Hierarchical effects of historical and environmental factors on lizard assemblages in the upper Madeira River, Brazilian Amazonia

Gabriela Marques Peixoto; Rafael De Fraga; Maria C Araújo; Igor Luis Kaefer; Albertina Pimentel Lima

doi:10.1371/journal.pone.0233881

. 2020 Jun 2;15(6):e0233881. doi: 10.1371/journal.pone.0233881

Hierarchical effects of historical and environmental factors on lizard assemblages in the upper Madeira River, Brazilian Amazonia

Gabriela Marques Peixoto ^1,^*,^#, Rafael De Fraga ^2,^#, Maria C Araújo ¹, Igor Luis Kaefer ^1,^3,^#, Albertina Pimentel Lima ^1,^#

Editor: Stefan Lötters⁴

PMCID: PMC7266318 PMID: 32484844

Abstract

Investigating the role of historical and ecological factors structuring assemblages is relevant to understand mechanisms and processes affecting biodiversity across heterogeneous habitats. Considering that community assembly often involves scale-dependent processes, different spatial scales may reveal distinct factors structuring assemblages. In this study we use arboreal and leaf-litter lizard abundance data from 83 plots to investigate assemblage spatial structure at two distinct scales in southwestern Brazilian Amazonia. At a regional scale, we test the general hypothesis that the Madeira River acts as a barrier to dispersal of some lizard species, which results in distinct assemblages between river banks. At a local scale, we test the hypothesis that assemblages are not evenly distributed across heterogeneous habitats but respond to a continuum of inadequate-to-optimal portions of environmental predictors. Our results show that regional lizard assemblages are structured by the upper Madeira River acting as a regional barrier to 29.62% of the species sampled. This finding suggests species have been historically isolated at one of the river banks, or that distinct geomorphological features influence species occurrence at each river bank. At a local scale, different sets of environmental predictors affected assemblage composition between river banks or even along a river bank. These findings indicate that environmental filtering is a major cause of lizard assemblage spatial structure in the upper Madeira River, but predictor variables cannot be generalized over the extensive (nearly 500 km) study area. Based on a single study system we demonstrate that lizard assemblages along the forests near the banks of the upper Madeira River are not randomly structured but respond to multiple factors acting at different and hierarchical spatial scales.

Introduction

Investigating historical and ecological factors structuring assemblages may reveal patterns of biodiversity distribution across time and space [1,2]. However, defining mechanisms and processes that potentially affect assemblage structure is often highly dependent on the spatial scale applied [3–5]. Such dependence results from the fact that assemblage composition (e.g. taxonomic diversity) is influenced by complex hierarchical interactions among processes that operate at multiple spatio-temporal scales [6]. In highly heterogeneous habitats such as the Amazonian tropical rainforests the relative contribution of historical and ecological processes to assemblage structuring is poorly understood for many taxa, mainly because multi-scale ecological approaches depend on standardized sampling systems, which have been specifically designed for such purpose [e.g. 7–12]. Regarding lizards, poor knowledge on assemblage structure also results from lack of refined data on individual species distribution [13], despite few unpublished studies have shown assemblage spatial structure defined by environmental heterogeneity [e.g. 14–16].

At broad spatial scales (e.g. Amazon Basin), it has been suggested that many organisms are restrictedly distributed by their inability to cross large rivers. From the classic studies of Alfred R. Wallace on primate distribution across the Amazon Basin [e.g. 17], it has been known that the Amazon River and some of its main tributaries (e.g. Madeira, Negro) may be important biogeographic barriers to dispersal. Testing the Wallace´s hypothesis has revealed the riverine barrier as a major factor explaining limited distribution of plants, frogs, birds, spiny rats, and primates [18–26]. Additionally, studies have shown that gene flow reduced or blocked by a riverine barrier may cause genotypic and phenotypic divergence in Amazonia [27–29]. Specifically for lizards, riverine barriers may cause intraspecific genetic divergence [27], although they do not necessarily produce different morphotypes [30]. Interspecifically, species distribution regionally limited to a single river bank may cause distinct assemblage compositions between banks [12,22,31].

At local scales, environmental predictors may affect species occurrence and abundance due to the filter effect of the spatial variation in habitat suitability [32,33]. In general, it is expected that habitat-specialist species find inadequate-to-optimum continuums of environmental conditions for survival and reproduction [34]. Environmental filtering has been found in Amazonia for plants, frogs, lizards, snakes, and birds [15,35–43]. For lizards, local assemblages may differ due to variation in individual abundance or species turnover along gradients of distance from water courses [31,44], elevation [45], climate seasonality [46], and number of trees [43,47]. Additionally, lizard assemblages may be indirectly structured by species turnover along gradients of canopy openness affecting the availability of thermoregulation sites [48,49], understory-plant density affecting the availability of foraging sites for perching species [50], and clay content in the soil affecting plant composition and food availability [43].

Integrating multiple spatial scales is relevant to estimating simultaneous effects of historical and ecological factors on assemblage structure, especially in heterogeneous habitats such as rainforests in Amazonia [51]. However, designing a sampling system which is efficient to quantify assemblages and habitats at multiple scales may be challenging. The RAPELD [1] method (Brazilian acronym for rapid sampling plus long-term ecological research) has been shown to be efficient for this purpose in the region of the upper Madeira River [13], due to (i) the adequate distribution of plot sets (5 km² each) so that hypotheses based on the effects of historical factors on regional assemblages may be tested (e.g. riverine barriers), and (ii) the plots following altitudinal contours reduce within-plot environmental variation, which allows them to be assumed as environmental units to test hypotheses based on environmental filtering [1]. The rationale behind testing such hypotheses in southwestern Amazonia is that the Madeira River has been recognized as a barrier to dispersal of Squamata reptiles, which causes species turnover along a longitudinal gradient [52], and the region covers two endemism zones (Rondônia and Inambari) that are distinct regarding geological history and environmental heterogeneity [53].

In this study we use plot-based lizard abundance data from the upper Madeira River (southwestern Brazilian Amazonia) to investigate patterns of assemblage structure at two distinct spatial scales. At a regional scale, we test the hypothesis that lizard assemblages differ between the river banks. We expect differences in species composition and abundance as a consequence of the Madeira River historically limiting lizard dispersal. At local scale, we test the hypothesis that environmental heterogeneity causes species turnover, because species are absent or occur at low densities in suboptimal portions of environmental predictors. Specifically, we quantify the filtering effects on lizard abundance driven by gradients of number of trees, soil nutrient composition, shrub density, elevation, clay and sand content in the soil, and distance from the river bank. We expect that analyzing assemblages from two distinct perspectives will provide us with deep insights into factors that cause and maintain biodiversity at megadiverse regions such as the upper Madeira River basin.

Materials and methods

Study area

The study area is located near the banks of the upper Madeira River (coordinates of the centroid 08 ° 48004.0"S; 63 ° 56059.8"W), an important tributary of the Amazon River classified as a white and muddy river with a total length of 1,459 km. The upper Madeira River extends from the outskirts of Porto Velho (state of Rondônia) to about 600 km upstream, in southwestern Brazilian Amazonia, and its width varies from about 0.5 to 10 km depending on the river flow. The Madeira separates the Inambari and Rondônia endemism zones located along its left (west) and right (east) margins, respectively [20]. We also sampled plots close to the Jaci-Paraná River, a tributary on the east bank of the upper Madeira River (Fig 1).

Fig 1 — Sampling 5 km² modules (circles) near the banks. Gray circles show modules in the Inambari endemism zone, blue circles are modules in the Rondônia endemism zone. The acronyms summarize sampling modules´ local names: TO = Teotônio, IB = Ilha dos Búfalos, IP = Ilha das Pedras, JL = West Jirau, JR = East Jirau, JP = Jaci-Paraná, MO = Morrinhos. In detail on the right side, the standard configuration of each module, with 14 plots (squares), 250 m-long each, distributed along a gradient of distance from the river bank (0–5,000 m).

In this study we quantified environmental heterogeneity as continuous gradients that may be broadly classified for descriptive purposes in three main habitat types. They mainly differ in canopy height, soil texture, understory-plant density, and species composition [following 54]. In the upland (terra-firme) forests habitats are never flooded by overflowing large rivers, the canopy is 30 m high, and the understory-plant density and clay content in the soil often depends on elevation [55]. The várzea forests are seasonally flooded by overflowing sediment-rich rivers, which produces nutrient-rich soils that are water-saturated for long periods. The canopy is 20 m high, and the understory is rich in bromeliads. The campinaranas are patches of palm tree-rich forests growing on a white-sand soil, which is highly drained and nutrient-poor [54].

The climate of the study area is tropical humid, with annual average temperature at 25.5 °C and average precipitation at 2,287 mm. Precipitation is distributed throughout the year in well-marked dry (May to September) and rainy (October to April) seasons. During the dry season, small streams can dry completely [56].

Sampling design

We collected arboreal and leaf-litter lizard abundance data in seven 5 km² RAPELD sampling sites (hereinafter modules), that were installed perpendicularly to the river bank. RAPELD [1] is a modification of the Gentry´s sampling method based on 1-ha plots [57], with the main difference being that the RAPELD plot central lines follow the altitudinal curves to reduce environmental variation within plots (PPBio—http://ppbio.inpa.gov.br). We sampled three modules on the east bank of the Madeira River (East Jirau, Jaci-Paraná and Morrinhos), and four modules on the west bank (West Jirau, Ilha das Pedras, Ilha dos Búfalos and Teotônio). The average distance between neighboring modules was 120 km. Each RAPELD module was composed of two 5-km long parallel trails, separated by 1 km. We surveyed seven 250 m plots (20 m wide) on each trail, totaling 98 plots (14 plots in each of the seven modules). The plots were distributed along a gradient of distance from the river bank, at 0, 500, 1000, 2000, 3000, 4000, and 5000 m.

We were not able to find lizards in 15 plots, and the excess of zeros in the dataset prevented us to reliably estimate pairwise distances among plots to summarize assemblage composition (see Data analysis). Therefore, we excluded zero-valued plots and our analyzes are based on 83 plots.

Sampling effort and ethics

We sampled each plot in four different periods (24 February to 26 April 2010, 30 July to 19 August 2010, 5 November to 26 November 2010, and 13 January to 4 February 2011) to cover large portions of the regional variation in temperature and precipitation along a year. We used species’ maximum abundance values per plot in the analyzes.

We found lizards using active visual search, with two simultaneous observers positioned 10 m apart. In addition, we supplemented the sampling effort by sweeping the leaf litter and removing debris in a 2 m strip following the center line of the plot. This approach was particularly useful to increase the efficiency of sampling leaf-litter species (e.g. Alopoglossidae, Gymnophthalmidae). Search on the vegetation and on the leaf litter was systematically conducted in the first and second half of each visual sampling in the plots, respectively, thus constituting two different and complementary methods. The searching time in each plot varied between 40 and 60 minutes and was always conducted during the day.

We collected data under RAN-ICMBio / IBAMA permit 13777–2. IBAMA and ICMBio are institutes of Ministry of Environment, Government of Brazil. This permit was subject to approval of all procedures for collecting lizard abundance data.

Environmental variables

We measured eight environmental predictors in each plot in order to quantify spatial heterogeneity in habitat suitability. We quantified vegetation structure by measuring (i) number of trees and (ii) shrub density. Those predictors potentially affect abundance of tropical reptiles by influencing availability of foraging, resting, and thermoregulation sites [58–60]. We also measured edaphic gradients related to soil texture, fertility, and flat-level deviation, which are (iii) clay content, (iv) sand content, (v) nutrient composition (soil pH, Calcium, Magnesium, Potassium, Zinc, and exchangeable Aluminum), (vi) elevation, and (vii) terrain declivity. Those variables potentially affect lizard abundance by causing variation in the overall primary production [61] and availability of invertebrate prey [62]. Additionally, we measured (viii) distance from the river bank, because it has been found as a major factor structuring plant [36] and animal [31,38,39,41] assemblages in Amazonia. The methods used to measure each predictor are described in detail in Appendix 1.

Data analysis

To quantify assemblage composition, we applied the Bray-Curtis index to estimate pairwise distances in species abundance among plots. We reduced dimensionalities using Principal Coordinate Analysis (PCoA) and represented assemblage composition by the first one or two axes produced (see below).

At regional scale (riverine barrier effects) we modeled the PCoA using all data (83 plots). The two first axes captured 30% (PCoA 1 = 16%. PCoA 2 = 14%) of the original variance in species abundance, and we used them to represent assemblage composition. To assess assemblage structuring, we used Multivariate Analysis of Variance MANOVA to test differences in assemblage composition (PCoA axes 1 and 2) between the river banks. We implemented a MANOVA using the vegan [63] R-package [64].

Analyzes at regional scale revealed two distinct lizard assemblages between the river banks (see Results). In addition, preliminary analyzes at local scale revealed that in two modules (Ilha das Pedras and East Jirau) environmental predictors may affect assemblage composition in opposite directions compared to the other modules (S1 and S2 Figs). These findings suggested that the banks of the Madeira River and some of the sampling modules along the same river bank are distinct environmental units, which contain distinct spatial structures of lizard assemblage composition. Therefore, to assess assemblage structure at local scale we modeled four distinct PCoA ordinations, using data from (i) the west bank, except for the module Ilha das Pedras (37 plots), which captured 86% of the original variance (PCoA 1 = 0.50, PCoA 2 = 0.36); (ii) the east bank, except for the module East Jirau (23 plots), which captured 45% of the original variance (PCoA 1 = 0.30, PCoA 2 = 0.15); (iii) the module Ilha das Pedras (12 plots), which captured 45% of the original variance (PCoA 1 = 0.32, PCoA 2 = 0.13); and (iv) the module East Jirau (11 plots), which captured 85% of the original variance (PCoA 1 = 0.49, PCoA 2 = 0.36).

The environmental predictors measured are expressed in different units and therefore in different orders of magnitude, so we transformed them using the “scale” function of the vegan R-package. This function subtracts mean values from each variable and scales centralized variables by dividing them by their standard deviation [63]. We used Mixed Linear Models to test the effects of scaled environmental predictors on assemblage composition based on data from multiple sampling modules. By using this method, we were able to include sampling modules as random effects to minimize potential abrupt differences in environmental predictors and lizard assemblages among the modules analyzed in a same model [65]. We set up two different groups of mixed models, according to the assemblage compositions summarized by PCoA for the west and east banks of the Madeira River. Each group was composed of as many models as necessary to test all possible combinations of environmental predictors, except for those predictors that were highly correlated. For instance, clay and sand content in the soil were not used in a same model because they were highly correlated on both river banks (r ≥ 0.93). In addition, elevation was correlated with terrain declivity on both river banks (r ≥ 0.78) and soil-nutrient composition on the east bank (r = 0.66). The cut-off point was r = 0.51.

For the two modules that were analyzed separately (Ilha das Pedras and East Jirau), it was not necessary to control random effects of sampling sites, so we tested the effects of environmental predictors on the assemblage composition using multiple linear regression models. We tested models with assemblage composition (PCoA 1) as dependent variable, and all possible combinations of uncorrelated environmental predictors as independent variables.

To select the most parsimonious mixed-effects and multiple-regression models we ranked all the models by the corrected Akaike´s Information Criterion (AICc) [66]. We refined the model selection by penalizing nested models assuming ΔAICc < 2 as a cut-off point. All selected models were validated by normal distribution of residuals (Shapiro-Wilk W > 0.95, P > 0.05 in all cases).

For visually checking the distribution of lizard abundance values per species along river banks and environmental predictors (only those that significantly affected assemblage composition) we plotted ordinated sampling plots. These graphs will be used in this study for assessing how spread the distributions of abundance values are over the river banks and the environmental heterogeneity measured.

Results

We found 27 lizard species, which are classified in 18 genera and 10 families. The most frequently found species were Norops fuscoarautus (Dactyloidae), Gonatodes humeralis (Sphaerodactylidae), and Ameiva ameiva (Teiidae) (Table 1), which occurred in both banks of the Madeira River, in 55, 49, and 30% of the plots respectively. Contrarily, Alopoglossus angulatus (Alopoglossidae) and Enyalius leechii (Leiosauridae) were found in one single plot.

Table 1. List of lizard species sampled in the upper Madeira River, Brazil.

N = total abundance per species, East and West = Madeira River banks filled with presence (1) and absence (0) data.

Family/Species	N	East	West
Dactyloidae
Norops fuscoauratus (D’Orbigny, 1847)	103	1	1
Norops tandai (Wagler, 1830)	2	0	1
Norops ortonii (Cope, 1869)	2	1	1
Dactyloa punctata (Daudin, 1802)	27	1	1
Dactyloa transversalis (Dumeril, 1851)	9	0	1
Alopoglossidae
Alopoglossus angulatus (Linnaeus, 1758)	2	0	1
Gymnophthalmidae
Arthrosaura reticulata (O’Shaughnessy, 1881)	5	1	0
Cercosaura argulus (Peters, 1863)	5	1	1
Cercosaura eigenmanni (Griffin, 1917)	11	1	1
Cercosaura bassleri (Ruibal, 1952)	8	0	1
Iphisa elegans (Gray, 1851)	8	1	1
Loxopholis percarinatum (Muller, 1923)	10	1	1
Hoplocercidae
Enyalioides laticeps (Guichenot, 1855)	3	1	1
Hoplocercus spinosus (Fitzinger, 1843)	2	1	1
Leiosauridae
Enyalius leechii (Boulenger,1885)	2	1	0
Scincidae
Copeoglossum nigropunctatum (Spix, 1825)	14	1	1
Phyllodactylidae
Thecadactylus rapicauda (Houttuyn, 1782)	21	1	1
Sphaerodactylidae
Chatogekko amazonicus (Andersson, 1918)	12	1	1
Gonatodes hasemani (Griffin, 1917)	29	1	1
Gonatodes humeralis (Guichenot, 1855)	432	1	1
Teiidae
Kentropyx altamazonica (Cope, 1876)	14	1	1
Kentropyx calcarata (Spix, 1825)	37	1	0
Kentropyx pelviceps (Cope, 1868)	29	0	1
Ameiva ameiva (Linnaeus, 1758)	48	1	1
Tropiduridae
Plica plica (Linnaeus, 1758)	7	1	1
Plica umbra ochrocollaris (Spix, 1825)	21	1	1
Uranoscodon superciliosus (Linnaeus, 1758)	5	1	1
Total	868

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Regional assemblage structuring—Madeira River as a biogeographic barrier

We found 19 species on both banks of the Madeira River, which is equivalent to 70.37% of the total diversity sampled. This finding suggests that most of the species sampled are widely distributed throughout the study area. However, for several of the species found on both sides of the river (e.g. Loxopholis percarinatum, Kentropyx altamazonica, Cercosaura eigenmanni, Plica plica, Uranoscodon superciliosus, Copeoglossum nigropunctatum), plot-related frequency and abundance were not even between the river banks (Fig 2). Additionally, five species (18.52%) were restricted to the west bank–Alopoglossus angulatus, Norops tandai, Dactyloa transversalis, Cercosaura bassleri, and Kentropyx pelviceps, and three species (11.11%) were restricted to the east bank–Arthrosaura reticulata, Kentropyx calcarata, and Enyalius leechii. These findings suggest two distinct assemblage compositions delimited by the Madeira River, which is strongly supported by differences in the PCoA scores (based on 83 plots) between the river banks (MANOVA Pillai Trace = 0.315, F_1–81 = 18.40, P <0.001).

Fig 2 — The heights of the black rectangles are relative to species abundance values.

Local assemblage structuring—The role of environmental predictors

On the west bank of the Madeira River (except for the module Ilha das Pedras) three mixed-effects models were selected by ΔAICc < 2 (Table 2). All the selected models consistently returned number of trees as a major gradient affecting assemblage composition (P < 0.001 in all cases). Despite some species occupied large portions of the gradient of number of trees (e.g. Ameiva ameiva, Norops fuscoauratus), species absence or low abundance in specific intervals between 144 and 613 trees caused species turnover (Fig 3). According to the models selected, assemblage composition was not affected by elevation (P = 0.76), and soil-content of sand (P = 0.84) or clay (P = 0.91).

Table 2. Summary of the results returned by linear mixed-effects models.

The models were set up using data from the west (Teotônio, Ilha dos Búfalos, and West Jirau) and east (Morrinhos and Jaci-Paraná) banks of the upper Madeira River. The models were selected by ΔAICc < 2. Shapiro-Wilk tests were applied on the residuals from each model to test normality. Bolded p-values show cases in which the null hypothesis was rejected.

Margins	Fixed effects	AICc	Weight	df	t	p	Total variance	Shapiro-Wilk
West	Number of Trees and Elevation	12.89	0.314	Intercept:2.18	-6.92	<0.001	54%	P = 0.109
				Trees:3.01	18.8	<0.001
				Elevation:1.43	-0.31	0.76
	Sand and Number of Trees	12.88	0.309	Intercept:3.39	-14.69	0.001	69%	P = 0.066
				Sand:1.86	-0.19	0.84
				Trees:3.29	18.8	<0.001
	Clay and Number of Trees	12.88	0.305	Intercept:1.00	-9.07	<0.001	76%	P = 0.153
				Clay:9.76	0.10	0.91
				Trees:3.27	18.88	<0.001
East	Elevation and Margin distance	2.0	0.412	Intercept:2.30	6.37	<0.001	71%	P = 0.782
				Elevation:2.30	-6.27	<0.001
				Margin:2.30	1.72	0.09
	Number of Trees and Elevation	2.0	0.400	Intercept:2.30	5.92	<0.001	72%	P = 0.413
				Trees:2.10	18.8	0.10
				Elevation:2.30	-0.31	<0.001

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Fig 3 — The heights of the black rectangles depict the relative species abundances.

Three multiple-regression models were selected for Ilha das Pedras sampling module (Table 3), all of them containing elevation as an independent variable. This predictors significantly affected assemblage composition according to a model constructed with soil sand content as an additional independent variable (P = 0.05) (Fig 4). However, the effects of elevation on the assemblage composition were marginally significant in models containing number of trees (P = 0.06) and soil clay content (P = 0.07) as independent variables.

Table 3. Summary of the results returned by linear mixed-effects models.

The models were set up using data from the Ilha das Pedras (west river bank) and East Jirau (east river bank) modules to test the effects of environmental predictors on lizard assemblage composition. The models were selected by ΔAICc < 2. Shapiro-Wilk tests were applied on the residuals from each model to test normality. Bolded p-values show cases in which the null hypothesis was rejected.

Margins	Variables	AICc	Weight	Std. error	t	P	F	r2
West	Number of Trees and Elevation	15.7	0.31	Intercept:8.76	0.00	1.00	2.746	0.37
				Trees:9.19	0.90	0.39
				Elevation:9.19	-2.06	0.06
	Sand and Elevation	15.8	0.29	Intercept:8.81	0.00	1.00	2.66	0.37
				Sand:9.21	0.85	0.42
				Elevation:9.21	-2.18	0.05
	Clay and Elevation	16.2	0.25	Intercept: 1.21	0.1	1.10	2.45	0.35
				Clay:6.12	-0.64	0.53
				Elevation:1.88	-1.98	0.07
East	Clay and Distance from the margin	6.5	0.655	Intercept: 5.69	0.00	1.00	15.42	0.81
				Clay: -1.05	-1.69	0.13
				Margin:2.89	4.63	<0.001
	Sand and Distance from the margin	7.9	0.367	Intercept: 2.30	5.92	<0.001	13.07	0.78
				Sand:8.81	1.28	0.24
				Margin:2.85	4.15	<0.001

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Fig 4 — Model for the effects from the elevation and sand contents in the soil on lizard assemblage composition. Assemblage composition was summarized by the first axis of a Principal Coordinates Analysis based on abundance data of the upper Madeira River, state of Rondônia, Brazil. The shades of blue show values of sand content in the soil.

On the east river bank (except for the East Jirau module) two models were selected as most parsimonious. Both models consistently showed strong effects of elevation on assemblage composition (P < 0.001 in both cases). This finding suggests species turnover along an elevational gradient of 69.12–100.59 m (Fig 5). According to the same models, distance from the river bank (P = 0.09) and number of trees (P = 0.1) did not affect assemblage composition.

Fig 5 — The heights of the black rectangles depict the relative species abundances.

Two multiple-regression models were selected for the East Jirau module. Both models consistently returned distance from the river bank (Fig 6) as a relevant gradient affecting assemblage composition (P < 0.001 in both cases). Soil-content of sand (P = 0.24) and clay (P = 0.13) did not affect assemblage composition.

Discussion

At regional scale, we found that lizard assemblages are spatially structured by differences in assemblage composition between river banks. This finding is consistent with large Amazonian rivers acting as dispersal barriers for several organisms, which have caused different species subsets composed of plants [18], diurnal frogs [12], birds [19,21,22], and primates [24]. At local scale, we showed that lizard assemblages are spatially structured by species turnover along environmental predictors. However, a set of environmental predictors cannot be assumed as generalized predictors among sampling sites. Our overall results are broadly consistent with those obtained for frog assemblages sampled in the same plots [12], which suggests multi-taxa ecological patterns. We relied on a single dataset to provide understanding about assemblage structure based on interacting historical and ecological processes. Therefore, we highlight the relevance of investigating multi-scale assemblage structuring for ecology and conservation decision making.

In the upper Madeira River, assemblage divergence between river banks has been attributed to historical processes regionally reducing species dispersal [12], and delimiting the Amazonian endemism zones Inambari and Rondônia [20]. Approximately half of the species present in the assemblage of diurnal frogs (13 species) of that region were restricted to one of the river banks [12]. The smaller proportion of regionally isolated lizard species (29.63%) is reasonably explained by the lower dispersal capacity of small and site-attached frogs compared with most lizards. A taxonomic bias may be also contributing to this scenario, since there are consistent and recent efforts to investigate the taxonomic status of frog species in the region [12], and such efforts are unparalleled regarding lizards. Nonetheless, we investigated assemblages in which about 30% of the sampled species were isolated by the river, and another 30% of the species occurred at low relative frequency or abundance at one of the river banks. This was a sufficiently adequate scenario to assume the river as a historical factor segregating assemblages between the river banks. Even though the riverine-restricted geographic distribution of species such as Kentropyx calcarata observed in this study is supported by basin-level data, we highlight that most of regionally isolated species in our sample are widely distributed throughout Amazonia outside our study area [13]. Such inconsistency may be explained by the strength of the river as a dispersal barrier varying along the river course, or even being nullified in response to meandering shapes [30,67–69]. Additionally, the barriers may be seasonal, because bridges for stepping-stone dispersal may be revealed during the dry season, which allows gene flow between river banks [70]. Therefore, our results for assemblage structure at regional scale should not be extrapolated to unsampled stretches of the Madeira River or other Amazonian rivers, because lizards probably have found multiple dispersal routes through evolutionary time [27].

The isolation of species on one of the river banks may be related to the geomorphological heterogeneity of the Madeira River across our study area. The Madeira river flows over an incisive fluvial valley, with predominantly crystalline and a geologically ancient basement (ca. 16 Ma). The morphodynamical development was mainly influenced by the geomorphological and climatic changes resulting from the Andean Orogeny in the Cenozoic [71], which have produced a relatively stable course along recent geological times [72]. Such stability in the shape of the river course has prevented meandering across most of the study area, which could facilitate for species to cross the river [73]. Exceptionally, the modules located further upstream (East and West Jirau) have rocky outcrops that are exposed in the middle of the river course during the dry season, which can act as bridges for stepping-stone dispersal (field observation). Although lizard species used alternative dispersal routes to widespread their distribution throughout Amazonia, our study showed that they were regionally prevented from colonizing or maintaining populations on both banks of the upper Madeira River. One could argue that our results of a river-barrier effect are biased due to the low detection probability of lizards, which resulted in false absence of species [74,75]. However, we think that a possible sampling bias was mitigated by the large sampling effort associated to the combination of different visual sampling methods employed in this study.

Besides affecting assemblage composition, the effect of rivers as barriers can also be observed at the intraspecific level in different biogeographic domains, resulting in genetic and morphological divergence among lizard populations due to restriction to gene flow [30,76,77]. Studies on the genetic and phenotypic differentiation of populations of a same species on opposite banks of the Madeira River should be performed as they might help to understand the initial steps of allopatric speciation in Amazonian lizards.

At local scale, lizard assemblages were spatially structured by environmental filtering causing non-random assemblage composition. Environmental conditions selected species that were unable to survive and maintain viable conditions in given sampling plots [78]. Despite we sampled species that are generalist in relation to the environmental predictors measured (e.g. Ameiva ameiva, Norops fuscoauratus), species for which distributions were restricted to narrow regions of gradients (e.g. Cercosaura argulus, Norops ortonii, Uranoscodon superciliosus) caused species turnover across sampling plots. Species turnover mediated by environmental filtering is a major factor structuring local assemblages in Amazonia [e.g. 41,36,39], and in the upper Madeira River it has efficiently explained assemblage structure in frogs [12], snakes [79], and bats [80]. However, we cannot generalize a single environmental dataset as a predictor for assemblage composition in all plots. Environmental predictors for assemblage composition differed between the river banks or even along a same river bank. This finding suggests that the scale at which lizard assemblages respond to environmental heterogeneity may be more refined than the classification of the Madeira River banks as distinct endemism zones [20,81].

Number of trees was a major factor causing species turnover in the west bank of the Madeira River. This gradient ranged from 144 to 613 trees, which shows that the vegetation structure is quite heterogeneous throughout our study area. Heterogeneity in vegetation structure affects occurrence and abundance of tropical squamates due to variation in the availability of foraging, nesting, resting, and thermoregulating sites [58,60]. Additionally, tree cover may directly affect food availability, protection against predators, light intensity, temperature, humidity, and wind speed [59,60]. The evidence for assemblage structuring along a gradient of number of trees is of concern from a conservation point of view, because our study area has been intensely deforested by the agribusiness and large hydroelectric plants [82]. It is widely expected that species dependent on high levels of tree cover (e.g. Norops tandai, Norops ortonii, Dactyloa transversalis) will either be locally extinct or migrate to more suitable habitats.

We found species turnover along an elevational gradient, although this finding was most evident on the east bank of the Madeira River. On the east bank the plots were installed on the depression of the Ji-Paraná River, which generated elevation values below 30 m. Low elevation is often related to outcropping of groundwater and high drainage density [83,71], which favors the occurrence of habitat-specific species for high humidity. For instance, Arthrosaura reticulata and Uranoscodon superciliosus typically occupy humid low areas [84,85], and in this study those species were found only on the east bank of the Madeira River. Additionally, elevation indirectly influences assemblage composition because it affects water availability and soil fertility [86,87], and therefore the overall structure of available habitats [88,89]. Extreme variation in elevation may cause behavioral and morphological differentiation in lizards [90]. In this study we showed that even subtle variation in elevation (24 to 128 m) may be sufficient for species to be locally filtered. A similar finding was observed using frog assemblage data from the Guiana Shield [37].

The gradient of distance from the river caused species turnover in the East Jirau module. Although habitats may be classified in riparian and non-riparian zones [91], gradients of distance from water courses carry multiple continuous interacting variables of microclimate, nutrient availability, vegetation cover, and edaphic structure. Habitats continuously changing along gradients of distance from streams (< 12 m wide) have caused species turnover structuring plant [36], frog [38], snake [41], and bird [39] assemblages. We have shown a similar pattern using lizard abundance data, with the main difference being that the gradient we measured refers to the distance from the bank of one of the major tributaries of the Amazon River. However, no significant effect of distance from the river on assemblage composition was observed using data from the other modules. This finding suggests that assemblages diverging between riparian and non-riparian zones should not be generalized in relation to gigantic rivers, or assemblage segregation should occur at distances that are greater than 5 km away from the river bank.

Some of the results found may be associated to environmental variables that were not explicitly measured in this study. For example, Hoplocercus spinosus (Hoplocercidae) occurred on both banks of the upper Madeira River but occurrence was restricted to plots with rocky outcrops. Such condition was only found in the westernmost sampling modules of the study area (East and West Jirau), where the species finds optimal availability of thermoregulation and refuge sites [91]. This finding reflects relationships between species and habitats that are dependent of biological traits affecting survival [92,93] and dispersal capacity [94,95], such as body size, diet [96], specificity level in habitat use [89], reproductive [49], and foraging mode [97]. Therefore, although patterns of assemblage structure are usually described based on dissimilarities among plots regarding subsets of cooccurring species, they may be determined by ecological requirements of individual species.

We have shown that lizard assemblages in the upper Madeira River are structured by scale-dependent hierarchical factors. Historical processes related to the Andes uplift [98] have isolated regional assemblages between the river banks, and have also generated distinct habitat patches, which in turn generate distinct local lizard assemblages. It is generally well established that interacting historical and environmental factors explain hierarchical structures of assemblages [5]. However, empirical application is not common because it relies on efficient sampling designs to capture multiple scales [1]. In the megadiverse Amazonian rainforests this has been achieved by a few studies [12,22,31,73]. Considering the fine levels in which those studies have understood processes affecting biodiversity, efficient methods for multi-scale sampling should be prioritized by ecology and conservation biology.

Supporting information

S1 Data. Protocols for measuring the environmental predictors.

Predictors used as independent variables in the ecological models to test lizard assemblage structuring in the upper Madeira River, Brazilian Amazonia.

(PDF)

Click here for additional data file.^{(106.7KB, pdf)}

S1 Fig. Partials from Mixed Linear Models for the west bank of the Madeira River.

Effect of environmental predictors on lizard assemblages composition (PCoA axis 1). The models were selected by ΔAICc < 2. (A) Ilha das Pedras (B) Ilha dos Búfalos (C) West Jirau (D) Teotônio.

(PDF)

Click here for additional data file.^{(76.8KB, pdf)}

S2 Fig. Partials from Mixed Linear Models for the east bank of the Madeira River.

Effect of environmental predictors on lizard assemblages composition (PCoA axis 1). The models were selected by ΔAICc < 2. (A) Jaci-Paraná (B) East Jirau (C) Morrinhos.

(PDF)

Click here for additional data file.^{(59.5KB, pdf)}

Acknowledgments

We thank Stefan Lötters and an anonymous reviewer for insightful suggestions on this manuscript. Data collection was logistically supported by Programa de Pesquisas em Biodiversidade (PPBio), Centro de Estudos Integrados da Biodiversidade Amazônica (INCT-CENBAM), and Programa de Conservação da Vida Selvagem da Santo Antônio Energia S.A. Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) granted PhD scholarships to GMP. CAPES provided a PNPD postdoc grant to RF and CNPq provided productivity grants to ILK and APL.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

Data collection was financially supported by Programa de Conservação da Vida Selvagem da Santo Antônio Energia S.A.; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) granted PhD scholarships to GMP. CAPES provided a PNPD postdoc grant to RF and CNPq provided productivity grants to ILK and APL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0233881.r001

Decision Letter 0

Stefan Lötters

17 Jan 2020

PONE-D-19-15180

Hierarchical effects of historical and environmental factors on lizard assemblages in the upper Madeira river, Amazonian Brazil

PLOS ONE

Dear Mrs. Marques Peixoto,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

It was difficult to find referees and eventually only one replied.The review was very detailed, however, so that my decision "major revision" could well be justified.

The referee suggests that the paper has potential but the referee warns that results could be misinterpreted. The referee's concern is that data are based on count and presence/absence data only and gives concrete literature hints. The solution proposed is the employment of modeling techniques that account for potential sampling errors. Moreover, the referee proposes to compare findings with results from other lizard systems around the world. For details, see below.

I strongly go along with the critics and the recommendations by the referee.

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Reviewer #1: Partly

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Reviewer #1: No

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Reviewer #1: Yes

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Reviewer #1: Yes

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5. Review Comments to the Author

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Reviewer #1: In this manuscript, authors sample lizard assemblages in 83 plots from southwestern Amazon, Brazil to investigate spatial structure in two distinct scales. They found differences in species presences and relative abundances, suggesting that the Madeira River acts as a barrier for almost 30% of the species. At the local scale, they found that different predictors were important for species.

The manuscript is interesting and includes good data and sampling. However, I am not sure about the conclusions, since inference was based on data that may suffer from sampling errors. Additionally, the conclusions on the barrier effect may be overstated given the sampling design and time frame of the study. Below I detailed some major and minor comments and suggestions, which I hope help authors in some way.

Major comments

My main concern is that all inference here is based on count and presence/absence data, which may be influenced by sampling errors. For instance, you may find 20 individuals of species A and 19 individuals of species B in a given plot, when in fact there was 120 individuals of species A and 20 individuals of species B. This is typically a false-negative type error. Alternatively, you may have misidentified species B, inducing false-positive errors in your counts. Similar problems occur when using presence/absence data when absence is not included and estimated in the model. This subject is well covered in the literature. You may want to have a look at Kéry and Schmidt 2008 (Community Ecology 9: 207-216), Fitzpatrick et al. 2009 (Ecological Applications 19: 1673-79), Miller et al. 2012 (Ecological Applications 22: 1665-74) and so on. Since sampling effort seems to be enough, I recommend applying a modeling technique that accounts for sampling errors (for instance, N-mixture models), which I find more appealing to discuss results arising from count data. However, I understand that you would have to make a major change in your manuscript, especially if results change, which I would not be surprised. At least, I recommend including a paragraph discussing this subject, since I find hard to believe that you have captured the exactly number of individuals and species present at a given site, and thus, the results may be biased.

Your sample includes temporal variation (extended for a year, or so) but there are no temporal covariates among your predictors. Lizard activity is usually related to temperature, sunlight, rainfall and so on. You probably sampled plots in different days and different hours within a day, and ciclicity occurs even within individuals. How do you think the absence of temporal predictors affect your results? For instance, Figure 2 shows species ‘abundances on each river bank. Some species seems to be common on one bank, but not the other. Is this really the barrier effect of the river? By ignoring temporal variability, you assume sampling did not vary along the year.

Authors suggest that the pattern found – the river acting as a barrier – was important. However, they mention that the lizard fauna found is widely distributed over the Amazon. This is somewhat confusing to me because if species were well-distributed, they would probably be found everywhere. To explain that, authors mention that the barrier effect of the river may vary, suggesting seasonality. This makes me wonder if species are truly isolated, as suggested. The barrier effect could be only temporarily and the results found are explained by the time frame of the study. I would like authors to clarify that point.

I think your manuscript would benefit from contrasting your results with other lizard systems around the world. For instance, are there examples of rivers acing as barriers for lizards elsewhere? What about other reptiles? What are the covariates related to isolation for lizards or reptiles in general? In addition, you could provide some information about the Amazon basin. For instance, what is the mean width of the big rivers? Finally, concerning the species found. What is the habit of the most abundant species? Did you find terrestrial, arboreal and fossorial species equally? Does you results relate to species ‘habits? I believe that finding terrestrial species (the teiids, for instance) may be easier than arboreal (Enyalius?) or fossorial. In the same way, diurnal species will be easier to find during the day and the opposite for nocturnal. How was the sampling scheme? Can you really talk about lizards in general of a guild?

Minor comments

L62: substitute "design" by "designed"

L83: change "the latter" by "lizards"

L161: I am not sure I would exclude those sites since you may overestimate predictor effects.

L166-167: you have four surveys on each plot. I suppose there was variation on specie´s counts over surveys, due to several aspects, including seasonality (which you ignored). I advise on modeling this variability on counts.

L168-169: what time did you search? Are all lizards diurnal/nocturnal? Any potential bias here?

L175: I would rather call these ‘environmental predictors’, since gradients may remind other things as well.

L183: change ‘.’ by ‘,’

L221-223: maybe it is just me, but the way it is written seems that you avoided correlated models, not predictors.

L223-226: what was the cut-off point?

L233: what do you mean by “corrected for few parameters”?

L234-235: what about the non-nested models? Why using the cut-off point if important model weight may be left out? Isn´t model averaging a better strategy depending on the case?

L261-262: how can you be sure those species were not there?

Table 2. what kind of models did you build? Only those with main effects, excluding additive and interactive effects? Did you try combining all best predictors in one model?

Figure 3. which model did you use to plot the relationship between abundance and number of trees? The top model or all three models? Notice that model weights are similar and all of them include the number of trees.

Table 3: This table is somewhat hard to read. Please, insert a backspace between West and East or a line between them. In addition, maybe you could reduce the names of the predictors or show their first letters only.

L293: again, I would rather call ‘predictor’ or ‘covariate’ instead of ‘gradient’.

L294: which model? Not the top model nor the third best-ranked model, if I read the table correctly (please, revise the table configuration). However, all models present similar AIC weights. How was the rationale behind using the only model that presented a significant effect? Did you try model averaging? I would not pick just one model among several to present a result, especially if they diverge.

Figure 4. Same comment as in Figure 3.

L352: there is a typo in this line.

L389-390: I am not convinced about that without including temporal predictors in the analysis. Was it too hot or too cold during some of the samplings? Were there thunderstorms or heavy rainfall in, at least, some of the sampling occasions?

L400-405: I find the discussion on conservation implications interesting, but I am not sure you provided all the elements for that. For instance, what kind of forest did you measure, native on invasive? Were there pine plantations (which are also categorized as forests)? How is the relationship between lizard assemblage and forests in general?

L406-407: please, see my major comment on this result, which seemed to be based not on the top-model in one analysis.

L419: you start this paragraph stating that species turnover was influenced by the distance from the river. But this was not found in all samples, as you stated in the same paragraph. Given that your samples covered only 5 km from the river, which in the Amazon, does not seem to be a huge area, what distance do you think you would find such effect?

L427-428: what do you mean by "...was returned..."?

L451-452: what do you mean by "efficient methods for multi-scale sampling"? Can you provide suggestions about what you would say is an efficient method?

**********

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PLoS One. 2020 Jun 2;15(6):e0233881. doi: 10.1371/journal.pone.0233881.r002

Author response to Decision Letter 0

2 Mar 2020

Responses to editor comments:

Response: All PloS ONE style standards have been revised.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

Response: The satellite image used to compose Figure 1 was changed following the proposed guidelines and a new image was added in its place from The Gateway to Astronaut Photography of Earth (public domain).

Response to Reviewers

1. My main concern is that all inference here is based on count and presence/absence data, which may be influenced by sampling errors. For instance, you may find 20 individuals of species A and 19 individuals of species B in a given plot, when in fact there was 120 individuals of species A and 20 individuals of species B. This is typically a false-negative type error. Alternatively, you may have misidentified species B, inducing false-positive errors in your counts. Similar problems occur when using presence/absence data when absence is not included and estimated in the model. This subject is well covered in the literature. You may want to have a look at Kéry and Schmidt 2008 (Community Ecology 9: 207-216), Fitzpatrick et al. 2009 (Ecological Applications 19: 1673-79), Miller et al. 2012 (Ecological Applications 22: 1665-74) and so on. Since sampling effort seems to be enough, I recommend applying a modeling technique that accounts for sampling errors (for instance, N-mixture models), which I find more appealing to discuss results arising from count data. However, I understand that you would have to make a major change in your manuscript, especially if results change, which I would not be surprised. At least, I recommend including a paragraph discussing this subject, since I find hard to believe that you have captured the exactly number of individuals and species present at a given site, and thus, the results may be biased.

Response:

We agree with the reviewer that when we study ecological data based on abundance and presence / absence data, we may be masking sampling errors since every technique is biased and will depend on the probability of species detection. However, as in our study we conducted multiple data collections at every point, combined with more than one sampling method, we think that we minimized sampling bias as much as possible.

As for the suggested modeling technique (N-mixture models): we opted for the second option suggested by the reviewer, which would be to continue using the models already proposed and not change our analyzes. We add a paragraph discussing these type I and II errors and the possible biased results generated from the sampling and that could be altering our results in some way. We justify our decision not to use N-mixture models because the same consist of two linked generalized linear models, however, for the realization of this model, the ordering method chosen by us (principal coordinate analysis - PCoA) cannot be used since it does not support the input of negative values. Given that the PCoA returns negative values of scores on its axes we could not be using this sorting method. Therefore, we chose to continue with PcoA to the detriment of the other sorting methods that could be substituted to meet the assumption of generalized linear models, since our choices followed the one suggested by GOTELLI & ELLISON (2011, page 446 of the book Principles of Statistics in Ecology). These authors recommend the use of PcoA for “sorting applications in which the objective is to preserve the original multivariate distances between observations in the reduced space (sorting)”, especially when we intend to use the axes generated in the ordering in later analyzes. With regard to the use of other methods such as the widely used NMDS, the axes are not orthogonal, that is, we could not choose just one of the axes as a predictor of our species composition.

We carried out several previous analyzes with other sorting methods, different types of returns, and simulations including the removal of species that presented low abundance. In each simulation we had the precaution to show to other researchers in the area and request their opinions before deciding on the methods presented in this version of our manuscript. Among these simulations, we carried out the NMDS which captured 46.64% of the variation in species composition with two axes and demonstrated that the lizard assemblages differed significantly in relation to the banks of the Madeira River (MANOVA: Pillai trace = 0,205, F1,80= 10,229, p <0,001). As for the results of the regressions with the environmental variables, the ordering of the NMDS plots for the left side of the river was only possible using three NMDS axes due to the little variation in the data with two axes (which led us to prefer the analysis main coordinates - PcoA), and as a result captured 70% of the variation in species composition. The explanation for the right margin using two NMDS axes was 57%. As for the possible structuring environmental variables, the effect of the vegetation structure (number of trees) on the left side (F7,38=2,567, p=0,04), and for the right margin, only elevation was identified as a factor influencing the composition of the lizard assemblages (F1,70=7,737, p<0,001). With all the simulations with our data we can see a strong effect of the modules themselves and the need to analyze the modules Ilha da Pedra (west bank) and Jirau-D (east bank) separately from the rest of the sample units, so we arrive at our current results .

2. Your sample includes temporal variation (extended for a year, or so) but there are no temporal covariates among your predictors. Lizard activity is usually related to temperature, sunlight, rainfall and so on. You probably sampled plots in different days and different hours within a day, and ciclicity occurs even within individuals. How do you think the absence of temporal predictors affect your results? For instance, Figure 2 shows species ‘abundances on each river bank. Some species seems to be common on one bank, but not the other. Is this really the barrier effect of the river? By ignoring temporal variability, you assume sampling did not vary along the year.

Response:

Each sampling campaign was carried out at a different time of the year: Campaign I- 24 February to 26 April 2010; Campaign II- July 30 to August 19 2010; Campaign III- November 5 to 26 2010; and Campaign IV- January 13 to February 4 2011. In each of these campaigns, all plots inserted in the different modules were sampled, that is, each plot was sampled in four different times of the year. Therefore, we think we minimized the sampling bias regarding the presence / absence of species in the plots. In fact, the activity of the lizards is closely related to variations in temperature, light and precipitation in a given environment. With this in mind, the plots were sampled at different times. We believe that our sampling method that combined active visual search, with two simultaneous observers, supplemented by sweeping the leaf-litter, minimized the lack of temporal predictors in the presence/absence results.

3. Authors suggest that the pattern found – the river acting as a barrier – was important. However, they mention that the lizard fauna found is widely distributed over the Amazon. This is somewhat confusing to me because if species were well-distributed, they would probably be found everywhere. To explain that, authors mention that the barrier effect of the river may vary, suggesting seasonality. This makes me wonder if species are truly isolated, as suggested. The barrier effect could be only temporarily and the results found are explained by the time frame of the study. I would like authors to clarify that point.

Response:

Actually, most of the lizard species found in our study are widely distributed across Amazonia, and when we say that these species are isolated, we are not trying to extrapolate our results to the entire length of the river, so we were careful to keep that registered in the discussion (from line 385). as we do not have information for other locations along the length of the Madeira River.

We did not want to suggest seasonality when we wrote that the river barrier effect may vary. The different Amazonian rivers (and even different parts of a same large river) have distinct widhts, current speeds, water types and geological dynamics, which may affect their role as an effective geological barrier.

Even widely distributed species in Amazonia are not necessarily found everywhere, since the configuration of the Amazon Basin can be related to a mosaic of different phyto-regions, observed both on a local and regional scale, with different geological ages and origins between the different fractions of the basin, a fact that leads to historical evolutionary differences between areas and that become determinant factors for the differences in lizard species richness in the region. Even within each area of endemism, with no current geographical barriers, ecological differentiation can occur (Ferrão et al., 2017; Ortiz et al. 2018). The isolation of lizard species in one of the banks of the river may be related to geomorphological heterogeneity along the course of the Madeira River, and even at different sampling dates, we still observe the restricted presence of some species to certain banks of the river.

Ferrão, M.; Moravec, J.; Fraga, R.; Almeida, A.P.; Kaefer, I.L.; Lima, A.P. 2017. A new species of Scinax from the Purus–Madeira interfluve, Brazilian Amazonia (Anura, Hylidae). ZooKeys, 706: 137–162.

Ortiz DA, Lima AP, Werneck FP (2018) Environmental transition zone and rivers shape intraspecific population structure and genetic diversity of an Amazonian rain forest tree frog. Evolutionary Ecology 32(4):359-378

4. I think your manuscript would benefit from contrasting your results with other lizard systems around the world. For instance, are there examples of rivers acing as barriers for lizards elsewhere? What about other reptiles? What are the covariates related to isolation for lizards or reptiles in general? In addition, you could provide some information about the Amazon basin. For instance, what is the mean width of the big rivers? Finally, concerning the species found. What is the habit of the most abundant species? Did you find terrestrial, arboreal and fossorial species equally? Does you results relate to species ‘habits? I believe that finding terrestrial species (the teiids, for instance) may be easier than arboreal (Enyalius?) or fossorial. In the same way, diurnal species will be easier to find during the day and the opposite for nocturnal. How was the sampling scheme? Can you really talk about lizards in general of a guild?

Response:

We thank the reviewer and agree that some information needs to be added, so we have included in the discussion other examples of lizards in the Amazon and around the world, and data on the covariates associated with restriction of geographic distributions. Information on the width of the largest Amazonian rivers was added to the methodology when mentioning the Madeira River, as it is one of the largest and most important rivers in the Amazon basin.

Regarding the species habits, we found the tree species Gonatodes humeralis, and Norops fuscuoratus as the most abundant, followed by the teiid Ameiva ameiva. The fossorial or semi-fossorial species were the ones with the lowest abundances, a fact already expected and observed in different studies on lizard assemblages. We think that the results are related to the habits of the species, as included in the discussion (starting from line 428), where we associate the results of the gradient number of trees to some tree species found.

Regarding the species activity time (more detail in the following answers to the reviewer below), most species are diurnal or crepuscular and few are nocturnal, but easily detectable to experienced researchers in the region.

We observed that our sample is within the richness already highlighted in the local and regional literature for Amazonia, and we think we gave a good representation of how the lizard community in that region is structured.

With regard to the term guild, we decided not to apply because the observed species use different and complementary resources, thus not constituting a specific guild with regard to diet or habitat use, for example.

Minor comments

✓

L62: substitute "design" by "designed"

Response: Replaced in text

✓

L84: change "the latter" by "lizards"

Response: Replaced in text

✓

L161: I am not sure I would exclude those sites since you may overestimate predictor effects.

Response: We decided to maintain the exclusion, as some of these sites coincided with areas of difficult access, for example, due to fallen trunks, making it difficult to travel along the plot, or even partially flooded, and could contribute to the low probability of detection. Thus, we decided to exclude all sites that contained zero individuals.

✓

Response: Question previously answered.

✓

L168-169: what time did you search? Are all lizards diurnal/nocturnal? Any potential bias here?

Response: Most of Amazonian lizards are diurnal. Even those that start their activity at dusk, such as Uranoscodon superciliosus (Linnaeus, 758), are easy to detect because they usually rest over tree branches while inactive. Regarding campaign schedules, all were carried out during the day. However, the first and last were carried out in the afternoon and early evening, and the second and third were carried out in the morning and early afternoon. Thus, we contemplated different activity periods, maximizing detectability.

✓

L177: I would rather call these ‘environmental predictors’, since gradients may remind other things as well.

Response: Replaced in text.

✓

L183: change ‘.’ by ‘,’

Response: Replaced in text.

✓

L221-223: maybe it is just me, but the way it is written seems that you avoided correlated models, not predictors.

Response: The sentence was rewritten for clarity.

✓

L223-226: what was the cut-off point?

Response: From r = 0.51. We added this information in the text.

✓ L233: what do you mean by “corrected for few parameters”?

Response: We wanted to say that we did use Akaike´s Information Criterion (AIC) for model selection, but the one corrected for small samples: the AICc. The sentence was poorly written in the first version and rephrased for clarity.

✓

L234-235: what about the non-nested models? Why using the cut-off point if important model weight may be left out? Isn´t model averaging a better strategy depending on the case?

Response: I am not sure if I understood the question, and in what our analysis differs from what has already been done in the literature. We stipulated the ∆AICc <2 cut, due to the number of possible models with different combinations between the variables, and this cut would inevitably exclude some models at the expense of those more parsimonious models.

✓

L261-262: how can you be sure those species were not there?

Table 2. what kind of models did you build? Only those with main effects, excluding additive and interactive effects? Did you try combining all best predictors in one model?

Response: Yes, several models were built, with different combinations between different predictors that did not correlate with each other. In relation to figure 3, we show a direct ordination of the values from the gradient number of trees and the relative abundance of species by plots. With regard to the table, we added additional lines as suggested by the reviewer for a better visualization.

✓ L293: again, I would rather call ‘predictor’ or ‘covariate’ instead of ‘gradient’.

Response: Replaced in text.

✓

Figure 4. Same comment as in Figure 3.

Response: We decided to graphically show only the significant models (P < 0.05). We could really include the images of all the models (which were marginally significant), but we wanted to be synthetic and illustrate these results with a single image due to the length of the article.

✓

L352: there is a typo in this line.

Response: Corrected in text.

✓

Response: Samples were not taken in the rain. When this happened the samplings were postponed.

✓

Response: All sampling modules were installed in original, preserved Amazonian terra-firme forests that are not influenced by other types of vegetation.

✓

Response: We believe that species turnover is related to several aspects in addition to the distance from the river, and that stipulating a margin distance value for the Amazon is a risk, since the region has a large environmental heterogeneity between the interfluvial regions, and even over small distances of 5km we could have significant results in the turnover of species, especially those less vagile. However, many gaps need to be filled for Amazonian lizards, despite recent efforts to answer such a question.

✓

L427-428: what do you mean by "...was returned..."?

Response: We used returned in the sense that it was observed, but the word was replaced in the text.

✓

L451-452: what do you mean by "efficient methods for multi-scale sampling"? Can you provide suggestions about what you would say is an efficient method?

Response: Methods that present a spatial standardization, easily replicable for different species (modular), and preferably that can be carried out for a long time. An example of a good method is the one used here in our study, the RAPELD- Rapid Assessments and Long-term Ecological Research (Magnusson et al., 2005). These modules are part of a network of permanent standardized transects installed in the Amazon by the Biodiversity Research Program (PPBio) of the Brazilian Science, Technology, Innovations and Communications Ministry (Magnusson et al., 2013). An example of sample desing is shown in the figure below. In this method the plots follow the terrain level curve, minimizing possible topographic (environmental) effects within the plot (sampling unit). RAPELD has already been used in several localities around the world https://ppbio.inpa.gov.br/sites/default/files/Biodiversidade_e_monitoramento_ambiental_integrado.pdf. cited 10 March 2019.

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PLoS One. doi: 10.1371/journal.pone.0233881.r003

Decision Letter 1

Stefan Lötters

27 Apr 2020

PONE-D-19-15180R1

Hierarchical effects of historical and environmental factors on lizard assemblages in the upper Madeira river, Amazonian Brazil

PLOS ONE

Dear Mrs. Marques Peixoto,

There was one referee and this person and myself find that the authors have done a good job to improve their paper. There a few minor things pending only, see attachment.

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Stefan Lötters

Academic Editor

PLOS ONE

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Reviewer #2: All comments have been addressed

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PLoS One. 2020 Jun 2;15(6):e0233881. doi: 10.1371/journal.pone.0233881.r004

Author response to Decision Letter 1

6 May 2020

Manaus, May 5th 2020.

RESPONSE LETTER

PONE-D-19-15180 - Second round of review 

Hierarchical effects of historical and environmental factors on lizard assemblages in the upper Madeira River, Brazilian Amazonia

Dear Dr. Stefan Lötters,

Thank you very much for such detailed revisions on our manuscript. Please find below our point-by-point letter addressing all comments and suggestions. Responses are marked in blue.

Kind regards,

The authors

 Responses to comments:

Dear all,

A previous reviewer expressed his/her concerns on possible sampling errors due to the methods used to estimate species presence/abundance. The authors answered that some of those errors were minimized by using “more than one sampling method”. Indeed, besides the visual searching, they only referred searching amidst leaf litter or under debris, and stated that this could enhance detection of fossorial/semi-fossorial species. However, there is no strictly fossorial species in the assemblage reported, and such species in reality could barely be found just by sweeping the leaf-litter. Moreover, although the authors provided responses to most of the reviewer’s concerns, they made no comment on the possibility that some of the taxa indeed represent cryptic species. I suggest them to discuss this it a little bit.

Response: Thank you very much for these comments. In this revised version of the manuscript we highlighted that "search on the vegetation and on the leaf litter was systematically conducted in the first and second half of each visual sampling in the plots, respectively, thus constituting two different and complementary methods". With regard to the assemblage of fossorial species, now we acknowledge that it was not sampled in this study, thus restricting our sample universe and reinforcing confidence to our results. Now we state in the abstract and methods that "we use arboreal and leaf-litter lizard abundance data".

Some of the points raised by the reviewer on item 4 of his/her review also deserve additional efforts from the authors. As an example, mean width of the upper Madeira River was not mentioned anywhere, but this is an important issue when hypothesizing it could act as a riverine barrier. Also, information on sampling scheme are not yet clear enough. Visual searches were performed during the day only, or also during the night, since there are nocturnal species in the assemblage studied?

Response: We added the followed passages in order to attend to these pertinent suggestions: "...and its width varies from about 0,5 to 10 km depending on the river flow. The Madeira separates the Inambari and Rondônia endemism zones located along its left (west) and right (east) margins, respectively [20].". "The searching time in each plot varied between 40 and 60 minutes and was always conducted during the day.". We are not restricting the assemblage to diurnal lizards in the manuscript because species that are usually active during the night were also sampled in daytime hours.

The former reviewer also pointed that “authors suggest that the pattern found – the river acting as a barrier – was important. However, they mention that the lizard fauna found is widely distributed over the Amazon.” He then made some comments on this topic. The authors provided a pertinent response but I think the discussion on this topic could benefit from adding supporting literature on known distributions of some of the species treated therein, which are based in more historical datasets and which clearly show the pattern discussed.

Response: In Discussion, we clearly state that "We highlight that most of regionally isolated species in our sample are widely distributed throughout Amazonia outside our study area [13]...". In addition, in this revised version we acknowledge in the Abstract that "This finding suggests species have been historically isolated at one of the river banks, or that distinct geomorphological features influence species occurrence at each river bank.".

Minor comments Page 1

Line 3 - "Brasil Amazônico" does not make sense... Brazilian Amazonia would be more appropriated

Response: Replaced by "Brazilian Amazonia" throughout the text.

Page 2

Line 36 - you have used "river" in the title and "River" from now on. Check and standardize it throughout the text - River or river.

Response: Replaced by "Madeira River" throughout the manuscript.

Line 39 - delete space

Response: Space suppressed.

Line 44 - "along" would be better

Response: Replaced by "along" in three phrases.

Line 46 - "... over the extensive (nearly xxx km) study area." (I suggest you to briefly mention in the abstract the magnitude of the area to which these results aplly)

Response: Replaced by "over the extensive (nearly 500 km) study area. ".

Page 3

Line 35 - Brazilian Amazonia

Response: Replaced by "Brazilian Amazonia".

Line 72 - first cite frogs, then birds. Please also sort citations in ascending order, throughout the text.

Response: Replaced by "...explaining limited distribution of plants, frogs, birds, spiny rats, and primates". Citations were sorted in ascending order in this new version of the manuscript.

Line 72 - first cite spiny rats, then primates

Response: Replaced by "...explaining limited distribution of plants, frogs, birds, spiny rats, and primates".

Page 4

Line 82 - insert an Oxford comma before "and"; see additional remarks on it in line 182

Response: Comma added.

Line 82 - Ideally, you should cite them in an evolutionary rank. Then you will need to renumber references. Please also sort citations in ascending order.

Response: Now cited in evolutionary rank.

Line 84 - sort citations in ascending order

Response: Citations were sorted in ascending order in this new version of the manuscript.

Line 85 - sort citations in ascending order

Response: Citations were sorted in ascending order in this new version of the manuscript.

Page 5

Line 116 - basin (the "megadiverse region" you want to better understand is that one under the influence of the river, not the river itself).

Response: We added "basin".

Line 120 - delete space

Response: Space deleted.

Line 120 - insert space

Response: Space inserted.

Line 122 - As one of your hypothesis treats the upper Madeira as a barrier, please add some information on relevant characteristics of the river itself (e.g., river width).

Response: We added the following passage: "and its width varies from about 0.5 to 10 km depending on the river flow.".

Line 123 - Brazilian

Response: "Brazilian" inserted.  Line 123 - River (standardize)

Response: Standardized in uppercase letter.

Line 124 - River

Response: Changed to "River".

Line 124 - check standards of the journal (Fig instead of Fig.). Additionally, please add some information on the endemism zones (Rondônia and Inambari), so the mention to them can be properly evaluated when analyzing the elements in your Fig 1.

Response: Standardized to "Fig" instead of "Fig.". We added the following passage: "The Madeira separates the Inambari and Rondônia endemism zones located along its left (west) and right (east) margins, respectively [20].".

Page 6

Line 126 - And what does it mean? These two endemism zones are only briefly mentioned in the Introduction and here in the legend for Figure 1. You should briefly discuss on the implications of this in your results.

Response: We added the following passage to Discussion: "In the upper Madeira River, assemblage divergence between river banks has been attributed to historical processes regionally reducing species dispersal [12], and delimiting the Amazonian endemism zones Inambari and Rondônia [20].".

Page 7

Line 153 - check and standardize throughout the text: you used either with or without hyphen

Response: Standardized as East and West Jirau (without hyphen). Jaci-Paraná has a hyphen because this is the original name of the locality.

Line 154 - see comment on line 153

Response: Standardized as East and West Jirau (without hyphen). Jaci-Paraná has a hyphen because this is the original name of the locality.

Line 103 – replace “e” by “and”

Response: Replaced by "and".

Line 106 - Brazilian Amazonia

Response: Replaced by "Brazilian Amazonia".

Lines 165-166 - Dates should follow a standardized sequence (e.g.,day-month- year, as you used here)

Response: Replaced by "We sampled each plot in four different periods (24 February to 26 April 2010, 30 July to 19 August 2010, 5 November to 26 November 2010, and 13 January to 4 February 2011)".

Line 172 – there is no fossorial species in your list.

Response: "fossorial" was excluded.

Line 174 - at what time of the day/night?

Response: Altered to "The searching time in each plot varied between 40 and 60 minutes and was always conducted during the day.".

Page 8 Line 178 – replace squamates by lizards '

Response: Changed to "lizard".

Line 181 – soil in lowercase

Response: "soil" now in lowercase.

Line 182 - Note: check journal style and consistently use (or do not use) the Oxford comma throughout all the text. In this paragraph, as an example, you alternatively used it (see line 180 - "soil texture, fertility, and flat-level") and did not use it (e.g., in lines 179 - "...foraging, resting and thermoregulation sites", and line 182 - "...Potassium, Zinc and exchangeable Aluminum").

Response: We opted to use the Oxford comma throughout the manuscript. The entire manuscript was revised with regard to the commas.

Line 184 – remove the dot before citation [61]

Response: Dot removed.

Page 9 Line 204 - along the same river bank (replace "within a" by "along the same")

Response: Changed to "...modules along the same river bank...".

Page 10

Line 224 - insert space between "predictors" and "that"

Response: space inserted.

Line 247 – bring to the suggested point the mention to Table 1, or, alternatively, to a point immediately after the mention of the three most abundant species. The information on the number of plots where each species was found do not appear in the table.

Response: We brought the mention of Table 1 to the point immediately after the three most abundant species.

Page 11

Line 251 - remove the mention to Table 1 here, and insert it at any point in the previous sentence. There is no information in Table 1 on the number of plots where each species was found.

Response: Mention removed.

Table 1 – check correspondence of values to each species: the values highlighted possibly correspond to Alopoglossus angulatus. Please relocate them to the correct line of the table.

Response: Values relocated to Alopoglossus angulatus.

Page 12 Table 1 – complete year of description of Kentropyx calcarata and of

Uranoscodon superciliosus

Response: Values completed: 1825 and 1758, respectively.

 Line 265 - insert an Oxford comma after bassleri

Response: Comma added.

 Line 267 - "and" not in italics. Please also add an Oxford comma before "and" .

Response: Replaced by "Kentropyx calcarata, and Enyalius leechii...".

Line 272 - replace "of" by "in" ; replace “and” by “or”

Response: Changed to "Plots ordinated according to their position in the upper Madeira River (west or east bank).".

Line 273 – add “bank” after “east”

Response: Changed to "Plots ordinated according to their position in the upper Madeira River (west or east bank).".

Page 13

Line 286 - insert an Oxford comma after “Búfalos”

Response: Oxford comma added.

Line 288 - standardize mention to the Akaike's criterion: "?AICc < 2", as used before.

Response: Standardized to "ΔAICc < 2".

Page 14

Table 2 - correct the word "margim". Check it in every Table. It appears more than once.

Response: Word corrected to "margin" throughout the table.

Line 292 – insert "to" before "the number"

Response: Corrected to "position relative to the number of trees...".

Page 15

Line 305 - standardize mention to the Akaike's criterion: "?AICc < 2", as used before.

Response: Standardized.

Table 3 - correct the word "margim". Check it in the table.

Response: corrected to "margin".

Page 16

Table 3 - correct the word "margim".

Response: Word corrected.

Lines 312-313 – Principal Coordinates Analysis (cite it according to the abbreviation PCA)

Response: Replaced by "Principal Coordinates Analysis".

Page 17 Line 331 - “East Jirau” without hyphen. Standardize it throughout the text.

Response: East Jirau was standardized without hyphen.

Line 332 - insert an Oxford comma between "sand, and clay"

Response: Comma inserted.

 Line 333 - see comments on line 312.

Response: Changed to "Principal Coordinates Analysis".

 Line 342 - ...[18], diurnal frogs [12], birds... (cite diurnal frogs before birds)

Response: "diurnal frogs" now cited before "birds".

Line 346 – “...with those obtained for frog assemblages”

Response: Replaced by "Our overall results are broadly consistent with those obtained for frog assemblages sampled in the same plots...".

Page 18

Lines 355-356 or elsewhere: did you ever discuss on the possibility of taxonomic bias contributing to this scenario? There are consistent and recent efforts to investigate the taxonomic status of frog species in the region, and such efforts are unparalleled regarding lizards. Maybe you can use recent literature to briefly discuss on that. Only 19% of the species in Table 1 were described in the 20th century, while 31% of the frogs considered in Dias-Terceiro et al. (2015) were described in 21th century and other three species were recognized as undescribed by the same authors.

Response: With regard to this pertinent comment, in the revised version we state that "The smaller proportion of regionally isolated lizard species (29.63%) is reasonably explained by the lower dispersal capacity of small and site-attached frogs compared with most lizards. A taxonomic bias may be also contributing to this scenario, since there are consistent and recent efforts to investigate the taxonomic status of frog species in the region [12], and such efforts are unparalleled regarding lizards.".

Line 360 - you could mention the distribution patterns for some of the species considered herein, depicted in Ribeiro Jr. (2015), as an additional source of evidence that the pattern you found is real, even though a few specimens were recorded during your study (the distribution shown for E. leechii, as an example, based in a large dataset).

Response: In Discussion, now we clearly state that "Even though the riverine-restricted geographic distribution of species such as Kentropyx calcarata observed in this study is supported by basin-level data, we highlight that most of regionally isolated species in our sample are widely distributed throughout Amazonia outside our study area [13].". In addition, in this revised version we acknowledge in the Abstract that "This finding suggests species have been historically isolated at one of the river banks, or that distinct geomorphological features influence species occurrence at each river bank.".

Lines 362-363 - again, taxonomic bias may be also one of causes of such inconsistencies. What you are calling Kentropyx altamazonica, as an example, potentially represents a cryptic species, as other taxa in your Table 1. This is out of the scope of your work, but cannot be ignored

Response: In the revised version we wrote that "The smaller proportion of regionally isolated lizard species (29.63%) is reasonably explained by the lower dispersal capacity of small and site-attached frogs compared with most lizards. A taxonomic bias may be also contributing to this scenario, since there are consistent and recent efforts to investigate the taxonomic status of frog species in the region [12], and such efforts are unparalleled regarding lizards.".

Line 363 - sort citations in ascending order

Response: sorted in ascending order.

Page 19

Line 385 - you basically used "active visual search". Did you additionally use a systematic search using leaf litter plots? Or you merely searched leaf litter anurans instead, by "sweeping the leaf litter and removing debris"? Looking carefully at every microhabitat visually accessible and inspecting potencial retreats are common procedures during visual searches.

Response: Now we clearly state in Discussion: "However, we think that a possible sampling bias was mitigated by the large sampling effort associated to the combination of different visual sampling methods employed in this study.". We rephrased Methods for clarity and reached the followed wording: "We found lizards using active visual search, with two simultaneous observers positioned 10 m apart. In addition, we supplemented the sampling effort by sweeping the leaf litter and removing debris in a 2 m strip following the center line of the plot. This approach was particularly useful to increase the efficiency of sampling leaf-litter species (e.g. Alopoglossidae, Gymnophthalmidae). Search on the vegetation and on the leaf litter was systematically and respectively conducted in the first and second half of each visual sampling in the plots, thus constituting two different and complementary methods. The searching time in each plot varied between 40 and 60 minutes and was always conducted during the day.".

Line 401 - cite in an evolutionary rank

Response: Cited in evolutionary rank: "frogs [12], snakes [79], and bats [80].". References were reordered in the list.

 Line 403 – replace "within a" by "along a same"

Response: Replaced by "along a same riverbank".

Page 20

Line 410 - of which zoological group? is this a general trend for chordates? for reptiles? for tropical reptiles? your supporting references herein (58-60) are for snakes, including a temperate species. Can you replace or complement them with any citation on the effects of vegetation heterogeneity on tropical lizards?

Response: Changed to: "Heterogeneity in vegetation structure affects occurrence and abundance of tropical squamates due to variation in the availability of foraging, nesting, resting, and thermoregulating sites [58,60].". We changed "species" by "tropical squamates", and replaced the reference from a temperate environment [60] by a tropical one. We used references on snakes because we did not find examples related to lizards - probably due to the relative few studies on tropical lizards.

Line 413 - insert an Oxford comma after “humidity”

Response: Comma added after "humidity".

Page 21 Line 435 - insert an Oxford comma after “cover”

Response: Comma added after "cover".

Line 437 – “...snake [41], and bird [39] assemblages.”

Response: reordered in an evolutionary rank.

Line 447: insert “upper” before Madeira River

Response: changed to "both banks of the upper Madeira River...".

Page 22 Line 463 - sort citations in ascending order

Response: In the megadiverse Amazonian rainforests this has been achieved by a few studies [12,31,73].

Page 33 Line 725 - italicize Liolaemus monticola

Response: "Liolaemus monticola" italicized.

Page 34 Line 746 - not in italics

Response: Italics removed from "Boulenger".

Page 36 Line 803 - West Jirau (standardize)

Response: Standardized to "West Jirau".  Line 801 - check

Response: Changed to "Effect of environmental predictors on lizard assemblages composition".

Line 805 - check

Response: "Effect of environmental predictors on lizard assemblages composition".  Line 806 - East Jirau (standardize)

Response: Standardized to "East Jirau".   Line 806 – delete extra space before “Paraná”

Response: Extra space deleted.

Attachment

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PLoS One. doi: 10.1371/journal.pone.0233881.r005

Decision Letter 2

Stefan Lötters

15 May 2020

Hierarchical effects of historical and environmental factors on lizard assemblages in the upper Madeira River, Brazilian Amazonia

PONE-D-19-15180R2

Dear Dr. Marques Peixoto,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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Stefan Lötters

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0233881.r006

Acceptance letter

Stefan Lötters

21 May 2020

PONE-D-19-15180R2

Hierarchical effects of historical and environmental factors on lizard assemblages in the upper Madeira River, Brazilian Amazonia

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Data. Protocols for measuring the environmental predictors.

Predictors used as independent variables in the ecological models to test lizard assemblage structuring in the upper Madeira River, Brazilian Amazonia.

(PDF)

Click here for additional data file.^{(106.7KB, pdf)}

S1 Fig. Partials from Mixed Linear Models for the west bank of the Madeira River.

Effect of environmental predictors on lizard assemblages composition (PCoA axis 1). The models were selected by ΔAICc < 2. (A) Ilha das Pedras (B) Ilha dos Búfalos (C) West Jirau (D) Teotônio.

(PDF)

Click here for additional data file.^{(76.8KB, pdf)}

S2 Fig. Partials from Mixed Linear Models for the east bank of the Madeira River.

Effect of environmental predictors on lizard assemblages composition (PCoA axis 1). The models were selected by ΔAICc < 2. (A) Jaci-Paraná (B) East Jirau (C) Morrinhos.

(PDF)

Click here for additional data file.^{(59.5KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

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Attachment

Submitted filename: PONE-D-19-15180_R1.pdf

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Attachment

Submitted filename: Comments Rev#1.pdf

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Attachment

Submitted filename: Response to Reviewers R2 5May2020.pdf

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Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

Hierarchical effects of historical and environmental factors on lizard assemblages in the upper Madeira River, Brazilian Amazonia

Gabriela Marques Peixoto

Rafael De Fraga

Maria C Araújo

Igor Luis Kaefer

Albertina Pimentel Lima

Roles

Abstract

Introduction

Materials and methods

Study area

Fig 1. Location of the upper Madeira River, state of Rondônia, Brazil.

Sampling design

Sampling effort and ethics

Environmental variables

Data analysis

Results

Table 1. List of lizard species sampled in the upper Madeira River, Brazil.

Regional assemblage structuring—Madeira River as a biogeographic barrier

Fig 2. Plots ordinated according to their position in the upper Madeira River (west or east bank).

Local assemblage structuring—The role of environmental predictors

Table 2. Summary of the results returned by linear mixed-effects models.

Fig 3. Plots ordinated according to their position relative to the number of trees measured in the west bank of the upper Madeira River, state of Rondônia, Brazil.

Table 3. Summary of the results returned by linear mixed-effects models.

Fig 4. Partials from a multiple linear model for the Ilha das Pedras module.

Fig 5. Plots ordinated according to their position relative to a gradient of elevation (meters above the sea level) in the east bank of the upper Madeira River, state of Rondônia, Brazil.

Fig 6. Partials from a multiple linear model from the East Jirau module.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Stefan Lötters

Roles

Author response to Decision Letter 0

Decision Letter 1

Stefan Lötters

Roles

Author response to Decision Letter 1

Decision Letter 2

Stefan Lötters

Roles

Acceptance letter

Stefan Lötters

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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