigned for heterozygotes in PHASE (Stephens & Donnelly, 2003). Unique haplotypes and their relationships will be identified using statistical parsimony in TCS v1.21 (Clement et al., 2000) and Bayesian methods in MrBayes (Ronquist et al., 2005).

To further test for geographic patterns in nuclear DNA and to test for evidence of interbreeding in the contact zone, we will develop a panel of 30 taxon-specific microsatellites using 454 pyrosequencing (e.g. Castoe et al., 2010; Malausa et al., 2011). Microsatellites will be amplified by PCR. Standard PCR conditions will be: 2 min at 95°C; 30 cycles of: 95°C for 30 s, annealing temperature for 30 s, and 72°C for 1 min; 10 min final extension at 72°C. PCR products will be sized on an ABI 3730 sequencer. To search for clusters of similar individuals based on microsatellite allele frequencies, the program STRUCTURE v2.3 (Pritchard et al., 2000) will be used, further applying the Evanno et al. (2005) procedure to interpret the results. The spatial distribution of groups will be plotted in GENELAND (Guillot et al., 2005). To test the significance of structuring we will use AMOVA, implemented in GENALEX v6.4 (Peakall & Smouse, 2006). To test whether genetic distance between populations is significantly related to their geographic distance, a Mantel test will be performed in GENALEX (Peakall & Smouse, 2006). A Principal Coordinates Analysis (PCA) on microsatellite allele frequencies will be performed to look for genetic evidence of reproductive barriers among individuals in the populations. If reproductive isolation is complete, we expect two discrete groups to be visible in the PCA. If the two lineages can reproduce but produce infertile offspring, we would expect two discrete groups representing each lineage with a third discrete group between them, representing the F1 hybrids. If no reproductive barriers exist, or if reproductive barriers are present but incomplete, we would expect to see all individuals in a single cluster in the PCA analysis (O’Donnell & Mock, 2012).

4.4 Niche modeling

Presence/absence data is available from a previous sample collection carried out for molecular phylogeny analysis (Sacrivzoa team 2012b), and new data will be recorded for this study. Environmental data will include raster geographic information system grid layers summarizing yearly averages of remotely sensed environmental parameters, with different resolutions. The algorithm of maximum entropy (MAXENT, Phillips et al., 2004, 2006) will be used for niche modeling analysis (presence-only data). MAXENT models will be imported into GIS (ESRI). The resulting grids will be converted to a binary prediction of presence versus absence by choosing the lowest threshold at which the species was known to occur (Rice et al., 2003; Pearson et al., 2006). The significance test will be employed to evaluate model performance follows Anderson et al. (2002, 2003): given the proportion of pixels predicted present versus absent, we tested whether occurrence points fell into pixels predicted present more often than would be expected at random.

4.5 Transcriptomics

In order to explore the differences in gene expression patterns and variability of coding regions between cryptic species, RNA will be isolated and sequenced using 454 Roche pyrosequencing with 8-10x coverage, at the University of São Paulo. The resulting reads will be assembled using pre-existing pipelines based on python scripts developed in our lab (Sacrivzoa Team, unpubl. res.). To quantify expression patterns and explore influence of different factors on them, the software TopHat and Cufflinks will be used (Trapnell et al., 2012). Differences in gene expression between two cryptic lines of Sacrivzoa sp. will be tested (controlled for sex differences), as well as associations of differentially expressed genes with environmental variables.

4.6 SNP development and genotyping

A set of single nucleotide polymorphism (SNP) markers will be developed using RNA sequences of 12 individuals obtained with RNA-Seq approach. At least 25 specimens per population will be genotyped (350-450 SNPs). Then, we will detect loci under selection and elucidate patterns of adaptive genetic diversity by testing for correlations between these loci and environmental variables. The detection of such loci bases on a presumption that positive selection operating on a locus increases population divergence, while balancing selection keeps it low (Storz, 2005).


Table 1. Timeline – Chart showing different stages of the project based on a start date of January 1, 2013.





All researchers involved in this proposal are experts in their fields of study and have full support from their academic institutions. Each part of the project will be coordinated by the researchers, as follows:
- Morphological analyses: Nadia Bonnet & Luana Soares
- Population genetics: Munique Mendonça, Carolina Miño & Evgeniy Simonov
- Niche modeling: Lucília Miranda
- Transcriptomics: Feng Gao, Sergio Muñoz & Evgeniy Simonov


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