Changes in the structure and composition of the ‘Mexical’ scrubland bee community along an elevational gradient

Sergio Osorio-Canadas; Noé Flores-Hernández; Tania Sánchez-Ortiz; Alfonso Valiente-Banuet

doi:10.1371/journal.pone.0254072

. 2021 Jul 1;16(7):e0254072. doi: 10.1371/journal.pone.0254072

Changes in the structure and composition of the ‘Mexical’ scrubland bee community along an elevational gradient

Sergio Osorio-Canadas ^1,^*, Noé Flores-Hernández ^1,², Tania Sánchez-Ortiz ¹, Alfonso Valiente-Banuet ¹

Editor: François Rigal³

PMCID: PMC8248643 PMID: 34197555

Abstract

‘Mexical’ scrubland is a sclerophyllous evergreen Mediterranean-like vegetation occurring in the leeward slopes of the main Mexican mountain ranges, under tropical climate. This biome occupies an elevational range approximately from 1900 to 2600 meters above sea level, which frequently is the upper-most part of the mountains range. This puts it at risk of extinction in a scenario of global warming in which an upward retraction of this type of vegetation is expected. The Mexical remains one of the least studied ecosystems in Mexico. For instance, nothing is known about pollinator fauna of this vegetation. Our main objective is to make a first insight into the taxonomic identity of the bee fauna that inhabits this biome, and to study how it is distributed along the elevational gradient that it occupies. Our results highlight that elevation gradient negatively affects bee species richness and that this relationship is strongly mediated by temperature. Bee abundance had no significant pattern along elevational gradient, but shows a significant relationship with flower density. Interestingly, and contrary to previous works, we obtained a different pattern for bee richness and bee abundance. Bee community composition changed strongly along elevation gradient, mainly in relation to temperature and flower density. In a global warming scenario, as temperatures increases, species with cold preferences, occupying the highest part of the elevation gradient, are likely to suffer negative consequences (even extinction risk), if they are not flexible enough to adjust their physiology and/or some life-story traits to warmer conditions. Species occupying mid and lower elevations are likely to extend their range of elevational distribution towards higher ranges. This will foreseeably cause a new composition of species and a new scenario of interactions, the adjustment of which still leaves many unknowns to solve.

Introduction

Paleontological evidence indicates that evergreen-sclerophylous Mediterranean-like vegetation originally existed in a belt around North America and Eurasia where the climate was wet and warm during the mid-Eocene [1, 2]. Currently, relictual patches of this ecosystem type are found in the five Mediterranean areas under Mediterranean climate. We also found it in the form of the sclerophyllous evergreen scrublands occurring in the leeward slopes of the main Mexican mountain ranges under tropical climate (Sierra Madre Oriental, Sierra Madre Occidental, Eje Neovolcánico and Oaxaca mountains), in an elevational range approximately from 1900 to 2600 meters above sea level (m asl). These plant communities grow in semi-arid tropical climates with summer rainfall and are known as the ‘Mexical’ in contraposition to the ‘Chaparral’ from California and Baja California, developing under Mediterranean climate [3]. Surprisingly, the Mexical remains one of the least studied ecosystems in Mexico, even though the scant existing evidence indicates that it harbors high levels of biodiversity [3]. In addition, most studies on Tropical mountain systems assume that these are extensions of lowland ecosystems [4]. But in the case of Mexical, this biome has an origin, common genera, and ecological traits (evergreening, sclerophyllous, leaf angle, resprouting ability), that are much closer to other Mediterranean ecosystems than to the low deciduous forest and derived scrublands, located a few meters below in the same mountain, and whose botanical lineage belongs to a different (neotropical) geoflora [1, 2, 5, 6].

As the Mexical occupies relatively high elevations in mountain systems (in some cases the upper-most part of the mountains), climate change is expected to have a strong impact on this ecosystem. Temperature increases are likely to cause the Mexical to retreat to higher elevations which would seriously compromise its very existence. Reduction and/or alteration of the Mexical would threaten plant species characteristic of this ecosystem, as well as their herbivores, including pollinators [7, 8]. Most of the plants of the Mexical show floral traits compatible with the syndrome of Mediterranean systems of the Tertiary [9, 10], and thus are likely to depend on pollinators for fruit and seed set. However, and in contrast to the pollinator fauna of other Mexican ecosystems, such as the semi-arid areas of Mexico valley [11], the lowland deciduous forest of the Jalisco coast [12], and the lowland tropical forest of the Yucatán Peninsula [13], the pollinating fauna of the Mexical is totally unknown.

Pollinator insects in general, and bees in particular, play a key role in the functioning of terrestrial ecosystems. As much as 85% of the Angiosperms (including 75% of human food crops) depend on insect pollination for sexual reproduction [14, 15]. At the same time, bees and other pollinators have experienced important abundance and diversity declines during the last century [16–20]. The drivers of these declines are partially known and include habitat loss and fragmentation, as well as agricultural intensification and, for some species, the arrival of new parasites and pathogens [21]. Climate change is another likely threat to pollinator populations but the information currently available is insufficient to establish whether climate change should be considered an important driver of bee declines [22–25]. Pollinator populations may respond to climate change in different ways. For instance, they may mitigate the effects of increased temperature through phenotypic plasticity and adaptation [26, 27]. On the other hand, they may migrate to new areas tracking favorable weather conditions [8, 28–30]. If none of these mechanisms works, populations will decline, potentially leading to extinction [16]. Migrations tracking weather conditions may occur along latitudinal and elevational gradients [31, 32]. Elevational gradients, in particular, afford an ideal scenario to study the effects of climate change because they provide a pronounced yet gradual change of climatic conditions (especially temperature) across a relatively small geographical area. Even though experimental manipulation and laboratory studies may help understand short-term responses of organisms (and ecosystems), understanding long-term responses (acclimatization, adaptation, species turnover, changes in community structure) is best accomplished through the study of climatic gradients [4]. For this reason, elevational gradients are increasingly being used as study models to predict potential effects of climate change [4, 33, 34].

In this study we apply a standardized sampling method to characterize bee communities along an elevational gradient of Mexical. We have three objectives: 1) To describe the hitherto unknown bee community of the Mexical; 2) To analyze how the structure (richness and abundance) and composition of this community change with elevation, and 3) To test some abiotic (temperature and precipitation) and biotic variables (flower richness and density) that could explain bee elevational variability.

Based on previous studies [35–47], one would expect bee abundance and richness to decrease along the altitudinal gradient. We also expect changes in community composition along our elevational gradient [45, 46, 48, 49]. Regarding the drivers behind these patterns, we expect that decreasing trends of bee richness and abundance would be associated to decreasing temperatures along the elevational gradient [42, 44, 47, 50]. We also expect a positive relationship between bee abundance and flower abundance, as it has been reported along elevational gradients [40, 42, 51, 52], and it is also known from some studies not associated with elevational gradients [53, 54].

Materials and methods

Ethics statement

Field work was conducted with the permission of Subsecretaría de Gestión para la Protección Ambiental (SGPA) (permit No. SGPA/DGVS/6790/19), belonging to the Secretaría de Medio Ambiente y Recuros Naturales (SEMARNAT). Our study does not involve any endangered nor protected species according to the NOM-059 of SEMARNAT. The study area is not located in a protected area by the Mexican government.

Study area

The study area chosen to conduct field work is in the ‘Mixteca Alta’ region, in north Oaxaca state (Mexico), in the boundaries between Oaxaca and Puebla states (between 16°45´ and 18°22´ N latitude, and between 96°59´ and 98°27´ W longitude). The climatology of the region includes a rainy season (May-October) and a dry season (November—April). Due to elevation, temperature is relatively smooth or even cold, swinging annually, between 6°C to 28°C (only rarely bellow 2°C or above 32°C). Great part of rainfall occurs along 31 days centered at around September 5th, with a total mean accumulation of 166 mm (although there is also a first peak of rainfall along 31 days centered at around June 28th, with a total mean accumulation of 163 mm) [55]. Evergreen sclerophyllous vegetation (‘Mexical’) in this zone is located along a belt covering the mountain ranges between Puebla and Oaxaca, between 1850 and 2500 m asl. More specifically, our study sites are located in an area that covers the municipalities of Villa de Tamazulápam del Progreso, Villa de Chilapa de Díaz, and San Andrés Lagunas.

We selected 19 plots of 450 m² (30 x 15 m) approximately, encompassing an overall area of 280 km² (Fig 1). Distances between plots ranged from 0.5 to 15.8 km. Plots ranged in elevation from 1850 to 2500 m asl (see S1 Appendix). The 19 selected plots share the same basic vegetation type (Mexical-type scrubland), soil type and recent disturbance history. Plant composition varies locally from plot to plot, but is always largely dominated by basic Mexical elements [3]. Other taxonomic elements could be mixed in the upper and lower parts of our elevational range, as the highest elevation plots are close to the ecotone with pine or oak-type vegetation, and the lower elevation plots are close to the ecotone with lowland deciduous forest.

Variable elevation

The orography of our study zone displayed a distribution in which elevation changes do not occur along a typical lineal mountain, but more or less irregularly over the territory. Specifically, we found Mexical-type scrubland in this area at as low as 1850 m asl (transitioning to low deciduous forest, a little bit lower), and at as high as 2500 m asl (transitioning to pinewood-type vegetation or oak-type vegetation, depending on the sites, a little bit higher). We studied changes in elevation taking the variable elevation as continuous, although plots were selected relatively grouped in three levels: 6 plots ranging between 1850 and 1925 m asl, 6 plots between 2110 and 2176 m asl, and 7 plots between 2405 and 2500 m asl. The minimum and maximum elevations correspond to those at which we found Mexical-type scrubland. This yielded a total of 19 plots (see Fig 1). We choose plots with similar conditions of slope and aspect as far as possible.

We also considered variable Elevation as a categorical variable to conduct all analyses described below (see ‘Statistical Analysis’ section below) for comparison. We established three elevation categories: ‘low’ (including 6 plots around 1850 m asl), ‘mid’ (including 6 plots around 2150 m asl) and ‘high’ (including 7 plots around 2450 m asl). As analyses results were qualitatively equivalent they are not shown in main text, but they are shown in S5 Appendix.

Climatic variables

Temperature gradient associated to elevational gradients is thought as one of the main drivers behind changes in biotic communities along elevational gradients [34]. Precipitation is another important climatic variable that can affect direct or indirectly bee presence along elevational gradients [56]. We obtained these two variables from corresponding raster layers for GIS of ‘Climatic Atlas of Mexico’ [57]. We obtained Mean Annual Temperature (°C) (MAT, henceforth) and Mean Annual Precipitation (mm) (MAP, henceforth) for each plot. These data represent an average from a series of 109 years recorded data for during years 1902 to 2011.

Bee sampling

We conducted 3 surveys (late September 2019, late October 2019, and late January 2020-early February 2020). In each survey and plot, we placed 6 sampling stations distributed in two parallel rows (3 stations in each of the two rows), with a distance of 15 m between stations in the same row, and with a distance of 15 m between the two rows. Following Westphal et al. [58], each station was composed of 3 pan traps (19-cm-diameter plastic bowls painted yellow, white and blue, respectively, with UV-reflecting paint in the case of yellow and blue, bright in the case of white). Traps were not held in metallic bars as in Westphal el al. [58]. Instead, they were located on the ground, the bowls being ~3 m apart from each other in each station. We searched for clears in the flowering vegetation to set the bowls on the ground. On each sampling day, traps were set on the ground and filled with water containing a small amount of detergent and we took note of the exact hour at which all the bowls were full of soapy water. These bowls were collected at the same hour next day, so that all bowls were active for 24 hours. We were able to complete this process for 6–7 plots on each day. To avoid the influence of weather conditions, surveys were conducted simultaneously in six random plots, two belonging to each one of our three groups of elevation. Then, we were able to survey our 19 plots in 6 consecutive days (one day to set pan-traps in each plot, and the following day to collect insect samples in traps laid the day before). Pan trapping has been shown to underestimate bee species richness compared to netting of flower visiting insects [58]. However, this method avoids collector bias and allows to apply the same sampling effort to each plot, so that samples of all our 19 plots were totally comparable, which was our main concern. Captured specimens were dried and pinned for taxonomic identification in the laboratory. This collection is deposited at the Ecology Institute Entomological Collection, Autonomous National University of Mexico (UNAM), Mexico City, Mexico. From these samples we obtained measures of bee species richness (number of bee species captured), bee abundance (number of bee individuals captured) and bee community composition (abundance of each bee species) for each plot. To obtain the final value for each variable, we lumped together all bee species and all bee individuals sampled per plot for the three surveys. Honey bee (Apis mellifera) is known to be a human-managed species in our study area, especially for honey production. As their species distribution could have been modified by human managing, we exclude this species from all analyses. This species was present in all plots.

Flower resources

We considered flower variables, as principal bee food resource and, in consequence, important co-variables determining presence and abundance of bees [59]. We quantified two variables: flower species richness and flower density in each of our 19 plots. To estimate flower species richness and flower density, we counted all flowers belonging to every flower species along two 20 m² transects arranged following the two lines in which sampling stations were settled. This was done three times, one for each bee sampling conducted. Some previous studies have shown that pollen and nectar density per plot are highly correlated, and both variables are also correlated to flower density [60]. Therefore, we used flower density as a measure of flower resources in all analyses (number of flowers/m² in each plot). We also used flower species richness as a variable in our analyses. To obtain the final value for each variable, as in the case of bees, we lumped together all flowering species and all flower individuals sampled per plot for the three surveys.

Statistical analysis

Bee community structure, flower community structure and climate vs elevation

We determined the relationship between bee species richness, bee abundance, flower species richness, flower density, MAT and MAP, each one as a response variable versus the variable elevation (six different models), taking into account a possible variation of these variables in relation to geographic distance (spatial autocorrelation). Previously, we used Moran’s I test to explore spatial autocorrelation of each one of this response variables. These analyses were conducted with the statistical package ‘ape’ [61] for R version 4.0.1 [62]. We found that bee species richness, flower species richness, MAT and MAP showed spatial auto-correlation. Bee abundance and flower density did not show spatial auto-correlation (see Results). In the case of the four auto-correlated response variables, we followed procedure as described in Zuur et al. [63], to control for spatial auto-correlation. For each response variable vs elevation, we build up seven different models. We used generalized least squares (GLS) models with five different spatial covariance structures (Spherical, Linear, Ratio, Gaussian, and Exponential type of spatial correlation). Then, for each relationship, we compared these five models and the model with no spatial covariance structure, and the ‘null model’ (with no explanatory variable and no spatial covariance structure). Then, we selected the best-fit model using second-order Akaike information criterion. Finally, to obtain unbiased parameter estimates, we calculated the selected model with restricted maximum likelihood estimates (REML). GLS analyses were conducted with the R package ‘nlme’ [64]. Adjusted-pseudo R² of these GLS models were calculated based on the likelihood-ratio test performed with the ‘r.squaredLR’ function of R package ‘MuMIn’ [65]. In the case of the two no auto-correlated response variables (bee abundance and flower density) we run a GLS model with no spatial covariance structure and with REML estimation. For each model, we also obtained and adjusted-pseudo R² in the same way as described above. In all cases, we checked if models complied with the assumptions of normality and homoscedasticity.

As our bee community had three clearly dominant species (see ‘Results‘ section), we decided decomposing bee abundance variable, considering the three most abundant species abundances separately as variables vs elevation, to check if general pattern could be determined by the specific patterns of these three species. We also considered the variables: ‘abundance of bees without 3 most abundant species’ and ‘abundance of bees without the most abundant bee’ vs elevation to check directly the effects of these 3 most abundant species in bee abundance vs elevation model. The statistical procedure to conduct these analyses was exactly equivalent to that described for analyses in the previous paragraph. Analyses were conducted considering elevation as a continuous and as a categorical variable. Details for these analyses and results for these models are shown in the S6 Appendix.

Bee community structure vs explanatory variables (flower and climate variables)

Disentangling the effect of elevation is problematic since it is often correlated with several abiotic and biotic environmental variables [66], which is also our case (see S2 Appendix). To deal with this problem, we decided to analyze separately elevation as explanatory variable (analyses in previous paragraph), and all those abiotic and biotic variables to which is related, in our case: MAT, MAP, flower species richness and flower density. To test if these four explanatory variables associated to elevational gradient influenced bee community structure response variables i.e., bee species richness and bee abundance, for each one of these two bee response variables we built a series of ‘lm’ models with all possible combinations of the four explanatory variables (including a ‘null model’ with no explanatory variables), and then we selected the best-supported models using second-order Akaike information criterion (AICc). This approach reduces the problems associated with multiple testing, collinearity of explanatory variables, and small sample sizes [67]. The best supported models were selected based on their AICc weights, which reveal the relative likelihood of a given model—based on the data and the fit—scaled to one. Model selection was carried out using the ‘dredge’ function in theMuMIn package for R. The relevant variables were those that were retained in the best-supported models (except, obviously, when the best-supported model consisted only of the intercept). We selected those models with a delta (AICc difference) of Δ<2 and then proceed to run a model-average effect sizes for the parameters with most support across these models with the ‘model.avg’ function of MuMIn package for R. Model averaging consists in making inference based on a subset of best candidate models, instead of basing conclusions on a single ‘best’ model [68]. To control for spatial auto-correlation, for each of the two response variables (bee species richness and bee abundance) set of models, we selected the best-supported model based on AICc weight (containing only significant explanatory variables resulting from the model-averaging procedure) and then proceed as described in Zuur et al. [63], as explained in the above paragraph.

Community composition vs elevation analyses

To assess the significance of community dissimilarity along the elevation gradient, we used PERMANOVA as implemented in the ‘adonis2’ function in the ‘vegan’ package [69] for R. We used ‘adonis2’ function (instead of ‘adonis’) to run marginal tests in order to obtain variable effects considering the presence of all other variables in the model (i.e., effects controlling for all other variables). We used NMDS ordinations to visualize community dissimilarities vs elevation. In this specific case we use elevation as a categorical variable for visualization purposes (we defined three elevational categories: ‘low’, ‘mid’, and ‘high’ categories, as described in ‘Variable Elevation’ section). Ordination plots were created using the ‘metaMDS’ function in ‘vegan’ package, which incorporated a square root transformation and Wisconsin double-standardization of species abundances. In both NMDS and PERMANOVA computation we used abundance-based metrics (Bray–Curtis dissimilarity index). We also used PERMANOVA analyses to quantify the contribution of climatic variables (MAT and MAP), and flower variables (flower species richness and flower density). Similarly as explained in above paragraph, we analyzed separately elevation as explanatory variable, from all those abiotic and biotic variables to which is correlated (MAT, MAP, flower species richness and flower density, see S2 Appendix). So, we run a first analysis with community composition as response variable vs elevation as unique explanatory variable, and then a second analysis with community composition as response variable vs climatic & flower variables as explanatory variables. To take into account the effects of undersampling and rare species on community dissimilarity, we compared our NMDS and PERMANOVA results to those generated with all singletons removed, and all singletons and doubletons removed, and to results calculated with presence-absence distance metrics (Jaccard dissimilarity index). To explore patterns of spatial autocorrelation, we used the ‘ecodist’ package [70] in R to do Mantel and partial Mantel tests. Community composition showed correlation with geographic distance (see Results), so we tested the significance of elevation and climatic and flower variables on communities while statistically constraining the variation attributable to distance alone. To do so, we used two approaches. First, we used a partial Mantel test to test if there was a relationship between elevation and bee community composition once the effects of geographic distance are removed. Second, we also used the procedure described in Zimmerman and Vitousek [71], conducting a series of constrained distance-based redundancy analysis (‘dbRDA’) implemented with ‘dbRDA’ function (‘vegan’ package) using principal components of neighbor matrices (PCNM). We obtained seven vectors from applying PCNM procedure with ‘pcnm’ function (‘vegan’ package). Then we selected significant PCNM vectors with ‘capscale’ and ‘ordistep’ functions (‘vegan’ package), using a both forward and backward selection method. Only significant PCNM vectors were used in dbRDA analyses. We run a first dbRDA analysis including community composition distances as response variable vs elevation + significant PCNM vectors as explanatory variables. Then, we run a second dbRDA analysis including community composition distances as response variable vs PCNM significant vectors + climatic variables (MAT and MAP) + flower variables (flower species richness and flower density) as explanatory variables. We used marginal anova testing in order to obtain results controlling for all other variables in the model (i.e. we obtain effects of explanatory variables after controlling for spatial effects (PCNM vectors)). We conducted these dbRDA analyses for our principal analysis using abundance-based dissimilarities (Bray-Curtis), and also for secondary comparative analyses removing singletons, singletons+doubletons, and for analysis using presence/absence-based dissimilarities.

Finally, we also conducted all analyses described above considering variable Elevation as a categorical variable, for comparison. As results were qualitatively equivalent they are not shown in main text, but they are shown in S5 Appendix.

Results

General results

A total of 1726 specimens of bees were captured and sorted into 62 species and morphospecies, included in five families: Apidae (27 species/morphospecies), Megachilidae (7), Andrenidae (9), Halictidae (18) and Colletidae (1) (S3 Appendix). Sixteen species/morphospecies represented 95.08% of the specimens captured, and 23 of the remaining 46 species/morphospecies were singletons. Macrotera sp1 was the most abundant species (29.3% of total specimens), followed by Lasioglossum (Dialictus) sp1 (18.6%), and Lasioglossum (Lasioglossum) sp1 (16.4%). These three species together constitute almost two thirds of the collected individuals. Plot species richness ranged between 6 and 21, and abundance between 18 and 151 (considering 3 surveys merged together). The relationship between bee species richness and bee abundance failed significance (Pearson r = -0.20, p = 0.4). Of the total 1726 specimens, 226 individuals corresponded to Apis mellifera, and 1500 to ‘solitary bees’. Only solitary bees were used for analyses (as explained in ‘Materials & Methods’).

Bee community structure, flower and climate variables vs elevation

Bee species richness significantly decreased with elevation (Table 1, Fig 2A), as did MAT (Table 1, Fig 2C). Richness of flower species significantly increased with elevation (Table 1, Fig 2E). Bee abundance and flower density tended to increase with increasing elevation but failed significance in both cases (Table 1, Fig 2B–2F respectively). MAP showed a “U”-pattern with significant lower values at mid-elevation and significant higher values at higher elevation (Table 1, Fig 2D). These results were not affected by spatial auto-correlation as, in all cases, models including no spatial correlation structure were selected (had the lower values of AICc).

Table 1. Best GLS models for different response variables vs elevation.

GLS model parameters
GLS Model		F	p-value	pseudo-R²
Bee species richness ~ Elevation		15.44	0.0011	0.48
Bee abundance*¹ ~ Elevation		1.57	0.226	0.08
MAT ~ Elevation		806.14	<0.0001	0.98
MAP ~ Elevation + Elevation²	Elevation	45.40	<0.0001	0.85
MAP ~ Elevation + Elevation²	Elevation²	43.27	<0.0001	0.85
Flower species richness ~ Elevation		17.13	0.0007	0.51
Flower density ~ Elevation		1.87	0.19	0.10

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Best GLS models for different response variables vs elevation (considered as continuous variable), after controlling for spatial autocorrelation. These results are plotted in Fig 2. Significant values are marked in bold. Abbreviations: MAT: Mean Annual Temperature (°C); MAP: Mean Annual Precipitation (mm). (*¹: log10-transformed)

Fig 2 — Effects of elevation (as a continuous variable, in meters above sea level, [m asl]), on different response variables: (A) bee species richness (number of bee species); (B) bee abundance (number of bee individuals); (C) Mean Annual Temperature (MAT,°C); (D) Mean Annual Precipitation (MAP, mm); (E) flower species richness (number of flower species); (F) flower density (number of flowers/m2); along elevation. Continuous blue lines represents best adjust of significant models, and the gray bands represent 95% confidence intervals. Blue broken lines without gray band denote non-significant models. See in Table 1 the statistic parameters of these models.

Bee community structure vs flower and climate explanatory variables

The model including only temperature as explanatory variable was the best ranked model based in AICc weight for bee species richness as response variable. Model-averaging estimation of parameters confirmed this result, as temperature was the only explanatory variable that resulted significant (see Table 2 and Fig 3). In the case of bee abundance as response variable, the model including only flower abundance as explanatory variable was the best ranked model based in AICc weight. This was confirmed by the model-averaging estimation of parameters, as flower abundance was the only explanatory variable that resulted significant (see Table 2 and Fig 3). Both analyses sets (bee species richness and bee abundance), yielded almost identical results for full average and conditional average, so only conditional average is shown (Table 2). We selected the two best models in each case (Bee species richness ~ temperature, and Bee abundance ~ flower abundance) and checked them for spatial correlation. Results were not affected by spatial auto-correlation as, in two cases, models including no spatial correlation structure were selected (had the lower values of AICc). Finally, parameters of these GLS best models after controlling for spatial autocorrelation were calculated (see Table 2).

Table 2. Results from model selection relating bee response variables vs climatic and flower variables.

2A. Model selection results (only models ΔAICc < 2 are presented)
Response variable: Bee species richness							Response variable: Bee abundance
Model	Explanatory variables included	Estimate	loglik	AICc	ΔAICc	weight	Model	Explanatory variables included	Estimate	loglik	AICc	ΔAICc	weight
1	MAT	2.18	-46.20	100.00	0.00	0.69	1	Flower density	0.59	-88.77	185.14	0.00	0.71
2	MAT	2.81	-45.37	101.60	1.58	0.31	2	Flower density	0.55	-88.05	187.0	1.82	0.29
	Flower species richness	0.21						MAT	-5.87
2B. Model-averaging of parameters included in best ranked models (conditional average)
Response variable: Bee species richness							Response variable: Bee abundance
Explanatory variable		Estimate	adj. se	z-value	p(>\|z\|)		Explanatory variable		Estimate	adj. se	z-value	p(>\|z\|)
MAT		2.37	0.72	3.28	0.001		Flower density		0.58	0.14	4.18	<0.0001
Flower species richness		0.21	0.18	1.12	0.26		MAT		-5.88	5.66	1.04	0.29
2C. Best GLS models (after controlling for spatial autocorrelation)
Model		F	p		pseudo-adj.R²		Model		F	p		pseudo-adj.-R²
Bee species richness ~ Mean Annual Temperature		16.25	<0.001		0.49		Bee abundance ~ Flower density		21.95	<0.001		0.56

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Best fitting models relating Bee species richness and Bee abundance (as response variables) vs climatic (MAT, MAP) and flower variables (Flower species richness, Flower density). In Table 2A. best ranked models (ΔAICc < 2) are represented for each of bee response variables (Bee species richness (left) and Bee abundance (right)). In Table 2B. model-averaged parameters for explanatory variables are presented for each bee response variable. In Table 2C. parameters for best (GLS) models (after controlling for spatial correlation) are presented. Abbreviations: MAT: Mean Annual Temperature (°C); MAP: Mean Annual Precipitation (mm); adj.: adjusted; se: standard error. Significant values are marked in bold.

Fig 3 — (A) Bee species richness vs Mean Annual Temperature (MAT;°C), and (B) bee abundance vs flower density (number of flowers/m²).

Community composition vs elevation and flower and climate explanatory variables

Community composition dissimilarity showed correlation with increasing geographic distance (Mantel test: r = 0.428, p = 0.002). However, community composition dissimilarities still showed positive correlation with increasing elevation once the effects of geographic distance are removed (partial Mantel test: Rm = 0.23, p = 0.02).

We found significant community composition dissimilarities along elevation (PERMANOVA: F = 4.99, p = 0.002, R² = 0.23; Table 3A). These community differences along elevation can be visualized in NMDS ordination plot (Fig 4). Elevation effect still was significant after controlling for geographic distance effect (dbRDA: Elevation: F = 2.15, p = 0.044, R² = 0.08; Table 3A), and geographic distance also resulted significant in explaining differences in community composition (dbRDA: pcnm1: F = 2.34, p = 0.045, R² = 0.09; pcnm6: F = 2.52, p = 0.031, R² = 0.10; Table 3A). In analyses considering climatic and flower explanatory variables, MAT and flower density significantly explained a part of the variation in community composition dissimilarities (PERMANOVA: MAT: F = 3.63, p = 0.003, R² = 0.13; flower density: F = 3.38, p = 0.009, R² = 0.12; Table 3B), and this effect still remained significant after controlling for geographic distance effect, which also explained a part of variation (dbRDA: MAT: F = 2.75, p = 0.024, R² = 0.09; flower density: F = 3.03, p = 0.015, R² = 0.10; pcnm6: F = 2.30, p = 0.043, R² = 0.07; Table 3B). These results were qualitatively almost the same comparing them with analyses removing singletons, and singletons and doubletons at once, even after controlling by geographic distance effect (Tables A and B in S4 Appendix, respectively). In analyses with binary composition data, results were similar: elevation explained significantly differences in community composition after controlling by geographic distance, and geographic distance failed significance. Further, considering climatic and flower explanatory variables, only temperature had a significant effect (Table C in S4 Appendix).

Table 3. Community composition vs elevation, geographical distance, climatic variables and flower variables.

3A. Considering Elevation and geographic distance as explanatory variables
PERMANOVA
variable	Df	Sum of Squares	R²	F	P(>F)
Elevation	1	0.692	0.226	4.991	0.002
Residual	17	2.357	0.773
Total	18	3.050	1
dbRDA (controlling for geographic distance)
variable	Df	Sum of Squares	R²	F	P(>F)
pcnm1	1	0.277	0.09	2.341	0.045
pcnm6	1	0.299	0.10	2.526	0.031
Elevation	1	0.255	0.08	2.152	0.044
Residual	15	1.77	0.58
Total	18	3.05	1
3B. Considering climatic (temperature and precipitation), flower (flower species richness and flower density), and geographic distance as explanatory variables
PERMANOVA
variable	Df	Sum of Squares	R²	F	P(>F)
Mean Annual Temperature (°C)	1	0.4	0.13	3.63	0.003
Flower density (flowers/m²)	1	0.37	0.12	3.38	0.009
Flower species richness	1	0.14	0.05	1.28	0.247
Mean Annual Precipitation (mm)	1	0.16	0.05	1.47	0.168
Residual	14	1.54	0.51
dbRDA (controlling for geographic distance)
variable	Df	Sum of Squares	R²	F	P(>F)
pcnm1	1	0.13	0.04	1.32	0.258
pcnm6	1	0.23	0.07	2.30	0.043
Mean Annual Temperature (°C)	1	0.27	0.09	2.75	0.024
Flower density (flowers/m²)	1	0.30	0.10	3.03	0.015
Flower species richness	1	0.15	0.05	1.52	0.198
Mean Annual Precipitation (mm)	1	0.03	0.01	0.26	0.972
Residual	12	1.19	0.39

Open in a new tab

PERMANOVA and dbRDA analyses. In Table 3A., Elevation (in PERMANOVA), or Elevation + geographic distance variables (pcnm1 and pcnm6) (in dbRDA) are considered as unique explanatory variables. Elevation is considered as a continuous variable. In Table 3B., climatic variables (Mean Annual Temperature and Mean Annual Precipitation) and flower variables (flower species richness and flower density) (in PERMANOVA), or climatic and flower variables + geographic distance variables (pcnm1 and pcnm6) (in dbRDA), are considered as explanatory variables. In all cases community composition is response variable, and quantitative matrix and Bray-Curtis dissimilarity index is applied.

Fig 4 — NMDS for quantitative bee community composition (relative abundance of all species in each plot, considering three surveys lumped together). In this analysis we considered Elevation as a categorical variable for visualization purposes. Each dot corresponds to a plot (red dots = ‘high’ elevation category, orange dots = ‘mid’ elevation category, green dots = ‘low’ elevation category). Dots with white center are ’centroids’ for each elevation category. Polygons encompass all sites within an elevation category. Ellipses represent 0.95% confidence intervals. Only two of the three dimensions obtained in the analyses (k = 3) are displayed.

Discussion

Our study shows that bee richness significantly decreases with increasing elevation, which seems to be strongly mediated by the effect of decreasing temperature with elevation. On the other hand, bee abundance follows no significant trend along our elevational gradient, although it shows a significant positive relationship with flower density. Bee community composition shows significant changes with elevation that can be explained by temperature and flower density. These results highlight the importance of climatic conditions on bee community structure and composition [40, 42], and are relevant to our understanding on how bee communities may respond to climate change [23]. In addition, our study represents, as far as we know, the first bee community record for a Mexical-type scrubland (S3 Appendix), which undoubtedly is the least studied ecosystem in Mexico [3, 6]. Some other studies have focused on bee communities in nearby areas, but under different climates and vegetation types and at lower elevations (below 1800 m asl) [11, 72].

Bee richness and abundance

In our study, as expected, bee species richness showed a significant trend to decrease with increasing elevation, which is in agreement with previous literature describing a pattern commonly observed in insects in general, with either a mid-elevation peak or a monotonical decrease in species diversity over the entire elevational gradient [34, 35, 37, 38, 50]. Specifically for bee species richness, a monotonically decrease with increasing elevation is mostly reported [39–47]. We also found that mean annual temperature was the best predictor for bee species richness, as we expected, showing a clear positive relationship. This is in agreement with other studies which also found a clearly stronger effect of temperature, outweighing the effect of resources [40, 42]. Temperature is known to have effects on species richness directly and indirectly (mediated via an influence on abundance; e.g. [42]). First, only a few species are expected to physiologically tolerate the harsh and cold climates of high-elevation or high-latitude habitats, in animals and plants in general [36, 73, 74], and in bees in particular [75, 76]. In this sense, elevation has been described as acting as an environmental filter on bee communities, excluding individuals that are not adapted to stressful mountain conditions (cold temperatures, wind, short growing seasons) [40, 77]. Bees are known to be typically associated with warm and sunny environments and their species richness peaks in arid-temperate climates [76, 78, 79]. Second, ambient temperature may determine how much of the potential available resources are accessible to ectothermic organisms, as net profit of foraging animals declines with decreasing temperatures [80, 81]. Such limitations could result in shrinking population densities and increasing probabilities of species extinction in cooler climates [42].

In the case of bee abundance, we found no pattern along our elevation gradient, which is in disagreement with our expectations based on several studies that show a decrease or a mid-elevation peak in bee abundance with increasing elevation [39–41, 43, 45, 51, but see 42, 52]. Nevertheless, we also found, as expected, that flower density was the best predictor of bee abundance, showing a clear positive relationship, which is in agreement with previous works along elevational gradients [40, 42, 51, 52], and it is also known from some studies not associated with elevational gradients [53, 54].

Most studies along elevational gradients have found similar trends for bee richness and abundance [39–45, 47, 51]. By contrast, we found different patterns for bee richness and abundance. This result is probably related to a differential effect of climatic conditions on these two variables along our elevational gradient. Our results suggest that temperature is restrictive enough in our elevational gradient to negatively affect bee richness, resulting in a decreasing trend of bee richness with increasing elevations (and lower temperatures). In the case of bee abundance, we found no pattern along the elevational gradient, but a positive relationship with flower density. Flower density may also be negatively affected by low temperature [e.g. 82], but temperature seems to be not restrictive enough in our study, as we found no pattern in flower density along the elevational gradient. Consequently, the lack of pattern in flower density may explain the lack of pattern in bee abundance along our elevational gradient. Due to the low latitude of our study area (~17°N), the climatic conditions, even at the highest sites, are not extreme enough to affect flower density. Another study conducted in a low latitude area (Mt. Kilimanjaro; 2°S) also reports similar levels of flower abundance along an elevational gradient (870 to 4550 m asl), but a clear decreasing trend in bee richness with elevation [42]. By contrast, a study from higher latitudes (Alps; 45°N; 970 to 2700 m asl) found a clear elevational decrease both in flowering plants abundance and bee richness beginning at mid-elevations (1772 m asl) [51].

Although our bee community has three clearly dominant species, the lack of abundance elevational pattern was not determined by the specific patterns of these three species. The overall lack of pattern was maintained when either the most abundant species (Macrotera sp1) or the three most abundant species together, were excluded from the analysis (see S6 Appendix). Interestingly, these three species showed contrasting trends: abundance of Macrotera sp1 (29.3% of total individuals) increased with elevation, Lasiglossum (Dialictus) sp1 (18.6%) showed no pattern, and Lasioglossum (Lasioglossum) sp1 (16%) showed a hump-shaped pattern (see S6 Appendix). The case of Macrotera sp1 is especially interesting because its pattern is inverse to that found in most of the previous elevational studies [39–41, 43, 45, 51]. This suggests a cold-adaptation of this species which would allow it to exploit high elevation flower resources.

Our results are relevant for the response of the Mexical bee community to a foreseeable scenario of climate change [24], with potentially different impacts on bee richness and abundance. In areas in which the Mexical occupies the top of the mountains, precluding upwards migration, some cold-adapted species might go extinct [8, 31, 83, 84]. However, overall bee richness is expected to increase at higher elevations, as global warming would allow low-elevation species to extend their range towards higher elevations [8, 30–32]. A net increase in bee richness in the higher areas of the gradient could result in greater functional complementarity due to a more diversified pollinator community, which could favor pollination function [85, 86]. Bee abundance, on the other hand, is likely to be indirectly affected by climate change through the effects of climate change on flower abundance. In this vein, contrasting results have been reported, with some plant species increasing their flower production with increasing temperatures, while others reacting conversely [23, 87]. However, in the Mexical scrubland, dominant plant species belong to old lineages (Tertiary) which are C3, evergreen-sclerophyllous species [3, 6]. These physiological traits make these plants particularly vulnerable to suffer significant physiological and structural damages, and even plant mortality, under increasing temperatures and hydric stress [88]. In addition, the Mexical vegetation is highly sensitive to long drought episodes, which are expected to increase in duration and intensity, as documented in Mexico in the last two decades [89–91]. This vulnerability becomes especially relevant where the Mexical occupies the top of the mountains, unable to shift its distributional range upwards tracking more favorable conditions. Therefore, we would expect a decrease in primary productivity and flower abundance in the Mexical community, leading to a lowered pollinator abundance [23]. Consequently, we would expect a decline in pollination function [85, 86]. This trend might be reinforced by a population decline of the cold-adapted most abundant bee species at the high elevations (Macrotera sp1).

Bee community composition

Regarding community composition, we found significant and consistent differences along our elevation gradient, as we expected, in agreement with previous works [45, 46, 48, 49]. Community composition changes along elevation are mainly due to temperature and flower density, and to some extent, also to geographic distance. The fact that we found bee compositional differences along the elevational gradient, suggest certain degree of specialization in species ecological niche in terms of elevation and temperature. We found two species abundantly represented (at least 30 individuals at high elevation) that were clearly more abundant at high elevations than in mid or low elevations (Macrotera sp1, Pseudopanurgus sp1). We also found one species that was exclusive from high and mid-elevations (Lasioglossum (Dialictus) sp2). On the other hand, we found some species that were clearly more abundant (Halictus sp1b) or exclusively represented (Ceratina sp3) in low elevations. As this elevational species distribution is in great part conditioned by ambient temperature, we can expect that increasing temperatures due to climate change [24] will determine shifts of species’ distributions along the elevational gradient, with a gradual replacement of cold-adapted species by warm-adapted ones [8, 31, 32, 92, 93]. In our bee community, those species clearly more abundant at high elevations (Macrotera sp1, Pseudopanurgus sp1, Lasioglossum (Dialictus) sp2), which are expected to have some kind of cold-environment adaptations or preferences, are likely to face physiological affectations or life-cycle mismatches, which could reduce their populations or even go extinct as their habitat progressively shrinks [8, 23, 31, 83, 84]. Meanwhile, we also would expect that those species occupying at present mostly lower to mid-elevation as, for instance, Lasioglossum (Lasioglossum) sp 1, Ceratina sp3, C. sp4b, Eucerini sp3, E. sp6, or Halictus sp1b, with apparent warmer-environment preferences, would be able to expand their distributional ranges upwards [8, 30]. Similar upwards shifts are expected for plant communities along the elevational distribution, associated to climate change [92–95]. If flowering plants and pollinators react to temperature changes at different rates, this could provoke plant-pollinator distributional mismatches [30, 96]. Nevertheless, pollination systems tend towards generalization [97]. If most of bee and plants species are generalist, they may be able to use different partners across their range, so diverging spatial ranges will not necessarily have any fitness impacts on either group [96], and impacts on pollination function could be relatively low. But, it is likely that specialist species would be the most affected in this context [98], and novel communities could be impoverished by the loss of some of these species.

Concluding remarks

In our system, bee richness is affected mostly by temperature. It is likely that expected increased temperatures under climate change will enhance bee richness in the highest parts of the mountains, as bee species from lower and mid-elevations will tend to move upwards [8, 31, 32]. Nevertheless, extinction of cold-adapted bee species is also possible [8, 31, 83, 84]. On the other hand, we found bee abundance to be affected mostly by resource availability (flower abundance), which, in turn, is dependent on climatic conditions. Increasing temperatures and prolonged drought episodes, are likely to have a strong negative impact on the Mexical scrubland, especially the mountain top areas. This would lead to a decline in flower abundance which, in turn, would result in decreased bee abundance. Although difficult to predict, changes in both flower and bee composition are also expected, potentially altering plant-pollinator interactions. Climate change is also likely to affect the phenology and distributional range of plant and pollinators, potentially leading to temporal and/or spatial mismatches if these two groups of organisms respond differently to weather variables [96], which would further enhance changes in plant-pollinator interactions and potentially threaten pollination function, especially in specialized plant-pollinator systems. Future research could be focused on understand how these changes will be taking place, what community variables will result affected first and in what relative extent, and how it will affect pollination function.

Supporting information

S1 Appendix. General database.

(XLSX)

Click here for additional data file.^{(11.6KB, xlsx)}

S2 Appendix. Correlations among all response and explanatory variables.

(DOCX)

Click here for additional data file.^{(52.9KB, docx)}

S3 Appendix. List of species and/or morphospecies.

(DOCX)

Click here for additional data file.^{(21.9KB, docx)}

S4 Appendix. Mantel test, PERMANOVA and dbRDA analyses for quantitative data excluding sigletons.

excluding sigletons and doubletons, and for binary data (considering elevation as a continuous variable).

(DOCX)

Click here for additional data file.^{(35.3KB, docx)}

S5 Appendix. Results of all analyses considering elevation as a categorical variable.

(DOCX)

Click here for additional data file.^{(137.8KB, docx)}

S6 Appendix. Abundance of the three most abundant species in our Mexical community vs elevation, and abundance of bees excluding most abundant species vs elevation.

(DOCX)

Click here for additional data file.^{(170.5KB, docx)}

Acknowledgments

We are most grateful to ‘Comisariado de Bienes Comunales’ and Municipal Authorities of Villa de Tamazulápam del Progreso, Villa de Chilapa de Díaz, and San Andrés Lagunas (including delegated authorities of San Isidro Lagunas, belonging to San Andrés Lagunas municipality), and in general, to all people of these villages for their welcome and kindness. We are also grateful to all people of San Isidro Lagunas, and especially to Mrs. Angélica Hernández and family, who kindly hosted us and offer us their hospitality and delicious dishes. We are also thankful to Germán Estocapan, Ana Edith Alcántara, Katia Haydeé Torres, Nicolás Osorno, Noé Guzmán, Mario Flores, Benajmín Cruz, Josué Palma and Alan Flores, for their friendship and field assistance. Finally, we are also grateful to the editor and two anonymous reviewers, who contributed with their comments and suggestions to improve this manuscript.

Data Availability

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

Funding Statement

This study was supported by Dirección General de Asuntos del Personal Académico (DGAPA), Universidad Nacional Autónoma de México (UNAM) with the project PAPIIT clave IN-214020, (https://dgapa.unam.mx/index.php/impulso-a-la-investigacion/papiit). This study was also supported by Consejo Técnico de la Investigación Científica (CTIC)-Dirección General de Asuntos del Personal Académico (DGAPA), Universidad Nacional Autónoma de México (UNAM), with one post-doctoral grant (CJIC/CTIC/0980/2019 and CJIC/CTIC/4698/2020) (to SO-C) (https://dgapa.unam.mx/index.php/formacion-academica/posdoc). 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.0254072.r001

Decision Letter 0

François Rigal

7 Jan 2021

PONE-D-20-34943

Changes in the structure and composition of the ‘Mexical’ scrubland bee community along an altitudinal gradient

PLOS ONE

Dear Dr. Sergio Osorio-Canadas,

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overall: The authors did a good job summarizing and documenting bee species abundance and richness along their Mexican elevation gradient. Further I like the additional at looking at correlated variables such as temperature, precipitation and floral availability. I had just a few minor comments

Line 29: You don’t talk about this in your paper so you can probably just remove this sentence

Line 45 & line 423: You make the statement throughout your paper that the area you sampled is one of the most diverse places globally. However, this is not the case for bee. Given that you paper is largely about bee diversity I find this statement odd. Can you restructure this claim in your manuscript? See Orr et 2020 Current Biology they talk about the distribution bee richness.

Line 101: generally, you see bees more abundant in lower elevations and flies more abundant in high elevations. Here you claim that bees and flies are equally abundant in low elevations however the three papers you cite, Arroyo, Lefebvre and McCabe all show low abundance of flies at lower elevations. Please correct this sentence in your manuscript

Methods: Can you add a sentence or two in your methods about where you deposited your pinned specimens and who ID these? It also looks like most of your specimens were only IDed down to morphospeices. Is this the lowest taxonomic resolution that you could get them down too?

Do you say what year you did this sampling? I don’t think I saw it in there?

Line 130: Why not put this in km since its such a high value in m?

Line 159: Were these plots all on the same side of the mountain? If not do you think this influence the variation between sites?

Line 168: was this an average between 1902 – 2011 or were you able to extract yearly data based on your sampling time? Either way please specify

Line 171: Was this the time frame that you would have expected to see the most bee activity? Not being from this area I am not sure what kind of bee activity you have during this sampling periods

Line 172: How did you have 3 stations in two rows? Was there an uneven sampling design or was this 2 rows with 3 stations in each row? Please specify

Line 194: Did this remove all your replications? Did you only combine your “seasons”? Could you instead do a mixed model where “season” was a random variable and keep your replications? I only think this is needed if you have an indication that you would get significance in abundance if you increased your samples size.

Line 318: Did you include singletons in your NMDS and PerMANOVA?

Lines 319: Have you thought about documenting the abundance differences for these two species? How do they change along the gradient? Is this driving your abundance trend? I think this should be discussed more

Line 433- 437: I think that the fact that you didn’t find changes in abundance is really interesting and should be discussed more. What do you think is causing this constant? Do you have really common generalist species that occur in all of your sites?

Line 456: Consider adding McCabe et al 2019 “Environmental filtering of body size and darker coloration in pollinator communities indicate thermal restrictions on bees, but not flies, at high elevations” PeerJ They show that in high elevation communities that size and darkness (largely due to temperature) is contributing to the change in pollinator communities along elevation gradients.

Line 482- 483: This sentence is confusing please reword.

Reviewer #2: Review of the manuscript entitled “Changes in the structure and composition of the ‘Mexical’ scrubland bee community along an altitudinal gradient” for PLOS ONE (PONE-D-20-34943).

Comments to the author(s)

In this manuscript the authors sampled bees in different elevations in mountains covered by the Mexical scrubland in Mexico to investigate their patterns of community structure and composition. They found that bee species richness declined with increasing elevation and that this relationship is mediated by temperature, while bee abundance did not follow any pattern, but was positively related to flower density. The bee species composition was influenced by elevation, temperature and flower density. In my opinion, the study is well designed, and the manuscript is well written. However, I have some suggestions that I think that need to be addressed to improve the manuscript quality.

Broad suggestions:

My first major suggestion is that I think the authors could focus their Discussion more on the main findings of their study. I thought it very interesting the contrasting results of bee species richness and abundance because although the lack of pattern against elevation for abundance is rare, as it was related to resources (flower density), it matches ecological theory. I was expecting a deeper discussion of these two patterns in the light of ecological theory and implications for the conservation of ecosystem processes and services. For example, the alterations in temperature will affect the bee species composition and richness, but it will be the alterations in plant communities (and consequently the alterations in flowers) that will most affect the quantity of functions and services that the bees would deliver (mediated by bee abundance). I think the authors could improve this part of discussion, linking it to the paragraph that starts in line 490.

My second suggestion is about the choice of analysing elevation separated in three categories. I understand the rationale and I am aware that the elevations of each plot form three groups but still, there is some variation. So, I think it would be possible to run linear regressions instead of comparing groups (in the same way the authors did for temperature, which is highly correlated to elevation, and there was enough variation). I do not think the way the authors did the analyses is wrong, but quite the opposite, because the statistical analyses were very well conducted, and I congratulate the authors for that. But I think it would be possible to improve the representation of the patterns by considering elevation as a continuous variable.

A third suggestion is to consider changing altitude to elevation throughout the text. Myself used to use altitude when talking about the elevation above sea level, until I understood that the correct form in English is elevation. Altitude is more suitable to meters above land, for example when in a plane.

Minor suggestions:

Line 31 – Change to “…highlight that altitude gradient negatively affects bee species…”

Line 32 – Bee abundance had no… with no comma

Lines 49 to 54 – This sentence is too long. Consider dividing into two.

Lines 59 to 60 – Develop more the ideas. How are the origins and ecology of the Mexical different from the lowlands? Explain it.

Line 61 – Change to “As the Mexical occupies relatively high elevations in mountain systems…”

Line 62 to 63 – The mexical is repeating here. Maybe change to “strong impact on this ecosystem.”

Line 82 – I think the authors could improve the link between this paragraph and the previous. I thought it was too abrupt.

Line 84 – On the other hand is repeated here. The previous sentence also started with “on the other hand”.

Line 85 – Insert a comma: “…populations will decline, potentially leading…”

Lines 95 to 102 – I thought this paragraph is out of the logical flow of the introduction, especially the part between lines 98 and 102. Consider rephrasing it.

Line 106 – Cut the s in “changes”.

Lines 108 to 109 - I think the end of this paragraph could be improved a lot. I would expect at least some hypothesis and prediction for example for objectives 2 and 3. The author could also explain what they were expecting to find that would help to “provide insights to predict how climate change may affect pollinator communities”.

Line 130 – Delete the word apart.

Line 176 – Brilliant could be replaced by shiny?

Line 190 – Change to “…totally comparable, which was our main concern.”

Line 197 to 198 – Was this species present in all plots? Include this information here.

Lines 207 to 209 – Change the verbs to the past.

Line 237 – There is something strange in this sentence: “…estimation, and obtained and adjusted-pseudoR2 in the…”

Line 238 – Change to “In all cases, we checked if models complied…”

Line 331 – Remove the word And before MAP.

Figures – All the figures seem to be with low resolution. I do not know if it was just because it is a first version for review, but I think it worth to look at it.

Figure 2 and 3 – I suggest removing the grey background to make a cleaner version of the graphs.

Lines 354 and 357 – The word resulted could be changed to remained?

Lines 396 to 399 – I did not understand this sentence. If geographic distance failed significance, why would it need to be controlled firstly?

Lines 399 to 401 – The way the sentence is written is strange. “Flower density failed significance explaining community composition.” In addition, what about climatic variables? Were they significant?

Lines 422 to 426 – I think the authors could start their discussion with the implications of the most important results. Although relevant, the fact that this was the first study on bees in the Mexical is not the most important part of this manuscript. The authors can use these sentences in the end of the first paragraph of discussion, but I suggest starting it with a general view of the implications of their results.

Line 427 – Change tendency to trend.

Lines 428 to 429 – I do not think the authors could say there is a trend while it is not statistically significant.

Line 434 – Explore better the contrast between your findings on the abundance patterns and the literature. In this sentence you just say your results disagree with other studies, but how and why? I think this whole paragraph could be improved with the suggestion I made above about discussing the general patterns and the contrast between richness and abundance.

Line 464 – Change find to found.

Lines 467 to 469 – This is one of the most intriguing result, so the authors could explore more the reasons why it happens.

Line 472 – Change work to works.

Line 485 to 489 – This inference is too abrupt. The authors should develop more these ideas. That climate change can be important in determining changes in the altitudinal distribution is something that we already know, but the authors have evidence to discuss how it will happen? Which ecosystem processes and functions will be impaired?

Lines 522 to 524 – I agree that functional ecology will play an important role in elucidating these patterns, but how this study contributed to this? For example, in this manuscript the authors found that richness is affected by temperature, but abundance is affected by resource availability. Future research, for example, should focus on understand which community parameter will be affected first and consequently will affect ecosystem processes. I suggest developing more these ideas based on what the authors found.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Jul 1;16(7):e0254072. doi: 10.1371/journal.pone.0254072.r002

Author response to Decision Letter 0

31 Mar 2021

Response to Reviewers

Reviewer #1:

Overall: The authors did a good job summarizing and documenting bee species abundance and richness along their Mexican elevation gradient. Further I like the additional at looking at correlated variables such as temperature, precipitation and floral availability. I had just a few minor comments

Authors: Thank you, we appreciate your comment.

Line 29: You don’t talk about this in your paper so you can probably just remove this sentence

Authors: We have removed this sentence in the new version of manuscript.

Line 45 & line 423: You make the statement throughout your paper that the area you sampled is one of the most diverse places globally. However, this is not the case for bee. Given that your paper is largely about bee diversity I find this statement odd. Can you restructure this claim in your manuscript? See Orr et 2020 Current Biology they talk about the distribution bee richness.

Authors: We have deleted these sentences referring to Mediterranean richness because they were referred specifically to vegetation species, and we agree with the referee that our main concern are bees in this paper.

Authors: We agree. Nevertheless, we have rewritten this final paragraph of Introduction as a Referee #2 suggestion, and we have deleted the sentence to which this suggestion of Referee #1 is referred to.

Methods: Can you add a sentence or two in your methods about where you deposited your pinned specimens and who ID these? It also looks like most of your specimens were only IDed down to morphospecies. Is this the lowest taxonomic resolution that you could get them down too?

Authors: We have added a sentence in the new version of the manuscript (see lines 197-198). All bees collected were curated and identified to genus-level by the authors in the Ecology Institute of Autonomous National University of Mexico (UNAM). Bees were identified authors using the ‘Bee Genera IDnature guide’ from DiscoverLife.org (Ascher and Pickering 2011) and ‘The Bee Genera of North and Central America’ (Michener et al. 1994). For most part of genera in our study area, there are no taxonomical revisions published. So, for species-level identification we would have needed to visit a reference collection from a locality relatively close to ours (Zapotitlán Salinas Valley, Puebla; ~130 km far away from our study zone), which is deposited at the Entomological Collection, Universidad de las Americas-Puebla, Cholula, Puebla, Mexico (Vergara & Ayala 2002). This collection has a total of 3487 specimens corresponding to 259 species. Although the Zapotitlán Salinas Valley is located in a more arid region and with lower altitudes than our study area, its relative proximity allows us to think that it may contain a good representation of the regional fauna and many of the species in this area may be common to our study area. Unfortunately, restrictions on mobility due to the covid-19 pandemic did not allow us to visit this collection, so we were forced to present bee identifications to morphospecies-level in our manuscript.

Do you say what year you did this sampling? I don’t think I saw it in there?

Authors: Yes, this information is mentioned in the first sentence of ‘Bee sampling’ paragraph (“We conducted 3 surveys (late September 2019, late October 2019, and late January 2020-early February 2020”) (see lines 176-177).

Line 130: Why not put this in km since it’s such a high value in m?

Authors: Done (line 133).

Line 159: Were these plots all on the same side of the mountain? If not do you think this influence the variation between sites?

Authors: Yes, all the plots were on the same side of the mountain. We carefully took this point into account in the plot selection process. We had pointed out this in the manuscript, as follows: ‘We choose plots with similar conditions of slope and aspect as far as possible’. (See lines 162-163).

Line 168: was this an average between 1902 – 2011 or were you able to extract yearly data based on your sampling time? Either way please specify

Authors: Data obtained from raster layers for GIS of ‘Climatic Atlas of Mexico’ were one value per plot corresponding to an average between 1902 and 2011. We did not obtain a value for our sampling time. We have rephrased this sentence to make this clearer, as follows: ‘We obtained these two variables from corresponding raster layers for GIS of ‘Climatic Atlas of Mexico’ [49]. We obtained Mean Annual Temperature (ºC) (MAT, henceforth) and Mean Annual Precipitation (mm) (MAP, henceforth) for each plot. These data represent an average from a series of 109 years recorded data for during years 1902 to 2011.’ (See lines 171-174).

Line 171: Was this the time frame that you would have expected to see the most bee activity? Not being from this area I am not sure what kind of bee activity you have during this sampling periods.

Authors: There is no study about bee phenology in our study zone. However, it exists a study about bee phenology in Zapotitlán Salinas (Vergara & Ayala 2002), a locality not far away to our study zone (~ 130 Km far), although with lower elevation and a more arid climate. Even considering this aridity differences, our study area climogram exhibit two peaks of precipitation in summer (June and September), which is a very similar pattern to that found in Zapotitlán Salinas climogram. In our latitudes, rain is the main trigger of flowering events, which are likely tracked by main events of pollinators abundance. As in Zapotitlán Salinas’ study most of the bee genera showed a very clear abundance peak in September, we assumed that a similar pattern could be expected in our study zone.

Line 172: How did you have 3 stations in two rows? Was there an uneven sampling design or was this 2 rows with 3 stations in each row? Please specify

Authors: We had 2 rows with 3 stations in each row (for a total of 6 stations per plot and survey). We have added a short phrase in the new version of the manuscript to clarify this point, as follows: ‘In each survey and plot, we placed 6 sampling stations distributed in two parallel rows (3 stations in each of the two rows)’. (See lines 177-178).

Authors: We only lumped our “seasons”, which did not were our replications. Our replications were the plots (n=19, 6 for ‘low-elevation category, 6 for ‘mid’ and 7 for ‘high’). Although we had no clear indication that we would get significance in bee abundance increasing sample size (keeping seasons without lumping them together), we conducted the analysis that reviewer suggested us. The result was similar, bee abundance failed significance (Linear Mixed Model with ‘Elevation’ as a continuous variable: F1,53=0.46, p=0.5; Linear Mixed Model with ‘Elevation’ as a categorical variable: F2,52=0.72, p=0.49; in both cases after controlling for spatial autocorrelation).

Line 318: Did you include singletons in your NMDS and PerMANOVA?

Authors: We conducted four versions of NMDS, PerMANOVA and dbRDA analyses: with all individuals (including singletons), without singletons, without singletons and doubletons, and a qualitative version (with presence/absence matrix). Results were almost identical (only qualitative version showed some little differences). We show analysis with all individuals (including singletons) in the main manuscript text, and we included the three remaining versions as ‘Supporting information’ (S4 Appendix, S5 Appendix, S6 Appendix). This is described in the lines 291-294 and 315-318 of ‘Material and Methods’.

Authors: In the new version of the manuscript, at this point of community description, we have included a third most abundance species (Lasioglossum (Lasioglossum) sp1) that was not mentioned in the first version of the manuscript. Now this sentence is as follows: ‘Macrotera sp1 was the most abundant species (29.3% of total specimens), followed by Lasioglossum (Dialictus) sp1 (18.6%), and Lasioglossum (Lasioglossum) sp1 (16.4%). These three species together constitute almost two thirds of the collected individuals’. (See lines 329-331).

With regard to the suggestion of the reviewer, we have conducted some exploratory analyses to take a look in the abundance of each one of this three most abundant species along the elevational gradient. We found that each species showed a different trend: Macrotera sp1 tend to increase its abundance towards higher altitudes (Linear model (LM) with Elevation as a categorical variable: F=10.8, p=0.001, R2=0.52; LM with Elevation as continuous variable: F=15.33, p=0.0011, R2=0.44, in both cases abundance log10-transformed); Lasioglossum (Dialictus) sp1 showed no trend (LM with Elevation as categorical variable: F=0.35, p=0.71, R2=-0.07; LM with Elevation as continuous variable: F=0.028, p=0.87, R2=-0.057), and Lasioglossum (Lasioglossum) sp1 showed a hump-shaped trend (LM with Elevation as categorical variable: F=3.68, p=0.048, R2=0.23; LM with Elevation as continuous variable: Elevation: F=0.02, p=0.88, Elevation2: F=10.14, p=0.006, R2=0.31, in both cases abundance log10-transformed).

(Caution: see plots in the word document attached "Response to Reviewers")

These three trends combined could, effectively, determine the lack of a clear trend in the global analysis ‘Bee abundance vs Elevation’. However, although these three most abundant species could be contributing to mask any possible pattern, it seems that they are not determinant in the global pattern found, as the lack of pattern remains even when we exclude the most abundant species (Macrotera sp1: LM with Elevation as categorical variable: F=1.65, p=0.22, R2=0.067; LM with Elevation as continuous variable: Elevation: F=0.8, p=0.38; Elevation2: F=2.85, p=0.11; R2=0.084); or the three most abundant species together from analyses (LM with Elevation as categorical variable: F=0.82, p=0.46, R2=-0.02; LM with Elevation as continuous variable: F=1.66, p=0.21, R2=0.035, in both cases abundance log10-transformed).

We have included a paragraph at the ‘Discusion’ in the new version of the manuscript, making reference to these results (see lines 473-482), and we also have included a new document of Supporting Information with the detailed results and plots (see S7 Appendix).

Authors: We agree with the reviewer. Thanks. We have included a paragraph in the new version of the manuscript discussing this point (see lines 483-499). We think that, as bee abundance is positively correlated with flower abundance, the fact that we found no pattern in flower abundance along the elevational gradient may explain the lack of pattern in bee abundance along our elevational gradient. We also think that, the lack of pattern in flower abundance in our study is due to the low latitude of our study area (~17ºN), which make that the climatic conditions, even at the highest sites, are not extreme enough to affect flower density. Similar results have been described in previous low-latitudes studies (Classen et al. 2015).

Unfortunately, we have no information about generalization/specialization habits of the bee species we found, so we are not able to respond the last question.

Authors: We have added this new reference (line 459). Thanks.

Line 482- 483: This sentence is confusing please reword.

Authors: We have rewritten this sentence in the new version of manuscript (see lines 531-532).

--------------

Comments to the author(s):

Authors: Thank you for your comments. We appreciate them.

Broad suggestions:

Authors: We agree with the reviewer, thanks. We have included a paragraph in the new version of the manuscript where we discuss a possible explanation (see lines 483-522 in the new version of the manuscript).

We think that this result is probably related to a differential effect of climatic conditions on bee richness and abundance along our elevational gradient. Our results suggest that temperature is restrictive enough in our elevational gradient to negatively affect bee richness, resulting in a decreasing trend of bee richness with increasing elevations (and lower temperatures). In the case of bee abundance, we found no pattern along the elevational gradient, but a positive relationship with flower density. Flower density may also be negatively affected by low temperature, but temperature seems to be not restrictive enough in our study for plants, as we found no pattern in flower density along the elevational gradient. Consequently, the lack of pattern in flower density may explain the lack of pattern in bee abundance along our elevational gradient. We think that this could be related to the low latitude of our study area (~17ºN), determining that the climatic conditions, even at the highest sites, are not extreme enough to affect flower density. Similar results have been described in previous low-latitudes studies (Classen et al. 2015).

We also discuss how climate change, and specially increasing temperatures, may determine alterations in flower abundance which, in turn, may determine possible effects for bee abundance and pollination services.

Authors: We run linear regressions considering elevation as a continuous variable as the reviewer suggested, but the results were almost identically to those comparing groups (we display the results and plots in the next pages). We also conducted multivariate analyses (PERMANOVA and dbRDA) using elevation as a continuous variable. Again results were very similar, in all cases elevation resulted significant (and also one of the ‘pcnm’ vectors representing geographic distance now resulted significant), but amount of variability (R2) explained by elevation was lower than using elevation as categorical variable. As a disadvantage of using Elevation as a continuous variable, in the NMDS plot, it is not possible to represent groups of elevation, so this visualization of results would lose clarity. We consider that, taking into account that results were qualitatively equivalent using elevation as a continuous variable, we would prefer to maintain analyses comparing groups of elevation, as the study was designed for it (but we have added these results using Elevation as a continuous variable as a Supplementary Information, see S8 Figures and Tables). However, if the editor and the reviewer consider that it is preferable presenting analyses with elevation as a continuous variable, we have no inconvenient in changing it.

At the moment, we have shown this results (using Elevation as a continuous variable) as a Supplementary material (see S8 Appendix), and we have made reference to that in the main text of the new version of the manuscript (see lines 164-166 and 319-321).

(Caution: see Fig.2 and tables in attached word document "Response to Reviewers)

New results for linear model analyses:

Table 1. Best GLS models for different response variables vs Elevation (as a continuous variable).

GLS model parameters

GLS Model F p-value pseudo-R2

Bee species richness ~ Elevation 15.44 0.0011 0.48

Bee abundance* ~ Elevation 1.57 0.226 0.08

MAT ~ Elevation 806.14 <0.0001 0.98

MAP ~ Elevation + Elevation2 Elevation 45.40 <0.0001 0.85

Elevation2 43.27 <0.0001

Flower species richness ~ Elevation 17.13 0.0007 0.51

Flower density ~ Elevation 1.87 0.19 0.10

(*log10-transformed)

New results for multivariate analyses (Community composition vs Elevation):

Table 3. Community composition vs Elevation (as a continuous variable), geographical distance, climatic variables and flower variables. (Complete quantitative matrix)

3.A. Considering Elevation as explanatory variable (continuous)

PERMANOVA

variable Df Sum of Squares R2 F P(>F)

Elevation 1 0.692 0.226 4.991 0.002

Residual 17 2.357 0.773

Total 18 3.050 1

dbRDA (controlling for geographic distance)

variable Df Sum of Squares R2 F P(>F)

pcnm1 1 0.277 0.09 2.341 0.045

pcnm6 1 0.299 0.10 2.526 0.031

Elevation 1 0.255 0.08 2.152 0.044

Residual 15 1.77 0.58

Total 18 3.05 1

New results for multivariate Analyses included as Supporting Information in the former version of manuscript:

S4 Table. Community composition vs Elevation, geographical distance, climatic variables and flower variables. (Quantitative matrix excluding singletons).

S4.A. Considering Elevation as explanatory variable (continuous)

PERMANOVA

variable Df Sum of Squares R2 F P(>F)

Elevation 1 0.696 0.233 5.173 0.001

Residual 17 2.289 0.766

Total 18 2.985 1

dbRDA (controlling for geographic distance)

variable Df Sum of Squares R2 F P(>F)

pcnm1 1 0.273 0.091 2.391 0.036

pcnm6 1 0.298 0.100 2.619 0.030

Elevation 1 0.250 0.084 2.197 0.047

Residual 15 1.711 0.573

Total 18 2.985 1

S5 Table. Community composition vs Elevation, geographical distance, climatic variables and flower variables. (Quantitative matrix excluding singletons and doubletons)

S5.A. Considering Elevation as explanatory variable (continuous)

PERMANOVA

variable Df Sum of Squares R2 F P(>F)

Elevation 1 0.688 0.235 5.244 0.002

Residual 17 2.233 0.764

Total 18 2.922 1

dbRDA (controlling for geographic distance)

variable Df Sum of Squares R2 F P(>F)

pcnm1 1 0.265 0.090 2.405 0.044

pcnm6 1 0.305 0.104 2.765 0.028

Elevation 1 0.244 0.083 2.212 0.040

Residual 15 1.656 0.566

Total 18 2.922 1

S6 Table. Community composition vs Elevation, geographical distance, climatic variables and flower variables. (Qualitative (binary) matrix)

S6.A. Considering Elevation as explanatory variable (continuous)

PERMANOVA

variable Df Sum of Squares R2 F P(>F)

Elevation 1 0.654 0.153 3.087 0.001

Residual 17 3.601 0.846

Total 18 4.255 1

dbRDA (controlling for geographic distance)

variable Df Sum of Squares R2 F P(>F)

pcnm1 1 0.264 0.062 1.304 0.171

pcnm5 1 0.298 0.070 1.468 0.079

Elevation 1 0.338 0.079 1.668 0.034

Residual 15 3.044 0.715

Total 18 4.255 1

Authors: We have changed ‘altitude’ to ‘elevation’ throughout the manuscript.

Minor suggestions:

Line 31 – Change to “…highlight that altitude gradient negatively affects bee species…”

Authors: Done (lines 29-30).

Line 32 – Bee abundance had no… with no comma

Authors: Done (line 31).

Lines 49 to 54 – This sentence is too long. Consider dividing into two.

Authors: Done (line 48).

Lines 59 to 60 – Develop more the ideas. How are the origins and ecology of the Mexical different from the lowlands? Explain it.

Authors: Mexical has an evolutionary origin and ecological traits that are much closer to other Mediterranean ecosystems, than to the low deciduous forest and derived srublands, located a few meters below in the same mountain. In fact, Mexical biome represents the same vegetation associated to Mediterranean climates, sharing common plant genera, and common traits (such as evergreen-esclerophyllous leaves, leaf angle, and the ability to resprout). So, Mexical scrubland could be considered a relict of the evergreen-sclerophylous Mediterranean-like vegetation that originally existed in a belt around North America and Eurasia during the mid-Eocene, from which the other five Mediterrean-type ecosystems existing nowadays also evolved [Axelrod 1958, 1975]. During the transition from warm and wet Tertiary to dry Quaternary, Mediterranean climate appeared and surviving taxa seek refuge in today’s current Mediterranean areas, whereas Mexical, which did not experienced this climatic transition, refuged along the principal mountain chains of Mexico, between ~1850 to 2500 meters above sea level (m asl) (Valiente-Banuet & Verdú 2020). This would explain all the ecological traits and plant genera that the Mexical shares with the species of these other Mediterranean areas (Valiente-Banuet & Verdú 2020). Contrarily, deciduous forest and derived srublands (spiny scrublands, for instance), located at lower strata of the mountains (around 1800 m a.s.l. and below), belong to a different botanical lineage (neotropical geoflora), and have different ecological traits (Rzedowski 1978).

We have included a couple of sentences explaining this in the new version of the manuscript (see lines 58-62).

Line 61 – Change to “As the Mexical occupies relatively high elevations in mountain systems…”

Authors: Done (line 63).

Line 62 to 63 – The mexical is repeating here. Maybe change to “strong impact on this ecosystem.”

Authors: Done (line 64-65).

Line 82 – I think the authors could improve the link between this paragraph and the previous. I thought it was too abrupt.

Authors: We agree with the reviewer, thanks. We have modified this link between paragraphs (see line 83).

Line 84 – On the other hand is repeated here. The previous sentence also started with “on the other hand”.

Authors: Done (We have changed ‘On the one hand’ in the first sentence to ‘For instance’) (line 84).

Line 85 – Insert a comma: “…populations will decline, potentially leading…”

Authors: Done (line 87).

Lines 95 to 102 – I thought this paragraph is out of the logical flow of the introduction, especially the part between lines 98 and 102. Consider rephrasing it.

Authors: We have rewritten and relocated this paragraph in the new paragraph at the final part of Introduction, as a part of revised literature in which we base our expectations for objectives 2 and 3 (see lines 103-110).

Line 106 – Cut the s in “changes”.

Authors: Done (line 100).

Authors: We have rewritten this final paragraph of the Introduction, introducing some expectations for objectives 2 and 3, based on revised literature (see lines 103-110).

Line 130 – Delete the word apart.

Authors: Done (line 133).

Line 176 – Brilliant could be replaced by shiny?

Authors: We have changed “brilliant” to “bright” following some literature* about pan traps (line 181).

(*see, for instance: ‘The Utility of Aerial Pan-Trapping for Assessing Insect Pollinators Across Vertical Strata’. Nuttman et al. 2011).

Line 190 – Change to “…totally comparable, which was our main concern.”

Authors: Done (line 195).

Line 197 to 198 – Was this species present in all plots? Include this information here.

Authors: Yes, this species was present in all plots. We have included this information in the new version in the manuscript (see line 205).

Lines 207 to 209 – Change the verbs to the past.

Authors: Done (see lines 214 and 215).

Line 237 – There is something strange in this sentence: “…estimation, and obtained and adjusted-pseudoR2 in the…”

Authors: We have rewritten this sentence in the new version of the manuscript (see lines 244-245).

Line 238 – Change to “In all cases, we checked if models complied…”

Authors: Done (line 245).

Line 331 – Remove the word And before MAP.

Authors: Done (line 342).

Figures – All the figures seem to be with low resolution. I do not know if it was just because it is a first version for review, but I think it worth to look at it.

Authors: The figures were checked to accomplish quality standards of the journal, using the software (‘PACE’) provided by the journal itself in its web page. In my screen, the figures look fine, but, may be, they do not have resolution enough. I would like the journal could confirm to me that figures are good enough, please.

Figure 2 and 3 – I suggest removing the grey background to make a cleaner version of the graphs.

Authors: Done.

Lines 354 and 357 – The word resulted could be changed to remained?

Authors: We prefer to maintain the word ‘resulted’, because ‘remained’ seems to make reference to some other variable that in a former analysis was significant, but, after a following analyses was not significant any more, but this is not the case. In our analyses, first we obtained a best model following AICc criteria (but no significance values), and in the second analytical step, we obtained significance of the variables included in the best AICc model (lines 365 and 368 in the new version of the manuscript).

Lines 396 to 399 – I did not understand this sentence. If geographic distance failed significance, why would it need to be controlled firstly?

Authors: In this case, the first analytical step to decide if we need to taking into account geographic distance was a Mantel test (Community composition dissimilarities ~ Geographic distance), which resulted significant. So, geographical distance seems to be playing a role in the composition differences, and we need to taking into account it in the next analytical step, which is the dbRDA (or PERMANOVA), where we consider if there are a relationship between changes in composition and elevation, controlling for geographic distance. When we conduct this second analytical step (dbRDA), considering the model: Composition ~ Elevation + Geographical distance, is when we obtain the result that geographical distance is not significant, when we consider Elevation variable in the same model. It is likely that geographical distance is not significant in these second analyses because Elevation is explaining a more significant part of composition variability.

In the first version of this manuscript we only specify the result of Mantel test for the complete quantitative version of our compositional matrix which was the one described in main text, but we did not give the Mantel test for each of the three other complementary analyses that we gave as supporting information (without singletons, without singletons and doubletons, and with a qualitative compositional matrix). In the new version of the manuscript, we give the result of Mantel test for each one of these complementary analyses (see Supporting information S4 Appendix, S5 Appendix and S6 Appendix).

Authors: We have rewritten this sentence (only temperature was significant). See lines 410-411.

Authors: We agree. We have rewritten the first paragraph of the Discussion including a summary of the most important results and relate it to broad implications in climate change scenario. See lines 433-440.

Line 427 – Change tendency to trend.

Authors: Done (line 446).

Lines 428 to 429 – I do not think the authors could say there is a trend while it is not statistically significant.

Authors: We agree. We have deleted this mention of a ‘trend’ (see line 466).

Authors: We have added a new sentence to explain this abundance pattern in relation with literature (see lines 466-468). We discuss the contrast between richness and abundance a little later (see lines 483-499).

Line 464 – Change find to found.

Authors: Done (line 469).

Lines 467 to 469 – This is one of the most intriguing result, so the authors could explore more the reasons why it happens.

Authors: We have rewritten this part of manuscript. Now we discuss this point at lines 469-472 and 483-499.

Line 472 – Change work to works.

Authors: Done (line 525).

Authors: We have rewriting this paragraph in the new version on the manuscript (see lines 534-554). We now make reference to how raising temperatures could affect bee communities, especially causing upwards migrations of species that in the present are occupying mid and lower elevations. We also refer to how changes in climatic conditions could affect flowering plants-bee relationships, causing possible mismatches in their relative position along the elevational gradient, which could determine affectations in pollination function.

Authors: We agree. We have deleted this reference to functional ecology. Following reviewer suggestions, we have rewriting this final paragraph in the new version of the manuscript, making reference to our bee richness and abundance, and flower abundance results, and how they could be affected by increasing temperatures in a climate change scenario (see lines 556-572).

PLoS One. doi: 10.1371/journal.pone.0254072.r003

Decision Letter 1

François Rigal

9 May 2021

PONE-D-20-34943R1

Changes in the structure and composition of the ‘Mexical’ scrubland bee community along an elevational gradient

PLOS ONE

Dear Dr. Sergio Osorio-Canadas,

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 yet, although these minor corrections should be very easy to handle. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised by reviewer #2.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: I have no further comments. I believe that the authors have addressed my original comments thoroughly.

Comments to the author(s)

I congratulate the authors for their effort in addressing the comments and suggestions made by me and the other reviewer. In my opinion, the authors solved most of the issues and explained the remaining problems. I have only very minor suggestions to help polishing the manuscript.

I understood the authors’ explanation for why to maintain the categorical approach in the manuscript. However, in my opinion, the patterns are much clearer to see with the scatter plots and regression curves, specially because we can see the spread of data, which is not possible to visualise with the current plots. The authors could maintain the Figure 4 with the NMDS using the categories just for visualisation purposes and explain that in Methods. Hence, my suggestion is to invert the graphs showed in the manuscript and S8 Appendix, i.e., moving the categorical approach to supplementary material and bringing the continuous approach to the main manuscript. Again, this is just a suggestion and I believe that the decision of presenting the results of continuous or categorical elevation in the main manuscript is up to the authors.

Line 160 – Change to “Hence, our elevation categories…”

Line 245 – Change to “For each model, we also obtained an adjusted-pseudo R²…”

Line 248 – Change ‘explicative’ to ‘explanatory’.

Line 256 – Change to “…response variables i.e., bee species richness and bee abundance, for each one of these two bee…”

Line 452 – Change ‘MAT’ to ‘mean annual temperature’.

Lines 474 to 483 – I think this paragraph could come after the next one (from lines 484 to 500). In addition, I suggest the authors including one or two sentences in Methods’ section to explain why and how they did these analyses.

**********

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Reviewer #1: No

Reviewer #2: No

PLoS One. 2021 Jul 1;16(7):e0254072. doi: 10.1371/journal.pone.0254072.r004

Author response to Decision Letter 1

17 May 2021

Response to Reviewers

Reviewer #1

Reviewer #1: I have no further comments. I believe that the authors have addressed my original comments thoroughly.

Authors: Thank you for your comments. We appreciate them.

Reviewer #2

Comments to the author(s)

Authors: Thank you for your comments. We appreciate them.

Authors: We have followed the suggestion of the reviewer, and we have included all analyses results and plots considering elevation as continuous variable in the main manuscript, and we have moved all the analyses results and plots considering elevation as categorical variable to supporting information (S5 Appendix in this new version). Further, in the new version, we have reorganized data contained in other Appendixes: we have included all results for multivariate complementary analyses considering elevation as a continuous variable in only one Appendix (information included in S4, S5 and S6 Appendixes in previous version, is now included in the new version in only one Appendix: S4 Appendix). That’s why we have only 6 Appendixes in this new version (instead of 8 Appendixes in the previous version).

Also, as suggested by the reviewer, we have maintained the Figure 4 (NMDS) using variable elevation as three categories for visualization purposes, and we have explained that in ‘Methods’ (see lines 294-297).

Line 160 – Change to “Hence, our elevation categories…”

Authors: This sentence has been rewritten in the new version on the manuscript (see lines 158-172).

Line 245 – Change to “For each model, we also obtained an adjusted-pseudo R²…”

Authors: Done. Thank you. (see lines 249-250).

Line 248 – Change ‘explicative’ to ‘explanatory’.

Authors: Done. Thank you. (see line 262).

Line 256 – Change to “…response variables i.e., bee species richness and bee abundance, for each one of these two bee…”

Authors: Done. Thank you. (see line 270).

Line 452 – Change ‘MAT’ to ‘mean annual temperature’.

Authors: Done. Thank you. (see line 483).

Authors: We have relocated the first paragraph (lines 474 to 483 in previous version) after the next one (lines 484 to 500 in previous version), following reviewer’s suggestion (see now lines 523-532 in the new version of the manuscript).

As the reviewer suggests, we also have included a couple of sentences in Methods’ section to explain why and how we did these analyses (see lines 252-261). Details for these analyses and results for these models are shown in the S6 Appendix.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(23.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0254072.r005

Decision Letter 2

François Rigal

21 Jun 2021

Changes in the structure and composition of the ‘Mexical’ scrubland bee community along an elevational gradient

PONE-D-20-34943R2

Dear Dr. Sergio Osorio-Canadas,

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

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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François Rigal

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Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0254072.r006

Acceptance letter

François Rigal

23 Jun 2021

PONE-D-20-34943R2

Changes in the structure and composition of the ‘Mexical’ scrubland bee community along an elevational gradient

Dear Dr. Osorio-Canadas:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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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 Appendix. General database.

(XLSX)

Click here for additional data file.^{(11.6KB, xlsx)}

S2 Appendix. Correlations among all response and explanatory variables.

(DOCX)

Click here for additional data file.^{(52.9KB, docx)}

S3 Appendix. List of species and/or morphospecies.

(DOCX)

Click here for additional data file.^{(21.9KB, docx)}

S4 Appendix. Mantel test, PERMANOVA and dbRDA analyses for quantitative data excluding sigletons.

excluding sigletons and doubletons, and for binary data (considering elevation as a continuous variable).

(DOCX)

Click here for additional data file.^{(35.3KB, docx)}

S5 Appendix. Results of all analyses considering elevation as a categorical variable.

(DOCX)

Click here for additional data file.^{(137.8KB, docx)}

S6 Appendix. Abundance of the three most abundant species in our Mexical community vs elevation, and abundance of bees excluding most abundant species vs elevation.

(DOCX)

Click here for additional data file.^{(170.5KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(23.9KB, docx)}

Data Availability Statement

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

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PERMALINK

Changes in the structure and composition of the ‘Mexical’ scrubland bee community along an elevational gradient

Sergio Osorio-Canadas

Noé Flores-Hernández

Tania Sánchez-Ortiz

Alfonso Valiente-Banuet

Roles

Abstract

Introduction

Materials and methods

Ethics statement

Study area

Fig 1. Study area map.

Variable elevation

Climatic variables

Bee sampling

Flower resources

Statistical analysis

Bee community structure, flower community structure and climate vs elevation

Bee community structure vs explanatory variables (flower and climate variables)

Community composition vs elevation analyses

Results

General results

Bee community structure, flower and climate variables vs elevation

Table 1. Best GLS models for different response variables vs elevation.

Fig 2. Effects of elevation.

Bee community structure vs flower and climate explanatory variables

Table 2. Results from model selection relating bee response variables vs climatic and flower variables.

Fig 3. Best GLS models relating bee response variables vs climatic and flower variables.

Community composition vs elevation and flower and climate explanatory variables

Table 3. Community composition vs elevation, geographical distance, climatic variables and flower variables.

Fig 4. Community composition vs elevation.

Discussion

Bee richness and abundance

Bee community composition

Concluding remarks

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

François Rigal

Roles

Author response to Decision Letter 0

Decision Letter 1

François Rigal

Roles

Author response to Decision Letter 1

Decision Letter 2

François Rigal

Roles

Acceptance letter

François Rigal

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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