Geodiversity is recognized as one of the most important drivers of ecosystem characteristics and biodiversity globally. However, in the northern Neotropics, the contribution of highly diverse landscapes, environmental conditions, and geological history in structuring large-scale patterns of aquatic environments and aquatic species associations remains poorly understood. We evaluated the relationships among geodiversity, limnological conditions, and freshwater ostracodes from southern Mexico to Nicaragua. A cluster analysis (CA), based on geological, geochemical, mineralogical, and water-column physical and chemical characteristics of 76 aquatic ecosystems (karst, volcanic, tectonic) revealed two main limnological regions: (1) karst plateaus of the Yucatán Peninsula and northern Guatemala, and (2) volcanic terrains of the Guatemalan highlands, mid-elevation sites in El Salvador and Honduras, and the Nicaraguan lowlands. In addition, seven subregions were recognized, demonstrating a high heterogeneity of aquatic environments. Principal component analysis (PCA) identified water chemistry (ionic composition) and mineralogy as most influential for aquatic ecosystem classification. Multi-parametric analyses, based on biological data, revealed that ostracode species associations represent disjunct faunas. Five species associations, distributed according to limnological regions, were recognized. Structural equation modeling (SEM) revealed that geodiversity explains limnological patterns of the study area. Limnology further explained species composition, but not species richness. The influence of conductivity and elevation were individually evaluated in SEM and were statistically significant for ostracode species composition, though not for species richness. We conclude that geodiversity has a central influence on the limnological conditions of aquatic systems, which in turn influence ostracode species composition in lakes of the northern Neotropical region.
Geodiversity is defined as the natural variety of geological (bedrock), geomorphological (elevation), soil features and assemblages (mineralogy, sedimentology), fossils, and hydrological features of a landscape (Gray, 2004, 2019; Schrodt et al., 2019). Geodiversity influences aspects of regional or local climate (Vartanyan, 2006b; Hu et al., 2020), and by interacting with the biosphere and the atmosphere, can also contribute, through sediment delivery, to the input of nutrients into ecosystems and determine the chemical composition of environments (Vartanyan, 2006a; Bravo-Cuevas et al., 2021). Geodiversity of a given region can be evaluated from different perspectives by using selected indicators such as elevation or a broad range of measured elements (Gray, 2018; Zarnetske et al., 2019; Bravo-Cuevas et al., 2021; de Paula et al., 2021).
Biodiversity, defined as the variety of life forms in a place on Earth (Huston, 1995), is strongly related to geodiversity, as species are and have been distributed in response to landscape features in the geological time (Mittelbach et al., 2007; Etienne and Apol, 2009; Smith et al., 2010; Bryson et al., 2013; Gillespie and Roderick, 2014; Steinbauer et al., 2016). Linkages between biodiversity and geodiversity are complex and usually difficult to track, since geodiversity patterns and ecological processes may be discernible only to a certain geographical extent, from landscapes to territories (Zarnetske et al., 2019; Alahuhta et al., 2020; Ren et al., 2021). Understanding causal relationships between geodiversity parameters and biodiversity patterns is currently a priority and a hot topic globally because of their relevance for conservation (bio- and geoconservation; Crofts, 2019), ecosystem management (Bravo-Cuevas et al., 2021), and prediction of ecosystem responses to future climate scenarios (Martiny et al., 2006; Hulsey et al., 2010; Jiménez-Alfaro et al., 2018).
In areas with highly dynamic and complex biological systems, such as tropical regions (Rull, 2011; Antonelli et al., 2018; Matzke-Karasz et al., 2019; Moguel et al., 2021), it is difficult to discern the spatial and temporal contribution of either individual or joint geodiversity-related factors that have shaped the regional species pool (Rossetti and Toledo, 2006).
The northern Neotropical region extends from central–southern Mexico to Central America and includes the Caribbean. It is characterized by a dynamic geological history, caused by the interplay of the North American, Cocos, and Caribbean tectonic plates (Molnar and Sykes, 1969; Marshall, 2007). The region is characterized by broad ranges of elevation and soil types, displays frequent volcanic and seismic activity, and has been subjected to repeated marine regressions and transgressions (Brezonik and Fox, 1974; Horn and Haberyan, 1993; Umaña et al., 1999; Haberyan et al., 2003; Obrist-Farner et al., 2021).
Numerous studies have attempted to elucidate the indirect relationship between geodiversity and biodiversity and identify the factors that account for the current biogeographic patterns in the northern Neotropics (Wallace, 1853; Patton et al., 1994; Gillespie and Roderick, 2014). Most evidence from terrestrial taxa suggests in situ diversification, resulting from repeated colonization events by North and South American taxa, before and after the closure of the Isthmus of Panama, estimated to have occurred between 15 and 4 Ma (Bacon et al., 2015; Montes et al., 2015). Molecular evidence suggests that extant Mesoamerican terrestrial taxa (i.e., angiosperms, ferns, birds, reptiles, and mammals) originated primarily in the Amazon Basin, with ancestors arriving by dispersal during the last 10 Myr (Antonelli et al., 2018).
These large-scale species movements between the American continents were mainly associated with large-amplitude Pleistocene climate fluctuations such as glacial and interglacial cycles and episodes of shorter, centennial to millennial fluctuations, including the Last Glacial Maximum, Heinrich, and Dansgaard–Oeschger stadials (Behling et al., 2000; Carnaval and Moritz, 2008; Bouimetarhan et al., 2018; Baker et al., 2001, 2020).
In aquatic environments of the northern Neotropics, relationships between geodiversity and biodiversity are less well known than those that operate in terrestrial environments. The limnological conditions of a region, defined as the set of physical, chemical, and biological components of inland waters, and its interactions with terrestrial, atmospheric, anthropogenic, and geological elements (Last, 2002; Azim, 2009; McCullough et al., 2021), are key to understanding such relationships. Limnological conditions represent the interface between geodiversity and biodiversity in aquatic environments and are generally accepted as a fundamental driver of the diversification and distribution of aquatic species (Matamoros et al., 2015).
During the last 50 years, anthropogenic influences on limnological
conditions of aquatic environments have altered biodiversity and species distributions because of the modification of natural conditions
(Albert and Reis, 2011; Wehrtmann et al., 2016; Franco-Gaviria et al.,
2018). Currently, most aquatic environments in the northern Neotropics are
used as potable water sources and for agriculture; in large lakes, fishing
and aquaculture have caused eutrophication and introduced invasive species
(e.g.,
Freshwater ostracodes are a well-suited group to evaluate past and present drivers of species distribution in the northern Neotropics. Ostracodes are bivalved microcrustaceans that are abundant, diverse, and widely distributed in aquatic ecosystems (Pérez et al., 2011b, 2013; Cohuo et al., 2016, 2020; Macario-González et al., 2018; Echeverria-Galindo et al., 2019). This taxonomic group shows levels of endemism (restricted distribution) as high as 74 %, sometimes confined to a single lake, or found in surface waters throughout the region (Cohuo et al., 2016). In sediment sequences from lakes of the northern Neotropics, ostracode remains are abundant, particularly in late Pleistocene deposits (Pérez et al., 2011b, 2013; Cohuo et al., 2020). The greatest limitation for using freshwater ostracodes to identify drivers of species distribution in the northern Neotropics is the scarcity of integrated and comparable regional studies, for which detailed spatial and temporal limnological and biological data were collected.
In this study using a set of selected geodiversity variables (geology, mineralogy), measured limnological conditions (physical and chemical variables of water) and biological data of freshwater ostracodes from southeast Mexico to Nicaragua, we aim to answer two main questions: (1) To what extent does geodiversity control limnological variables and thus define limnological regions? (2) How do geodiversity and limnological conditions influence biological richness and diversity of ostracode species?
Our study area covers the northernmost Neotropics, ranging from southern
Mexico (Yucatán Peninsula) to Nicaragua (Fig. 1). This region is
considered a biodiversity hotspot (Mesoamerican hotspot; Myers et al.,
2000), with more than 5000 endemic vascular plants (De Albuquerque et
al., 2015), and about 1120 bird (
Simplified geological map of the northern Neotropical region showing the locations of the 76 studied aquatic ecosystems. Colors indicate geological units based on bedrock type and age of the sediments. Geological data were obtained from Garrity and Soller (2009). Black dots and numbers represent sampling localities. Detailed information on sampling sites can be found in Table S1. Legend: K – Cretaceous sedimentary rocks; Kg – Cretaceous plutonic rocks; PZ, PZvf, PZx – Paleozoic sedimentary rocks; Q – Quaternary sedimentary rocks; Qvf, Qvm, TQv – Quaternary volcanic rocks; T – Tertiary sedimentary rocks of undetermined age; TRJ – Jurassic sedimentary rocks; eT – Eocene sedimentary rocks; mT – Miocene sedimentary rocks; mTvfi – Miocene volcanic rocks; nT – Eocene sedimentary rocks; oT – Oligocene sedimentary rocks; paT, pgT – Paleocene sedimentary rocks.
A total of 76 aquatic ecosystems located in 5 countries across the
northern Neotropical region (Fig. 1) were sampled during July–October 2013,
coinciding with the rainy season in the region. These systems are situated
on the Yucatán Peninsula, Mexico (
Water samples for analysis of major anions (
Biological samples were collected from the littoral zone and the deepest area of the profundal zone. In most lakes, we collected five samples in the littoral areas, using a 250
The final data set included 23 variables, of which 21 were numerical and the
remaining 2 were categorical (Table S1 in the Supplement). Numerical variables included
elevation, physical and chemical properties of water (temperature, dissolved
oxygen, pH, conductivity,
Prior to the statistical analysis, numerical data were log-transformed, except for pH, which is already a log-transformed value, to achieve an approximate normal distribution of variables. Normality was verified for all variables using the Shapiro–Wilk test. Missing data represented
We performed a cluster analysis (CA) to define groups of lakes based on the similarity of their measured attributes. For this analysis, we included all numerical variables. We used the unweighted pair group method with arithmetic mean (UPGMA) for the CA, and Euclidean distance to investigate the grouping similarity of sampling points. Calculations were conducted in R software (R Core Team, 2017), using the Vegan package (Oksanen et al., 2017).
We then used a Principal component analysis (PCA) for each of the main groups discriminated by the cluster, to identify correlated and explanatory variables of the data sets. For each group, the first PCA run included all 23 variables measured (numerical and categorical), and those represented by superimposed arrows in the graphs were considered correlated and excluded from further statistical analysis. A second PCA run, using uncorrelated variables, was used to identify explanatory variables of the data sets. The PCAmixdata package implemented in R software (Chavent et al., 2014) was used because of its ability to handle quantitative and categorical data simultaneously. The loading values for all parameters were obtained using normalized rotation.
To provide a graphical representation of the most meaningful variables of
the data sets detected in the PCA, we created an environmental
variable-specific map using kriging interpolation. Resulting maps represent
measured data and estimates from unmeasured locations. The software
Surfer® from Golden Software, LLC
(
Ostracode extraction and counting were carried out using 15
To disentangle the relationships between the geodiversity and limnological conditions with species composition (as a function of distribution) and richness we used structural equation modeling (SEM), the R software, and the package Lavaan (Rosseel, 2012). This is a multivariate statistical technique that enables one to model pre-defined causal relationships between observed (measured parameters) and latent variables which are not observed directly but rather mathematical modeled from observed variables (e.g., geodiversity and limnological conditions) and tests their statistical significance (Fan et al., 2016; Sarstedt and Ringle, 2020). Our conceptual model for SEM was based on the assumption that geodiversity variables in the northern Neotropics are heterogenous. Consequently, limnological conditions of aquatic systems were partially or entirely influenced by underlying geodiversity. At the regional scale, geodiversity and limnological conditions were expected to exert a direct or indirect influence on ostracode species richness and composition. The individual influence of variables that display environmental gradients such as elevation, conductivity, and TOC, were also tested in the models evaluated. In such cases, the variables were excluded from the construction of latent variables in the respective model. For this conceptual framework, “geodiversity” (latent and exogenous variables) was constructed with all or a subset of geological (bedrock type and age and elevation) and mineralogical variables. “Limnological conditions” (latent and endogenous variables) were constructed using geochemistry, and physical and chemical variables of water (major anion and cation, temperature, pH, and conductivity). Species richness was treated as an observed variable, and the latent variable “species composition” was constructed using NMDS associations. Using a covariance matrix with a set of uncorrelated variables, we fitted five models using this conceptual framework. For all models, statistical significance was tested with root mean square error of approximation (RMSEA), comparative fit index (CFI) and standardized root mean squared residuals (SRMR). The predictive power of the model (R-square) was measured based on the amount of variation of the biological data. The most parsimonious model fitting our data set was selected as the explanatory model (Sect. S1 in the Supplement).
The cluster analysis (CA) identified two main groups that represent limnological regions (Fig. 2). The first group (YG: Yucatán and Guatemala) consists of lowland lakes from the Yucatán Peninsula (Mexico), the Petén district (northern Guatemala) and the Pacific lowlands of southern Guatemala. The second group (GSHN: Guatemala, Salvador, Honduras, Nicaragua) consists of Guatemalan highland lakes, El Salvador and Honduras mid-elevation lakes, and Nicaraguan lowland lakes.
Table S1 shows a list of all the studied aquatic ecosystems located in the YG and GSHN limnological regions and subregions, as well as detailed results of water physicochemical, geochemical, mineralogical, and geological measurements for all studied water ecosystems.
For the YG region, the first PCA run identified 13 uncorrelated
variables. The second PCA run clearly explained the variation of the data
set (Fig. 3a). The first (PC1) and second (PC2) components explained 38 %
of the total variance of the data set (Fig. 3a and Table S2.1 in the Supplement). The PC1 accounted for 23.4 %, and the PC2 for 14.6 % of the total variance, respectively. The biplot based on component 1 and 2 indicated that conductivity (ranging from 175 to 3479
For the GSHN region, the PCA based on 13 uncorrelated variables, explained
49.6 % of the total variance of the data set, within the first (PC1) and
second (PC2) components (Fig. 4a and Table S2.2 in the Supplement). The first component (PC1) accounted for 26.8 % and PC2 for 22.8 % of the total variance. The PCA biplot based on components 1 and 2, respectively, showed that water ionic composition, particularly content of bicarbonates (
Water ionic dominance was graphically evaluated with ternary plots (Figs. S1 and S2 in the Supplement) and information on lake water types is shown in Table S1. Here, we highlight the most relevant characteristics for YG and GSHN limnological subregions.
In the YG limnological region, four subregions were identified in the
cluster analysis. The first subregion (YG1;
The mineralogical analysis reveals that most lakes are dominated by carbonates in the YG limnological region. In subregions YG1 and YG3 (central and northern Yucatán Peninsula), most lake sediments have calcite as the dominant mineral (Table S1 and Fig. S2). Lakes Chichancanab and Salpetén both belong to YG but show carbonates with a co-dominance of phyllosilicates and gypsum. Lakes from YG2 are mainly dominated by phyllosilicates and feldspars. The mineralogical composition of lake sediments of the YG4 subgroup varies. Sediments of the lakes such as Vallehermoso, Emiliano Zapata, and Chacanbacab are dominated by phyllosilicates with or without feldspars. Sediments of lakes such as Miguel Hidalgo, on the other hand, are dominated by carbonates, with calcite as main mineral, whereas Lake Silvituc is characterized by exotic minerals such as silver and gold (Table S1).
In the GSHN region, three subregions were identified by CA. Lakes of GSHN1 (
Sediment mineralogy of Central American lakes (GSHN region) shows that most subgroups are dominated by feldspars. Co-dominance with other minerals such as phyllosilicates and carbonates occurs. The GSHN1 combined lakes are dominated by phyllosilicates and feldspars, whereas quartz is the dominant mineral in lakes Yojoa and Ticamaya (Honduras). The GSHN2 region included large lakes dominated by feldspars. In Lake Nicaragua, this dominance is also shared with phyllosilicates. For the GSHN3, we detected two main mineral assemblages. The first is dominated by phyllosilicates and feldspars, with clay minerals and feldspars as main minerals, and the second is dominated by feldspars and phyllosilicates.
We found ostracode species in 74 of the 76 aquatic systems we studied
in the northern Neotropics. In the volcanic Lake Alegría (El Salvador)
and the karstic cave San Miguel (Yucatán, Mexico), ostracodes were not
observed. Living adult specimens were encountered in samples from all
systems, except those from lakes Chicabal, Tekoh, Yaxhá, Verde, and
Cenote Mucuyche, where only empty shells or single valves were recovered.
A taxonomic analysis of species enabled us to identify 70 species (Table S3 in the Supplement), out of which 31 were recorded at single sites, whereas the remaining 39 were observed in at least 2 systems. Species richness ranged between 1 to 9 with an average of 4 species per site, whereas the maximum value of the Shannon diversity index (
The NMDS ordination, based on species occurrence data, revealed five major
species associations (OST 1–5) with a reliable stress value of 0.08 (Fig. 5a) (Clarke, 1993). The PERMANOVA test showed statistically significant
differences between group centroids (
Five models of the relationships of geodiversity, limnological conditions,
and species composition and richness were tested with structural equation
modeling (SEM). Four models used geodiversity only as exogenous variables
(models 1–2, 4–5), and in one model (model 3) “limnological conditions” were also considered as an exogenous variable (two exogenous variables,
explaining an endogenous variable). Descriptions of the rationale behind
variable selection and relationships tested in each model are found in
Sect. S1. Model 1 evaluated the direct influence of geodiversity on limnological conditions and the influence of limnological conditions on species composition and richness, resulting in the following metrics of global fit: CFI – 0.63 (values close to 1.00 indicate
better fit of the model), RMSEA – 0.19 (values
Structural equation modeling of the influence of geodiversity and limnological conditions with freshwater ostracode species composition and distribution. Metrics of global fit of the optimal model
Our cluster analysis based on 76 lakes and 23 lake attributes shows limnological regionalization in the northern Neotropics. Two main regions, corresponding to Yucatán Peninsula–northern Guatemala (Group YG) and northern Central America (Group GSHN), were identified (Fig. 2). The group YG is located in karstic plateaus of sedimentary origin, dominated by limestone, dolomite, evaporites, and carbonate-rich impact breccia (Hildebrand et al., 1995; Schmitter-Soto et al., 2002a, b; Vázquez-Domínguez and Arita, 2010). The group GSHN is located in volcanic bedrock terrains of Guatemala, El Salvador, Honduras, and Nicaragua, where pyroclastic and volcanic epiclastic materials, usually reworked, are abundant, reflecting active or past volcanic activity (Dengo et al., 1970; Stoiber and Carr, 1973; Carr, 1984). The YG and GSHN groups were further subdivided into four (YG1–4) and three (GSHN1–3) subgroups, representing limnological subregions. This proposed regionalization therefore reveals high heterogeneity of aquatic systems in the northern Neotropics. Multivariate statistics (PCA) show that regions and subregions can be distinguished by the ionic composition of waters. Geochemical variables related to sediments, such as TOC and mineral composition, are recognized as the second most important characteristics (Figs. 3 and 4).
In the YG karst region, lakes are characterized by carbonate, calcium and calcite signatures, which is expected because waters interact with limestone and dolomite-rich bedrock on the Peninsula (Schmitter-Soto et al., 2002a, b; Perry et al., 2009). This is also responsible for the dominance of calcium, sodium, and magnesium ions in waters, which in turn are related to the generally alkaline surface waters in most aquatic systems of the region (Alcocer et al., 1998; Schmitter-Soto et al., 2002a, b) (Fig. 3a and d). In specific areas, such as YG3, dominance of chloride is also relevant. This can be explained by two main processes: (1) marine intrusion and (2) input of subterranean waters that have interacted with evaporites. The spatial distribution map of chloride contents in lake waters (Fig. 3c) shows a clear tendency to higher values on the northern Yucatán Peninsula where marine intrusion is probably the most important source of chloride (Sánchez-Sánchez et al., 2015; Saint-Loup et al., 2018). Marine intrusions in northern Yucatán have been mapped as far as 100 km inland (Steinich and Marín, 1996). Pérez-Ceballos et al. (2012) found that several water systems, mainly cenotes, in this same region are characterized by marine waters below freshwater lenses, with water intermixing.
Sulfate is an interesting component of some lakes of the YG1 systems (Socki
et al., 2002; Pérez-Ceballos et al., 2012). The presence of sulfates in
lake waters may be attributed to the K/T anhydrite/gypsum-bearing impact
breccia and dissolution of
High TOC values in sediments of most lakes of YG2 probably reflect the
trophic state of lake waters. Our data confirm results by Pérez et al.
(2011a), who recorded a TOC increase from north to south on the Yucatán
Peninsula. This may be attributed to the combined effect of soil,
precipitation, and vegetation type, which changes from north to south. The
northern part of the Yucatán Peninsula is characterized by leptosols,
which are shallow soils with high amounts of exposed hard rock and
calcareous material (Bautista et al., 2011; Estrada-Medina et al., 2013).
There, precipitation of about 450
In Central American, volcanism is the most common mode of lake formation.
These lakes are classified as caldera lakes, crater lakes in (partially)
active or inactive volcanoes, maar lakes, or are located in volcanic bedrock
basins (Golombek and Carr, 1978; Newhall and Dzurisin, 1988; Dull et al.,
2001; Vallance and Calvert, 2003) (Table S1). The existence of at least
three limnological subregions highlights that these lakes are additionally
influenced by regional factors related to orography (elevation), climate and
the level of volcanic activity, including magmatic heat and gas input. Ionic
dominance of Central American lakes is highly variable, but anions
Given the origin of discriminating variables in PCA for both YG and GSHN, we
found three main sources controlling limnological conditions in the northern
Neotropics: (1) bedrock type, which determines specific mineral and ionic
composition of lake sediments and host waters; (2) volcanic and marine
influence, which determines the presence of dominant and conservative ions
such as
We found ostracode species in almost all the aquatic systems we studied.
Seventy species were recognized, demonstrating that this group is abundant
and diverse in the northern Neotropics. The number of species per lake
(species richness) was relatively low. In most sites, we found between two
and six species, and in large lakes, such as Petén Itzá and
Nicaragua, we found a maximum of nine species. Diversity metrics
substantiated this tendency, with values of the Shannon diversity index always
This pattern may be influenced by sampling effort, as sampling sites did not
always cover the lake extent. In addition, sampling was conducted in a
single climatic period. Ostracode phenology, such as differences in hatching
time and functional traits, varied within species and are mostly influenced
by photoperiod, conductivity, and water temperature (Rossi et al., 2013; Rosa
et al., 2021). Therefore, the estimated values of biodiversity may partially
represent the true diversity in the region. Low species in Neotropical
lakes, however, was also observed in previous studies in the region. In Lake
Petén Itzá, for example, a maximum of 11 species was reported by
Pérez et al. (2010). In Lake Nicaragua, the largest lake in Central
America, seven species were found (Hartmann, 1959). In Colombian aquatic
systems, such as the La Fe reservoir (Saldarriaga and Martínez, 2010) and the Magdalena River basin (Roessler, 1990a, b), the number of ostracode species is similar to that in Central America (
The NMDS analysis shows the existence of at least five species associations
in the region (OST1–OST5), emphasizing that ostracodes do not conform to a
faunal unit, but rather display disjunct faunas (Fig. 5a), similar to what
is observed in other studies of ostracodes (Cohuo, et al., 2018) and
freshwater fishes of the region (Miller, 1966; Matamoros et al., 2015).
Ostracode associations are geographically delimited, and no overlap was
observed (Fig. 5b). Ostracode groups OST1 and OST2 belong to the YG
limnological region, whereas the OST3, OST4, and OST5 associations belong to
the GHSN limnological region (Fig. 5a and b). Few species were present in more than three limnological subregions, and these can be considered of wide
Neotropical distribution, e.g.,
Correspondence between species associations and limnological regions and
subregions suggests a major influence of physical and chemical properties of
lake environments on biological systems. The SEM analysis exposed the significant influence of geodiversity on limnological conditions, and of limnological conditions on species associations, identified in the NMDS. This illustrates that limnological conditions, particularly geochemistry, and water chemistry, is the primary factor responsible for species distributions in the study area. We were, however, unable to find statistical significance to explain the relationships between geodiversity and limnological conditions with ostracode species richness. This suggest that the number of species per lake may not be fully governed by our predictors. For instance, the individual influence of conductivity and elevation in model 4 and TOC and latitude in model 5, also failed to explain species richness. This revealed that other intrinsic or extrinsic factors such historical water level fluctuations and precipitation–evaporation balance, might instead control species richness. In our SEM models, we also tested the direct influence of geodiversity and its indirect influence through limnology on species distribution and richness. The optimal SEM model demonstrated the significance of paths describing the direct influence of geodiversity on species richness and composition, but significance of standardized coefficients was of minor importance (0.1 and 0.04, respectively). Conversely, strong ties were discovered when we analyzed the indirect effect of geodiversity (via limnology) on species composition (
The northern Neotropics is a region characterized by diverse environmental conditions, abundant aquatic systems, and high biodiversity. Our limnological survey of 76 aquatic environments identified 2 main limnological regions in the northern Neotropics. The YG region is associated with karst plateaus in southern Mexico and northern Guatemala, whereas the GSHN region is associated with landscapes formed by volcanic activity in southern Guatemala, El Salvador, Honduras, and Nicaragua. At least seven limnological subregions were identified, illustrating the high heterogeneity of aquatic systems in the northern Neotropics. Low ostracode species richness in the northern Neotropics seems to be strongly related to the geological history of the region. The low number of species per lake contrasts with the number of species per lake in temperate regions, which is at least 5 times higher. The SEM analysis highlights that geodiversity has a direct influence on limnological regions, and an indirect but relevant influence on freshwater ostracodes. This is the first study to integrate data on geodiversity including watershed geology, sediment mineralogy, limnological conditions such as physical and chemical characteristics of the water column, geochemistry, and biota in aquatic ecosystems of southern Mexico and Central America. Further studies should focus on the establishment of a more detailed regionalization, by including a greater number of lakes, more environmental variables, and samples collected at different times throughout the seasonal cycle.
Water physicochemical, sediment geochemistry, mineralogical and geological data from the 76 aquatic ecosystems sampled in this study are available in Table S1 in the Supplement and in the Pangaea repository:
The supplement related to this article is available online at:
LMG and SC conducted the fieldwork, performed data analysis, and wrote the manuscript. AS, LP, and MEG developed, managed, and coordinated the project and contributed to data interpretation and manuscript writing. PH contributed and interpreted mineralogical data and gave scientific input to the manuscript. MC developed water chemistry analyses and gave scientific input to the manuscript. AO, MP, and MRA provided support for sampling in their respective countries and organized sampling permits.
The contact author has declared that none of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank all colleagues and institutions involved in this work, including the student team: Christian Vera, León E. Ibarra, Miguel A. Valadéz, and Cuauhtémoc Ruiz (Instituto Tecnológico de Chetumal, Mexico); Ramón Beltran (Centro Interdisciplinario de Ciencias Marinas, Mexico); and Lisa Heise (Universidad Autónoma de San Luis Potosí, Mexico) for their excellent contributions during field work. We also thank the following colleagues and institutions that made the analysis of field sampling and water chemistry possible: the team from the Asociación de Municipios del Lago de Yojoa y su área de influencia (AMUPROLAGO, Honduras); Margaret Dix, Eleonor de Tott, Roberto Moreno (Universidad del Valle de Guatemala, Guatemala); Consejo Nacional de Áreas Protegidas (CONAP, Guatemala); Néstor Herrera (Ministerio de Medio Ambiente, San Salvador), Teresa Álvarez (El Colegio de la Frontera sur, Chetumal Unit, Mexico); María Aurora Armienta (Laboratorio de Química Analítica, Instituto de Geofísica, Universidad Nacional Autónoma de México); and Adriana Zavala (El Colegio de la Frontera Sur, Mexico). We also gratefully acknowledge anonymous reviewers and Mark Brenner for their constructive feedback and language editing.
Funding was provided by the Deutsche Forschungsgemeinschaft (DFG, Project no. 5297191), CONACYT (Mexico; project no. 319857) and Tecnológico Nacional de México (projects nos. 14502.22-P and 14698.22-P).This open-access publication was funded by Technische Universität Braunschweig.
This paper was edited by Gabriel Singer and reviewed by two anonymous referees.