Variation in patterns of fish assemblage and their environmental correlates in a tropical river basin from the Gulf of Mexico slope

Abstract

Understanding patterns of freshwater fish assemblage structure is key to protect them from ongoing human-induced threats to aquatic biodiversity. Yet, studies on associations between fish assemblages and habitat are lacking from many areas of high diversity in Middle America. We assessed fish assemblage structure and environmental associations from a portion of the Lacantún River sub-basin (Usumacinta River, Chiapas, Mexico). Based on environmental data and 17,462 individuals (56 species, 46 genera, and 22 families) captured from 13 sites sampled between 2017–2019, we found that stream order, distance to the Usumacinta, forest cover, temperature, and dissolved oxygen are key to explaining assemblage composition. Four clusters were found via multivariate regression tree analysis, with stream order and dissolved oxygen as defining variables. Our findings suggest that fish communities remain spatially structured even at small scales, in association to environmental gradients among habitats.

Keywords:
Community structure; Ecological gradients; Middle America; Southern Mexico; Usumacinta basin

Resumen

Comprender los patrones de estructuración en los ensamblajes de peces dulceacuícolas es una clave para protegerlos de las amenazas humanas a la biodiversidad acuática. No obstante, los estudios sobre asociaciones entre ensamblajes de peces y su hábitat son aún escasos en muchas áreas de alta diversidad de América Media. Evaluamos las asociaciones entre la estructura de los ensamblajes de peces y el ambiente de una porción de la subcuenca del Río Lacantún (cuenca del Río Usumacinta, Chiapas, México). Con base en datos ambientales y un total de 17,462 individuos (56 especies, 46 géneros, y 22 familias) capturados de 13 sitios muestreados entre 2017–2019, encontramos que el orden del cauce, distancia al Usumacinta, cobertura vegetal, temperatura y oxígeno disuelto son clave para explicar la composición de los ensamblajes. Cuatro grupos se detectaron utilizando un árbol de regresión multivariada, definidos por el orden del cauce y el oxígeno disuelto como variables definitorias. Nuestros hallazgos sugieren que las comunidades de peces retienen su estructura espacial aún a pequeñas escalas, en asociación a gradientes ambientales entre hábitats.

Palabras clave:
América Media; Cuenca del Usumacinta; Estructura de comunidades; Gradientes ecológicos; Sur de México

INTRODUCTION

Fish is the most biodiverse group among vertebrates, with over 36,000 currently recognized species occurring throughout the globe (Fricke et al., 2021). Roughly 40% of them are freshwater-dwelling species, and most of these concentrate in the tropics (Albert et al., 2020), making tropical freshwater ecosystems biodiversity hotspots (Dudgeon, 2010; Strayer, Dudgeon, 2010). Despite this, freshwater systems are also among the most degraded ecosystems in the world, attaining greater rates of biodiversity loss in comparison to marine or terrestrial systems (Dudgeon et al., 2006). This is particularly true in tropical regions, which have sustained elevated rates of land-use change in the last decades (Song et al., 2018; Díaz et al., 2019), boosting biodiversity losses.

Assemblage composition in fluvial ecosystems is driven (among others) by interacting factors of ecological (e.g., water chemistry and temperature), geological (e.g., channel geomorphology, macro- and mesohabitat) and biological (e.g., competition and predation) nature (Ricklefs, 1987; Brown, Lomolino, 1998; Fine, 2015). Among the many conceptual models in river ecology, the Flood Pulse Concept - FPC (Junk et al., 1989), Riverine Productivity Model - RPM (Thorp, Delong, 1994), Riverine Productivity Synthesis (Thorp et al., 2008), and River Wave Concept - RWC (Humphries et al., 2014) not only address the spatial components of basal resource change, but also their seasonal component. Under this view, freshwater systems are home to highly dynamic assemblages in which species composition is prone to change with resource and habitat availability (Lowe-McConnell, 1987; Junk et al., 1989), which in turn are affected by their position in space and time. In the tropics, marked environmental shifts among wet and dry seasons (Winemiller et al., 2004; Pease et al., 2020) commonly create a series of notable changes in water level, connectivity, and habitat characteristics.

Within the Neotropics, freshwater systems in Middle America (sensuWinker, 2011) are critical to the region’s diversity. Not only is Middle America a biodiversity hotspot (Myers et al., 2000; Matamoros et al., 2015; Velázquez-Velázquez et al., 2016), but it also represents a faunal discontinuity between the Nearctic region and the South American portion of the Neotropics (Leroy et al., 2019), hosting a particularly distinct biota. The Grijalva-Usumacinta system, lying in the heart of Middle America, hosts a great proportion of the regional biodiversity (March Mifsut, Castro, 2010; Matamoros et al., 2015). It is the largest freshwater system in Middle America, and second only to the Mississippi River in North America (Yáñez-Arancibia et al., 2009; Sánchez et al., 2015; Soria-Barreto et al., 2018; Herrera-Silveira et al., 2019). Despite the socioeconomic (Inda-Diaz et al., 2009; Mendoza-Carranza et al., 2018; Herrera-Silveira et al., 2019; Vaca et al., 2019), and ecological (Rodiles-Hernández et al., 1999; Soria-Barreto, Rodiles-Hernández, 2008; Pease et al., 2020; Soria-Barreto et al., 2021) importance of the Grijalva-Usumacinta system, much remains unknown about the spatial and temporal dynamics of its fish assemblages. Understanding the factors associated to the occurrence and development of such dynamics is crucial in attaining comprehensive management decisions in future conservation strategies for the region (Beard et al., 2018).

Here we examine patterns of spatial and temporal heterogeneity, and associations between fish assemblages and environmental factors based on the monitoring of fish assemblages and environmental variables from 13 localities in the Lacantún sub-basin (in the upper Usumacinta basin) (Fig. 1) sampled between 2017 and 2019. Using these data, we first explore whether assemblages cluster together in distinct groups and, should these clusters occur, which species and environmental traits are key to group formation. We hypothesized that changes among assemblages’ groups should occur in a gradual fashion, in association to gradients in characteristic variables. Following the RWC, our second goal was to test for the existence of a conspicuous temporal component on fish assemblage variation in the study area. For this purpose, we test for temporal effects using seasons and years as factors. We predicted fish assemblages would differ between seasons, and between years, due to changes in the magnitude of seasonal variation from one year to another. Our findings will contribute to better understand the ecology of fish assemblages in the Lacantún River and to the environmental mechanisms driving their structure.

MATERIAL AND METHODS

Study area. The study was conducted in a section of the Lacantún River sub-basin (hereafter referred to as “basin”), which is a tributary to the Usumacinta River, in the Lacandon tropical rainforest of Chiapas, Mexico, between 16°14’ and 16°35’N, and 91°17’ and 90°39’W (Fig. 1). The Lacantún River is a low gradient river that originates in the confluence of the Jataté and Santo Domingo Rivers and flows east for 189 km to its confluence with the Salinas-Chixoy and La Pasión Rivers from Guatemala. Numerous streams and rivers born in the highlands of the Mexican state of Chiapas and the departments of central Guatemala (INE, 2000) join its main stem, with the Ixcán, Chajul, San Pedro-Tzendales and Lacanjá being some of its major tributaries. Several other low-order, mid-to-high-gradient streams also contribute to flow in the Lacantún. The Lacantún has a total basin area of 17,658 km² and an annual discharge of 24,780 million m³ (SEMARNAT, 2016). Along with the Salinas-Chixoy and La Pasión Rivers, the Lacantún forms the middle portion of the Usumacinta River basin (Yáñez-Arancibia et al., 2009; Sánchez et al., 2015; Herrera-Silveira et al., 2019), providing approximately 44% of its total annual discharge (SEMARNAT, 2016). The region is characterized by a warm-humid climate with a mean temperature of 25°C, and an average precipitation of 2,226 mm (INE, 2000) displaying a unimodal pattern, with a well-defined dry season occurring between January-April (Saavedra et al., 2015).

FIGURE 1 |
Study area and sampling sites in the Usumacinta River basin (shaded area in figure box), Mexico. Site number and names of the main rivers in the region are enclosed in boxes (see Tab. 1 for names of watercourses). Black arrow indicates flow direction.

With an increasing human population surrounding the riverine network, the Lacantún plays a crucial role for water supply to rural communities and associated agriculture (Álvarez-Porevsky et al., 2014; Carabias et al., 2015; Gomez Ruiz, Hernández Garciadiego, 2020). Furthermore, it supports an important regional subsistence fishery (Inda-Diaz et al., 2009; Mendoza-Carranza et al., 2018). Main productive activities in the region include corn and bean plantations, and extensive cattle grazing, with oil palm plantations becoming increasingly successful, and a major driver of land use change during the last decade (Vijay et al., 2016; Castellanos-Navarrete, Jansen, 2018). Deforestation due to these activities has been a problem in the region since the second half of the twentieth century, and has worsened during the last 25 years (Carabias et al., 2015), resulting in a large decrease of the tropical forest cover (Conservation International, 2002; Carabias et al., 2015). Although no alarming reductions in water quality have been reported in the Lacantún River itself, it seems that persistence and abuse of agrochemicals and other agricultural practices maintain low levels of pollutants in some of its larger tributaries (Álvarez-Porevski et al., 2015).

Data collection. Fish community and environmental data were collected between January 2017 and August 2019 from 13 sites along a 94 km portion of the Lacantún (see Tab. 1 for a description of sampling sites). Sites included wadeable streams of 1^st to 3^rd order and 4^th and 5^th order non-wadeable streams flowing directly into the Lacantún River, as well as one reach of the Lacantún itself. Three surveys were made each year, one each between January-February, April-May (dry season), and August (wet season), covering the main environmental shifts in seasonal dynamics of the region (i.e., the beginning and peak of the dry season, and a low-flow period during the rainy season, traditionally known as “canícula”). These sampling efforts resulted in a total of 108 samples (key to site names, abbreviations, and codes in Tab. S1) available for analysis.

TABLE 1 |
Environmental attributes of 13 sampling sites in the Lacantún River basin. Mean values are shown. ORD, Strahler stream order; DO, dissolved oxygen (mg·ml^-1); DU, distance to the confluence with the Usumacinta River in km along the Lacantún thalweg; TC, water temperature (°C); FC, proportion of forest cover adjacent to the sampling site; SW, stream width (m); ALT, m.a.s.l.; EC, electric conductivity (µS·cm^-1); TDS, total dissolved solids (ppm).

Site	Site Name	ORD	DO	DU	TC	FC	SW	ALT	pH	EC	TDS
1	Ixcán River	4	7.48	158	23.6	0.30	67	166	7.98	438.83	221.82
2	Puerto Rico Stream	2	7.11	142	25.5	0.33	16	154	8.03	283.18	142.44
3	San Pablo Stream	2	6.36	133	23.6	1.00	5	157	8.09	476.10	254.03
4	Chajul River	4	7.81	134	25.3	0.25	53	150	8.00	308.59	154.58
5	Lacantún River	7	7.60	130	25.3	0.80	174	149	8.06	480.27	246.27
6	José Stream	3	6.65	130	23.6	1.00	5	150	7.97	802.24	404.34
7	Miranda Stream	4	6.33	128	26.1	1.00	12	149	7.86	652.84	329.16
8	Lagarto Stream	4	6.57	128	26.3	0.60	18	150	7.30	840.39	425.06
9	Danta Stream	1	3.38	124	23.8	1.00	2	165	7.50	406.66	207.71
10	Manzanares Stream	5	7.13	116	24.5	0.28	17	146	7.33	119.05	59.91
11	Tzendales River	5	7.44	94	25.3	1.00	36	142	7.75	716.14	364.22
12	Lacanjá River	5	7.10	61	28.0	1.00	34	123	7.98	652.38	325.73
13	Lacanjá Wetland	5	3.90	61	29.6	1.00	25	125	7.66	585.86	293.30

Samples were obtained using a 3x2 m seine net with 0.5 cm diameter mesh size and a DC backpack electrofisher. Each site was sampled simultaneously both by seine net hauling and electrofishing for a 30 min lapse. When possible, all available habitats (e.g., riffles, pools, run and rapids) on each site were sampled to maximize the number of species recorded. Sampling was focused on shallow habitats; deep pools and runs (i.e., > 2 meters deep) on some of the larger systems were missed due fishing gear limitations. All fishes captured were identified following current literature (Miller et al., 2005; Betancur-R. et al., 2007; Schmitter-Soto, 2017) and counted in situ. With exception of voucher specimens, most of the captured fishes were released unharmed after processing. In total, 436 voucher specimens of 42 species were deposited at the Colección Nacional de Peces, Instituto de Biología of the Universidad Nacional Autónoma de México, Mexico City, Mexico (IBH) (Tab. S2).

Environmental data consisted of 10 variables commonly considered as some of the determinants of fish assemblage structure (Ibarra, Stewart, 1989; Rodiles-Hernández et al., 1999; Ibanez et al., 2007; Fischer, Paukert, 2008; Mercado-Silva et al., 2012). Variables measured were altitude (ALT, m.a.s.l.), dissolved oxygen (DO, mg·L^-1), distance to the Usumacinta (DU, km), electric conductivity (EC, μS·cm^-1), forest cover (FC, proportion: 0 – 1), mean stream width (SW, m), pH (pH), Strahler stream order (ORD, 1 – 7), total dissolved solids (TDS, ppm), and water temperature (TC, °C). Physicochemical variables (i.e., DO, EC, pH, TDS, and TC) represent the average of three independent readings measured at a near-shore point in each site using a portable multiparameter meter (model HI9829, Hanna Instruments, Woonsocket, RI, USA). Both DU and FC were estimated using Google Satellite images processed on QGIS 3.2 “Bonn” (QGIS Development Team, 2021). Distance to the Usumacinta was measured as the distance between the downstream extreme of the sampling site to the main axis of the Lacantún River, and onward until the confluence with the Usumacinta. We measured FC as the proportion of the area covered by forest in a 100-meter-wide strip of land along both stream banks in the sampling site. Stream order (Strahler, 1957) was calculated for each site segment using river network shapefiles obtained from the Instituto Nacional de Estadística y Geografía (INEGI, Mexico) available at www.inegi.org.mx, and the Sistema Nacional de Información Territorial (SINIT-SEGEPLAN, Guatemala) available at www.segeplan.gob.gt.

Analyses. Two independent data sets were constructed from the collected data. The first was a matrix of species abundances per site (“assemblage” data from hereon; Tab. S2), and the second was on habitat, geographical and environmental (henceforward environment) data. In order to explore the effects of seasonal shifts on fish assemblages, we classified samples according to the corresponding season and year. Prior to analyses, environmental data was natural-log-transformed, and a Hellinger standardization (Legendre, Legendre, 1998) was applied to abundance matrices using the function decostand in R package “vegan” (Oksanen et al., 2018). Three exotic species, Ctenopharyngodon idella (Valenciennes, 1844), Pterygoplichthys disjunctivus (Weber, 1991), and Oreochromis aureus (Steindachner, 1864), and four native species, Lacantunia enigmatica Rodiles-Hernández, Hendrickson & Lundberg, 2005, Ictalurus meridionalis (Günther, 1864), Mugil curema Valenciennes, 1836, and Centropomus undecimalis (Bloch, 1792) were excluded from these analyses (although they are included in the data sets).

It is common for certain environmental variables to be correlated (e.g., water temperature and dissolved oxygen, stream order and distance from source). Therefore, a correlation (Spearman’s coefficients) matrix was calculated for the 10 environmental variables to examine interrelationships and reduce multicollinearity. We considered pairs of variables with a correlation coefficient > 0.70 as highly correlated (Dormann et al., 2013). In the case of high collinearity between variables, the most ecologically meaningful one was retained for construction of a priori models. After removal of redundant variables (SW, FC, TDS, and ALT) a principal component analysis (PCA) was conducted on a correlation matrix of the remaining variables, using the function rda from R package “vegan” (Oksanen et al., 2018), to create a multidimensional space of habitat complexity. This facilitated the detection of spatial and/or seasonal environmental patterns. We assessed the association of individual variables to the first two principal components (which accounted for > 50% of the variation) using Spearman correlation coefficients of PCA eigenvectors vs. the environmental matrix. Variables significantly correlated (p < 0.05) to PC1 and PC2 were retained for the subsequent analyses. The final environmental matrix contained the variables FC, DU, EC, DO, TC, and ORD.

To disentangle associations between assemblage composition and environment, we used a combination of multivariate metrics. We used a multivariate regression tree - MRT (De’ath, 2002) on standardized species abundances, and environmental variables as explanatory variables, to obtain a clustering scheme of assemblages and environmental traits associated to each node split. Multivariate regression trees serve as a method for multivariate regression and constrained clustering, in which clusters are explained, defined, and can be predicted by a set of environmental variables (De’ath, 2002; Borcard et al., 2011). The MRT was obtained using the function mvpart from R package “mvpart” (Therneau et al., 2014). An indicator species analysis (Dufrêne, Legendre, 1997) was performed to calculate indicator values of species for each MRT group using function indval from R package “labdsv” (Roberts, 2019). An indicator species analysis rates the degree of association of a species to groups of sites, with values ranging from 0 to 1 (Dufrêne, Legendre, 1997). The index attains its maximum values for species showing high fidelity (all individuals are found in only one group of sites) and high frequency (the species occurs in all sites of that group). We considered accurate indicators of each group only those species having a significant p-value (p < 0.05), and an indicator species value ≥ 0.6. Mean species richness and standard deviations were calculated for each MRT cluster. Differences in species richness among groups were compared using a one-way analysis of variance paired with a Tukey test for multiple comparisons.

To assess changes in assemblage structure, we calculated a multidimensional space via canonical analysis of standardized species abundances, using selected environmental variables as the constraining matrix. Canonical analyses allow for the direct comparison of both response variables “Y” (i.e., fish assemblage data) and a set of explanatory variables “X”, allowing to extract all of the variance of “Y” that is related to “X”, with minimum amounts of data loss (Legendre, Legendre, 1998). This was achieved using redundancy analyses - RDA (Rao, 1964; van den Wollenberg, 1977) in combination with a double-stopping-criterion forward selection procedure (Blanchet et al., 2008), to identify best-explanatory variables. RDA and forward selection were performed using functions rda and ordiR2step, also from R package “vegan”. Forward selection was conducted following two stopping rules: either exceeding the critical p value (p = 0.05) or the adjusted R² value of the reduced model exceeding that of the global model. Variance inflation factors - VIF (Chatterjee, Hadi, 1977; Belsey et al., 1980) were calculated before and after forward selection procedure to further ensure no collinearity was introduced into the final model. We considered variables with VIF >10 (Chatterjee, Hadi, 1977, 2012; Belsey et al., 1980; Borcard et al., 2011) as having high collinearity and excluded them from further analyses. We tested for significant linear dependencies for axes and individual variables via marginal and term-wise tests of significance.

To test for differences over spatial (i.e., MRT clusters) and temporal (i.e., seasonal and inter-annual) scales, and to assess the relative importance of these in explaining variation on a) fish assemblage structure and b) environmental characteristics, we used permutational multivariate analysis of variance PerMANOVA (McArdle, Anderson, 2001). For this purpose, we tested for differences among MRT clusters, years, and seasons. We performed two kinds of seasonal comparisons: 1) all-years-dry-season vs. all-years-wet-season (i.e., grouping all samples of each season among the three years), and 2) yearly seasons (i.e., considering every combination of years and seasons as an individual group). Yearly seasons are hereafter identified by the starting letter of the season (D - dry; W - wet) and the last two digits of the year (e.g., D17: dry 2017, W17: wet 2017, and so on). PerMANOVAs were performed using the function adonis2 in R package “vegan”, and whenever significant differences were found (p ≤ 0.05), a post-hoc pairwise PerMANOVA was performed to assess the differences between individual groups. Pairwise models were constructed using the package “pairwiseAdonis” (Martínez-Arbizu, 2017) and a Bonferroni correction for multiple comparisons was applied to the corresponding p-values. We tested for multivariate homoscedasticity using the function betadisper in the package “vegan”, to ensure no overdispersion was introduced due to the unbalanced nature of the data. All analyses were performed using R version 4.0.3 (R Development Core Team, 2020).

RESULTS

A total of 17,462 individual fishes representing 56 species, in 22 families and 46 genera were sampled from 13 sites (Tab. S2). Overall, we found a highly skewed distribution of species richness, with 56% of species belonging to the families Cichlidae (35%) and Poeciliidae (21%), while all other families were represented by either one (Lepisosteidae, Cyprinidae, Bryconidae, Lacantuniidae, Ariidae, Ictaluridae, Batrachoididae, Mugilidae, Belonidae, Hemiramphidae, Cynolebiidae, Centropomidae, and Gerreidae) or two species (Clupeidae, Characidae, Heptapteridae, Atherinopsidae, and Eleotridae). A high proportion of the total individual count (43%) was comprised by only four species (Fig. 2): Astyanax spp., Atherinella alvarezi (Díaz-Pardo, 1972), Poecilia mexicana Steindachner, 1863, and Pseudoxiphophorus bimaculatus (Heckel, 1848). After removal of rare and exotic species, sampling yielded a total of 17,349 individual fishes representing 49 species, in 20 families and 42 genera (Tab. 2).

TABLE 2 |
Fish assemblage composition of the Lacantún River sub-basin. Total recorded frequencies for species considered in analysis and species codes used for reference on subsequent figures are shown. For a complete list of recorded species, refer to Tabs. S2 and S3.

No.	Taxon	Code	Ct.
	Lepisosteidae
1	Atractosteus tropicus Gill, 1863
	Clupeidae
2	Dorosoma anale Meek, 1904	Dana	10
3	Dorosoma petenense (Günther, 1867)	Dpet	122
	Characidae
4	Astyanax spp.	Abre	4418
5	Hyphessobrycon compressus (Meek, 1904)	Hcom	353
	Bryconidae
6	Brycon guatemalensis Regan, 1908	Bgua	257
	Ariidae
7	Cathorops aguadulce (Meek, 1904)	Cagu	7
	Heptapteridae
8	Rhamdia guatemalensis (Günther, 1864)	Rgua	163
9	Rhamdia laticauda (Kner, 1858)	Rlat	51
	Batrachoididae
10	Batrachoides goldmani Evermann & Goldsborough, 1902	Bgol	26
	Eleotridae
11	Gobiomorus dormitor Lacepède, 1800	Gdor	4
12	Leptophilypnus guatemalensis Thacker & Pezold, 2006	Lgua	199
	Cichlidae
13	Chuco intermedius (Günther, 1862)	Cint	343
14	Cincelichthys pearsei (Hubbs, 1936)	Cpea	21
15	Maskaheros argenteus (Allgayer, 2002)	Marg	25
16	Mayaheros urophthalmus (Günther, 1862)	Muro	61
17	Oscura heterospila (Hubbs, 1936)	Ohet	2
18	Parachromis multifasciatus (Regan, 1905)	Pmul	22
19	Petenia splendida Günther, 1862	Pspl	133
20	Rheoheros lentiginosus (Steindachner, 1864)	Rlen	198
21	Rocio octofasciata (Regan, 1903)	Roct	32
22	Theraps irregularis Günther, 1862	Tirr	181
23	Thorichthys helleri (Steindachner, 1864)	Thel	576
24	Thorichthys meeki Brind, 1918	Tmee	373
25	Thorichthys pasionis (Rivas, 1962)	Tpas	392
26	Thorichthys socolofi (Miller & Taylor, 1984)	Tsoc	6
27	Trichromis salvini (Günther, 1862)	Tsal	163
28	Vieja bifasciata (Steindachner, 1864)	Vbif	174
29	Vieja melanura (Günther, 1862)	Vmel	102
30	Wajpamheros nourissati (Allgayer, 1989)	Wnou	86
	Atherinopsidae
31	Atherinella alvarezi (Díaz-Pardo, 1972)	Aalv	3123
32	Atherinella schultzi (Álvarez & Carranza, 1952)	Asch	131
	Belonidae
33	Strongylura hubbsi Collette, 1974	Shub	86
	Hemiramphidae
34	Hyporhamphus mexicanus Álvarez, 1959	Hmex	49
	Cynolebiidae
35	Cynodonichthys tenuis Meek, 1904	Cten	38
	Poeciliidae
36	Belonesox belizanus Kner, 1860	Bbel	66
37	Carlhubbsia kidderi (Hubbs, 1936)	Ckid	2
38	Gambusia sexradiata Hubbs, 1936	Gsex	377
39	Phallichthys fairweatheri Rosen & Bailey, 1959	Pfai	230
40	Poecilia kykesis Poeser, 2002	Pkyk	262
41	Poecilia mexicana Steindachner, 1863	Pmex	1722
42	Poecilia sphenops Valenciennes, 1849	Psph	33
43	Poeciliopsis spp.	Pspp	47
44	Pseudoxiphophorus bimaculatus (Heckel, 1848)	Pbim	1356
45	Xenodexia ctenolepis Hubbs, 1950	Xcte	257
46	Xiphophorus hellerii Heckel, 1848	Xhel	723
47	Xiphophorus maculatus (Günther, 1866)	Xmac	31
	Synbranchidae
48	Ophisternon aenigmaticum Rosen & Greenwood, 1976	Oaen	184
	Gerreidae
49	Eugerres mexicanus (Steindachner, 1863)	Emex	65

FIGURE 2 |
Relative contribution of common species to total fish abundance by seasons (left panel) and years (right panel). Species shown had a contribution above the 50% quantile for abundance. Refer to Tab. 1 for species acronyms.

Principal component analysis explained 52.51% of the observed variation in its first two principal components (Fig. 3). We found significant correlations for several environmental variables in at least one PCA axis (in descending order: FC, EC, DU, TC, DO, ORD; Tab. S3). Positive associations were found between PC1 (31.16% of variance explained) and variables FC (r = 0.777, p < 0.001), EC (r = 0.749, p < 0.001), and TC (r = 0.541, p < 0.001); while negative correlations were found for DU (r = -0.599, p < 0.001) and DO (r = -0.3, p = 0.002). PC2 (21.35% of variance explained) was positively correlated to ORD (r = 0.820, p < 0.001), TC (r = 0.507, p < 0.001) and DO (r = 0.470, p < 0.001), and negatively correlated to DU (r = -0.243, p = 0.011) and FC (r = -0.411, p < 0.001).

FIGURE 3 |
Principal component analysis plot of environmental data. DO, dissolved oxygen; DU, distance to the confluence with the Usumacinta River; EC, electric conductivity; FC, forest cover; ORD, stream order.

The MRT yielded a four-leaf tree (henceforward groups G1 - G4) with only 23.7% of total variance explained, and variables ORD and DO as decisive criteria (Fig. 4). Sample composition within groups was variable; while the first group (G1) was composed exclusively of samples from site 9, we found a complex cluster (G3) containing all medium to high order sites. We recovered significant differences using PerMANOVA among MRT clusters for both environment (F_3,104 = 12.787, R² = 0.269, p = 0.001) and assemblage (F_3,106 = 10.873, R² = 0.238, p < 0.001) data. Pairwise PerMANOVAs indicated that both environment and assemblage differed significantly among each cluster pair (Tab. 3). Indicator species analysis recovered species with significant indicator values for three out of the four groups (Tab. 4). In accordance with patterns recovered by the redundancy analysis, G1 was mainly defined by the cynolebiid Cynodonichthys tenuis Meek, 1904, the poeciliids Xiphophorus hellerii Heckel, 1848, and Pseudoxiphophorus bimaculatus, G2 was defined by Poecilia kykesis Poeser, 2002, and the cichlids Thorichthys pasionis (Rivas, 1962) and Mayaheros urophthalmus (Günther, 1862). G4 was defined by the poecilid Xenodexia ctenolepis Hubbs, 1950, and the eleotrid Leptophilypnus guatemalensis Thacker & Pezold, 2006. We found no indicator species for G3.

TABLE 3 |
Pairwise PerMANOVA comparisons of environmental variables and assemblage (standardized species abundances) between MRT clusters (G1 – G4), years, and yearly seasons. D17, dry 2017; W17, wet 2017; D18, dry 2018; W18, wet 2018; D19, dry 2019; W19, wet 2019. Significant comparisons (p < 0.05) are marked by *.

	Factor	Comparison	SS	F	R2	padj
Environment	Clusters R2 = 0.269, F = 12.787, p = 0.001	G1-G2	5.647	9.920	0.474	0.008 *
		G1-G3	11.934	18.422	0.199	< 0.001 *
		G1-G4	4.759	11.373	0.256	< 0.001 *
		G2-G3	5.731	8.863	0.111	0.001 *
		G2-G4	6.471	16.473	0.354	< 0.001 *
		G3-G4	6.385	11.108	0.107	< 0.001 *
	Years R2 = 0.041, F = 4.564, p = 0.011	2017-2018	0.105	0.156	0.004	1.000
		2017-2019	1.102	1.588	0.034	1.000
		2018-2019	0.536	0.763	0.021	1.000
	Yearly seasons R2 = 0.081, F =1.810, p = 0.021	D17-W17	2.064	2.584	0.052	0.555
		D17-D18	1.473	1.932	0.052	1.000
		D17-W18	0.636	1.006	0.030	1.000
		D17-D19	0.201	0.328	0.015	1.000
		D17-W19	1.760	2.253	0.062	0.895
		W17-D18	1.284	1.799	0.075	1.000
		W17-W18	1.288	1.856	0.053	1.000
		W17-D19	1.982	2.568	0.052	0.874
		W17-W19	0.960	1.324	0.037	1.000
		D18-W18	3.449	4.286	0.109	0.103
		D18-D19	2.500	3.321	0.126	0.253
		D18-W19	0.152	0.177	0.004	1.000
		W18-D19	0.105	0.156	0.004	1.000
		W18-W19	1.102	1.588	0.034	1.000
		D19-W19	0.536	0.763	0.021	1.000
Assemblage	Clusters R2 = 0.239, F =10.873, p < 0.001	G1-G2	4.067	13.680	0.554	0.005 *
		G1-G3	7.734	16.999	0.187	< 0.001 *
		G1-G4	4.981	18.157	0.355	< 0.001 *
		G2-G3	3.271	6.590	0.085	< 0.001 *
		G2-G4	4.133	11.673	0.280	< 0.001 *
		G3-G4	3.308	7.500	0.075	< 0.001 *
	Years R2 = 0.053, F = 6.023, p = 0.001	2017-2018	0.531	1.201	0.017	0.804
		2017-2019	3.045	5.690	0.074	0.003 *
		2018-2019	3.589	6.597	0.085	0.003 *
	Yearly seasons R2 = 0.117, F = 2.709, p = 0.001	D17-W17	0.644	1.509	0.043	1.000
		D17-D18	0.377	0.892	0.019	1.000
		D17-W18	0.512	1.152	0.032	1.000
		D17-D19	1.828	3.482	0.068	0.015 *
		D17-W19	2.917	6.070	0.147	0.015 *
		W17-D18	0.754	1.736	0.051	0.660
		W17-W18	0.530	1.108	0.050	1.000
		W17-D19	1.101	1.912	0.053	0.420
		W17-W19	1.771	3.321	0.131	0.015 *
		D18-W18	0.408	0.899	0.026	1.000
Assemblage	Yearly season R2 = 0.117, F = 2.709, p = 0.001	D18-D19	2.287	4.297	0.085	0.015 *
		D18-W19	3.368	6.885	0.168	0.015 *
		W18-D19	0.945	1.603	0.043	1.000
		W18-W19	1.775	3.193	0.121	0.015 *
		D19-W19	0.941	1.517	0.040	1.000

TABLE 4 |
Indicator values for diagnostic species of each MRT cluster according to the indicator species analysis of Dufrêne, Legendre, 1997. Ind. Val. = indicator species index value.

Species	Cluster	Ind. Val.	padj
Cynodonichthys tenuis	G1	0.958	< 0.001
Xiphophorus hellerii	G1	0.698	< 0.001
Pseudoxiphophorus bimaculatus	G1	0.668	< 0.001
Poecilia kykesis	G2	0.778	< 0.001
Thorichthys pasionis	G2	0.774	< 0.001
Mayaheros urophthalmus	G2	0.752	< 0.001
Xenodexia ctenolepis	G4	0.706	< 0.001
Leptophilypnus guatemalensis	G4	0.655	< 0.001

FIGURE 4 |
Multivariate regression tree of transformed species abundances and environmental data. Percentage of improvement in the model is shown under each node; discriminating environmental variables and threshold values are shown at each split. DO, dissolved oxygen; ORD, stream order; n, number of samples.

The forward selection procedure excluded a single variable from the RDA ordination (Fig. 5), which explained 58.92% of constrained variance in its first two axes using variables ORD, TC, DU, FC, and DO as constraining factors (Figs. 5A, B). Axis 1 (38.43% variance) represented a gradient in stream order, forest cover and dissolved oxygen, separating species associated to low-order streams with high proportion of forest cover and reduced oxygen availability (Pseudoxiphophorus bimaculatus, Xiphophorus helleri, and Cynodonichthys tenuis) from other species such as Astyanax spp., Poecilia mexicana, and Strongylura hubbsi Collette, 1974, found in higher-order, oxygen-rich streams (Fig. 5C; Tab. S4). Axis 2 (20.49% variance) represented a gradient mainly associated to the distance from the mouth, stream order and water temperature, where abundances of Atherinella alvarezi, Xenodexia ctenolepis, Leptophilypnus guatemalensis, and Chuco intermedius (Günther, 1862) got separated from species from sites having higher water-temperature and stream order, and low oxygen content (Thorichthys meeki Brind, 1918, T. pasionis and Mayaheros urophthalmus, and to Poecilia kykesis). All five variables were found to have a significant effect on assemblage variance, most of which was accounted by stream order (Tab. S4).

FIGURE 5 |
Plots for the redundancy analysis of fish communities and environmental data, displaying weighted averages of species scores (A), fitted site scores (B), and species scores of significant species (C). Centroids of factor “ORD” are indicated by a “×”. Convex hulls (A, C) and point shape (A, B) show cluster affiliation. DO, dissolved oxygen; DU, distance to the confluence with the Usumacinta River; FC, forest cover; ORD, stream order; TC, water temperature.

Permutational multivariate analysis of variance on environmental conditions recovered significant, but poorly explained variation (R² range: 0.004 – 0.12) among years (F_3,106 = 4.564, R² = 0.041, p = 0.012) and yearly seasons (F_5,102 = 1.810, R² = 0.081, p = 0.02), while a wet-dry seasonal comparison was non-significant (F_1,106 = 0.646, R² = 0.006, p = 0.62). Post-hoc pairwise PerMANOVAs among years indicated that environmental conditions did not differ between 2017–2018, but varied significantly between 2019 and the former two years (Tab. 3). As for yearly seasonality, we found no single pair of seasons to be different after having their p-values corrected for multiple comparisons (Tab. 3). In contrast with the environment, we found significant but still poorly explained (R² range: 0.026 – 0.17) differences for fish assemblage structure among years (F_3,106 = 6.023, R² = 0.053, p = 0.001), yearly seasons (F_5,102 = 2.709, R² = 0.117, p = 0.001), and overall seasonal comparison (F_1,106 = 1.955, R² = 0.018, p = 0.028). Pairwise tests among years mirrored those from environmental PerMANOVA, with only 2019 being distinctive from the former two years. Accordingly, except for W18–D19, we found both wet and dry 2019 seasons differing from those of 2017 and 2018, and no significant variation was found between intra-year seasons (Tab. 3).

DISCUSSION

Freshwater fish assemblages are structured by environmental factors occurring at multiple spatial and temporal scales (Ricklefs, 2004; Hoeinghaus et al., 2007; Elías et al., 2020), some of which rely on habitat characteristics (Angermeier, Karr, 1983; Ibarra, Stewart, 1989; Lamouroux et al., 2002; Hoeinghaus et al., 2007; Fischer, Paukert, 2008) and seasonality (Lowe-McConnell, 1987, 1979). Our study revealed patterns of both spatial and temporal structure present in studied assemblages. However, we found unexpected contrasts between environmental and assemblage dynamics.

The PCA on environmental data showed a dominant gradient among sites defined by longitudinal increases in FC, EC, and TC. Most sites associated to low EC and TC, were located on tributaries in the right margin of the Lacantún River, in the municipalities of Maravilla Tenejapa and Marqués de Comillas (hereafter MC). Low EC was unexpected on sites located in tributaries on this margin of the Lacantun given extensive presence of human activity and settlements (vs. sites on tributaries along the left margin, located inside the Montes Azules Biosphere Reserve - MA). It is likely that the differences in ion contributions between the pre-Pleistocene alluvial deposits from MC and the upper Cretaceous limestones, and Tertiary lutites and sandstones found in MA’s bedrock (Saavedra et al., 2015) obscured anthropogenic effects and played a large role in this pattern.

Low-order streams, which usually have a larger proportion of riffle cover, had lower oxygen content than high-order streams. This could be caused by a number of factors, such as lower rates of algal growth and oxygen released through photosynthetic activity when compared to wider channel rivers (Soria-Barreto et al., 2021). Proportionally large pools relative to their discharge, may also dampen vortex formation and sediment movement (Thompson, 2013), allowing for high rates of litter decomposition depleting oxygen levels.

Spatial effects on assemblage composition. As expected, habitat shifts played an important role in assemblage structure, as four distinctive groups resulted from the regressive model, with varying species composition and environmental affinities. Major divisions among assemblages related to increasing stream order on groups G1 (ORD = 1), G4 (ORD = 2 and 3), and G3 (ORD = 4 – 7). Group G1 clustered all samples from site 9, a small, first-order stream dominated by small-bodied generalists in the Poeciliidae and Cynolebiidae, but that also contains Rhamdia guatemalensis (Günther, 1864), and the ubiquitous Astyanax spp. These species efficiently exploit a rich aquatic insect community (Ramírez-Martínez et al., 2015; Castillo et al., 2018), terrestrial prey, and other allochthonous nutrients. Despite its closeness to the main stem of the Lacantún (i.e., < 1 km), low water flow makes for limited connectivity and therefore a strong barrier to many larger-bodied fish species. Furthermore, although continuous, reduced water flow during the dry season provides additional isolation for larger-bodied fish, here represented by juvenile individuals of Chuco intermedius and Rocio octofasciata (Regan, 1903).

Groups G3 and G4 portrayed the core of the regional fish assemblage, with mostly rheophilic species (Rheoheros lentiginosus (Steindachner, 1864); Theraps irregularis Günther, 1862; Strongylura hubbsi; Hyporhamphus mexicanus Álvarez, 1959) occurring alongside larger plant-eating cichlids (i.e., Cincelichthys pearsei (Hubbs, 1936)) and predatory fish (i.e., Batrachoides goldmani Evermann & Goldsborough, 1902, and Gobiomorus dormitor Lacepède, 1800). Yet, while both groups consist of samples taken in sites with medium to high water-discharge, the sites forming group G4 (sites 2, 3, and 6) retain the status of low-order streams, and are consequently associated with higher gradients and faster flowing water (Davies et al., 2008), making them ideal for species with presumably high oxygen requirements such as Xenodexia ctenolepis and Leptophilypnus guatemalensis (Miller, 2009; Espinosa-Pérez et al., 2014), typical of such sites. These sites also had large amounts of debris and timber, which on larger streams is usually washed away by higher flow rates, making for complex habitats. It is also interesting that while G4 sites are highly connected to the main channel, large predators such as Petenia splendida Günther, 1862 and Parachromis multifasciatus (Regan, 1905) were mainly present as juvenile fish, and adults were seldomly found. While not considered in statistical analyses given their rarity, some species of large predatory fishes (i.e., Centropomus undecimalis, Ictalurus meridionalis, and Megalops atlanticus Valenciennes, 1847) were either seen or captured during the study period in some sites belonging to group G3 but not in the other sites. Such pattern has been associated with preferences of large predatory fish towards sites with short transitions between deep channels and pools while smaller fish gather in shallow-water habitats for protection and food (Hoeinghaus et al., 2004; Ibanez et al., 2007; Thorp, 2008), or sites with higher habitat complexity (Willis et al., 2005).

Contrasting with the previous clusters, group G2 was largely composed by samples from site 13, the only lentic body included in this study, and was characterized by low DO. This site, part of the Lacanjá River floodplain, has water and nutrient dynamics very different from those in lotic systems in the study. A nearly four month-long period of intermittent flood pulses in the rainy season, during which extended macrophyte and filamentous algae blooms occur, followed by an extended period of slow shrinking, allows for high fish densities (observed in this study) in the fashion of similar systems elsewhere (Hoeinghaus et al., 2004; Castillo-Domínguez et al., 2015; Camacho-Valdez et al., 2020; Soria-Barreto et al., 2021). Fish assemblages in this group were dominated by lowland cichlids (i.e., Mayaheros urophthalmus and Vieja bifasciata (Steindachner, 1864)) and contained many species only captured in this site, such as the poeciliids Poecilia kykesis, and Carlhubbsia kidderi (Hubbs, 1936), and the cichlid Thorichthys pasionis. Site 13 is used as a shelter and nursery site for many species, which either lay their eggs during flood pulses, or their fry/juveniles take advantage of the expanded water coverage to colonize isolated pools. These oxbow lakes, which are relatively isolated from the river network for a long period, provides a large nutrient supply resulting from high primary production rates of these systems (Cazzanelli et al., 2021). This energy, produced through photosynthesis, is not “leaked” via downstream flow (Davies et al., 2008) and allows for higher densities of benthic algae and plankton (otherwise scarce in lotic systems), as well as other primary consumers. In these sites predation on some species gets suppressed due to the reduced abundance of adult predatory fish, which tend to remain in the main riverine network.

Environmental associations inferred from the RDA model coincide with the MRT splitting decisions, suggesting a dominant gradient characterized by a low-to-high order and lotic-to-lentic transition indicated by the patterns found on variables ORD, OD, and DU. This gradient is further supported by the associations to fish assemblages such as those from sites 9, 3, 6, and 13 discussed above. Despite sharp shifts in environmental conditions found among clusters, the model showed that, except for G1, their assemblages’ composition does not form discrete clusters; instead, they overlap forming a compositional continuum. We suggest that fish communities in the Lacantún might be composed of a “core” community which includes a majorly ubiquitous species group, easily distinguishable herein in group G3 (hence with a lack of indicator species), with a slight species turnover both towards lower and higher order reaches of the basin. Such compositional pattern was also found by Esselman et al., (2006) at the Monkey River, in Belize. Moreover, the pattern of distinctive assemblages among lotic-to-lentic environments is common for freshwater fish communities (Angermeier, Karr, 1983; Willis et al., 2005), and also represents a nodal concept of most riverine ecosystem functioning models (Vannote et al., 1980; Junk et al., 1989; Thorp, Delong, 1994; Thorp et al., 2006; Thorp, 2008; Humphries et al., 2014).

The RDA model also showed that while FC represented a major environmental gradient (from the PCA results), it had minor (though significant) effects on assemblages, most likely as an artifact of the large differences in relative contribution among dominant, common, and rare species, in particular among the families Cichlidae and Poeciliidae. In general, assemblages from sites with low proportions of FC, found in MC, were more speciose on poecilids, while those in MA were found to have richer cichlid communities and a lower overall abundance of poeciliids. Poeciliid fishes are known to have a great host of reproductive strategies (Thibault, Schultz, 1978; Pollux et al., 2014; Furness et al., 2021; Reznick et al., 2021), and either to retain long breeding periods or have multiple reproductive peaks throughout the year (Milton, Arthington, 1983; Contreras-MacBeath, Espinoza, 1996; Machado et al., 2002). Such traits enable rich poecilid communities to be highly resilient and capable of efficiently establishing in disturbed environments (Rosen, Bailey, 1963). Cichlids on the other hand, are characterized by long periods of parental care and more discrete breeding seasons (McKaye, 1977; McKaye et al., 2010). The differences in the contributions of each of these families to sites from MA and MC could potentially result from adverse effects of human activities hampering the development of non-resilient species. However, data relating to human impact in our possession is still insufficient to properly test this. We believe further work tackling this subject is urgent in the region. In line with this, it is important to note that our surveys failed to capture native species which had previously reported in the basin, or common through the Gulf of México slope (Rodiles-Hernández et al., 1999; Esselman et al., 2006; Lozano-Vilano et al., 2007), such as Dajaus monticola (Bancroft, 1834) and Joturus pichardi Poey, 1860 (Mugilidae), and Ictiobus meridionalis (Günther, 1868) (Catostomidae). It is unknown whether this follows only from our collections being performed in unsuitable habitats, or from a decrease in their densities (Tab. S5).

Fish assemblage seasonality. Previous studies in the region have addressed the effect of temporality on whole fish assemblages (Rodiles-Hernández et al., 1999), particular fish families (Soria-Barreto, Rodiles-Hernández, 2008), and basal resources supporting these assemblages (Pease et al., 2020; Cazzanelli et al., 2021; Soria-Barreto et al., 2021). While PerMANOVA models addressing temporal variability showed that there were significant differences between years and yearly seasons, in the case of environmental conditions, and on all three temporal comparisons they consistently accounted for low proportions of total explained variation (R²: 0.018–0.117), regardless of whether these were performed on environmental or assemblage data. These results had been previously suggested, although not formally tested. Rodiles-Hernández et al., (1999) found changes on assemblages sampled among dry and rainy seasons in the Lacanjá River. Yet, the species varying the most were either found to be rare (i.e., Theraps irregularis, Parachromis multifasciatus, Ictalurus meridionalis) or the most dominant (i.e., Brycon guatemalensis Regan, 1908), with little effects on the overall relative abundances of most species conforming the assemblages. Soria-Barreto and Rodiles-Hernández (2008) found that communities of cichlids in the Tzendales River showed no variation on a temporal scale. The low amounts of explained variation in our results could represent a signature of sampling inconsistencies caused by water-level fluctuations, limiting available microhabitats or reduced capture efficiency during particular sampling events, and we therefore suggest that these results be taken cautiously.

Although the spatial distribution of our study sites does not follow the common headwater-to-mouth pattern (Vannote et al., 1980; Sedell et al., 1989; Thorp et al., 2006), such gradients are herein present as a patchy pattern, mainly driven by changes in stream order. Samples from small first-order streams had mainly generalist species (e.g., Astyanax spp., Pseudoxiphophorus bimaculatus, Xiphophorus hellerii, Rhamdia guatemalensis) which commonly feed on both aquatic and terrestrial insects (Pease et al., 2019, 2020), while samples from higher-order and increasingly larger-width sites tended to have more complex assemblages and ecologically specialized species (i.e., piscivore Petenia splendida and substrate sifters Thorichthys spp.). Still, we are missing crucial information on the overall environmental and biological gradients happening from the main headwaters to the river mouth. The Lacantún is the last unimpounded main tributary from the upper Usumacinta basin, which plays an important role as a source to the regional biodiversity (Elías et al., 2020), and predictions on how future impacts will affect their biodiversity calls for an overall improvement of our understanding on the dynamics of its assemblages.

We provide an assessment of the associations between freshwater fish assemblages and the environment in the Lacantún River basin based on quantitative analyses. Although we are aware of the limitations imposed by a relatively small number of sites and the extent of the study area surveyed, this is to our knowledge the first time a standardized, “medium-term” study has been made on multiple tributaries in this portion of the basin. Our findings suggest that even at small scales, there is a prevalence of gradients in fish assemblage structure in association to particular macrohabitats. Aside from providing information about spatiotemporal variability in the region, we believe this study could provide future research directions on the fish fauna in the Lacantún.

ACKNOWLEDGEMENTS

We thank Angélica D. Cruz, Arbey Sánchez, Brenda Vega, Fernando C. Tapia, Gabriel C. Martínez, Isaac Vázquez, Julio C. Salinas, Oralia B. Ballesteros, and Said Ramírez, for their assistance sampling fishes; Carlos R. Martínez supported field logistics, and the staff at Estación Chajul, Estación Tzendales and Estación Lacanjá provided valuable support during field work. This study is a partial result of the project “Evaluación del estado de conservación de los ecosistemas naturales y especies indicadoras de las áreas naturales protegidas de la Selva Lacandona, Chiapas y zonas de influencia”, by Natura y Ecosistemas Mexicanos A. C., financed by Alianza WWF-Fundación Carlos Slim. We dedicate this paper to Héctor Espinosa-Pérez (1954–2022), Mexican Ichthyologist, Colleague, Mentor and Friend.

REFERENCES

• Albert JS, Tagliacollo VA, Dagosta F. Diversification of neotropical freshwater fishes. Annu Rev Ecol Evol Syst. 2020; 51(1):27–53. https://doi.org/10.1146/annurev-ecolsys-011620-031032
  » https://doi.org/10.1146/annurev-ecolsys-011620-031032
• Álvarez-Porevski P, Hernández Garciadiego L, Gómez-Ruiz H, Ramírez-Martínez C. Calidad del agua en la subcuenca del río Lacantún. In: Carabias J, De la Maza J, Cadena R, editors. Conservacion y desarrollo sustentable en la Selva Lacandona. 25 años de actividades y experiencias. Seccion 4: El deterioro. Ciudad de México: Natura y Ecosistemas Mexicanos; 2015.
• Álvarez-Porevsky P, Gómez-Ruiz H, Hernández-Garciadiego L. Comparison of Soxhlet extraction, ultrasonic bath and focused microwave extraction techniques for the simultaneous extraction of PAH´s and pesticides from sediment samples. Sci Chromatogr. 2014; 6(2):124–38. https://dx.doi.org/10.4322/sc.2014.026
  » https://dx.doi.org/10.4322/sc.2014.026
• Angermeier PL, Karr JR. Fish communities along environmental gradients in a system of tropical streams. Environ Biol Fishes. 1983; 9:117–35. https://doi.org/10.1007/BF00690857
  » https://doi.org/10.1007/BF00690857
• Beard ZS, Quist MC, Hardy RS, Ross TJ. Patterns in fish assemblage structure in a small western stream. Copeia. 2018; 106(4):589–99. https://doi.org/10.1643/CE-17-712
  » https://doi.org/10.1643/CE-17-712
• Belsey DA, Kuh E, Welsch RE. Regression diagnostics: Identifying influential data and sources of collinearity. Wiley; 1980. https://doi.org/10.1057/jors.1981.33
  » https://doi.org/10.1057/jors.1981.33
• Betancur-R. R, Willink PW. A new freshwater ariid (Otophysi: Siluriformes) from the Río Usumacinta basin. Copeia. 2007; 2007(4):818–28. https://doi.org/10.1643/0045-8511(2007)7[818:anfaos]2.0.co;2
  » https://doi.org/10.1643/0045-8511(2007)7[818:anfaos]2.0.co;2
• Blanchet FG, Legendre P, Borcard D. Forward selection of explanatory variables. Ecology. 2008; 89(9):2623–32. https://doi.org/10.1890/07-0986.1
  » https://doi.org/10.1890/07-0986.1
• Borcard D, Gillet F, Legendre P. Numerical ecology with R. 2011. https://doi.org/10.1007/978-1-4419-7976-6
  » https://doi.org/10.1007/978-1-4419-7976-6
• Brown JH, Lomolino M. Basic Biogeography. 2nd ed. Sinauer Associates; 1998. https://doi.org/10.4324/9781315841236
  » https://doi.org/10.4324/9781315841236
• Camacho-Valdez V, Saenz-Arroyo A, Ghermandi A, Navarrete-Gutiérrez DA, Rodiles-Hernández R. Spatial analysis, local people’s perception and economic valuation of wetland ecosystem services in the Usumacinta floodplain, Southern Mexico. PeerJ. 2020; 2020(1):1–26. https://doi.org/10.7717/peerj.8395
  » https://doi.org/10.7717/peerj.8395
• Carabias J, De la Maza J, Cadena R, editors. Conservación y desarrollo sustentable en la Selva Lacandona. 25 años de actividades y experiencias. México: Natura y Ecosistemas Mexicanos; 2015.
• Castellanos-Navarrete A, Jansen K. Is oil palm expansion a challenge to agroecology? Smallholders practising industrial farming in Mexico. J Agrar Change. 2018; 18(1):132–55. https://doi.org/10.1111/joac.12195
  » https://doi.org/10.1111/joac.12195
• Castillo MM, Barba-Álvarez R, Mayorga A. Riqueza y diversidad de insectos acuáticos en la cuenca del río Usumacinta en México. Rev Mex Biodiv. 2018; 89(18):45–64. https://doi.org/10.22201/ib.20078706e.2018.4.2177
  » https://doi.org/10.22201/ib.20078706e.2018.4.2177
• Castillo-Domínguez A, Melgar-Valdes CE, Barba-Macías E, Rodiles-Hernández R, Jesús Navarrete A, Perera-García MA et al. Composición y diversidad de peces del río San Pedro, Balancán, Tabasco, México. Hidrobiológica. 2015; 25(2):285–92.
• Cazzanelli M, Soria-Barreto M, Castillo MM, Rodiles-Hernández R. Seasonal variations in food web dynamics of floodplain lakes with contrasting hydrological connectivity in the Southern Gulf of Mexico. Hydrobiologia. 2021; 848(4):773–97. https://doi.org/10.1007/s10750-020-04468-8
  » https://doi.org/10.1007/s10750-020-04468-8
• Chatterjee S, Hadi AS. Regression analysis by example. Fifth. New Jersey: Wiley; 2012.
• Chatterjee S, Hadi AS. Regression analysis by example. New York: John Wiley & Sons; 1977.
• Conservation International. Selva Lacandona Siglo XXI. Estrategia conjunta para la conservación de la biodiversidad. Mexico, Tuxtla Gutiérrez, Chiapas; 2002.
• Contreras-MacBeath T, Espinoza HR. Some aspects of the reproductive strategy of Poeciliopsis gracilis (Osteichthyes: Poeciliidae) in the Cuautla River, Morelos, Mexico. J Freshw Ecol. 1996; 11(3):327–38. https://doi.org/10.1080/02705060.1996.9664455
  » https://doi.org/10.1080/02705060.1996.9664455
• Davies PM, Bunn SE, Hamilton SK. Primary production in tropical streams and rivers. Tropical stream ecology. Elsevier Inc.; 2008. p.23–42. https://doi.org/10.1016/B978-012088449-0.50004-2
  » https://doi.org/10.1016/B978-012088449-0.50004-2
• De’ath G. Multivariate regression trees: A new technique for modeling species-environment relationships. Ecology. 2002; 83(4):1105–17. https://doi.org/10.1890/0012-9658(2002)083[1105:MRTANT]2.0.CO;2
  » https://doi.org/10.1890/0012-9658(2002)083[1105:MRTANT]2.0.CO;2
• Díaz S, Settele J, Brondízio ES, Ngo HT, Agard J, Arneth A et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science. 2019; 366(6471). https://doi.org/10.1126/science.aax3100
  » https://doi.org/10.1126/science.aax3100
• Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013; 36:27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
  » https://doi.org/10.1111/j.1600-0587.2012.07348.x
• Dudgeon D. Prospects for sustaining freshwater biodiversity in the 21st century: Linking ecosystem structure and function. Curr Opin Environ Sustain. 2010; 2(5–6):422–30. https://doi.org/10.1016/j.cosust.2010.09.001
  » https://doi.org/10.1016/j.cosust.2010.09.001
• Dudgeon D, Arthington AH, Gessner MO, Kawabata Z-I, Knowler DJ, Lévêque C et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol Rev Camb Philos Soc. 2006; 81(2):163–82. https://doi.org/10.1017/S1464793105006950
  » https://doi.org/10.1017/S1464793105006950
• Dufrêne M, Legendre P. Species assemblages and indicator species: the need for flexible asymmetrical approach. Ecol Monogr. 1997; 67(3):345–66.
• Elías DJ, McMahan CD, Matamoros WA, Gómez-González AE, Piller KR, Chakrabarty P. Scale(s) matter: Deconstructing an area of endemism for Middle American freshwater fishes. J Biogeogr. 2020; 47(11):2483–501. https://doi.org/10.1111/jbi.13941
  » https://doi.org/10.1111/jbi.13941
• Espinosa-Pérez H, Martínez-C. A, Sepúlveda JD Leptophilypnus guatemalensis Thacker & Pezold, 2006 (Gobiiformes: Eleotridae): First record in México. Check List. 2014; 10(6):1535–37. https://doi.org/10.15560/10.6.1535
  » https://doi.org/10.15560/10.6.1535
• Esselman PC, Freeman MC, Pringle CM. Fish-assemblage variation between geologically defined regions and across a longitudinal gradient in the Monkey River Basin, Belize. J North Am Benthol Soc. 2006; 25(1):142–56. https://doi.org/10.1899/0887-3593(2006)25[142:FVBGDR]2.0.CO;2
  » https://doi.org/10.1899/0887-3593(2006)25[142:FVBGDR]2.0.CO;2
• Fine PVA. Ecological and evolutionary drivers of geographic variation in species diversity. Annu Rev Ecol Evol Syst. 2015; 46:369–92. https://doi.org/10.1146/annurev-ecolsys-112414-054102
  » https://doi.org/10.1146/annurev-ecolsys-112414-054102
• Fischer JR, Paukert CP. Habitat relationships with fish assemblages in minimally disturbed great plains regions. Ecol Freshw Fish. 2008; 2008(17):597–609. https://doi.org/10.1111/j.1600-0633.2008.00311.x
  » https://doi.org/10.1111/j.1600-0633.2008.00311.x
• Fricke R, Eschmeyer WN, Van der Laan R, editors. Eschmeyer’s catalog of fishes: genera, species, references [internet]. San Francisco: California Academy of Sciences; 2021. Available from: http://researcharchive.calacademy.org/research/ichthyology/catalog/fichcatmain.asp
  » http://researcharchive.calacademy.org/research/ichthyology/catalog/fichcatmain.asp
• Furness AI, Avise JC, Pollux BJA, Reynoso Y, Reznick DN. The evolution of the placenta in poeciliid fishes. Curr Biol. 2021; 31(9):2004–11. https://doi.org/10.1016/j.cub.2021.02.008
  » https://doi.org/10.1016/j.cub.2021.02.008
• Gomez Ruiz H, Hernández Garciadiego L. Validation of the use of ion chromatography to assess compliance with water quality regulations by studying water quality in the Lacantún River in southwest Mexico. Int J Environ Anal Chem. 2020; 102:8108–23. https://doi.org/10.1080/03067319.2020.1845325
  » https://doi.org/10.1080/03067319.2020.1845325
• Herrera-Silveira JA, Lara-Domínguez AL, Day JW, Yáñez-Arancibia A, Ojeda SM, Hernández CT et al. Ecosystem functioning and sustainable management in coastal systems with high freshwater input in the southern Gulf of Mexico and Yucatan Peninsula. Coast Estuar. 2019:377–97. https://doi.org/10.1016/B978-0-12-814003-1.00022-8
  » https://doi.org/10.1016/B978-0-12-814003-1.00022-8
• Hoeinghaus DJ, Winemiller KO, Birnbaum JS. Local and regional determinants of stream fish assemblage structure: Inferences based on taxonomic vs functional groups. J Biogeogr. 2007; 34(2):324–38. https://doi.org/10.1111/j.1365-2699.2006.01587.x
  » https://doi.org/10.1111/j.1365-2699.2006.01587.x
• Hoeinghaus DJ, Winemiller KO, Taphorn DC. Compositional change in fish assemblages along the Andean piedmont - Llanos floodplain gradient of the río Portuguesa, Venezuela. Neotrop Ichthyol. 2004; 2(2):85–92. https://doi.org/10.1590/S1679-62252004000200005
  » https://doi.org/10.1590/S1679-62252004000200005
• Humphries P, Keckeis H, Finlayson B. The river wave concept: Integrating river ecosystem models. Bioscience. 2014; 64(10):870–82. https://doi.org/10.1093/biosci/biu130
  » https://doi.org/10.1093/biosci/biu130
• Ibanez C, Oberdorff T, Teugels G, Mamononeke V, Lavoué S, Fermon Y et al. Fish assemblages structure and function along environmental gradients in rivers of Gabon (Africa). Ecol Freshw Fish. 2007; 16:315–34. https://doi.org/10.1111/j.1600-0633.2006.00222.x
  » https://doi.org/10.1111/j.1600-0633.2006.00222.x
• Ibarra M, Stewart DJ. Longitudinal zonation of sandy beach fishes in the Napo River basin, Eastern Ecuador. Copeia. 1989; 1989(2):364–81.
• Inda-Diaz E, Rodiles-Hernández R, Naranjo EJ, Mendoza-Carranza M. Subsistence fishing in two communities of the Lacandon Forest, Mexico. Fish Manag Ecol. 2009; 16:225–34. https://doi.org/10.1111/j.1365-2400.2009.00668.x
  » https://doi.org/10.1111/j.1365-2400.2009.00668.x
• Instituto Nacional de Ecología (INE). Programa de manejo Reserva de la Biósfera Montes Azules. México, D.F.: Instituto Nacional de Ecología, Secretaría de Medio Ambiente, Recursos Naturales y Pesca; 2000.
• Junk WJ, Bayley PB, Sparks RE. The flood pulse concept in river-floodplain systems. In: Dodge DP, editor. Proceedings of the International Large River Symposium, vol. 106. Can J Fish Aquat Sci. 106; 1989. p.110–27.
• Lamouroux N, Poff NL, Angermeier PL. Intercontinental convergence of stream fish community traits along geomorphic and hydraulic gradients. Ecology. 2002; 83(7):1792–807.
• Legendre P, Legendre L. Numerical ecology. Second. Amsterdam: Elsevier; 1998. https://doi.org/10.1016/S0167-8892(12)70001-5
  » https://doi.org/10.1016/S0167-8892(12)70001-5
• Leroy B, Dias MS, Giraud E, Hugueny B, Jézéquel C, Leprieur F et al. Global biogeographical regions of freshwater fish species. J Biogeogr. 2019; 46(11):2407–19. https://doi.org/10.1111/jbi.13674
  » https://doi.org/10.1111/jbi.13674
• Lowe-McConnell RH. Ecological studies in tropical fish communities. Cambridge: Cambridge University Press; 1987.
• Lowe-McConnell RH. Ecological aspects of seasonality in fishes of tropical water. Symp Zool Soc Lond. 1979; 44:219–41.
• Lozano-Vilano ML, García-Ramírez ME, Contreras-Balderas S, Ramírez-Martínez C. Diversity and conservation status of the ichthyofatma of the Río Lacantún basin in the Biosphere Reserve Montes Azules, Chiapas, México. Zootaxa. 2007; 1410:43–53. https://doi.org/10.11646/zootaxa.1410.1.2
  » https://doi.org/10.11646/zootaxa.1410.1.2
• Machado G, Giaretta AA, Facure KG. Reproductive cycle of a population of the Guaru, Phallocerus caudimaculatus (Poeciliidae), in Southeastern Brazil. Stud Neotrop Fauna Environ. 2002; 37(1):15–18. https://doi.org/10.1076/snfe.37.1.15.2115
  » https://doi.org/10.1076/snfe.37.1.15.2115
• March Mifsut I, Castro M. La cuenca del Río Usumacinta: Perfil y perspectivas para su conservación y desarrollo sustentable. In: Cotler-Ávalos H, editor. Las cuencas hidrográficas de México: Diagnostico y priorización. Primera. Ciudad de México: Secretaría de Medio Ambiente y Recursos Naturales, Instituto Nacional de Ecología; 2010. p.193–97.
• Martínez-Arbizu P. pairwiseAdonis: Pairwise multilevel comparisons using Adonis. R package version 0.4. 2017. Available from: https://github.com/pmartinezarbizu/pairwiseAdonis
  » https://github.com/pmartinezarbizu/pairwiseAdonis
• Matamoros WA, McMahan CD, Chakrabarty P, Albert JS, Schaefer JF. Derivation of the freshwater fish fauna of Central America revisited: Myers’s hypothesis in the twenty-first century. Cladistics. 2015; 31(2):177–88. https://doi.org/10.1111/cla.12081
  » https://doi.org/10.1111/cla.12081
• McArdle BH, Anderson MJ. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology. 2001; 82(1):290–97.
• McKaye KR. Competition for breeding sites between the cichlid fishes of Lake Jiloá, Nicaragua. Ecology. 1977; 58(2):291–302.
• McKaye KR, Hale J, van den Berghe EP. The reproductive biology of a Central American cichlid Neetroplus nematopus in Lake Xiloá, Nicaragua. Curr Zool. 2010; 56(1):43–51. https://doi.org/10.1093/czoolo/56.1.43
  » https://doi.org/10.1093/czoolo/56.1.43
• Mendoza-Carranza M, Arévalo-Frías W, Espinoza-Tenorio A, Hernández-Lazo CC, Álvarez-Merino AM, Rodiles-Hernández R. La importancia y diversidad de los recursos pesqueros del río Usumacinta, México. Rev Mex Biodivers. 2018; 89:131–46. https://doi.org/10.22201/ib.20078706e.2018.0.2182
  » https://doi.org/10.22201/ib.20078706e.2018.0.2182
• Mercado-Silva N, Lyons J, Díaz-Pardo E, Navarrete S, Gutiérrez-Hernández A. Environmental factors associated with fish assemblage patterns in a high gradient river of the Gulf of Mexico slope. Rev Mex Biodivers. 2012; 83:117–28.
• Miller RR. Peces dulceacuícolas de México. Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Sociedad Ictiológica Mexicana A. C., El colegio de la Frontera Sur y Consejo de los Peces del Desierto México-Estados Unidos. México, D.F; 2009.
• Miller RR, Minckley WL, Norris SM. Freshwater fishes of Mexico. Chicago and London: Museum of Zoology, University of Chicago Press; 2005.
• Milton DA, Arthington AH. Reproductive biology of Gambusia affinis holbrooki Baird and Girard, Xiphophorus helleri (Gunther) and X. maculatus (Heckel) (Pisces; Poeciliidae) in Queensland, Australia. J Fish Biol. 1983; 23(1):23–41. https://doi.org/10.1111/j.1095-8649.1983.tb02879.x
  » https://doi.org/10.1111/j.1095-8649.1983.tb02879.x
• Myers N, Mittermeier RA, Mittermeier CG, Da Fonseca GAB, Kent J. Biodiversity hotspots for conservation priorities. Nature. 2000; 403(6772):853–58. https://doi.org/10.1038/35002501
  » https://doi.org/10.1038/35002501
• Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D et al. Package ‘vegan’: Community ecology package. 2018.
• Pease AA, Capps KA, Rodiles-Hernández R, Castillo MM, Mendoza-Carranza M, Soria-Barreto M et al. Trophic structure of fish assemblages varies across a Mesoamerican river network with contrasting climate and flow conditions. Food Webs. 2019; 18:e00113. https://doi.org/10.1016/j.fooweb.2019.e00113
  » https://doi.org/10.1016/j.fooweb.2019.e00113
• Pease AA, Soria-Barreto M, González-Díaz AA, Rodiles-Hernández R. Seasonal variation in trophic diversity and relative importance of basal resources supporting tropical river fish assemblages in Chiapas, Mexico. vol. 149. 2020. https://doi.org/10.1002/tafs.10269.
  » https://doi.org/10.1002/tafs.10269.
• Pollux BJA, Meredith RW, Springer MS, Garland T, Reznick DN. The evolution of the placenta drives a shift in sexual selection in livebearing fish. Nature. 2014; 513(7517):233–36. https://doi.org/10.1038/nature13451
  » https://doi.org/10.1038/nature13451
• QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project; 2021. Available from: https://qgis.org/en/site/
  » https://qgis.org/en/site/
• R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available from: https://www.r-project.org/
  » https://www.r-project.org/
• Ramírez-Martínez C, Naranjo E, Caspeta JM, Espinosa-Pérez H, Barba-Álvarez R. Ecosistemas acuáticos. In: Carabias J, De la Maza J, Cadena R, editors. Conservacion y desarrollo sustentable en la Selva Lacandona. 25 años de actividades y experiencias. Seccion 2: Subcuenca del río Lacantún: medio físico y biodiversidad. Ciudad de México: Natura y Ecosistemas Mexicanos; 2015.
• Rao CR. The use and interpretation of principal component analysis in applied research. Indian J Stat. 1964; 26:329–58.
• Reznick DN, Travis J, Pollux BJA, Furness AI. Reproductive mode and conflict shape the evolution of male attributes and rate of speciation in the fish family Poeciliidae. Front Ecol Evol. 2021; 9:1–20. https://doi.org/10.3389/fevo.2021.639751
  » https://doi.org/10.3389/fevo.2021.639751
• Ricklefs RE. A comprehensive framework for global patterns in biodiversity. Ecol Lett. 2004; 7(1):1–15. https://doi.org/10.1046/j.1461-0248.2003.00554.x
  » https://doi.org/10.1046/j.1461-0248.2003.00554.x
• Ricklefs RE. Community diversity: relative roles of local and regional processes. Science. 1987; 235(4785):167–71. https://doi.org/10.1126/science.235.4785.167
  » https://doi.org/10.1126/science.235.4785.167
• Roberts DW. labdsv: Ordination and multivariate analysis for ecology. R package version 2.0-1. 2019.
• Rodiles-Hernández R, Díaz-Pardo E, Lyons J. Patterns in the species diversity and composition of the fish community of the Lacanja River, Chiapas, Mexico. J Freshw Ecol. 1999; 14(4):455–68. https://doi.org/10.1080/02705060.1999.9663704
  » https://doi.org/10.1080/02705060.1999.9663704
• Rosen DE, Bailey RM. The poeciliid fishes (Cyrinodontiformes), their structure, zoogeography and systematics. Bull Am Mus Nat Hist. 1963; 126(1):1–176. https://doi.org/10.2307/1441257
  » https://doi.org/10.2307/1441257
• Saavedra GA, López LD, Castellanos FL. Aspectos físicos (hidrografía, geoulogía, suelos clima y vegetación) de la cuenca media del Río Usumacinta México (CMUM). In: Carabias J, de la Maza J, Cadena R, editors. Conservacion y desarrollo sustentable en la Selva Lacandona. 25 años de actividades y experiencias, vol. Seccion 2: Ciudad de México: Natura y Ecosistemas Mexicanos; 2015.
• Sánchez AJ, Salcedo MÁ, Florido R, Mendoza J de D, Ruiz-Carrera V, Álvarez-Pliego N. Ciclos de inundación y conservación de servicios ambientales en la cuenca baja de los ríos Grijalva-Usumacinta. ContactoS. 2015; 97:5–14. Available from: http://www2.izt.uam.mx/newpage/contactos/revista/97/pdfs/inundacion.pdf
  » http://www2.izt.uam.mx/newpage/contactos/revista/97/pdfs/inundacion.pdf
• Schmitter-Soto JJ. A revision of Astyanax (Characiformes: Characidae) in Central and North America, with the description of nine new species. J Nat Hist. 2017; 51(23–24):1331–424. https://doi.org/10.1080/00222933.2017.1324050
  » https://doi.org/10.1080/00222933.2017.1324050
• Sedell JR, Richey JE, Swanson FJ. The river continuum concept: A basis for the expected ecosystem behavior of very large rivers? In: Dodge DP, editor. Proceedings of the International Large River Symposium. Can Spec Publ Fish Aquat Sci. 106. 1989. p.49–55.
• Secretaria de Medio Ambiente y Recursos Naturales (SEMARNAT). Acuerdo por el que se actualiza la disponibilidad media anual de las aguas nacionales superficiales de las 757 cuencas hidrológicas que comprenden las 37 regiones hidrológicas en que se encuentra dividido los Estados Unidos Mexicanos. Mexico: 2016.
• Song XP, Hansen MC, Stehman SV, Potapov PV, Tyukavina A, Vermote EF et al. Global land change from 1982 to 2016. Nature. 2018; 560:639–43. https://doi.org/10.1038/s41586-018-0411-9
  » https://doi.org/10.1038/s41586-018-0411-9
• Soria-Barreto M, González-Díaz AA, Castillo-Domínguez A, Álvarez-Pliego N, Rodiles-Hernández R. Diversidad íctica en la cuenca del Usumacinta, México. Rev Mex Biodivers. 2018; 89. https://doi.org/10.22201/ib.20078706e.2018.0.2462
  » https://doi.org/10.22201/ib.20078706e.2018.0.2462
• Soria-Barreto M, Montaña CG, Winemiller KO, Castillo MM, Rodiles-Hernández R. Seasonal variation in basal resources supporting fish biomass in longitudinal zones of the Usumacinta River basin, southern Mexico. Mar Freshw Res. 2021; 72(3):353–64. https://doi.org/10.1071/MF19341
  » https://doi.org/10.1071/MF19341
• Soria-Barreto M, Rodiles-Hernández R. Spatial distribution of cichlids in Tzendales River, Biosphere Reserve Montes Azules, Chiapas, Mexico. Environ Biol Fishes. 2008; 83(4):459–69. https://doi.org/10.1007/s10641-008-9368-0
  » https://doi.org/10.1007/s10641-008-9368-0
• Strahler AN. Quantitative analysis of watershed geomorphology. Trans Am Geophys Union. 1957; 38(6):913–20.
• Strayer DL, Dudgeon D. Freshwater biodiversity conservation: Recent progress and future challenges. J North Am Benthol Soc. 2010; 29(1):344–58. https://doi.org/10.1899/08-171.1
  » https://doi.org/10.1899/08-171.1
• Therneau TM, Atkinson B, Ripley B, Oksanen J, De’ath G. mvpart: Multivariate partitioning. Version 1.6–2. 2014.
• Thibault RE, Schultz RJ. Reproductive adaptations among viviparous fishes (Cyprinodontiformes: Poeciliidae). Evolution. 1978; 32(2):320–33. https://doi.org/10.2307/2407600
  » https://doi.org/10.2307/2407600
• Thompson DM. Pool-Riffle. In: Shroder J, Wohl E, editors. Treatise on Geomorphology, vol. 9. San Diego, CA: Academic Press; 2013. p.364–78. https://doi.org/10.1016/B978-0-12-374739-6.00246-3
  » https://doi.org/10.1016/B978-0-12-374739-6.00246-3
• Thorp JH. The Riverine Ecosystem Synthesis. Academic Press, Elsevier. UK. 2008.
• Thorp JH, Delong MD. The riverine productivity model: An heuristic view of carbon sources and organic processing in large river ecosystems. Oikos. 1994; 70(2):305–08. https://doi.org/10.2307/3545642
  » https://doi.org/10.2307/3545642
• Thorp JH, Thoms MC, Delong MD. The riverine ecosystem synthesis. Towards conceptual cohesiveness in river science. Academic Press; 2008.
• Thorp JH, Thoms MC, Delong MD. The riverine ecosystem synthesis: Biocomplexityin river networks across space and time. River Res Appl. 2006; 22(2):123–47. https://doi.org/10.1002/rra.901
  » https://doi.org/10.1002/rra.901
• Vaca RA, Golicher DJ, Rodiles-Hernández R, Castillo-Santiago MA, Bejarano M, Navarrete-Gutiérrez DA. Drivers of deforestation in the basin of the Usumacinta River: Inference on process from pattern analysis using generalised additive models. PLoS ONE. 2019; 14(9):1–21. https://doi.org/10.1371/journal.pone.0222908
  » https://doi.org/10.1371/journal.pone.0222908
• Vannote RL, Minshall GW, Cummins KW, Sedell JR, Cushing CE. The river continuum concept. Can J Fish Aquat Sci. 1980; 37:130–37.
• Velázquez-Velázquez E, López-Vila JM, Gómez-González AE, Romero-Berny EI, Lievano-Trujillo JL, Matamoros WA. Checklist of the continental fishes of the state of Chiapas, Mexico, and their distribution. Zookeys. 2016; 632:99–120. https://doi.org/10.3897/zookeys.632.9747
  » https://doi.org/10.3897/zookeys.632.9747
• Vijay V, Pimm SL, Jenkins CN, Smith SJ. The impacts of oil palm on recent deforestation and biodiversity loss. PLoS ONE. 2016; 11(7):e0159668. https://doi.org/10.1371/journal.pone.0159668
  » https://doi.org/10.1371/journal.pone.0159668
• Willis SC, Winemiller KO, Lopez-Fernandez H. Habitat structural complexity and morphological diversity of fish assemblages in a Neotropical floodplain river. Oecologia. 2005; 142:284–95. https://doi.org/10.1007/s00442-004-1723-z
  » https://doi.org/10.1007/s00442-004-1723-z
• Winemiller KO, Welcomme RL, Petr T. Floodplain river food webs: Generalizations and implications. 2nd International Symposium on the Management of Large Rivers for Fisheries. 2004(2):285–309.
• Winker K. Middle America, not mesoamerica, is the accurate term for biogeography. Condor. 2011; 113(1):5–06. https://doi.org/10.1525/cond.2011.100093
  » https://doi.org/10.1525/cond.2011.100093
• van den Wollenberg AL. Redundancy analysis: An alternative for canonical correlation analysis. Psychometrika. 1977; 42(2):207–19.
• Yáñez-Arancibia A, Day JW, Currie-Alder B. Functioning of the Grijalva-Usumacinta River Delta, Mexico: Challenges for coastal management. Ocean Yearb. 2009; 23(1):473–501. https://doi.org/10.1163/22116001-90000205
  » https://doi.org/10.1163/22116001-90000205

ADDITIONAL NOTES

• HOW TO CITE THIS ARTICLE

  Larre-Campuzano S, Espinosa-Pérez H, Mercado-Silva N, Rosales-Quintero N, Matamoros WA. Variation in patterns of fish assemblage and their environmental correlates in a tropical river basin from the Gulf of Mexico slope. Neotrop Ichthyol. 2023; 21(2):e220098. https://doi.org/10.1590/1982-0224-2022-0098

Edited-by

Ana Cristina Petry

Publication Dates

• Publication in this collection
  22 May 2023
• Date of issue
  2023

History

• Received
  14 Oct 2022
• Accepted
  05 Apr 2023