Darwinian shortfall and macroecological patterns in genetic data of Tocantins-Araguaia basin fishes

Gabriel Nakamura^1,2 , Leonardo Carlos Jeronimo Corvalán^3,4, Laura Barreto de Paula-Souza^2,5, Lívia Estéfane F. Frateles⁶, Guilherme Rogie Gonçalves Tavares⁶, Jullia Costa Pereira^3,6, Diego Ortiz da Silva^3,6, Carmen Barragán-Ruiz^2,3, Flavia de Paiva Breseghello⁷, Nina Machado Suzigan⁶, Cintia Pelegrineti Targueta^3,8, Geiziane Tessarolo², Fabrício Barreto Teresa⁹, Murilo Sversut Dias⁵, Lucas Jardim¹⁰, Rafael Barbosa Pinto^2,11, Rhewter Nunes^3,4, Mariana Pires de Campos Telles^3,12 and José A. Felizola Diniz-Filho¹³

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Abstract​

Introduction​

Biodiversity data is often limited by sampling biases, gaps, and other problems that affect our ability to understand underlying ecological and evolutionary patterns (Hortal et al., 2015). These kinds of limitations can hinder effective data-driven conservation strategies (Whittaker et al., 2005; Bini et al., 2006; Moura, Jetz, 2021; Urbina-Cardona et al., 2023). Hortal et al. (2015) presented seven key biodiversity shortfalls – Darwinian, Eltonian, Hutchinsonian, Linnean, Prestonian, Raunkiæran, and Wallacean, pinpointing knowledge gaps for different aspects of biodiversity and stressing the consequences of these shortfalls for ecological and evolutionary research. Of these shortfalls, three are more associated with a systematic (taxonomy and evolutionary relationships) and biogeographic perspective: a lack of knowledge regarding how many species exist for a given group (Linnean), their geographic distribution (Wallacean), and their evolutionary and phylogenetic relationships (Darwinian). The other shortfalls involve gaps in knowledge about population and demographic processes (Prestonian), functional traits among species (Raunkiæran), and the different components of species’ ecological niches (Hutchinsonian and Eltonian). Yet other shortfalls have been proposed subsequently (e.g., Roque et al., 2019; Faria et al., 2021; García-Girón et al., 2023), and the overall framework is being continuously developed.

The Linnean shortfall has at least two components that would explain the increasing number of species in the future (Hortal et al., 2015). The first relates to the unknown species, referring to taxa that have not yet been collected in poorly known regions or unsuspected material available in museums and collections (alpha taxonomy). The second component refers to refining taxonomic criteria to delimit already known species and better organize the hierarchical structure among taxa (beta taxonomy). For instance, Freeman, Pennell (2021) revealed a geographical structure in the “quality” of species’ delimitation, leading to variation in biological patterns and, consequently, in the interpretations of macroecological and macroevolutionary patterns.

The second component of the Linnean shortfall (i.e., beta taxonomy) may lead to changes in subdivisions of previously known species, thus with minor impacts on the overall estimates of phylogenetic diversity (e.g., Mace et al., 2003; Diniz-Filho et al., 2013, 2023; Lum et al., 2022), but with pronounced impacts in other biodiversity estimates. For example, splitting of known species in two or more can lead to changes in short branch lengths, which in turn can bias the estimates of diversification rates, reduce the Darwinian shortfall, and change the interpretations, for instance, of the evolutionary dynamics underlying the latitudinal gradients of species richness or patterns of phylogenetic endemism (Diniz-Filho et al., 2023; Tavares et al., 2024). These issues reveal that biodiversity shortfalls cannot be considered independent and may act independently, depending on the geographical and phylogenetic scales of the patterns under investigation.

Thus, when challenging both Linnean and Darwinian (as well as Wallacean) shortfalls in a systematic context, it is essential to explain biodiversity patterns and define more efficient strategies for conservation. The strategies could involve, for example, the development of techniques or sampling methods that allow a better delimitation of species (see Urbina-Cardona et al., 2023, for a recent review and compilation). Species delimitation, i.e., correctly identifying an individual to the lowest taxonomic level (species), is of pivotal importance in biodiversity studies. Given the importance, there is an increasing interest in finding variables that could be proxies or indicate situations in which the species delimitation is dubious or uncertain. The poor delimitation of species, especially in tropical regions, can be associated with macroecological variables (e.g., more speciation in the tropics) and sociological variables (e.g., less infrastructure, historical exploration, to cite a few) that could be used as surrogates of the quality of species delimitation and could be used to evaluate the magnitude of the second component of the Linnean shortfall — beta taxonomy. For instance, Cerezer et al. (2023) postulated that small-bodied Neotropical freshwater fishes inhabiting high-elevation small rivers are more likely to experience rapid allopatric speciation, increasing overall species richness for the group.

Considering the above issues, increasing the number of population genetic structure and phylogeography studies across a wider range of species is essential to mitigate Linnean and Darwinian shortfalls. This would ensure better criteria for species delimitation and more accurate knowledge of phylogenetic structure, allowing better estimates of phylogenetic and biogeographical patterns. Furthermore, the use of DNA barcodes and similar molecular markers, assuming they are properly geo-referenced and available, are important sources of data that can increase the accuracy of phylogeny estimates. Therefore, a first step towards better understanding species delimitation is identifying species with poor coverage in genetic information to obtain more refined genetic and genomic data for multiple taxa, allowing the development of more refined molecular markers that could be used for further investigation.

Here, we aim to describe the current understanding of genetic and genomic data for fish species in the Tocantins-Araguaia River basin and discuss how Linnean and Darwinian shortfalls limit our ability to understand underlying ecological and evolutionary patterns. In doing so, we also suggest guidelines for filling conspicuous gaps in genetic information that challenge ongoing conservation efforts in the basin. We compiled species-level descriptors that considered the genetic information available for species from public databases we sampled. We also evaluated the relationship between the availability of the most commonly used genomic regions and potential molecular markers in phylogenetic studies with macroecological variables (e.g., number of occurrences for each fish species in occurrences database) and taxonomic patterns (e.g., description dates, number of synonyms for each valid species the Darwinian shortfall – the amount of phylogenetic information accounted by missing species in the most comprehensive phylogeny available).

The Tocantins-Araguaia River basin is a good model for exploring Darwinian and Linnean shortfalls. The basin has a highly diverse and endemic fish fauna, with about 700–750 species (e.g., Dagosta, de Pinna, 2019; Chamon et al., 2022; Reis et al., 2024), which challenges taxonomic inventories and stresses the importance of high-quality schemes of species delimitations and investments. Most of the basin is also located within the Cerrado domain, a global biodiversity hotspot with high plant endemism (Myers et al., 2000; Klink, Machado, 2005; Latrubesse et al., 2019; Colli et al., 2020). In the socioeconomic context, the Tocantins-Araguaia basin is important for supplying water to Brazil’s northern and central-west regions (Latrubesse et al., 2019). However, the basin is also threatened by several factors, including establishing dams, pollution, illegal hunting, and excessive water exploitation for intense agricultural expansion (Pelicice et al., 2021; Santana et al., 2021). Given these threats, the fish fauna of the Tocantins-Araguia basin is under threat of extinction and localized extirpations.

Material and methods

Species list. We used a list of species in the Tocantins-Araguaia basin based on Dagosta, de Pinna (2019), which we updated by including missing species, synonyms, and new occurrence records provided by Chamon et al. (2022) and references therein. From this list, we recognized only species considered valid by Fricke et al. (2024). This resulted in a final list with 748 species, out of 751 species listed by Chamon et al. (2022). In the context of Linnean shortfalls, taxonomic uncertainty metrics for each species were represented by counting the total number of synonyms, past valid names, and the date of first description of the species, once again based on Fricke et al. (2024).

Phylogenetic relationships and Darwinian shortfall. We used Cassemiro et al. (2023), a phylogenetic tree for South American freshwater fishes, as our framework for assessing Darwinian shortfalls. For the fish fauna of the Tocantins-Araguaia River basin, only 446 species out of the 748 in our initial list were contained in this phylogeny, so we performed a phylogenetic imputation procedure for missing species at genus (n = 213) or family (n = 88) levels, using the rtrees package (Li, 2023) implemented in R (R Development Core Team, 2023). To do so, we grafted missing species to Cassemiro et al. (2023) tree, via a sequential process. First, congeneric species were grafted into the tree. Then, if there were still missing species in the tree, those were grafted to the node corresponding to the most recent common ancestor (MRCA) of the species’ family (Li, 2023). In both cases, the missing species was grafted as a polytomy at the MRCA of the genus or the family. In the case of a genus or family consisting of only one species, the target species was grafted at half of the size of the branch length of the single genus/family representative of the species in the backbone tree (Nakamura et al., 2021; Li, 2023).

The imputed phylogeny was used to evaluate the availability and distribution of genetic data on the phylogenetic tree, estimate the phylogenetic signal in genomic information (i.e., how the genetic data is distributed among species in the tree), and account for inflated Type I errors when modeling the unique and individual genes as a function of macroecological variables and taxonomic uncertainty metrics for each species. Controlling the type I error in this analysis is relevant since the species are not phylogenetically independent entities (species are related to each other), which can influence statistical models by indicating an effect of macroecological variables even if it is not the underlying cause.

The magnitude of the overall Darwinian shortfall was evaluated following the proposition by Nakamura et al. (2022), which comprises a ratio between the length of the branches inserted into the phylogeny (PD inserted) and the total amount of phylogenetic diversity (PD total). Each species has a Darwinian shortfall value, with those species already present in the backbone tree having a value of zero (i.e., no branch length was added into the tree – no Darwinian deficit) and values close to one, indicating a high value of Darwinian shortfall (i.e., the species inserts a long branch accounting for a high amount of phylogenetic history missing in the tree – high phylogenetic deficit).

Molecular data. We obtained the available genomic data for each species from BOLD (Barcode of Life Data System) and the DNA sequence databases of NCBI (National Center for Biotechnology Information). The BOLD database is a workbench for acquiring, storing, and processing DNA barcode records (Ratnasingham, Hebert, 2007). The NCBI is a section of the National Institutes of Health that houses a series of genetic databases and the most significant genetic information, including data about genomes, transcriptomes, raw sequence reads, and short DNA sequences like DNA barcode records. We used BOLD and NCBI since they are complementary and the most commonly used databases for retrieving genetic information about species. We used the refdb R package (Keck, Alternmatt, 2023 – CRAN v. 0.1.1) to query genetic information for all 748 fish species in the Tocantins-Araguaia River basin, using their genus and epithet format for all valid, synonyms and past valid species names. We made two different searches, one in BOLD (using the function refdb_import_BOLD) and another in NCBI (using the function getNCBI). The searches were done to encompass genes and genomes, including both deposited genomic regions and complete genomes. The results were combined in one table (through the function refdb_merge) containing the unique register for all individuals/genome regions of all species and their synonyms. Finally, we merged the synonym names to their current accepted taxonomic name in the table with genetic information.

With this merged table, we calculated species-level descriptors, including the total number of genomic regions (the total number of individual regions in which that region was sequenced in each species), the number of different genomic regions (the total number of unique genomic regions sequenced by each species), and the number of individual regions sequenced for the six most common DNA regions in our database (Cytochrome c oxidase subunit I – COI, cytochrome B – cytB, recombination activating genes-1 – RAG1, recombination activating genes-2 RAG2, myosin heavy chain 6 – MYH6 and ATP synthase subunit 8 – atp8). Despite not ranking among the six most used genomic regions, we also included in the analysis information about 12S since it is widely known as one of the most important regions used in environmental DNA analysis in fishes (Polanco et al., 2021). The number of individual regions sequenced for the six most common DNA regions plus 12S for every species, and the number of unique genomic regions for each species are the response variables for statistical analyses and modeling.

Body size. Body size data for 698 species, expressed as maximum body length, were available from FISHMORPH (Brosse et al., 2021) and FISHBASE (Froese, Pauly, 2021; Humphries et al., 2023). We used a phylogenetic imputation approach for 50 species with missing body size information (Swenson, 2014). We fitted a multi-regime Ornstein-Uhlenbeck model using the l1ou package (Khabbazian et al., 2016) to identify evolutionary shifts in body size optima. Modeling trait distribution on large phylogenetic clades requires a realistic representation of the underlying evolutionary model (Beaulieu et al., 2013). Large clades are probably under different environmental pressures that could result in multiple adaptive landscapes for traits like body size. Two main models have been proposed to identify evolutionary shifts in trait’s mean expectation, Brownian motion (BM) models and Ornstein-Uhlenbeck (OU) models (Beaulieu et al., 2012; Uyeda, Harmon, 2014; Khabbazian et al., 2016; Bastide et al., 2018; Castiglione et al., 2018). By using l1ou, we were able to select models that best described the trait distribution on the tips, optimizing the number of shifts, their positions, evolutionary variance (σ²), and selective strength (ɑ) (Khabbazian et al., 2016). Therefore, we selected the model to impute missing traits based on their selective strength that could differentiate BM to OU model and identify the number and position of the evolutionary shifts.

We imputed missing data as the sum of species regime optima and their residual deviations from the root state (Garland, Ives, 2000):

Y = XB + ℇ

where XB is the regime optima and ∈(CiC-1 (X – µ))is the phylogenetic residual mean. C is the variance-covariance of all species in the phylogeny transformed by the fitted Ornestein-Uhlenbeck model. C_i is the species column in C, X is the observed species body sizes and µ the fitted root state.

Occurrence records. We downloaded occurrence records of the 748 species from three sources: 1) Global Biodiversity Information Facility (GBIF) (https://www.gbif.org/); 2) SpeciesLink (https://specieslink.net/search/), Sistema de Informação sobre a Biodiversidade Brasileira (SiBBR)(https://www.sibbr.gov.br/); 3) Portal da Biodiversidade (https://www.gov.br/icmbio/pt-br/assuntos/programas-e-projetos/portal-da-biodiversidade – currently deactivated) and Neotropical Freshwater Fishes (Tonella et al., 2022).

Once all records were compiled, we recorded the total number of occurrences of each species, the proportion of occurrences within the Tocantins-Araguaia basin, and endemism (i.e., a value indicating if the species have no records out of the basin, if species have occurrences found only within the basin it is considered endemic). For species with more than three occurrences in the occurrence databases searched, we calculated the area of occurrence (in square meters) by calculating a convex hull and transforming it into a spatial polygon using the sf package (Pebesma, 2018). Some of these variables were used in the statistical models (number of synonyms, total occurrence records, date of first description, Darwinian shortfall, and body length) and some were used only as general descriptor variables to support our interpretation of the patterns in genetic information distribution among species (endemic status and area of occurrence).

Statistical analyses and modelling. We initially described the frequency of individual genomic regions and unique genomic regions across the species list. We explored their phylogenetic patterns based on Moran’s Iphylogenetic correlograms (Legendre, Legendre, 2013) after log-transformation of the number of sequences for each species to reduce skewness in their distributions. Moran’s I is a metric that is used to determine whether the distribution of a characteristic is influenced by the phylogenetic relationships among species (phylogenetic signal). Significant positive values of Moran’s I statistic indicate that species that are phylogenetically similar also present similarities regarding their number of genomic regions and/or unique genomic regions sampled. To calculate Moran’s I patristic distances among species based on the phylogeny were scaled to vary between 0 and 1 and divided into five equal classes to obtain the correlograms.

We modeled the log-transformed number of individual genomic regions (for each one of the seven regions mentioned in subsection Molecular data) and the number of unique genomic regions for each species as a function of the date of first description, number of synonyms, number of occurrences in the Tocantins-Araguaia basin, relative PD (Darwinian shortfall), and log-transformed body length. The number of genomic region sequences can be viewed as a proxy for the genetic sampling effort of a given species for each region. However, it is worth noting that this quantity differs from the number of individuals with genetic information since it is hard to differentiate the individuals in the BOLD and NCBI databases. The number of synonyms can be viewed in two ways. First, as a proxy of taxonomic effort; species with more synonyms may be more taxonomically stable since they were submitted to greater taxonomic scrutiny. On the other hand, the number of synonyms indicates more taxonomic uncertainty, where there is discordance regarding the taxonomic status of a given species. In the context of a species’ relationship with genetic information, the second case is of greater interest since genetic data can help to clarify the taxonomic status of species.

Because of the phylogenetic signal detected using Moran’s I correlograms in the response variables and residuals of an Ordinary Least-Squares (OLS) multiple regression, a Phylogenetic Generalized Least Squares (PGLS) was applied, using a flexible λ, allowing the incorporation of different strengths of phylogenetic structure in data (with λ = 1 indicating strong phylogenetic structure assuming a Brownian motion, and λ = 0 indicating no phylogenetic structure in data, so PGLS converges to an OLS), avoiding potential inference problems (type I error). Nagelkerke’s pseudo-R² was calculated based on likelihood ratios. Phylogenetic analyses were performed in the R packages ape (Paradis, Schliep, 2019), phyloglm (Ho, Ané, 2014), and phytools (Revell, 2024).

Results​

Out of the 748 species in the reviewed Tocantins-Araguaia River basin ichthyofauna list, about 16% (N = 117) of species are endemic to the basin, with a median of 22% of occurrence of all records for each species found within the basin. Accounting for missing data (46 species) in our compiled list, we estimated that 21% of the species are endemic to the basin, a value close to the one reported by Chamon et al. (2022).

Information about molecular sequences is available for about 67% of the species studied here, with an average of 30% of the species with sequences available for the seven genomic regions. We obtained genetic information for 69% of non-endemic species, versus only 26% of species for endemic species. Across both endemic and non-endemic species, the COI region emerges as the primary repository of genetic data per species, encompassing approximately 59% (N = 444 species) (Fig. 1A). Conversely, the atp8 region yielded fewer sequences, providing no information for any endemic species. Among the five genetic regions with the highest number of species with available data, only 107 species offered information for all five regions (COI, cytB, rag1, rag3,and myhB) (Fig. 1B). Accordingly, the mean Darwinian shortfall estimated for the endemic species is three times higher than for non-endemic species (F = 71.2; P << 0.01). A general overview of the phylogenetic relationship of 748 fish species with their respective information on the six most common molecular markers is shown in Fig. 2.

FIGURE 1| Fish species in the Tocantins-Araguaia River basin with genetic information for COI, cytB, rag2, rag1, myh6, 12S, atp8 regions. A. Number of species with genetic information per region; B. Venn Diagram illustrating the five regions with the highest number of species with genetic information. The numbers within each shape represent the species with genetic information for a combination of regions.

FIGURE 2| The phylogenetic tree and distribution of genetic information are represented by the five most common genetic regions and 12s gene (concentric tiles) for 748 fish species. The gray areas close to the tips highlighted in the phylogeny represent the five most common families regarding the amount of genetic information (Characidae, Loricariidae, Serrrasalmidae, Anostomidae, and Pimelodidae). The color in the tiles represents the presence of a given genetic region for each species in the phylogeny. The tip points in the phylogeny indicate if the species was grafted in the backbone phylogeny.

Moran’s I correlograms reveal a phylogenetic signal in coverage of genetic regions. The total number of unique genomic regions available for the species shows a low but significant autocorrelation in short phylogenetic distance classes (I = 0.146 for species related at 10% of maximum distance), followed by Moran’s I close to zero for all other distance classes (Fig. 3). The number of individuals sequenced for each of the most used genetic regions have higher values for Moran’s I at these short phylogenetic distances, ranging from 0.317 for RAG2 to 0.029 for 12S (Fig. 3; Tab 1). The correlogram for RAG2 shows a more clinal pattern in which there is a gradient in phylogenetic autocorrelation up to mean phylogenetic distances (Fig. 3). All regions follow the same patterns, with significant Moran’s I only in the first distance class (except for MYH6 and atp8, for which no significant autocorrelation is found for Moran’s I).

TABLE 1 | PGLS standardized regression coefficients for the effect of the number of synonyms (Synonyms), date of first description (Date), number of occurrences (Occurrence), Darwinian shortfall (PD), and Body Length (BL) on the number of unique genetic regions across species, and for the number of individual regions of each species sequenced for six genes more commonly used (COI, CytB, RAG2, RAG1, MYH6, atp8,and 12S). Coefficients in bold indicate significant (P < 0.05) effects. Also shown are the λ fitted in PGLS and the pseudo-R² of the model. Moran’s I coefficients estimate the phylogenetic autocorrelation coefficient in the first distance class of the correlogram for each region (see Fig. 3).

FIGURE 3| Phylogenetic correlograms were built using Moran’s I autocorrelation coefficient for five distance classes, the number of different genetic regions (open circles), and the number of individual genetic regions sequenced for RAG2 (closed circles).

Given the statistically significant Moran’s I phylogenetic autocorrelation equal to 0.102 (P < 0.019), we used a PGLS with a flexible λ to model the effects of predictors on genetic regions. The PGLS models with the high explanatory power (i.e., R² > 0.5) were found for the number of unique regions and the number of individual regions sequenced for COI (Tab. 1), with regression coefficients with similar magnitudes and directions. The species best characterized for genetic regions are those with more occurrences and exhibit a lower Darwinian shortfall in phylogeny. Smaller effects were found for a higher number of synonyms (more genes for species with more synonyms) and species described earlier (i.e., more unique genes and more number of total genes for species described first than species described recently) (Tab. 1). Regarding body size, we found coefficients close to zero in all analyses but COI (with a small but positive coefficient).

Discussion​

Our analyses reveal patterns of lack of knowledge on genetic and molecular data for Tocantins-Araguaia ichthyofauna. The knowledge gaps are associated mainly with the poor coverage of genetic information on endemic species. This is probably associated with the low coverage of genetic samples for tropical species, since most of genetic samples are mainly related to non-tropical species in the databases we used (BOLD and NCBI), as identified for other taxonomic groups and tropical megadiverse countries (Noreña et al., 2018; Arana et al., 2024). To our knowledge, this is the first assessment of genetic information available in one of the main tropical basins in South America. Also, the analyses here point out a series of methodological issues related to the analyses of Linnean, Wallacean, and Darwinian shortfalls. Since molecular data is currently the most reliable information to construct phylogenetic relationships among species (e.g., Hughes et al., 2018; Rabosky et al., 2018) and also permits the use of barcoding techniques for species identifications and delineations (e.g., Boza et al., 2022; Souza et al., 2023), or to evaluate overall biodiversity patterns (i.e., Sales et al., 2021; Lin et al., 2023; Diniz-Filho et al., 2024), the lack of genetic information in endemic species of Tocantins-Araguaia hampers the understanding of biodiversity patterns and processes in this important tropical basin.

It is worth noticing that this very low coverage of molecular information for endemic species in the Tocantins-Araguaia basin is coherent with the relatively high number of species that were imputed in Cassemiro et al. (2023) phylogeny for analyses, resulting in a much higher Darwinian shortfall for endemic species using Nakamura et al. (2021) approach. The PD deficits (amount of Darwinian shortfall by each species) are one of the most important predictors for both the number of unique genetic regions and the total number of individual regions by species, according to our PGLS model. This result indicates that the lack of knowledge regarding phylogenetic relationships among fish species in the Tocantins-Araguaia River basin is related to the lack of genetic information needed to build reliable phylogenies once molecular information is essential in producing megaphylogenies.

By considering the overall lack of knowledge and sampling in the Tocantins-Araguaia (Almeida et al., 2021), even species that have some information on molecular data available, it is more likely that the populations or individuals from which the genetic data were taken are not within the basin (although this is not straightforward to evaluate due to a generalized lack of coordinates of genetically evaluated individuals). Once the effects of environmental changes depend on the scale and local context, the lack of local samples and coordinates in genomic data impedes a better understanding of the genetic structure and historical processes acting in these local populations. Therefore, we also stress the need to increase efforts to obtain spatially explicit genetic information on populations within the Tocantins-Araguaia River basin, especially in the face of climate change, which can drive the loss of genetic diversity of natural populations, homogenizing genetic variability and hampering these natural populations by limiting its capacity to deal with environmental changes (Lima et al., 2017).

Also, there is a bias regarding the genetic information for fish species, as COI is the region most available in BOLD and NCBI. Part of this is because our search was based on BOLD, and COI was the first standardized DNA barcode region (i.e., Hebert et al., 2004). Additionally, using COI region to DNA barcode exhibits a high ability to distinguish between species (Zangl et al., 2022). However, it is also important to highlight that the use of other regions must refine the phylogenetic relationship and molecular identification. It is also worth noting that other important regions to construct phylogenetic hypotheses are scarce for fish species of Tocantins-Araguaia (e.g., 12s, 16s, rag1 and rag2 to cite a few, see Rabosky et al., 2018, for a list of markers used). Therefore, if we also aim to advance toward a better understanding of the phylogenetic relationships of Tocantins-Araguaia fishes, greater efforts should be made to increase the coverage of genetic regions beyond the COI.

We found some short-distance phylogenetic signal in this knowledge of molecular data, which can be likely related to life-history and ecological traits that make species more available for sampling or associated with the systematic practice of reviewing and analyzing entire genus or families using molecular approaches. The phylogenetic signal at shorter distances may be because systematists always make revisions and study genera or entire families (e.g., Lum et al., 2020; Freitas et al., 2021; Diniz-Filho et al., 2023), or also due to phylogeography studies involving species from the same genus of family. Reviewing genera and families will produce more molecular data to separate species and would explain the autocorrelation of genetic data in the first phylogenetic distance classes. Further analyses of phylogenetic endemism in the basin could also reinforce the link between Wallacean and Darwinian shortfalls in a biogeographic context, reinforcing the lack of knowledge of endemic species. This signal must be considered when modeling these molecular data with the PGLS, which provides several insights about coverage and biases in the knowledge of the Tocantins-Araguaia ichthyofauna.

The knowledge of genetic data is positively correlated with the number of occurrences for each species, with more information available for those species with more occurrences (a surrogate for large geographic ranges). This is expected and would indicate a passive sampling effect in which more information is available for wide-ranged species. Reinforcing this argument, PGLS coefficients also showed that more molecular information is available for species described earlier and those with more synonyms. The positive relationship between the availability of genetic information and the number of synonyms can be interpreted in different ways. First, this high number of synonyms may indicate more stability of species when they are related to more taxonomic revisions throughout species’ history. Second, it may reveal more complexity in population structure, generating genetic diversity and eventually hiding new species to be described after future taxonomic revisions (e.g., Parsons et al., 2022). In the last case, we reinforce the need to increase the genetic sampling efforts of local species populations in the Tocantins-Araguaia basin.

There are important consequences for Linnean shortfall in the region if we couple our results (mainly the relationship between the number of occurrences and genetic information) with the relatively poor knowledge of endemic species previously discussed by considering patterns of species discovery in Neotropical fishes, which can be associated with recent patterns of speciation (Freitas et al., 2021; Cerezer et al.,2023). Chamon et al. (2022) pointed out that at least 20 new species were in the process of description at the date of publication of their paper. Although it is impossible to define, solely from this information, how many of these new taxa came from revisions of previously described taxa. Our results based on the data available in NCBI and BOLD suggest that the revisionary process of current species (one of the components of the Linnean shortfall) can be important to achieve a better understanding of the number of species in the Tocantins-Araguaia basin. The main issue is that, as pointed out by Cerezer et al. (2023), widespread species in different hydrographical basins with particular ecological and life-history characteristics, such as small-bodied species with narrow dietary niches and living in specific habitats (lotic environments), are most likely isolated and would turn to be considered, shortly, as new species, or at least new independent evolutionary units for conservation purposes. Here, we obtained a positive relationship between the amount of genetic information available and the number of occurrences. Still, it is important to note that this information might not come from individuals or populations within the Tocantins-Araguaia basin since a considerable portion of the genetic data is from individuals of the same species with occurrence in Tocantins-Araguaia but lacks precise geographic information or, when present, it was sampled from other areas. Thus, evaluating in more detail the population structure and phylogeographical patterns in these species is important because it could lead to the description of new hidden species.

Perspectives and recommendations on the use and sampling of genetic information for freshwater fish species from public databases

From an applied perspective, our analyses point out some pathways for further studies to advance the knowledge of the genetic and molecular diversity of ichthyofauna in the basin. For instance, it would be possible to establish priorities for obtaining new genetic data for the species based on several possibilities. A higher priority should be given to endemic species of the Tocantins-Araguaia basin. By considering the correlation between endemism and the Darwinian shortfall among species, sequencing endemic species would be a goal to get more robust estimates of phylogenies, allowing a more accurate evaluation of ecological and biogeographical patterns in the region.

On a more elaborated level, the other alternative would be, following Cerezer et al. (2023), focusing on describing population structure and phylogeographical patterns of widespread, small-bodied species with particular ecological and life-history characteristics, which would potentialize the probability of finding new species by better delimiting the already described species (the second component of the Linnean shortfall) – even though the effect of body size in our analyses was negligible. Finally, the importance of expanding the genomic regions sampled and targeting regions adequate to assemble phylogenetic relationships among species is worth mentioning, as also highlighted by Janzen et al. (2022).

Our results based on describing and modeling the knowledge patterns of molecular data in the Tocantins-Araguaia ichthyofauna are important to define strategies for mitigating the lack of data and filling gaps, providing a better description of biodiversity patterns and, hopefully, a more accurate basis for further data-driven conservation actions (e.g., Monchamp et al., 2023). Moreover, it is important to consider, when trying to account for these gaps, to disentangle the important interactions between Linnean, Wallacean, and Darwinian shortfalls at a regional scale. Future studies should also focus on integrating the conservation status of fishes in the Tocantins-Araguaia River basin with the availability of genetic information.

Acknowledgments​

This work is a contribution of the Institutos Nacionais de Ciência e Tecnologia (INCT) in Ecology, Evolution, and Biodiversity Conservation funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq grant 465610/2014–5) and Fundação de Amparo à Pesquisa do Estado de Goiás (FAPEG grant 201810267000023), and of the “Araguaia Vivo 2030” program developed under the agreement between the Tropical Alliance Water Research (TWRA) and FAPEG (proc. 202210267000536), with grants to MPCT, JAFDF, CPT, FBT, and RN. LCJC, LEFF, GRGT, JCP, DOS and NMS receive MsC or Doctoral fellowships (PROAP and PROEX) from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and FB a PIBIC fellowship from CNPq. This paper is also part of the “PPBio Araguaia” project supported by CNPq (proc. 441114/2023–7), and work by JAFDF, MPCT, MD, FBT is also supported by several CNPq productivity grants.

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Authors

Gabriel Nakamura^1,2 , Leonardo Carlos Jeronimo Corvalán^3,4, Laura Barreto de Paula-Souza^2,5, Lívia Estéfane F. Frateles⁶, Guilherme Rogie Gonçalves Tavares⁶, Jullia Costa Pereira^3,6, Diego Ortiz da Silva^3,6, Carmen Barragán-Ruiz^2,3, Flavia de Paiva Breseghello⁷, Nina Machado Suzigan⁶, Cintia Pelegrineti Targueta^3,8, Geiziane Tessarolo², Fabrício Barreto Teresa⁹, Murilo Sversut Dias⁵, Lucas Jardim¹⁰, Rafael Barbosa Pinto^2,11, Rhewter Nunes^3,4, Mariana Pires de Campos Telles^3,12 and José A. Felizola Diniz-Filho¹³

^[1]    Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo (USP), Rua do Matão, 321, Cidade Universitária, 05508-090 São Paulo, SP, Brazil. (GN) gabriel.nakamura.souza@gmail.com (corresponding author).

^[2]    Instituto Nacional de Ciência & Tecnologia (INCT) em “Ecologia, Evolução e Conservação da Biodiversidade. ICB, UFG, 74690 900 Goiânia, GO, Brazil. (NMS) ninasuzigan@discente.ufg.br, (GT) geites@gmail.com, (RBP) rafaelbpinto@ueg.br.

^[3]    Laboratório de Genética & Biodiversidade (LGBio), Instituto de Ciências Biológicas I (ICB-I), Universidade Federal de Goiás (UFG), 74690-900 Goiânia, GO, Brazil. (LCJC) leonardocorvalan@discente.ufg.br, (JCP) julia_costa@discente.ufg.br, (DOS) ortiz.dsilva@gmail.com, (CBR) car.hele.ruiz@gmail.com, (CPT) cincintia@hotmail.com, (RN) rhewter@gmail.com, (MPCT) tellesmpc@gmail.com.

^[4]    Laboratório de Bioinformática e Biodiversidade (LBB), Instituto Acadêmico de Ciências da Saúde e Biológicas (IACSB), Universidade Estadual de Goiás (UEG) – Campus Oeste, UnU Iporá, 76200-000 Iporá, GO, Brazil.

^[5]    Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade de Brasília (UnB), 70910-900 Brasília, DF, Brazil. (LBPS) laurabarretops@gmail.com, (MSD) msdias@unb.br.

^6]    Programa de Pós-Graduação em Ecologia & Evolução, ICB, UFG, 74690-900 Goiânia, GO, Brazil.

^[7]    PIBIC/UFG, Graduação em Ciências Biológicas, ICB, UFG, 74690-900 Goiânia, GO, Brazil. (FPB) flavia.breseghello@gmail.com.

^[8]    Hospital Veterinário, Escola de Veterinária e Zootecnia, UFG, 74690-900 Goiânia, GO, Brazil.

^[9]    Programa de Pós-Graduação em Recursos Naturais do Cerrado (RENAC), UEG, Câmpus Central, BR-153, km 99, Zona Rural, 75132-903 Anápolis, GO, Brazil. (FBT) fabricioteresa@yahoo.com.br.

^[10]    Laboratório de Macroecologia, Instituto de Biociências, Universidade Federal de Jataí (UFJ), Campus Jatobá, 75801-615 Jataí, Goiás, Brazil. (LJ) lucas.ljardim9@gmail.com.

^[11]    Universidade Estadual de Goiás, Unidade Universitária de Iporá, Iporá, GO, Brazil.

^[12]    Escola de Ciências Médicas e da Vida, Pontifícia Universidade Católica de Goiás, 74605-010 Goiânia, GO, Brazil.

^[13]    Departamento de Ecologia, ICB, Ecologia UFG, 74690-900 Goiânia, GO, Brazil. (JAFDF) jafdinizfilho@gmail.com.

Authors’ Contribution

Gabriel Nakamura: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing-original draft.

Leonardo Carlos Jeronimo Corvalán: Conceptualization, Data curation, Methodology, Writing-review and editing.

Laura Barreto Paula-Souza: Data curation, Formal analysis, Writing-review and editing.

Lívia Estéfane F. Frateles: Conceptualization, Formal analysis, Methodology, Writing-review and editing.

Guilherme Rogie Gonçalves Tavares: Data curation, Formal analysis, Methodology, Writing-review and editing.

Jullia Costa Pereira: Data curation, Formal analysis, Writing-review and editing.

Diego Ortiz da Silva: Data curation, Formal analysis, Writing-review and editing.

Carmen Barragán-Ruiz: Conceptualization, Data curation, Methodology, Writing-review and editing.

Flavia Breseghello: Data curation, Writing-review and editing.

Nina Machado Suzigan: Data curation, Writing-review and editing.

Cintia Pelegrineti Targueta: Conceptualization, Data curation, Formal analysis, Writing-review and editing.

Geiziane Tessarolo: Data curation, Formal analysis, Writing-review and editing.

Fabrício Barreto Teresa: Conceptualization, Data curation, Supervision, Writing-review and editing.

Murilo Sversut Dias: Conceptualization, Data curation, Supervision, Writing-review and editing.

Lucas Jardim: Formal analysis, Methodology, Writing-review and editing.

Rafael Barbosa Pinto: Conceptualization, Formal analysis, Writing-review and editing.

Rhewter Nunes: Conceptualization, Formal analysis, Supervision, Writing-review and editing.

Mariana Pires de Campos Telles: Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing-review and editing.

José Alexandre Felizola Diniz-Filho: Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing-original draft, Writing-review and editing.

Ethical Statement​

Analyses based on data available in BOLD Systems and NCBI plataforms, registered in SisGen (Sistema Nacional de Gestão do Patrimônio Genético e do Conhecimento Tradicional Associado), license number A104BAE.

Competing Interests

The author declares no competing interests.

How to cite this article

Nakamura G, Corvalán LCJ, Paula-Souza LB, Frateles LEF, Tavares GRG, Pereira JC, Silva DO, Barragán-Ruiz C, Breseghello FP, Suzigan NM, Targueta CP, Tessarolo G, Teresa FB, Dias MS, Jardim L, Barbosa Pinto R, Nunes R, Telles MPC, Diniz-Filho JAF. Darwinian shortfall and macroecological patterns in genetic data of Tocantins-Araguaia basin fishes. Neotrop Ichthyol. 2025; 23(1):e240047. https://doi.org/10.1590/1982-0224-2024-0047

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