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Patterns of Rotifer Diversity in the Chihuahuan Desert

Department of Biological Sciences, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA
Departamento de Ciencias Químico Biológicas, Instituto de Ciencias Biomédicas, Universidad Autónoma de Ciudad Juárez, Av. Benjamin Franklin no. 4650, Zona PRONAF, Cd. Juárez 32315, Chihuahua, Mexico
Centro de Ciencias Basicas, Departamentos de Química y Biología, Universidad Autónoma de Aguascalientes, Avenida Universidad 940, Ciudad Universitaria, C.P., Aguascalientes 20131, Ags., Mexico
Department of Biology, 300 Seward St., Ripon College, Ripon, WI 54971, USA
Author to whom correspondence should be addressed.
Diversity 2020, 12(10), 393;
Submission received: 28 August 2020 / Revised: 4 October 2020 / Accepted: 7 October 2020 / Published: 13 October 2020
(This article belongs to the Special Issue Biodiversity of Rotifers)


Desert aquatic systems are widely separated, lack hydrologic connections, and are subject to drought. However, they provide unique settings to investigate distributional patterns of micrometazoans, including rotifers. Thus, to understand rotifer biodiversity we sampled 236 sites across an array of habitats including rock pools, springs, tanks, flowing waters, playas, lakes, and reservoirs in the Chihuahuan Desert of the USA (n = 202) and Mexico (n = 34) over a period of >20 years. This allowed us to calculate diversity indices and examine geographic patterns in rotifer community composition. Of ~1850 recognized rotifer species, we recorded 246 taxa (~13%), with greatest diversity in springs (n = 175), lakes (n = 112), and rock pools (n = 72). Sampling effort was positively related to observed richness in springs, lakes, rivers, and tanks. Nestedness analyses indicated that rotifers in these sites, and most subsets thereof, were highly nested (support from 4 null models). Distance was positively correlated with species composition dissimilarity on small spatial scales. We predicted species richness for unsampled locations using empirical Bayesian kriging. These findings provide a better understanding of regional rotifer diversity in aridlands and provide information on potential biodiversity hotspots for aquatic scientists and resource managers.

Graphical Abstract

1. Introduction

Delineating patterns of species distributions is important for understanding basic and applied questions in biogeography, ecology, and evolutionary biology [1,2]. Species distributions can be used in modeling current communities and in predicting outcomes to both short-term (e.g., acute pollution episodes) and long-term events (e.g., increases in temperature due to climate change). They also inform biogeography and macroecology [3]. Unfortunately, the biogeographic patterns of many small and understudied species have not been well-documented. As members of the Syndermata, rotifers offer a good example of this challenge. While they comprise an important component of freshwater ecosystems and contribute to both the microbial loop and typical aquatic food webs, it is unclear whether their distribution follows ubiquity theory [4,5], or whether they exhibit some level of endemicity [6,7,8,9]. Due to their ability to produce small resting stages that are easily transported by hydrochory [10], zoochory [11,12], or anemochory [13,14], it has been assumed that most rotifers were widely dispersed by passive means and that the majority of species would have cosmopolitan distributions [8,15,16]. However, recent studies have shown that the distribution of rotifer species encompasses the range from cosmopolitanism to biogeographies that are restricted to certain biogeographic realms, hotspots of biodiversity [7,17,18], or habitat types [4,17,18,19]. Two examples illustrate this point. (1) In his analysis of the genus Trichocerca, Segers [9] concluded that strict cosmopolitanism was evident in >1/3rd of the species analyzed, endemism was lacking in tropical regions but that it was strongly evident in the Northern hemisphere, and latitudinal variation was evident in >25% of the species. (2) Segers and De Smet [18] grouped species of Keratella into four categories: cosmopolitans (n = 8), Holarctic (n = 5), widespread (n = 3), and regional and local endemics, with seven subcategories: Afrotropical (n = 2), Australian (n = 6), Nearctic (n = 8), Neotropical (n = 8), Oriental (n = 2), Palearctic (n = 6), and marine (n = 5). To distinguish between the opposing views of cosmopolitanism versus endemism, additional studies are needed of larger geographic regions, with repeated sampling.
Since deserts contain waterbodies that are often widely separated, highly fragmented, possess limited hydrologic connections, and subject to unpredictable drought [20,21,22], they are ideal systems to determine patterns in aquatic species distributions. However, within a basin, assemblages of aquatic habitats can be quite complex. For example, a series of spring-fed pools can lead to a stream, each with its own edaphic conditions, that support a substantial number of species [22]; both can be hotspots of aquatic biodiversity, but maintain different arrays of species. Deserts also are considered ecological paradoxes. While generally low in terrestrial productivity, their varied habitats support striking levels of taxonomic diversity, often with a high degree of endemism. The Chihuahuan Desert of Mexico and the southwest USA is a prime example of such a system. This desert is a complex of intergrading plant communities arrayed across a broad series of elevational and latitudinal sequences [23]. It covers some 6.29 × 105 km2, largely in the central Mexican plateau, but extending northward into west Texas, south-central New Mexico, and the southeastern Arizon. This well-defined ecoregion is the only desert system included in The Global 200 conservation priority listing as being recognized for its critical biodiversity values for both terrestrial and freshwater habitats [24].
An analysis specific to the Chihuahuan Desert [25] has designated 98 specific habitats or localities as priority sites for investigation and evaluation with respect to biodiversity resources; 37 are freshwater habitats. Of these, the highest priorities are assigned to systems with high intactness and high richness and/or endemism. An important array of these freshwater habitats is found in an arc from Big Bend National Park (BIBE, Texas) into Mexico, with the priority sites falling largely along the western boundary of the Sierra Madre Occidental, but extending as far south as the state of Hidalgo. A particularly important locality is the renowned Cuatro Ciénegas thermal spring system in Coahuila, perhaps the most studied of all Chihuahuan Desert aquatic systems [26,27,28,29]. This system of thermal springs, marshes, rivers, and large permanent lakes is home to a diversity of aquatic and mesic habitats that supports high levels of endemism in aquatic species [26,27,30]. Chihuahuan Desert springs and other water sources are recognized as sites of high biodiversity with high rates of endemism of macroinvertebrates, especially springsnails [31,32]. To complement that knowledge, more attention should be given to aquatic microinvertebrates of these systems.
While some aquatic sites in these deserts are relatively permanent over geologic time (playas and rivers), others are ephemeral over ecologic time (wet seasonally, monthly, weekly, even daily). Hydroregime (i.e., the duration, frequency, and timing of wet phases) is an important indicator of species richness, with increasing species diversity positively correlated with length of the filling cycle [33,34,35]. Connectivity among sites is also an important consideration, as connected sites will likely share large portions of their species pools. In the Chihuahuan Desert, connectivity among sites in different drainage basins is reduced by vast stretches of arid landscape [36]. Thus, system isolation may be a driving force in speciation and endemism. This certainly seems to hold true for fishes [37,38,39,40], springsnails [41,42,43,44,45], and amphipods [46,47]. In addition, communities may be structured through recent processes such as local and regional interactions (competition and dispersal) [48,49,50], habitat permanence [51,52], or local physiochemical conditions [48].
Prior to our work [53,54,55,56,57], there were few surveys of rotifers in the Chihuahuan Desert, with some notable exceptions. These mostly focused on smaller geographic areas and shorter time scales [58,59,60,61,62,63]. However, there have been numerous studies of rotifers from deserts and aridlands of the world, but in general, they have been limited to reports of species composition in specific habitats. These studies include the following: Sonoran [58,60,64,65,66,67,68], Algeria [69], Australia [70,71,72,73,74,75,76,77,78,79,80,81], Kalahari [82], Namib [83], Oman, Saudi Arabia, and Yemen [84], Spain [85], and Western Sahara [86]. The semi-arid regions in Mongolia also have been studied by several researchers [87].
Here we characterized patterns of rotifer species distribution in 236 aquatic systems that we sampled through a broad range of the Chihuahuan Desert. As appropriate to the system, we sampled the water column, sediments, and littoral vegetation during a period of ≥20 years. As part of our study, we tested the following hypotheses: (1) recovered richness will be positively correlated with sampling effort, (2) species are associated with particular habitats, (3) species composition will show nestedness, and (4) richness and assemblage composition possess a geographic pattern. In addition, using our dataset, we employed empirical Bayesian kriging to predict rotifer diversity across unsampled locations within the Chihuahuan Desert. Finally, we compared our results with those from five other desert systems and six studies from cool, temperate, and tropical systems. Our findings and analyses will help identify areas with high conservation value for zooplankton, including rotifers and add to our understanding of rotifer biogeography on a regional scale. They also inform the Baas-Becking (ubiquity) hypothesis in providing an indirect test of the assumption that for microinvertebrates, everything is everywhere [4,5].

2. Materials and Methods

2.1. Collection Sites

We collected samples from 236 sites, 202 USA and 34 Mexico during 1998–2020 (Figure S1; Appendix A). We sampled a variety of habitats including permanent lakes and reservoirs (n = 21), tanks (n = 11), temporary playas (n = 16), rock pools (n = 60) and artificial rock pools (n = 6), rivers and streams (n = 15), and springs (n = 95). Sampling effort varied among the sites from 1 visit to >20 visits; frequencies were used as ranks (1 = 1 sampling event; 2 = 2–5 events; 3 = 6–10 events; 4 = 11–20 events; 5 = >20 events), and at some sites only one type of sample was taken (e.g., plankton), while at others a variety of microhabitats were sampled. We compiled species lists at each site overall sampling dates using presence/absence criteria.
We described the sites at Big Bend National Park (BIBE) (Brewster Co., Alpine, TX, USA) in our previous work [53,54,88]. General characteristics for rock pools sites at Hueco Tanks State Park & Historic Site (HTSPHS) (El Paso Co., San Antonio, TX, USA) were provided by Schröder and colleagues [89] and springs in northern Mexico were described in detail by Ríos-Arana and colleagues [90].
Sampling techniques included using plankton nets (64 µm), aspirating samplers for flocculent bottom sediments, as well as taking grab samples (i.e., aquatic macrophytes for sessile species) [53,54]. We did not sample hyporheic habitats. The equipment was cleaned using distilled water rinses and, whenever possible, dried between uses in different systems. Although we usually took multiple samples at each site, we attempted to minimize environmental damage of the smaller systems by keeping the total amount of each sample to about 250 mL of source water. We recorded GPS coordinates using a Brunton Multi-Navigator® and used Google Earth to verify locations.

2.2. Species Identification

We identified morphospecies of rotifers (hereafter, species) primarily from live material using a Zeiss Axioscope with Neofluar objectives equipped with DIC, but when necessary, some specimens were preserved in 4% buffered formalin to view key taxonomic characters. For example, specimens of Lecane and Lepadella were fixed to view characteristics of the lorica, and in some cases trophi were examined using SEM. Keys to the Rotifera used in this study were as follows: Bdelloidea—[81,91,92]; Monogononta—[93,94,95,96,97,98,99,100,101,102,103,104,105]. We identified taxa to species or, if that was not possible, to genus: e.g., Lecane sp. We conducted all of the analyses using the lowest level of identification that we determined. For most specimens, we took voucher images with a SPOT camera and, when possible, voucher specimens were preserved in 70% ethanol and/or 4% buffered formalin. We housed all voucher specimens in UTEP’s Biodiversity Collections.

2.3. Diversity Indices

To assess diversity of sites we calculated Hill numbers (q) of order 0 (richness, S), 1 (Shannon Index), and 2 (Simpson Index), and Sorensen’s Index (SI). Species incidence was characterized at a variety of spatial grains by overlaying 0.1°, 0.25°, 1.0°, 1.25°, and 2.0° grids on the site map. We calculated incidence within these grids cell from presence/absence data from each collection site occurring within the boundaries of the grid cell.

2.4. Sampling Effort

We tested the relationship between species richness and sampling effort using linear regression in R version 4.0.2 (R Core Team, 2020) for all sites combined, as well as for each habitat type separately.

2.5. Indicator Species Identification

We determined indicator species for habitat types by testing for significant associations using the indicspecies package 1.7.8 version in R version 4.0.2 (R Core Team, 2020; This analysis calculates an Indicator Value (IndVal) index to measure the association between species and sites and combinations of sites based on the methods of Dufrene and Legendre [106] and De Caceres et al. [107]. The statistical significance is determined by permutation tests (n = 999).

2.6. Nestedness

We tested the hypothesis that smaller assemblages of rotifers are nested subsets of larger assemblages based on the habitats in which they are found by using the algorithms implemented in ANINHADO 3.0 (Bangu) [108,109,110]. In this program, the matrix is rearranged (packed) to achieve the densest grouping of species in the habitats [111]. We employed both the Temperature calculator () and nestedness metrics based on overlap and decreasing fill (NODF) [109], but because the packing is only marginally different, here we report . We tested all packed matrices using the 4 null models described by Guimarães & Guimarães [110]. For comparison purposes we also included a meta-analysis of 11 published datasets of rotifers from other biomes including aridlands (n = 5), cold (n = 2), temperate (n = 2), and tropical regions (n = 2). In our previous nestedness study [90] we determined species or habitats to be idiosyncratic when their individual was ≥1 SD than the mean of the matrix . Since species and site often exhibit large variance, we decided to employ a more rigorous criterion, and here we note idiosyncratic species or habitats when their value is ≥2 SD of the mean of matrix .

2.7. Relationship between Species Richness and Geographic Distance

To determine whether distances between sites were contributing to differences in species composition, we conducted Mantel tests. Geographic distances between sites were estimated using Haversine distances based on GPS coordinates using the R package geosphere 1.5-10 [112]. Bray-Curtis dissimilarity matrices of species composition were constructed using the vegdist function from the R package vegan 2.5-6 [113]. We used Mantel tests, based on Spearman rank correlations, to determine whether species composition was related to (1) geographic distances between collection sites, (2) spatial scale (e.g., grid cells size), and/or (3) habitat type.

2.8. Prediction of Biodiversity Hotspots

Based on our survey data, we estimated richness throughout the Chihuahuan Desert using empirical Bayesian kriging [114]. Using kriging as a method to predict species richness in unsampled areas has the benefit of illustrating general trends in richness across broad geographic regions. This process uses a probabilistic predictor that models spatial dependence with functions (i.e., semivariograms). A semivariogram model was estimated from the species richness data we obtained in our surveys, and then used that estimate to simulate the richness in unsampled geographic areas. From these newly simulated data, another semivariogram was estimated and evaluated against previous models using Bayes’ rule. This process was iterated (n = 100) and the simulated data were used to predict richness at unsampled locations. Richness values were log-empirically transformed (a multiplicative skewing normal score approximation based on the log of our survey richness data) prior to semivariogram fitting. This process ensures that negative richness values are not predicted. Kriging was conducted on species richness at each site and for each grain size.

3. Results

3.1. Species Composition

We identified 246 rotifer species, which represents a substantial portion of known rotifer species, genera, and families (~13, 50 & 77%, respectively) [17,115]. Given that the Chihuahuan Desert comprises only about 0.35% of the global landmass (excluding the poles), it includes a large percentage of known rotifer biodiversity. Species richness ranged from 1 to 44 at a given locality. The site with the highest richness was Laguna Prieta at HTSPHS (S = 44). This site was sampled >20 times during this study. The site with the second highest richness was Lago Colina located in Chihuahua, Mexico (S = 43), but this site was sampled only four times over a 2-year period. Species found in all habitat types (except rock pools) include Brachionus quadridentatus, Cephalodella catellina, Cephalodella forficula, Cephalodella gibba, Colurella obtusa, Euchlanis dilatata, Lecane bulla, Lecane hamata, Lecane luna, and Platyias quadricornis. Lecane quadridentata was found in all habitats except streams.

3.2. Diversity Indices

Of the five most common habitat types, springs had the highest richness (S = 175) while rock pools had the lowest (S = 53) (Figure 1A). Former cattle tanks also exhibited relatively low diversity (S = 53). In the few rivers (2 rivers, 26 sites) and streams (5 streams, 7 sites), sampled richness was 95 and 26, respectively. When compared to all other sites, springs also had the highest percentage of unique species (34.3%), followed by lakes and tanks (10.5%), playas (9.1%) and finally rock pools (5.7%) (Table 1). For these systems, Sorensen’s Index ranged from 0.36 to 0.54, and most habitats share about 40% of their species (Table 1) with springs and lakes having the most divergent rotifer species communities. Diversity was highest at the largest spatial scale investigated, with the mean diversity for cells at the largest grid size being 48, 35, 27 for q = 0, 1, and 2, respectively. Diversity found for q = 0, 1, and 2 increased at higher spatial grains (r2 = 0.16, 0.15, 0.12, respectively; p-value < 0.05 for each; Figure 1). The strength of this relationship decreased with increasing Hill number.

3.3. Sampling Effort

There was a positive relationship between observed species richness and sampling effort when we included all sites in the analysis, although S is only weakly explained (r2 = 0.01, p < 0.05; Figure 2). However, when analyzed by habitat type, the relationship was stronger (r2 = 0.32, 0.17, 0.40, 0.56 for springs, lakes, rivers, and tanks, respectively). Although, in some cases, such as in rock pools, S was weakly explained by sampling effort (r2 = 0.02, p < 0.05). Playas and streams did not show a significant relationship with sampling effort.

3.4. Indicator Species Identification

In the indicator species analysis, 144 species were associated with one habitat type, while only 4 species were associated with 6 of the 7 habitat types. Indicator species were identified for all habitat types and some combinations of habitat types (Table 2). Playas and Lake + Tanks had the most indicator species (n = 5). While two species (C. gibba and L. luna) were indicators of all habitat types except rock pools. Not surprisingly, Hexarthra n. sp. is an indicator species for rock pools. Indicator species with highly significant associations (p < 0.001) include Hexarthra n. sp. with rock pools, Epiphanes brachionus with playa habitats, B. quadridentatus with playa + river + tank habitats, E. dilatata with playa + river + stream + tank habitats, and L. bulla with lake + playa + river + spring + stream habitats. Species that were indicators of combinations of five habitat types include: L. bulla, Philodina megalotrocha, L. luna, and C. gibba.

3.5. Nestedness

We evaluated nestedness in rotifers from the 236 Chihuahuan Desert aquatic habitats at several levels: (1) the completed dataset; (2) by habitat type (lakes, playas, tanks, springs, cascading pools, and rock pools); (3) by geospatial scale (0.1°, 0.25°, 1.0°, 1.25°, and 2.0°). As a comparison, we completed a meta-analysis on data from 11 published studies that examined rotifer assemblages from other biomes (see above). We report results of these analyses in Table 3 and summarized them below.
The complete dataset exhibited nestedness, with support from 4 null models (p < 0.001). At this scale, only two idiosyncratic species (identified as those with a ≥ 2SD above the mean matrix = 2.55): Hexarthra n. sp. and Trichocerca similis. Of these two species, Hexarthra n. sp. [89] had the most restrictive distribution. It was confined to a group of 25 isolated rock pools at HTSPHS, indicating that it is a rock pool specialist. (See also the discussion below on rock pools.) The other idiosyncratic species, T. similis, was present in 24 habitats (~10% of all the sites we studied), including rock pools (n = 18), lakes (n = 4), one pond, and one spring. However, while it also seems to be a rock pool specialist, it was not present in the HTSPHS system. We found T. similis in two rock pool systems of BIBE possessing very different edaphic conditions. In our analysis of the complete dataset several sampling sites (n = 8) were identified as idiosyncratic habitats, but there was no common feature among them: springs (n = 2); lakes and reservoirs (n = 3); ponds (n = 2); cascading pools (n = 1).
We subdivided the dataset by habitat type to examine the distribution of rotifers separately in lakes, playas, tanks, springs, cascading pools, and isolated rock pools at HTSPHS. Lakes and reservoirs (n = 21) possessed five idiosyncratic species (Encentrum cf. algente; Lecane arcula; L. quadridentata; Polyarthra vulgaris; Synchaeta cf. oblonga), but only one idiosyncratic reservoir, Presa Chihuahua. Playas (n = 16) possessed two idiosyncratic species (Lecane hornemanni and L. thalera), but no idiosyncratic habitats. There were three idiosyncratic species in the tanks (n = 11) (Brachionus durgae, E. brachionus, and Lepadella patella and one idiosyncratic habitat, Tule Cattle Tank (BIBE). The spring habitats exhibited more diversity with 10 idiosyncratic species (Adineta vaga, Aspelta aper, C. catellina, Cephalodella tenuiseta, Colurella adriatica, Encentrum saundersiae, Filinia brachiata, Lepadella acuminata, Mytilina mucronata, and Notommata cf. haueri). Six of the spring habitats (n = 95) idiosyncratically distinct (n = 6); these included Balmorhea Main Pool, Balmorhea wetland 2, Miller Ranch 96 Well, Oak Creek BIBE, Ojo de la Punta ANPMS, and Sitting Bull Falls LNF. In a previous study of 7 springs in Mexico [90] we found four idiosyncratic species Cephalodella cf. graciosa and Cephalodella megalocephala, Pleurotrocha petromyzon, and Pleurotrocha sigmoidea and one small, idiosyncratic habitat: Ojo de en Medio.
We also examined a portion of the dataset that included only BIBE habitats in which one pool cascaded into another (n = 40). In that analysis two species (Epiphanes daphnicola and T. similis) and one habitat (a pool surrounded by lush vegetation) possessed idiosyncratic . Since the edaphic conditions of these pool habitats are different, we separated them by location (n = 5) to explore whether they exhibited unique species distributions. In the Cattail Spring pools (n = 12) four species (C. obtusa, Lecane pyriformis, Proales cryptopus, and Tripleuchlanis plicata) and one small pool isolated from the main flowage yielded idiosyncratic . Surprisingly in Ernst canyon, none of the 16 species or 12 rock pools proved to be idiosyncratic. Tuff canyon pools (n = 6) also possessed no idiosyncratic species and only one idiosyncratic habitat (one small pool). In the rock pool flowage of the Window Trail pools (n = 10 sites) one species (L. pyriformis) and one habitat (a small tinaja nearly filled with small rocks and sediment, surrounded by plants) possessed idiosyncratic . The rock pools at HTSPHS yielded no idiosyncratic species. However, as noted above Hexarthra n. sp. was found in all sites except for two artificially enlarged, sheltered rock pools. Those rock pools were also possessed idiosyncratic . In a separate study of six artificial rock pools (mesocosms) placed at HTSPHS, only one species (Lecane nana) had an idiosyncratic . Interestingly, this species was not found in natural habitats of HTSPHS during our extensive sampling effort (n > 20 for most sites over 20 years).
Nestedness was evident across all five geospatial scales (0.1°, 0.25°, 1°, 1.25°, and 2.0°), with support from 4 null models (P < 0.001) at each scale. A total of 38 idiosyncratic species were identified in the geospatial analysis and of these eight were identified at more than one spatial scale: Brachionus plicatilis; Brachionus variabilis; Cephalodella cf. misgurnus/pachyodon; Euchlanis calpidia; Paradicranophorus sordidus; P. vulgaris; T. similis; and Wulfertia ornata. Ten regions were identified as idiosyncratic across the five geospatial grids. No obvious pattern of habitats emerged from the scale analysis.
Of the 246 species identified in this study, 59 possessed idiosyncratic in one or more of the analyses. Of that set we recorded 10 species twice (A. vaga, B. plicatilis, B. variabilis, C. cf. misgurnus/pachyodon, E. calpidia, F. brachiata, L. hornemanni, L. pyriformis, S. cf. oblonga, and W. ornata), while three other species occurred more often: three (P. sordidus), four (P. vulgaris), and five (T. similis) times.
For comparison purposes we reviewed published datasets from four other biomes, including aridland (n = 5), tropical (n = 2), temperate (n = 2), and cold (n = 2) biomes. In 13 billabongs of Australia three species (M. mucronata; E. daphnicola; Trichocerca rattus), but no habitats, possessed idiosyncratic . Similar results were found in the desert habitats of Oman (n = 9 sites) (C. gibba; C. obtusa; Trichocerca tenuior), Saudi Arabia (n = 23 sites) (Lecane ungulata), and Yemen (n = 12 sites) (Brachionus urceolaris; C. forficula; C. adriatica; Lophocharis salpina). In each of these datasets, a single habitat possessed an idiosyncratic : Ravine (Wadi O7), Sabkhat (S7), and Wet Wadi (Y30) with Phragmites, respectively. An analysis of 32 dune pools in Spain also yielded similar results: three idiosyncratic taxa (L. salpina; Trichocerca bidens; T. rattus) and two idiosyncratic habitats: mobile dune region; stable dune region and close to a salt marsh. The two tropical datasets we evaluated offered very different results. In 29 Costa Rican habitats we found six idiosyncratic species (Ascomorpha klementi; Keratella americana; L. nana; L. patella; Resticula melandoca; Trichocerca dixonnuttalli) and three idiosyncratic habitats (an artificial Lake; Lake Turrialba; bromeliads). On the other hand, no idiosyncratic taxa or habitats were present in five tropical fishponds. We found similar results in two temperate regions. In 31 sites on the North Island of New Zealand six species (Filinia cf. pejleri; Keratella australis; Keratella tropica; Lecane flexilis; L. acuminata; Trichocerca longiseta) and three lakes (Lake Okaro; Lake Ototoa; Lake Tutira) yielded idiosyncratic T°. In seven habitats of the Develi Plain (Turkey) three species (L. quadridentata; Lepadella biloba; Scaridium longicauda), but no habitats with idiosyncratic . We examined published data from two habitats in cold biomes: one each in the Antarctica (n = 14) and Arctic (n = 8 sites). These habitats yielded a moderately rich fauna of 24 and 70 taxa, with two (B. quadridentatus; Notholca hollowdayi) and four (Collotheca sp. 2; C. catellina; Squatinella sp.; Trichocerca sp.) idiosyncratic taxa, respectively.
Of the 246 taxa identified in our Chihuahuan Desert dataset, 114 were also reported in the four comparison biomes: Aridlands (5 studies; n = 89 species); Tropical (2 studies; n = 63 species); Temperate (2 studies; n = 72 species); and Cold (2 studies; n = 30 species). In spite of this overlap, fewer species with idiosyncratic were found among all datasets. Of the 59 idiosyncratic species identified from the Chihuahuan Desert, only 11 also were identified as being idiosyncratic in the comparison biomes: Aridlands (n = 5) (C. adriatica, C. obtusa, L. ungulata, M. mucronata, and E. daphnicola); Tropical (n = 3) (K. americana, L. nana, and L. patella); Temperate (n = 2) (L. quadridentata and L. acuminata); Cold (n = 1) (C. catellina). None of those 11 species were present in more than one of the comparison biomes.

3.6. Relationship between Species Richness and Geographic Distance

Mantel tests showed a significant correlation between distance and species composition for grid cell sizes below 1.25°. The effect became progressively larger at smaller grid cell size, being the most substantial at cell size 0.1° (p = 0.01) and the least significant at the largest grid cell size (2°; p = 0.1). Species composition in springs demonstrated no significant correlation with distance at any spatial scale investigated. In contrast, playa species composition showed significant correlations with distance at all grain sizes. Tank composition was significant at all grain sizes with the exception of 0.25°. All other habitats showed significant correlation at small grain sizes, but little correlation at large grain sizes (See Table 4). Stream sites were too few (n = 3) to adequately assess using Mantel tests, and thus were not analyzed as a separate habitat.

3.7. Prediction of Biodiversity Hotspots

Generally, patterns of predicted species richness were similar among the spatial scales investigated (Figure 3). At smaller scales, localized hotspots of richness are apparent within the Chihuahuan Desert. At the site level, 0.1° and 0.25° grid cell sizes, predicted species richness was highest in a band spanning from the southern Chihuahuan Desert northward along the western border to the El Paso/Juarez area, and a band spanning from Guadalupe Mountains National Park (TX) to Balmorhea State Park (TX), with low predicted richness along the Rio Grande in this area. When we excluded the site level, a band of high predicted richness exists from Samalayuca across the Rio Grande to Balmorhea State Park, each with localized hotspots (Figure 3B,C). Cuatro Ciénegas showed high richness at most scales (Figure 3B–D). At grid cell sizes >0.25°, distinct hotspots are less apparent (Figure 3D). At these higher scales, local hotspots are more difficult to resolve due to the lower number of grid cells present within the Chihuahuan Desert (n = 24 for 1° grid cells).

4. Discussion

Comprehensive studies of rotifer distributions are common, but vary widely in their focus. For example, many emphasize long-term, ecological questions across several water bodies [125,126,127,128], the dynamics in a particular lake [129,130,131,132,133,134] or region [13,14,117,135,136,137,138,139,140], or examine a single taxon [141,142,143,144,145,146,147,148]. Collectively, such studies provide insight into the biogeography of the phylum. However, to obtain a thorough understanding of the biogeography of rotifers, long-term, systematic survey data is required. Unfortunately, that level of effort is difficult to accomplish, so most studies provide a short-term, snapshot of a region or of a particular habitat [149,150,151,152,153,154,155]. On the other hand, extensive regional studies have been published, which illustrate the diversity of rotifers that may be present in one area: three studies illustrate this point. (1) The study by Segers and Dumont [84] of >110 sites across the Arabian Peninsula, which included five countries, yielded >115 species. (2) In examining 33 lakes on the North Island of New Zealand, Duggan and his colleagues [135] reported 79 species. (3) In a long-term study (1982 onward) of the zooplankton of seven water bodies in the Trout Lake LTER [140], ~75 species have been recorded.
While our choice of collection sites was pragmatic and based on accessibility, sampling >225 diverse habitats over a 20-year period, with many sites visited multiple times, this study comprises an extensive survey. Due to its thorough nature, our analysis of Chihuahuan Desert aquatic systems offers additional insight to the understanding diversity of rotifers in aridlands, and it offers testable predictions regarding the presence of biodiversity hotspots at a regional level.
Among habitats, rotifer species richness was highest in springs (n = 175) and lowest in rock pools (n = 53) followed closely by tanks and playas (n = 57, 66, respectively). This difference in diversity may reflect the relative stability of these habitats in terms of hydroperiod and/or connectivity with other sites. For example, the ephemeral rock pools at HTSPHS are unique in character from all other rocky basins examined in our study. All of the HTSPHS rock pools have nearly identical edaphic conditions, and the Hexarthra found in these pools was identified as a strong indicator species for rock pools (Table 2). For rotifers, the use of the indicator species concept has been used mostly in regard to water quality [99]; thus, our application is somewhat unique. It should be noted that some species have been highly associated with acidic habitats (e.g., Cephalodella hoodi [156], Cephalodella acidophila [157], Keratella taurocephala [158]), and function as indicators. The five species with significant indicator values associated with a combination of five habitat types (L. bulla, P. megalotrocha, L. luna, and C. gibba) possess wide ecological tolerances. Another implication is that these morphospecies likely represent cryptic species complexes [159,160] (see below).
Locations we identified possessing high predicted richness generally overlap the proposed wetland priority sites for the Chihuahuan Desert [25]. However, we found low richness in the Rio Grande and at aquatic sites in White Sands National Park (NM). Several priority areas were sparsely sampled in our study (i.e., the Apachean and the Meseta central subregions); making the predicted richness within these regions less reliable. However, some unusual outcomes occurred at various spatial scales. At our smallest scale (e.g., site level;) some areas that contain highly sampled locations yielded low overall predicted richness. For example, at HTSPHS large numbers of ephemeral rock pools are in close proximity to more speciose playas such as Laguna Prieta, the site with the highest richness in our survey (n = 44). The low diversity of these rock pools decreased our predicted richness for the entire area at the smallest spatial scale. At the 0.1° grid size, the low diversity rock pools and high diversity playas of HTSPHS are combined, resulting in a hotspot on the kriging map. We found similar scenarios at Cuatro Ciénegas (Mexico), BIBE (TX) and Bottomless Lakes State Park (NM). At the largest spatial scale (grid size 1°), the pattern seemed to be more influenced by sampling intensity.
Of the 17 different ways we examined nestedness in the Chihuahuan Desert sites, only three did not exhibit nestedness. The rock pools of Tuff Canyon had no support from the null models; Window Trail Canyon had support from only two; and the artificial rock pools (mesocosms) had support from only one model. These results are not surprising as the basins within of each of these systems are quite similar: Tuff Canyon (basalt larva and tuff deposits); Window Trail (limestone); Mesocosms (plastic basins filled with artificial pond water). This indicates that, for nestedness to be present, the inclusive habitats must possess environmental heterogeneity, and if nestedness were not present, we would expect the species assembly to be random within the habitats [161,162].
In the 18 ways that we analyzed nestedness in our Chihuahuan Desert dataset, we recorded a large number of species to be idiosyncratic (n = 59; ~24%). These species are those, that within the context of the data, contributed disproportionately to the overall matrix temperature; i.e., their occurrence is, therefore, unexpected in that nested group (Table 3). It is notable that most of the idiosyncratic species are generally considered cosmopolitan or having broad environmental tolerances. Our analyses also show that rotifer assemblages are correlated with distance at smaller spatial scales but are more homogenous at the regional level (Table 4). Other papers have reported similar patterns in multiple studies analyzing species assemblages or populations of a single species [147,160,163,164,165,166,167]. Thus, our results seem to support the Baas Becking Principle—“Everything is everywhere, but, the environment selects”—the ubiquity hypothesis [168]. That is, organisms with small dispersal stages (<1 mm) are easily, and widely, dispersed, but arrival does not necessarily guarantee persistence in a habitat [169].
We know that in rotifers, community structure may result from a combination of their high dispersal capacity and their ability to create resting egg banks [5,170]. These two traits can lead to the monopolization of local habitats if the initial colonization and subsequent production of an egg bank leads to rapid adaptation and then to the exclusion of other species. This construct has been named the monopolization hypothesis [171,172]. Thus, at small spatial scales, monopolization leads to high dissimilarity among sites, as may be the case of rock pools and springs in our study (lowest v. highest species richness). However, the high dispersal capability of rotifers may lead to increasing community similarity at larger spatial scales. In general, community composition of organisms with high dispersal ability are less impacted by geographic distances than those with low capacity. Local edaphic conditions, including the arrival sequence, ultimately selects the composition of assemblages that endures.
At larger spatial scales, a greater degree of habitat heterogeneity is present within each region, resulting in a reduction of assemblage differences among regions because of shared habitat types occurring within the larger geographic areas. We have previously reported that rotifer assemblages are more homogenous at the regional level, thereby supporting the relative cosmopolitan nature of dominant rotifer species [57]. However, there can be significant associations between local environmental parameters and species assemblages [53]. Here we report that Chihuahuan Desert spring assemblages were not correlated with distance at any spatial scale investigated. This may be due to the unique edaphic conditions present in each habitat. This was seen in T. similis, which was found in a series of small to large rock pools lying along an erosional channel of Cretaceous limestone in Ernst canyon (n = 12 sites) [173], as well as in Tuff canyon (n = 6 sites) where the rocks pools are arrayed in a channel of eroded basalt lava and tuff deposits [174].
We note that our estimate of richness is likely underestimated, as we could not identify some specimens to species; this is especially true for the Bdelloidea. In addition, it is well known that many traditional species of rotifers are, in fact, complexes of cryptic species [175,176]. For example, two species common in our samples, E. dilatata and B. plicatilis, are comprised of at least 4 and 15 separate lineages, respectively [145,147]. Two of the four newly described species of the E. dilatata complex occur in the Chihuahuan Desert [147]. During the surveys undertaken for this study, they were all recorded as E. dilatata. Finally, several new species are pending formal description.
Our research identified rotifers that exhibited distribution patterns at two extremes: either widely or narrowly distributed. Five species were widely distributed: i.e., being present in 50 or more of the sites we sampled. These species were E. dilatata, L. bulla, L. luna, L. patella, and P. megalotrocha. The perception in the literature is that species with wide distributions have few specific growth requirements. However, as noted above some of these species may represent cryptic species complexes: E. dilatata [147], L. bulla [56], P. megalotrocha [177], and L. luna (Walsh, unpubl. data). On the other hand, some species were narrowly distributed. In our collections we found 70 species only once (e.g., Asplanchna intermedia, Brachionus rotundiformis, Cephalodella dentata, Filinia limnetica, Synchaeta tremula). These species may possess rigorous requirements for growth, be poor dispersers, and/or poor competitors, in each case restricting their distributions.
In addition, we did not sample all sites evenly. We sampled some sites only once at one station, while we sampled others >20 times and from multiple stations/microhabitats within the waterbody. We showed that for sites at BIBE, increased sampling effort increased the number of species recovered even up to seven collections [88]. Similarly, among all sampled habitat types, sampling effort increased richness found, although this relationship was weakest in rock pools, possibly due to their low diversity.

5. Conclusions

Understanding the biogeography of rotifers remains an important problem. Indeed, the general perception that they do not have a biogeography remains largely untested. Rousselet was the first to pose this idea; he argued that “… the Rotifera enjoy a cosmopolitan distribution which is not limited to continents, but extends to all places on the surface of the earth where suitable conditions prevail” [15]. This view, which presaged that of Baas Becking, had been the prevailing view until challenged by several researchers [4,8,169,178]. Yet a large part of the question of whether rotifers possess a biogeography remains rooted in three issues. (1) There is a rotiferologist effect—that the distribution of rotifers indicates more the distribution of researchers, and the habitats that they survey, than the rotifer species themselves [179]. (2) Currently, there are few venues where researchers can receive training in rotifer taxonomy and identification [180]. Thus, identification is often limited to easily recognized species. (3) Recently researchers have come to the realization that cryptic speciation is widespread within the phylum [145,147,181,182] (see also above). Thus, reports of a species from distant locations that are identified based solely by morphological characters may be insufficient to consider them as identical. Emerging science on cryptic speciation suggests that they may be genetically distinct enough to warrant the designation of separate species. Examples of previously unrecognized morphological and ecological differences in the B. plicatilis complex [145], among other species [159], support this contention. Until these issues are, to a large degree, settled, an adequate test of whether rotifers fit the ubiquity hypothesis is not possible.
Thus, our research effort addresses three important aspects in understanding species distributions and biogeography. We covered a broad geographic range, provided a long-term study, and used repeated sampling of sites. Thus, it is not surprising that our study yielded a large number of species. Supporting our previous study that focused on a smaller geographic region (i.e., BIBE), here, we found that sampling effort was positively correlated with rotifer richness in more permanent habitats (e.g., lakes, springs, rivers) and in anthropogenic tanks. In addition, for some sites our efforts spanned seasons and years. Our predictive maps show that it is probable that additional rotifer species remain undiscovered in the Chihuahuan ecoregion. They also give guidance for focusing efforts, as well as for conservation prioritization. Additional diversity also may be revealed by molecular applications such as DNA sequencing to delineate cryptic species and environmental sequencing of water and sediments to find rare species and/or to sample habitats during desiccated periods. In conjunction with environmental data (e.g., water quality data, land use patterns), our findings also can be used to determine ecological drivers of rotifer species assemblages.

Supplementary Materials

The following are available online at, Figure S1: Examples of Chihuahuan Desert aquatic systems.

Author Contributions

Conceptualization, E.J.W., R.L.W., P.D.B., T.S., J.V.R.-A., R.R.-M., and M.S.-B.; validation, P.D.B., E.J.W., and R.L.W.; formal analysis, P.D.B.; R.L.W.; E.J.W.; investigation, E.J.W., R.L.W., T.S., J.V.R.-A., R.R.-M., and M.S.-B.; resources, E.J.W., R.L.W., J.V.R.-A., R.R.-M., and M.S.-B.; data curation, E.J.W.; writing—original draft preparation, R.L.W., P.D.B., E.J.W.; writing—review and editing, E.J.W., R.L.W., P.D.B., T.S., J.V.R.-A., R.R.-M., and M.S.-B.; project administration, E.J.W., R.L.W.; funding acquisition, E.J.W., R.L.W. All authors have read and agreed to the published version of the manuscript.


This research was funded in part by an American Association for the Advancement of Science Women’s International Science Collaboration (WISC) travel grant award, the National Science Foundation Grant No. 0516032, NSF Advance #0245071 (UTEP), NIH 5G12RR008124, T & E, Inc., and Funds for Faculty Development (Ripon College). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the National Institutes of Health.


Assistance was provided by A. Adabache, R. Galván-De la Rosa, J. and B. Newlin, M. Sigla Arana, P.L. Starkweather, N. Lannutti and many undergraduate and graduate students in the Walsh lab. A. Sosa and H.J. Gonzalez-Martínez helped us locate several of the sampling sites in Mexico. Mexican samples were collected under permit #09436 from the Secretario de Medio Ambiente y Recursos Naturales to M. Silva-Briano. We thank the Comisión Federal de Electricidad for permission to sample Presa de la Boquilla. USA samples were collected under permits (to E. Walsh) BIBE-2001-SCI-0058, BIBE-2006-SCI-0003, BIBDE-2016-SCI-0057, BIBE-2001-SCI-0012, CAVE (CAVE-2008-SCI-0005, CAVE-2-12-SCI-0001, CAVE-2016-SCI-0012), GUMO (GUMO-2009-SCI-0009, GUMO-2010-SCI-0013, GUMO-2014-SCI-0017, GUMO-2015-SCI-0017, GUMO-2016-SCI-0017), TPW 02-04, #66-99, #07-02, 2011-13, 2013-01, 2014-01, 2015-03, 2017-R1-19, WHSA-2009-SCI-0011, WHSA-2010-SCI-0008, WHSA-2009-SCI-0011, WHSA-2009-SCI-0-007, WHSA-2012-SCI-0001, WHSA-2014-SCI-0011, WHSA-2016-SCI-009, and NM State Parks Division. We thank The Nature Conservancy and J. Karges for permission to sample the East Sandia Spring Reserve. We thank the local inhabitants for permission to sample Ojo de La Casa, Ojo de La Punta (Don Bruno) and Ojo de Santa María. Jeffrey Bennett provided logistical support in sampling the hotsprings in the lower canyons downstream of BIBE. Kevin Bixby provided access to La Mancha Wetlands. H. Segers provided expert review of some of our species identifications.

Conflicts of Interest

The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.

Appendix A

Table A1. Site name, locations, habitat types, and sampling intensities for waterbodies included in this study. APFFC = Área de Protección de Flora y Fauna Cuatrociénegas, ANPMS = Área Natural Protegida Médanos de Samalayuca, BANWR = Buenos Aires National Wildlife Refuge, BIBE = Big Bend National Park, CAVE = Carlsbad Caverns National Park, GUMO = Guadalupe Mountains National Park, HTSHPS = Hueco Tanks State Park and Historic Site; WHSA = White Sands National Park. Sampling effort: 1 = 1 sample date only, 2 = 2–5 sampling dates, 3 = 6–10 sampling dates, 4 = 11–20 sampling dates, 5 = >20 sampling dates.
Table A1. Site name, locations, habitat types, and sampling intensities for waterbodies included in this study. APFFC = Área de Protección de Flora y Fauna Cuatrociénegas, ANPMS = Área Natural Protegida Médanos de Samalayuca, BANWR = Buenos Aires National Wildlife Refuge, BIBE = Big Bend National Park, CAVE = Carlsbad Caverns National Park, GUMO = Guadalupe Mountains National Park, HTSHPS = Hueco Tanks State Park and Historic Site; WHSA = White Sands National Park. Sampling effort: 1 = 1 sample date only, 2 = 2–5 sampling dates, 3 = 6–10 sampling dates, 4 = 11–20 sampling dates, 5 = >20 sampling dates.
Site Name/LocationHabitat TypeLatitudeLongitudeSpecies RichnessSampling Effort
Triangle Pond, BANWRspring31.55−111.53388962
Lake Arivaca, BANWRlake31.531896−111.25313661
New Mexico
Lazy Lagoon, BLSPplaya33.3541666−104.341766632
Cottonwood Lake, BLSP lake33.3388666−104.334027762
Mirror Lake, BLSPlake33.3363666−104.332733322
Figure Eight Lake, BLSPlake33.3339333−104.332466622
Pasture Lake, BLSP lake33.3310666−104.3295666162
Lea Lake, BLSP lake33.3170833−104.330366682
Elephant Butte Reservoir lake33.1607361−107.188519422
Rio Grande, Williamsburg river33.10335−107.293983122
Caballo Reservoir lake32.8977222−107.2985583131
Dune Pond 1, WHSA playa32.7243−106.39336711
Dune Pond 3, WHSA playa32.72365−106.39491731
Lost River, WHSA stream32.8802−106.170883331
Lower Lost River Pool, WHSA stream32.8775333−106.178933311
Lake Holloman lake32.80745−106.122783361
Backcountry Trailhead, WHSA playa32.797−106.2696521
Garton Spring, WHSA spring32.775067−106.14526712
Lake Lucero, WHSA playa32.6976333−106.451166672
Cattle Tank, WHSA tank32.67485−106.4434542
Dripping Springs spring32.3231888−106.572513862
La Mancha Wetlands river32.278092−106.828626132
Red Lake lake32.8615027−104.17717912
Sitting Bull Falls, LNF spring32.243666−104.69659971
Sitting Bull Falls, LNF spring32.2434916−104.6962916192
Sitting Bull Falls Pool 1, LNF spring32.2390333−104.7025333191
Sitting Bull Falls Pool 2, LNF spring32.2385−104.70266731
Rattlesnake Spring, CAVE spring32.1097−104.471625332
404A Playa playa32.0125844−106.52342716
404B Playa playa32.022586−106.508957171
McKittrick Creek, GUMO stream31.985783−104.76938312
Smith Spring, GUMO spring31.9186111−104.80666731
Manzanita Spring, GUMO spring31.9103194−104.79855233
Chosa Spring south side, GUMO spring31.9065333−104.782116652
Chosa Spring north side, GUMO spring31.906397−104.78299642
Upper Pine Spring Pool #1, GUMO spring31.9032666−104.8178542
Upper Pine Spring Pool #2, GUMO spring31.9029666−104.8176572
Guadalupe Canyon Seepage 1, GUMO spring31.869527−104.838016631
Guadalupe Canyon Seepage 3, GUMO spring31.8696−104.837783351
Columbus Playa, NM playa31.805433−107.103833121
NM Highway 180 river32.508553−106.957176102
Rio Grande, Percha Dam river32.868149−107.30445452
Rio Grande, Anthony river32.005933−106.63973393
BRH, HTSPHSplaya31.927081−106.04114245
Heart, HTSPHS rock pool31.924848−106.04246725
Hex, HTSPHS rock pool31.924734−106.0422125
Stacia, HTSPHS rock pool31.924685−106.04259215
North Temp, HTSPHS rock pool31.924682−106.04234745
Vero, HTSPHS rock pool31.924675−106.04266225
Boo’s Pond, HTSPHS playa31.9246611−106.04582535
South Temp, HTSPHS rock pool31.924658−106.04228565
Cammie, HTSPHS rock pool31.924642−106.04266915
Laguna Prieta, HTSPHS playa31.9246388−106.046675175
Al, HTSPHS rock pool31.924634−106.04267415
Walsh, HTSPHS rock pool31.924628−106.04262825
Julie, HTSPHS rock pool31.924622−106.04249715
Luisa, HTSPHS rock pool31.924768−106.04261715
Jamie, HTSPHS rock pool31.92456−106.04243315
Behind East, HTSPHS playa31.919195−106.041106135
Mescalero Canyon, HTSPHS playa31.9188166−106.040366445
Clammation, HTSPHS rock pool31.922556−106.04250814
Shelby, HTSPHS rock pool31.924622−106.04266815
Pia, HTSPHS rock pool31.924544−106.04223914
Monica, HTSPHS rock pool31.925051−106.04572714
Kettle 1, HTSPHS rock pool31.918455−106.04010624
Kettle 2, HTSPHS rock pool31.918455−106.04010724
Kettle 3, HTSPHS rock pool31.918455−106.04010124
Kettle 4, HTSPHS rock pool31.918446−106.04010545
Kettle 5, HTSPHS rock pool31.918484−106.04008724
Behind Picnic, HTSPHS rock pool31.924831−106.04585523
1 of 4, HTSPHS rock pool31.924826−106.04566324
2 of 4, HTSPHS rock pool31.92482−106.0456714
3 of 4, HTSPHS rock pool31.924813−106.04566914
4 of 4, HTSPHS rock pool31.924799−106.04567314
Abelex, HTSPHS rock pool31.924624−106.04252613
Iceskating Pond, HTSHPS playa31.924729−106.04590943
Rio Grande, Borderland river31.8859527−106.5988777121
Crossroads Pond lake31.836988−106.58051842
Keystone Heritage Park Wetland spring31.8224694−106.564244452
Rio Grande, American Dam river31.786506−106.526992153
Ascarate Lake lake31.7501777−106.4047527334
Ascarate Duck Pond lake31.7473027−106.403552771
Feather Lake lake31.6890972−106.305242
Rio Bosque Wetland Cell 1 tank31.64202−106.31550321
Rio Bosque Wetland Cell 2 tank31.636467−106.31083382
Rio Grande, San Elizario river31.669737−106.337114183
Rio Grande, Fort Quitman river31.087533−105.6093342
Rio Grande, Presidio river29.60365−104.4519722
Rio Grande, C 50 river30.585217−104.89283352
Rio Grande, C 20 river30.36695−104.811832
Rio Grande, Candelaria river30.133417−104.6912
Rio Grande, Guadalupe POE river31.431854−106.14834342
Rio Grande, Montoya Drain river31.799933−106.556490113
Montoya and Doniphan river31.873037−106.59226242
Rio Grande Fabens river31.430277−106.14222182
Album Park playa31.783419−106.34634953
McNary Reservoir lake31.2242138−105.7890083121
Diamond Y Roadside spring31.0088−102.922533132
Diamond Y Spring spring31.0010666−102.9242833182
East Sandia Flow spring30.9910833−103.7286102
East Sandia Spring spring30.9909666−103.7288666222
Balmorhea Lake lake30.9663333−103.713452
Balmorhea Main Pool spring30.9445833−103.787666652
Balmorhea Wetland 1 spring30.9449166−103.7835273
Balmorhea Wetland 2 spring30.945413−103.78598252
Balmorhea Canal spring30.9444472−103.7851583323
Roadside Wetland river30.8551333−105.3608833171
Soda Spring spring30.8276388−105.3173055101
Beauty Spring B spring30.8243333−105.314861122
Stump Spring A spring30.8225883−105.315146671
Masims Spring spring30.8219666−105.31473321
Dynamite Spring spring30.8218833−105.3154561
Squaw Spring spring30.7972166−105.011183322
Corral Tank, IMRS tank30.785263−104.98408492
Peccary Tank, IMRS tank30.755556−105.00416731
Rattlesnake Tank, IMRS tank30.743611−105.00833311
Red Tank, IMRS tank30.7303083−104.989108322
Miller Ranch 96 Well spring30.6238533−104.673998892
Miller Ranch 2 (Spring) spring30.55025−104.66645131
Miller Ranch Glidewell spring30.571483−104.65731781
Pinto Canyon Stream stream30.0308666−104.468433101
Kimball Hole Miller Ranch spring30.585278−104.62666751
Sanderson Canyon rock pool29.8472−102.183705561
La Mesa Canyon Tule 2 rock pool29.829091−102.360993261
Rio Grande, Above Dryden river29.8090277−102.148113811
Lower Madison Falls Seep 1 spring29.7967666−102.377933372
Silber Hotspring 2 spring29.76835−102.563583321
Below Hotsprings Texas spring29.7484−102.540683331
Fuentes Ranch Shafter stream29.7936833−104.27665111
Buttrill Springs, BIBE spring29.54585−103.273862
McKinney Spring 1, BIBE spring29.4090166−103.0871531
Grapevine Spring, BIBE spring29.4075666−103.1908511
McKinney Wall Spring, BIBE spring29.407466−103.088516611
McKinney Tinaja, BIBE rock pool29.4073666−103.088683311
Dripping Spring Cliff, BIBE spring29.4066833−103.310316611
Dripping Spring, BIBE spring29.4049666−103.307858312
Dripping Spring Upper, BIBE spring29.4049491−103.307847011
Onion Tinaja, BIBE rock pool29.4014−103.3258511
Paint Gap Tank, BIBE tank29.3878555−103.302675103
San Felipe Creek Del Rio stream29.36985−100.883816611
Croton Spring, BIBE spring29.3446166−103.3471166103
Croton Stream, BIBE spring29.3437833−103.346542
Government Spring 2, BIBE spring29.3406167−103.255983322
Government Spring 1, BIBE spring29.3405666−103.256083324
Oak Creek, BIBE spring29.2828666−103.342183363
Window Trail Pool A, BIBE rock pool29.28003−103.329947222
Window Trail Pool B, BIBE rock pool29.28003−103.3342
Window Trail Pool C, BIBE rock pool29.28009−103.3301812
Window Trail Pool D, BIBE rock pool29.2802−103.3303822
Window Trail Pool E, BIBE rock pool29.28025−103.3304363
Window Trail Pool F, BIBE rock pool29.28031−103.330563
Window Trail Pool G, BIBE rock pool29.28035−103.330538843
Window Trail Pool H, BIBE rock pool29.2804138−103.330538842
Window Trail Pool I, BIBE rock pool29.2804611−103.330538862
Window Trail Pool Donut, BIBE rock pool29.2802722−103.33047552
Carlota Tinaja, BIBE rock pool29.2790833−103.035416611
Cattail Spring A, BIBE spring29.2731805−103.3355138354
Cattail Spring B, BIBE spring29.2731833−103.33555254
Cattail Spring C, BIBE spring29.2731833−103.3355861174
Cattail Spring C’, BIBE spring29.2731833−103.335630593
Cattail Spring C’’, BIBE spring29.2731833−103.33567583
Cattail Spring C-D, BIBE spring29.2731555−103.3357336133
Cattail Spring D, BIBE spring29.2731527−103.3358277174
Cattail Spring E, BIBE spring29.2731444−103.3359666184
Cattail Spring F, BIBE spring29.2731333−103.3360833214
Cattail Spring G, BIBE spring29.2731666−103.3361638294
Cattail Spring H, BIBE spring29.2731694−103.3362388234
Ernst Tinaja 1, BIBE rock pool29.2568666−103.010083363
Ernst Tinaja 2, BIBE rock pool29.2567416−103.010358353
Ernst Tinaja 3, BIBE rock pool29.2567415−103.010462
Ernst Tinaja 4, BIBE rock pool29.2562666−103.011291622
Ernst Tinaja 4A, BIBE rock pool29.2563611−103.011108362
Ernst Tinaja 5, BIBE rock pool29.2560416−103.011736183
Ernst Tinaja 6, BIBE rock pool29.2559972−103.011916663
Ernst Tinaja 7, BIBE rock pool29.2559944−103.0119553
Ernst Tinaja 8, BIBE rock pool29.2559888−103.011969412
Ernst Tinaja 9, BIBE rock pool29.2559805−103.011997253
Ernst Tinaja 10, BIBE rock pool29.255975−103.012013832
Ernst Tinaja Hueco, BIBE rock pool29.2551−103.014883361
Ward Spring 2, BIBE spring29.24445−103.350583311
Tule Cattle Tank, BIBE tank29.2424333−103.4438305213
Tule Spring A, BIBE spring29.2422833−103.442666663
Tule Spring B, BIBE spring29.24155−103.442833333
Burro Spring, BIBE spring29.2373−103.4259143
Rio Grande Village Cattail Pond, BIBE tank29.189−102.9716166283
Rio Grande Village Canal, BIBE river29.18615−102.9722562
Rio Grande Rio Grande Village, BIBE river29.18555−102.979666163
Langford Hot Springs, BIBE spring29.1794944−102.99546632
Rio Grande Village Pump House, BIBE river29.17945−102.95325162
Rio Grande Village Upper Pond, BIBE river29.1785472−102.9531833304
Rio Grande Village Lower Pond, BIBE river29.1785166−102.95375344
Glenn Springs, BIBE spring29.1744166−103.1575213
Trap Spring, BIBE spring29.1636333−103.419416632
Mule Ears Spring (Middle), BIBE spring29.1624−103.408266621
Mule Ears Spring (Lower), BIBE spring29.16235−103.408283352
Rio Grande, Santa Elena river29.15415−103.59868341
Tuff Canyon Falls (wall), BIBE rock pool29.15115−103.485521
Tuff Canyon 1, BIBE rock pool29.1507666−103.4860512
Tuff Canyon 3, BIBE rock pool29.1507666−103.485922
Tuff Canyon 4, BIBE rock pool29.15077−103.485766632
Tuff Canyon 5, BIBE rock pool29.1509−103.4857522
Tuff Canyon 6, BIBE rock pool29.15095−103.48538911
Presa Chihuahua lake28.5762166−106.1711833322
Delicias Beisbol Field Pool tank28.1648166−105.49850061
Presa Francisco Ignacio Madero lake28.1626166−105.6321833192
Lago Colina lake27.5724−105.4004666432
Presa de la Boquilla lake27.5361333−105.4011333232
Laguna La Leche playa27.2860833−102.916166671
San Jose del Anteojo, APFFC spring26.9693166−102.1208166212
Tio Julio, APFFC spring26.9462833−102.0592101
Poza Tortugas, APFFC spring26.93145−102.1247273
Poza Azul, APFFC spring26.9226666−102.122633332
Rio Mesquites, APFFC river26.9222222−102.108333382
Poza Marcelo, APFFC spring26.9104−102.036316662
Las Playitas, APFFC spring26.9085166−102.0174572
Los Gatos, APFFC spring26.88875−101.9980333142
Poza la Becerra, APFFC spring26.8784166−102.1377666132
Los Hundidos Main pool, APFFC spring26.8711666−102.0204166132
La Campana, APFFC spring26.8683666−102.027833331
Poza El Arco B, APFFC spring26.8683333−102.022861
Poza Churince, APFFCspring26.8404166−102.1342333153
Ejido El Venado Entrance, APFFCspring26.9146333−102.047141
Ejido El Venado Grande, APFFCspring26.8199−101.90483311
Ejido El Venado A, APFFCspring26.8194666−101.905316671
Presa Francisco Zarco Durangolake25.2693055−103.772722221
Ojos Altos Aspring31.40685−107.618183313
Ojos Altos Bspring31.4068−107.617966612
Ojos Altos Cspring31.4035166−107.616123
Ojos Altos Dspring31.4032666−107.616393
Ojo de la Punta, ANPMSspring31.3859166−106.6022666324
Ojo de en Medio ANPMSspring31.37885−106.5877833263
Ojo de la Casa ANPMSspring31.3656166−106.5322333213
DunasCampestre ANPMSspring31.335967−106.49133383
El Huerfano ANPMSspring31.294817−106.511017103
Ojo de Santa Mariaspring31.1552777−107.3172222222
Upper Mexican Hotspringsspring29.7460833−102.5455666112


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Figure 1. Observed species richness (S) of rotifers in 236 Chihuahuan Desert aquatic sites grouped by habitat type over >20 years. (A) Boxplots: horizontal lines indicate median, 95% confidence intervals are shown; dots represent outliers, (B) Richness at different geographic scales (grid cell sizes: 0.1°, 0.25°, 1.0°, 1.25°, 2.0°), numbers above bars are sample sizes, and are the same for panels C and D. (C) Effective richness eH; Hill number, order q = 1. (D) Effective richness based on inverse (inv) of the Simpson’s Diversity Index (SDI); Hill number, order q = 2.
Figure 1. Observed species richness (S) of rotifers in 236 Chihuahuan Desert aquatic sites grouped by habitat type over >20 years. (A) Boxplots: horizontal lines indicate median, 95% confidence intervals are shown; dots represent outliers, (B) Richness at different geographic scales (grid cell sizes: 0.1°, 0.25°, 1.0°, 1.25°, 2.0°), numbers above bars are sample sizes, and are the same for panels C and D. (C) Effective richness eH; Hill number, order q = 1. (D) Effective richness based on inverse (inv) of the Simpson’s Diversity Index (SDI); Hill number, order q = 2.
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Figure 2. Observed species richness (S) as a function of sampling effort in 236 Chihuahuan Desert aquatic sites over 20 years. We shifted some of the data points to reveal their location; some remain obscured by other data points. Lines are linear regressions of the data analyzed separately for each site type. We ranked sampling effort as follows: 1 = 1 sampling event; 2 = 2–5 events; 3 = 6–10 events; 4 = 10–20 events; 5 = >20 events.
Figure 2. Observed species richness (S) as a function of sampling effort in 236 Chihuahuan Desert aquatic sites over 20 years. We shifted some of the data points to reveal their location; some remain obscured by other data points. Lines are linear regressions of the data analyzed separately for each site type. We ranked sampling effort as follows: 1 = 1 sampling event; 2 = 2–5 events; 3 = 6–10 events; 4 = 10–20 events; 5 = >20 events.
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Figure 3. Empirical Bayesian kriging of predicted rotifer species richness within the Chihuahuan Desert ecoregion [123] interpolated from all sites (n = 236) and at a variety of spatial scales. (A) All collection sites (B) 0.1° grid cells, (C) 0.25° grid cells, and (D) 1° grid cells. Sites (panel A) and grid cell centroids (panels B–D) are represented by purple dots. We obtained state boundaries from the USGS and ArcGIS online [124]; ArcGIS Mexican state boundary shapefile courtesy of M. Hoel (
Figure 3. Empirical Bayesian kriging of predicted rotifer species richness within the Chihuahuan Desert ecoregion [123] interpolated from all sites (n = 236) and at a variety of spatial scales. (A) All collection sites (B) 0.1° grid cells, (C) 0.25° grid cells, and (D) 1° grid cells. Sites (panel A) and grid cell centroids (panels B–D) are represented by purple dots. We obtained state boundaries from the USGS and ArcGIS online [124]; ArcGIS Mexican state boundary shapefile courtesy of M. Hoel (
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Table 1. Species richness, unique species, and Sorensen’s Index (below diagonal) and number of shared species (above diagonal) of rotifers from five selected habitat types in the Chihuahuan Desert.
Table 1. Species richness, unique species, and Sorensen’s Index (below diagonal) and number of shared species (above diagonal) of rotifers from five selected habitat types in the Chihuahuan Desert.
Habitat TypeSpecies Richness (S)Unique Species *Versus LakeVersus PlayaVersus Rock PoolVersus SpringVersus Tank
Lake11412 (10.5)42367733
Playa666 (9.1)0.47244526
Rock pool533 (5.7)0.440.404420
Spring17560 (34.3)0.540.380.3939
Tank576 (10.5) 0.390.420.360.34
*—Number of species and percentage of S occurring only in this habitat type compared to all sampling sites.
Table 2. Rotifer indicator species by habitat type for 236 waterbodies in the Chihuahuan Desert. Only those combinations of habitat types with significant associations are reported. Indicator value (IndVal) is the test statistic and p values were calculated using permutation tests.
Table 2. Rotifer indicator species by habitat type for 236 waterbodies in the Chihuahuan Desert. Only those combinations of habitat types with significant associations are reported. Indicator value (IndVal) is the test statistic and p values were calculated using permutation tests.
Habitat TypeNumber of Associated SpeciesIndicator SpeciesIndValp Value
Lake30Trichocerca pusilla0.4830.003
Asplanchna priodonta0.3780.008
Playa16Epiphanes brachionus0.5380.001
Rhinoglena ovigera0.4580.011
Filinia cornuta0.4330.002
Asplanchna sieboldii0.3870.012
Lacinularia flosculosa0.3540.048
Rock Pool6Hexarthra n. sp.0.6320.001
Stream3Dicranophorus grandis0.3780.027
Wulfertia ornata0.3780.027
Tank13Filinia cf. pejleri0.4810.005
Brachionus dimidiatus0.3600.018
Lake + River5Keratella americana0.4320.011
Lake + Rock Pool1Trichocerca similis0.5140.004
Lake + Stream3Colurella adriatica0.4230.014
Lake + Tank6Asplanchna brightwellii0.4330.013
Brachionus caudatus0.3540.031
Brachionus havanaensis0.3500.041
Euchlanis calpidia0.3450.042
Mytilina ventralis0.3320.042
Playa + Stream1Trichocerca rattus0.4450.004
River + Spring2 Dipleuchlanis propatula0.3960.034
River + Tank6Plationus patulus0.4460.015
Eosphora najas0.3970.022
Brachionus bidentatus0.3640.026
Lake + Playa + Spring3Lecane closterocerca0.4390.049
Lake + Playa + Stream2Brachionus plicatilis0.4860.006
Notommata glyphura0.3560.039
Lake + River + Spring7 Colurella uncinata0.4820.010
Lake + River + Tank4Keratella cochlearis0.4670.003
Brachionus variabilis0.4310.011
Polyarthra dolichoptera0.4310.028
Testudinella patina0.4030.040
Playa + River + Tank3Brachionus quadridentatus0.6740.001
Brachionus angularis0.4390.019
Lake + Playa + River + Stream1Cephalodella catalina0.455 0.019
Lake + Playa + River + Tank4Brachionus calyciflorus0.4320.026
Epiphanes chihuahuaensis0.3680.036
Playa + River + Stream + Tank2Euchlanis dilatata0.6280.001
Platyias quadricornis0.4620.012
Lake + Playa + River + Spring + Stream2Lecane bulla0.6680.001
Lake + River + Spring + Stream + Tank1Philodina megalotrocha0.5980.007
Lake + Playa + River + Spring + Stream + Tank4Lecane luna0.5640.008
Cephalodella gibba0.4950.017
Table 3. Comparative statistics of nestedness among selected studies based on presence/absence data of rotifer species. (See Table A1 for an explanation of the sites, including the abbreviations used here.).
Table 3. Comparative statistics of nestedness among selected studies based on presence/absence data of rotifer species. (See Table A1 for an explanation of the sites, including the abbreviations used here.).
Regions Analyzed 1Number of TaxaNumber of GeneraNumber of FamiliesPacked Matrix Null Support 2Idiosyncratic Species 3Idiosyncratic Habitats 4
Chihuahuan Desert(this study)
All sites24659252.44Hexarthra n. sp.; Trichocerca similisCaballo Reservoir, NM; Cattail Spring Pools C-D, BIBE, TX; Lake Lucero, WHSA, NM; Langford Hot Springs, BIBE, TX; Miller Ranch 2 (Spring), TX; Presa Chihuahua, MX; Rio Grande Village Cattail Pond, BIBE, TX; Rio Grande Village Upper Pond, BIBE, TX
By habitat type
1. All lakes112382414.24Encentrum cf. algente; Lecane arcula; Lecane quadridentata; Polyarthra vulgaris; Synchaeta cf. oblongaPresa Chihuahua, Chihuahua, MX
2. All playas66301911.94Lecane hornemanni; Lecane thaleraNone
3. All tanks57271411.14Brachionus durgae; Epiphanes brachionus; Lepadella patellaTule Cattle Tank, BIBE, TX
4. All springs17549235.04Adineta vaga; Aspelta aper; Cephalodella catellina; Cephalodella tenuiseta; Colurella adriatica; Encentrum saundersiae; Filinia brachiata; Lepadella acuminata; Mytilina mucronata; Notommata cf. haueriBalmorhea State Park Main Pool, TX; Balmorhea Wetland 2, TX; Miller Ranch 96 Well, TX; Oak Creek BIBE, TX; Ojo de la Punta, ANPMS, MX; Sitting Bull Falls LNF, NM
Selected springs in Mexico57241521.94Cephalodella cf. graciosa; Cephalodella megalocephala; Pleurotrocha petromyzon; Pleurotrocha sigmoideaOne small, impounded spring: Ojo de en Medio, ANPMS
5. Cascading pools (BIBE)
A. All rock pools7221145.44Epiphanes daphnicola; Trichocerca similisSecond pool of the flowage – surrounded by lush vegetation
B. Cattail Springs65191123.74Colurella obtusa; Lecane pyriformis; Proales cryptopus; Tripleuchlanis plicataSmall pool isolated from the main flowage at this site.
C. Ernst canyon169819.04NoneNone
D. Tuff canyon44311.70NoneShallow rock pool (Tuff Canyon Site #4)
E. Window Trail canyon167623.32Lecane pyriformisSmall tinaja nearly filled with small rocks and sediment, surrounded by plants
6. Rock pools at HTSPHS
A. Isolated rock pools141194.94None. However, Hexarthra n. sp. was found in all sites except for the two artificially enlarged, sheltered rock pools noted hereTwo, artificially enlarged, rock pools sheltered by an overhanging shelf
B. Mesocosms: artificial rock pools96522.91Lecane nanaNone
By Geospatial scale (grid size)
1. Grid 0.1°24659254.44Adineta vaga; Brachionus plicatilis; Brachionus variabilis; Cephalodella cf. misgurnus/pachyodon; Lecane hornemanni; Lecane inermis; Synchaeta cf. oblonga; Trichocerca similis20755: Northern BIBE (Cattail Springs, Window trail, Croton spring)
29355:Caballo reservoir and Percha dam
2. Grid 0.25°24659256.04Brachionus caudatus; Brachionus variabilis; Cephalodella cf. misgurnus/pachyodon; Epiphanes chihuahuaensis; Paradicranophorus sordidus; Polyarthra vulgaris; Trichocerca similis; Wulfertia ornata3310:Northern BIBE
3. Grid 1.0°246592511.64Brachionus bidentatus; Brachionus plicatilis; Cephalodella calosa; Euchlanis triquetra; Filinia brachiata; Keratella americana; Keratella cochlearis; Philodina acuticornis; Philodina megalotrocha; Proales cognita; Wolga spinifera; Wulfertia ornata177: Delicias Beisbol field pool and Presa Francisco Ignacio Madero (southern pond and reservoir respectively)
298: BLSP
4. Grid 1.25°246592510.54Dicranophorus mesotis; Euchlanis calpidia; Hexarthra n.sp.; Lacinularia flosculosa; Lecane aeganea; Lecane undulata; Paradicranophorus sordidus; Polyarthra vulgaris; Proales cf. halophila; Squatinella lamellaris f. mutica; Testudinella patina; Trichocerca similisEl Paso area including HTSPHS
5. Grid 2.0°24659259.54Encentrum cf. cruentum; Euchlanis calpidia; Paradicranophorus sordidus; Plationus patulus; Polyarthra vulgaris; Trichocerca similis64: El Paso/Juarez area including ANPMS, HTSPHS, IMRS
65: GUMO and Balmorhea SP
Other aridland biomes
1. Billabongs (Australia)52251839.32Mytilina mucronata; Epiphanes daphnicola; Trichocerca rattusNone
2. Various habitats (Oman)66201245.93Cephalodella gibba; Colurella obtusa; Trichocerca tenuiorRavine (Wadi O7)
3. Various habitats (Saudi Arabia)1910711.13Lecane ungulataBrackish water lagoon (Sabkhat S7)
4. Various habitats (Yemen)74261611.34Brachionus urceolaris; Cephalodella forficula; Colurella adriatica; Lophocharis salpinaWet Wadi (Y30) with Phragmites
5. Dune pools (Spain)34181216.54Lophocharis salpina; Trichocerca bidens; Trichocerca rattusTwo pools: (1) mobile dune region; (2) stable dune region and close to a salt marsh
Tropical biomes
1. Costa Rican habitats105331710.14Ascomorpha klementi; Keratella americana; Lecane nana; Lepadella patella; Resticula melandoca; Trichocerca dixonnuttalliArtificial Lake; Bromelia; Lake Turrialba
2. Eutrophic tropical fish ponds57221561.80NoneNone
Temperate biomes
1. North Island, NZ79322026.34Filinia cf. pejleri; Keratella australis; Keratella tropica; Lecane flexilis; Lepadella acuminata; Trichocerca longisetaLake Okaro; Lake Ototoa; Lake Tutira
2. Develi Plain, Turkey84331731.63Lecane quadridentata; Lepadella biloba; Scaridium longicaudaNone
Cold biomes
1. Antarctica & sub-Antarctica246322.72Brachionus quadridentatus; Notholca hollowdayiNone
2. Canadian High Arctic70261629.54Collotheca sp. 2; Cephalodella catellina; Squatinella sp.; Trichocerca sp.Small pool, 8 (P208)
1—Partitioning of the dataset. To run the nestedness analyses, we partitioned our Chihuahuan Desert dataset into units as follows. Chihuahuan Desert: All sites (n = 236). By habitat type: 1. Lakes (n = 21). 2. Playas (n = 16). 3. Tanks (n = 11). 4. Springs (n = 95). Selected springs in Mexico (n = 7) in Samalayuca, Chihuahua, Mexico; these data were previously published by Ríos-Arana, Agüero-Reyes, Wallace and Walsh [90]. 5. Cascading Pools: A. All pool habitats at Big Bend National Park (BIBE) (n = 40). B. Cattail Spring (BIBE) (n = 11). C. Ernst Canyon (BIBE) (n = 12). D. Tuff Canyon (BIBE) (n = 6). E. Window Trail (BIBE) (n = 10). 6. Isolated pools: A. Isolated rock pools (n = 27) at Hueco Tanks State Park and Historical Site (HTSPHS) (El Paso, TX). B. Mesocosms–Artificial rock pools (n = 6) developed over 9 weeks at HTSPHS [20]. By scale (grid size): 1. Gridded at 0.1° (n = 83 designations). 2. Gridded at 0.25° (n = 55 designations). 3. Grid 1.0° (n = 23 designations). 4. Gridded at 1.25° (n = 21 designations). 5. Gridded 2.0° (n = 14 designations). Other aridland biomes: 1. Billabongs (oxbows, cut–off meanders) (n = 13) in River Murray (southeastern Australia) [116]. 2, 3, 4. Various habitats ranging from permanent lakes and rivers to temporary pools in Oman (n = 9), Saudi Arabia (n = 19), and Yemen (n = 33), respectively [84]. 5. Ephemeral dune pools (n = 32) in Doñana National Park (Spain) [85]. Tropical biomes: 1. Costa Rica—various habitats including puddles, phytotelmata, ditches, and lakes (n = 29) [117]. 2. Eutrophic, tropical fish ponds (n = 5) in Darbhanga City (Bihar, India) [118]. Temperate biomes: 1. Lakes on North Island, New Zealand (n = 31) [119]. 2. Develi Plain (n = 8) Middle Anatolia, Kayseri, Turkey [120]. Cold Biomes: 1. Antarctica and sub-Antarctica—various habitats (n = 14) [121]. 2. Canadian High Arctic (Devon Island, Northwest Territories)—pools, ponds, and a small lake (n = 8) [122]. 2—Number of null models supporting nestedness. 3—Comments on species with individual ≥ 2 SD of the mean matrix . 4—Comments on sites or gridded regions with individual ≥ 2 SD of the mean matrix .
Table 4. Mantel correlation coefficients (r) between Haversine geographic distances and Bray-Curtis dissimilarity values for rotifer communities between sites (n) at each grid size investigated. Habitat types were then analyzed separately, with the exception of streams due to low number of samples (n = 3 at grid size 0.1°).
Table 4. Mantel correlation coefficients (r) between Haversine geographic distances and Bray-Curtis dissimilarity values for rotifer communities between sites (n) at each grid size investigated. Habitat types were then analyzed separately, with the exception of streams due to low number of samples (n = 3 at grid size 0.1°).
RegionMantel r StatisticP-Valuen
All sites
By habitat
Rock pools

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Brown, P.D.; Schröder, T.; Ríos-Arana, J.V.; Rico-Martinez, R.; Silva-Briano, M.; Wallace, R.L.; Walsh, E.J. Patterns of Rotifer Diversity in the Chihuahuan Desert. Diversity 2020, 12, 393.

AMA Style

Brown PD, Schröder T, Ríos-Arana JV, Rico-Martinez R, Silva-Briano M, Wallace RL, Walsh EJ. Patterns of Rotifer Diversity in the Chihuahuan Desert. Diversity. 2020; 12(10):393.

Chicago/Turabian Style

Brown, Patrick D., Thomas Schröder, Judith V. Ríos-Arana, Roberto Rico-Martinez, Marcelo Silva-Briano, Robert L. Wallace, and Elizabeth J. Walsh. 2020. "Patterns of Rotifer Diversity in the Chihuahuan Desert" Diversity 12, no. 10: 393.

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