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    Responses of small mammals to habitat characteristics in Southern Carpathian forests

    We surveyed small mammal communities in a montane area along the elevational gradient in relation to habitat characteristics and human impact, this study being the first to assess habitat use by small mammals in the Southern Carpathians.Compared to a similar study conducted in the Eastern Tatra Mountains31, the species richness (12 species captured) was lower in our survey; part of the reason could be that the North Carpathian endemic Microtus tatricus and the boreal species Sicista betulina are absent in our study area, which is beyond the limits of their geographical distribution. Species composition of small mammals was overall comparable to those reported for forested areas of Northern Carpathians17,32,33, although a high variability, both spatial and temporal, in the number and abundance of species characterized all surveyed communities. Although A. flavicollis was seldom captured in 2003 and 2005 and only at low elevations23, overall it, together with M. glareolus, dominated the small mammal community, representing over 75% of the captured individuals (Table 1). This is the common pattern of small mammal communities in temperate zones, i.e., to be dominated by two species, usually rodents34,35,36. M. glareolus and A. flavicollis are the dominant species in most forests of central and eastern Europe32,33,37,38, with one or the other being more numerous depending on habitat conditions and geographic position35. M. glareolus and A. flavicollis were also found to remain dominant in small-sized clearings39.Box-trapping results for shrews are often considered underestimates because of their small size40 and because seed baits are not attractive to them41. However, during our survey S. araneus had wider distribution than A. flavicollis; we captured it in low numbers in a large number of trapping sites, having the highest ratio between occurrence (45.2%) and relative abundance (16%) of all small mammal species (Table 1). S. araneus was higher in abundance in our research area in comparison to both natural and planted montane forests in Northern Carpathians17,32,33, possibly as an effect of the long-term conservation practices in the national park.Besides the three dominant species and S. minutus, all the other captured species are of regional conservation interest, being included in the Red Book of Vertebrates from Romania42, which highlights the conservational value of this landscape.Small mammals showed significant responses to habitat characteristics at population and community levels, regardless of the metrics considered. Tree cover was an important predictor for small mammal communities (Table 2, Table 3). Increased tree cover limits light available for understory plants, reducing habitat structure43, hence the usually negative correlation between canopy cover and both shrub and herbaceous cover. The reduced vegetation complexity of closed-canopy forests may limit resources important to small mammals. Most studies show that forests with a greater percentage of tree cover harbour less abundant small mammal communities44. In the Sierra Nevada mountains in North America, small mammals showed a limited response to canopy thinning, reflecting the generalist habits of the common species in those forests, which may be a legacy of more than a century of human impacts generating a process of biotic homogenization via differential success of some native species over the others45. In Europe, there is a legacy of much longer human impacts, thus common forest species should have even more generalist habits. However, in our research area tree cover was positively correlated with all parameters, except for the abundance of A. flavicollis, which did not significantly respond to it (Table 2). The small mammal fauna in our study area is a primarily forest fauna, with dominant species responding negatively to the decrease in tree canopy cover, even when this means an increase in the understory cover and complexity. The response to tree cover was strongest in M. glareolus (Fig. 2a, Fig. 3). In boreal forests of Scandinavia tall vegetation and structural heterogeneity of trapping stations positively influenced the total abundance of this species15. This may mean that there is an important geographic variability in the ecological behavior of M. glareolus. There are differences in the habitat preferences not only along the latitudinal gradient15,35,46,47 but also on elevation. At the foothills of Southern Carpathians M. glareolus is limited mainly to forest edges and riparian forests with tall hygrophilous vegetation48. During this study we did not find a significant effect of the interaction between elevation and tree cover, probably because of the relatively short elevational gradient (of 1200 m), which did not include lowland forests outside the ecological optimum of M. glareolus. The short gradient may also explain the lack of response by M. glareolus, both as absolute and relative abundance (Fig. 3) to elevation itself, although this species is known to increase in density towards the north and at higher elevations35.Although shrub cover is an important element of vegetation structure, and one which increases its complexity, it had a significant effect only on the abundance of A. flavicollis. In opposition to our expectations, we found increased abundances of A. flavicollis in forests with little or no shrub layer (Table 2). In forests, shrubs may serve as shelter for mice against physical disturbances such as soil compaction, trampling or rooting49, although some studies failed to find evidence for this50. A positive effect of cover and height of shrub layer was also found on the abundance of A. flavicollis in the Northern Carpathians in forest clearings51. However, besides the positive effects of greater vegetation complexity and increased availability of food and shelter resources, the shrub layer also reduces visibility and hinders rapid movement, so that mobile species such as mice, which rely on running rather than hiding to escape predation, are exposed to higher predation risk in habitats with dense undergrowth.The feature related to habitat heterogeneity to which small mammals responded positively in our study area was the abundance of rocks (Fig. 2a, Table 2). Rocky outcrops and large boulders are stable elements of the landscape that enhance the availability of shelters and refuges providing hard protection for nest sites50. Some species that do not burrow are dependent on rocks for shelter, occurring only in rocky sites. Among these is C. nivalis, but the small number of captured individuals did not allow testing its habitat use.Unlike rocks, woody debris is more ephemeral, and apparently it was less valued as a shelter resource (Table 3). Many studies show the importance of coarse woody debris as a quantitative habitat feature for forest small mammals44; their value increases in the late decay stages52. Woody debris in mid-to-late decay state is often a suitable substrate for lichen and fungi, and can support a rich insect fauna53, all potential foods for omnivorous rodents and shrews. In our research area the sites with the largest amounts of coarse woody debris were those recently logged, so availability of food resources for small mammals was not optimal.Soil moisture, which has a very strong effect on the primary productivity and vegetation diversity, may also have an important role in the habitat selection, with various effects on small mammal populations. In our study area the two dominant rodents had opposite responses to soil moisture, with M. glareolus showing a strong preference for dry habitats (Fig. 3, Table 2), in contrast to its response to moisture in other parts of its distribution. At the southern limit of its geographical distribution35 or at the limit of its elevational distribution48, M. glareolus is usually confined to damp habitats, but there it does not develop abundant populations, with Apodemus species usually dominating the small mammal community. In the northern part of its distribution, where Apodemus species are absent, M. glareolus also shows a preference for moist woodlands54. We may thus infer that the response of M. glareolus to soil moisture is modulated by the interaction with mice species, in our case A. flavicollis. This conjecture is also supported by the fact that moisture did not significantly affect community abundance, only species composition (Table 3). Other studies have also reported conflicting results of the role of soil moisture for A. flavicollis. For example, it was one of the most important factors influencing population dynamics of A. flavicollis in a beech forest in northern Germany55 but it did not predict its distribution in Britain56.Sites closer to watercourses are damper, so an overlap of the effect of the two variables—moisture and distance to water—would be expected. However, the significant negative effect of distance to water on the abundance of A. flavicollis also had a component that was independent of soil moisture (Table 2), and this may have a spatial significance. The increased abundance of A. flavicollis in sites close to watercourses could be explained by a potential fence effect that these may exert on small mammal populations. River banks are linear habitats bordered on one side by a physical barrier, more or less penetrable depending on the local habitat morphology. Linear habitats with favourable conditions sometimes shelter rodent populations at densities much higher than those in wide habitats, although the underlying mechanism, involving probably territoriality and dispersal, is not yet understood57. In our research area, river banks were important for A. flavicollis especially in low abundance years, when we captured this species exclusively here and only at low elevations, suggesting that besides a source of habitat heterogeneity watercourses may be involved also in the spatial dynamics of populations, with their banks being used as routes for dispersal.Neither species richness nor species abundance changed along the elevational gradient in our research area when also considering yearly fluctuations and habitat characteristics (Table 2), and our result is in contradiction with the pattern frequently described for mountains worldwide58,59, including the Eastern Tatras31, which shows a reduced species richness with the increase in elevation. But on the other hand, we found species composition to be affected by elevation, with A. flavicollis responding negatively and S. araneus positively. The thermophilous character of A. flavicollis is more evident in the Northern Carpathians, where this species was found only up to 1328 m, well below the timberline31. But as latitude compensates for elevation, at least in part, in our research area A. flavicollis was found along the entire elevational gradient, up to above 2000 m (Table 1), beyond the timberline, in the subalpine shrubs, perhaps as a result of its lack of preference for the tree cover. S. araneus had a similarly wide elevational distribution and, unlike A. flavicollis, it was captured at high elevations also in low abundance years23. This result supports the classification of S. araneus as a habitat generalist. In contrast to these species, M. glareolus was only once captured in the shrubs beyond the timberline, suggesting that in our study area this vole avoids habitats with no tree layer. This may also be because the subalpine sites that we surveyed were heterogenous, with relatively small patches of shrubs separated by open meadows, areas avoided by M. glareolus.Logging is the main human activity causing disturbance of forests. In our study area only selective logging was recent, while older clearcuts were already reforested. The overall impact was negative and significant on species richness and total abundance, as well as on the abundance of S. araneus. The sensitivity of S. araneus to logging may be one cause of its increased abundance at higher elevations, as in the study area recent timber exploitation was concentrated at low elevations (mostly in mixed forests). Although we did not find a significant response of M. glareolus to logging, other studies revealed that this species is influenced by habitat alterations caused by logging15 but also by the inter- and intraspecific competition, which is considered by some investigators to be the main mechanism causing the decline of vole populations in harvested forests60. We learned that timber exploitation caused a drastic reduction of the small mammal populations in the disturbed area, to the point where no animal was captured during a trapping session, with the neighbouring habitats being also affected. However, since habitat changes were not substantial, timber extraction had a relatively short time impact on the small mammals, and the year following logging the community structure resembled that of undisturbed areas. This suggests that selective logging with the extraction of a relatively small amount of timber affects small mammals rather by direct disturbance than by changes in habitat characteristics. The influence of logging on species of conservation interest, such as the mostly arboreal M. avellanarius and the rare S. alpinus, still needs to be evaluated. The main effect of logging is the decrease in canopy cover or its complete removal in case of clearcuts. But there are also other effects, such as degradation of shrub and herbaceous layers, soil compaction and erosion, and also direct disturbance involving presence of humans and sometimes domestic animals (in the research area logged trees were removed by horses and watch dogs usually roamed the logged forest patches and their surroundings), noise and soil vibrations. Following reduction of canopy cover, improvement in light conditions cause development of understory and decrease of soil moisture, affecting the abundance and composition of animal communities. Most studies on the influence of forest management on small mammals in Europe have focused mainly on clearcutting, one of the most common methods of forest harvest, and have revealed a positive effect on most analyzed small mammals, which can be attributed to an increase in forb and grass cover in the harvested areas61. In managed forest in Czech Republic it was found that the practice of felling within relatively small-sized clearings may help preserve the diversity of small mammal community39. However, the observed positive effect of clearcuts may be a biased result caused by the fact that most surveyed sites were in homogenous conifer plantations, a low-quality habitat for small mammals61.We found that tourism had less impact on small mammals compared to logging, with M. glareolus showing the only significant negative response. Tourism may also represent an additional source of food for the small mammal species that tolerate the presence of humans, such as A. flavicollis, which we found on campgounds. Touristic buildings may also represent important daily or hibernation shelters for some rodents, such as Glis glis, which we observed in autumn in a chalet. In contrast to logging, the effect of tourism on small mammals has been less researched and most such studies have focused on winter sports resorts and mainly on the impact of ski-run development, which involves substantial alteration of forest habitat, sometimes with a significant change in small mammal communities62. In case of ecotourism, damage to the vegetation and soil compaction that result from trampling during tourist season is only local and temporary, thus the regeneration of soil fauna and vegetation is possible63, hence the weaker effect of ecotourism on small mammals.Habitat characteristics had a stronger influence on community abundance than on species composition (Table 3), suggesting that, being primarily forest dwellers, the small mammal species in our study area have somewhat similar responses, especially towards tree cover, but they also show some differentiation, which is reflected by the divergent responses of A. flavicollis, M. glareolus, and S. araneus in their relative abundances in the community. The differences in the relative habitat use, along with the divergent dietary niche, enables their coexistence as dominant species, exploiting the same wide range of habitat resources.In conclusion, habitat use by small mammals in the continuous forest landscape in the Southern Carpathians was overall similar to that reported from the Northern Carpathians, with some notable differences related to recent and historical forest management practices and to latitude. Variation partitioning showed that yearly fluctuations were more important than habitat selection in shaping community composition. Temporal variations eclipsed the effects of habitat selection and elevational gradient, temporal fluctuations in community abundance and species composition having higher amplitudes than spatial variations. Relative habitat use by most species also changed among years. Thus, our results suggest that ignoring the time dimension of habitat selection may lead to the inability to comprehend the forces and processes that structure small mammal communities. More

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    Barrier crossings and winds shape daily travel schedules and speeds of a flight generalist

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    Impact of the female and hermaphrodite forms of Opuntia robusta on the plant defence hypothesis

    Study areaWe performed this study in San Nicolas Tecoaco village (20° 2′ 38.2ʺ N, 98° 35′ 16ʺ W), Hidalgo State, central Mexico, from March 2014 to October 2014. This location has an annual average temperature of 16 °C and an average altitude of 2600 m above sea level. The type of vegetation occurring in this area is classified as a xerophilous shrubland42.Study speciesOpuntia robusta (Cactaceae) is an endemic plant found in Meridional Altiplano, México43, which exists in the following three sexual forms: hermaphrodite, dioecious (male and female), and trioecious44. In a parallel study, Sandoval-Molina45 found that the most common herbivores of this plant were leaf-footed bugs, Chelinidea sp., Narnia sp. (Hemiptera: Coreidae), the cactus long-horned beetle, Moneilema sp. (Coleoptera: Cerambycidae), and mining insects. Before 2017, this population was considered to be gynodioecious; thus, we did not collect samples from male individuals in this study. In 2018, fewer than 15 male individuals were reportedly present in a population of more than 800, and most of these were hermaphrodites (Supplementary Information).Determination of plant sexWhite empty anthers, short style, and well-developed lobular stigma characterised female flowers, while a relatively longer style compared to that of the female and functional anthers characterised hermaphrodite individuals44.Comparison of tissue cost between female and hermaphrodite individualsIn March 2017, we undertook a census in San Nicolas Tecoaco, to identify the number of female and hermaphrodite plants with cladode and flower sprouts from the set of plants studied in the previous years. We selected 1–2 m tall plants, located 5–10 m apart for sampling. Finally, we randomly selected 19 plants (eleven female and eight hermaphrodite individuals) bearing flower buds and young cladodes on different branches for analysis and tagged the cladodes and flower sprouts using a permanent marker. We marked the flower sprouts on the adjacent side of their parental cladode surface.Between March 2017 and June 2017, we obtained sufficient data to estimate the relative growth rates of the species, in order to explore possible differences in the energy costs of cladodes and flower buds between the two sexual forms of O. robusta. We measured the length, width, and thickness of each cladode and flower bud twice during the study, once at the beginning, and once at the end of the study. Additionally, we also measured the lengths of the flowers from the base to the beginning of the sepals. Since the flower buds were spherical, we considered the thickness to be equal to the width. Subsequently, we calculated the flower volume immediately after the emergence of cladodes and flower buds, and the final volume after anthesis. We estimated the initial and final volumes (Vx) of the cladodes using the formula Vx = ((a/2))/((b/2)π)c, and those of the flowers using the formula Vx = 4/3πa2b. Here, x represents the time of measurement (initial or final), a and b represent the major and minor axes of the ellipsis, while c represents the cladode thickness. We measured all estimators to the nearest 1.0 mm and represented values in centimetres. We estimated the relative growth rate (RGR) using the formula proposed by Hunt46: RGR = (lnVf – lnVi)/(t2 – t1). Here, Vf represents the final volume [cm3], Vi represents the initial volume [cm3], t1 represents the initial time [day], and t2 represents the final time [day].We compared relative growth rate data using a generalized linear model (GLM) with gamma error distribution in the R software, using the log link function47. The explanatory variables included sex, type of structure, and their interactions. We performed partial regression using the ggeffects package in R48.We obtained meteorological variables, including total precipitation [mm], maximum temperature [°C], minimum temperature [°C], mean temperature [°C], global radiation [W(m2)−1], relative humidity [%], reference evapotranspiration [mm], and potential evapotranspiration [mm] for the Singuilucan municipality from March–October 2014, from the official Mexican Government weather station database of the Instituto Nacional de Investigaciones Forestales Agrícolas y Pecuarias49. We summed up the data for the per-day total precipitation, and that for the reference and potential evapotranspiration, from the beginning of each month through the sampling day. In the months (March, April, and May) or days when values from the meteorological database were underestimated, we averaged the values for the closest preceding and following days. If we lacked the data for more than one day and the data for such days could not be acquired, we considered a repetition of the averaged value for the days for which we lacked data, between the existing days. For July, we considered the values for the previous day (11/07/14), since we lacked the data for the days on which sampling was performed and the subsequent days. For the additive variables (total precipitation, reference, and potential evapotranspiration), we summed up data for 30 days, excluding data for one day, for the 31-day period.To determine the effects of the environmental variables on the concentration and presence/absence of secondary metabolites, we used R to formulate a structural equation model (SEM) in piecewiseSEM47,50. For concentrations, we fitted linear mixed-effects models using the nlme package51 and used the plant ID as a random factor. To evaluate the presence or absence of substances, we fitted generalized linear models with binomial error distributions and logits as the link functions. The concentration and presence/absence of 4-HBA, CGA, and QUE were dependent variables, and total precipitation, average temperature, global radiation, relative humidity, and potential evapotranspiration were explanatory variables. We analysed the sexes separately, and the substance concentration variables were log + 1 transformed. We assessed the goodness-of-fit using the Fisher function in the piecewiseSEM package50, where a larger p-value implies better data adjustment to the model. We conducted a visualisation of the SEM models using Biorender52, flaticon53, and CorelDRAW54.We estimated fruit traits (biomass [g], volume [cm3], and tissue density [g × cm−3]) and the number of fruits eaten by fructivores and compared them between the sexual forms using data reported by Janczur et al.18. The former comparison enabled the assessment of the possible differences in reproduction per fruit biomass between the sexual forms. The latter comparison enabled the assessment of the differences in preference for fruits eaten by animals in relation to the different sexual forms, and thus, the mechanisms by which this may increase the probability of seed dispersal. Higher zoochory of one sexual form may occur not only because of differences in fruit biomass density [g × cm−3], but also because of differences in the volatile substance content between the sexual forms.To test the effects of sexual form on fruit traits, we used generalized linear models in R. To analyse the number of fruits eaten, we used the negative binomial error distribution and log link function, and the Gaussian error distribution and identity link function for the other fruit traits47,55,56. We performed all post-hoc contrasts for fruit traits using the emmeans package47,57, and generated plots using the ggplot R package47,58. We compared the average number of fruits produced by the two sexual forms using the Kruskal–Wallis test.Comparison of secondary metabolite occurrence/concentration between female and hermaphrodite individualsWe obtained plant samples for secondary metabolite analysis using 100 m long Canfield lines, which were parallel to the contours of the hill and located 60 m from each other, and selected plants that were located near the lines and were 10 m apart for analysis. We randomly assigned each plant to one of the eight groups established herein, with three female plants and twelve hermaphrodite plants. The uneven number of individuals of each sex was attributable to the low proportion of females in the population. We tagged examined cladodes on their surface using a permanent marker.We used a stainless-steel punch (Ø = 0.5 cm) to remove two samples of vegetative tissue from cladodes belonging to the same order of each plant. We perforated the mid-section of the arc delimited by the border of the upper quarters of the cladodes, approximately 1 cm away from the edge. We placed samples in labelled Ziploc bags, stored them in a cooler containing ice, and then transported them to the laboratory in a portable refrigerator at − 20 °C. The samples were stored in the laboratory at − 40 °C until extraction.We performed homogenisation of approximately 1 g of the sample containing the cuticle in 35 mL of 100% methanol in an ultrasonic 6 L bath for 30 min at room temperature (21 °C). We filtered the methanol extracts, placed them in amber bottles, and stored the bottles at − 20 °C until further analysis59. We determined the types and concentrations of secondary metabolites in these tissues using high-performance liquid chromatography (HPLC), in accordance with the procedure described by Janczur and González Camarena59, using the following: Waters 717 liquid chromatograph with autosampler, Waters 2487 HPLC Absorbance UV–Vis Detector, Waters 1525 Binary HPLC Pump, Waters control module with SAT/IN Bus (Waters, Milford, MA, USA), Symmetry HPLC C18 column (particle size 5 µm, length 250 mm, internal Ø = 4.6 cm; Waters, Milford, MA, USA). We filtered the extracts using a 0.45 µm pore size nylon-membrane filter. The mobile phase consisted of 0.1% v/v acetic acid (A) together with 100% acetonitrile (B). For the mobile phase A, we dissolved 1 mL of glacial acetic acid with HPLC water, until the volume was 1 L. For the mobile phase B, we used 100% acetonitrile. We filtered both mobile phases using a 0.45 µm nylon membrane. We degasified them with an ultrasonic bath for 30 min. We set the column temperature at 25 °C, used the 254 nm UV detector, and established the flow of the mobile phase, injection volume, and run time as 0.2–0.8 mL/min, at 8 µL, and 35 min, respectively. To wash the piston seals, we used MeOH : H2O (60 : 50). To generate the calibration curves, we used standards for salicylic acid (SA), 4-hydroxybenzoic acid (4-HBA), chlorogenic acid (CGA), and quercetin (QUE) (Sigma-Aldrich). We generated the following calibration curves: yi = 1109.4xi + 481.67, yi = 296.01xi + 133.74, yi = 551.41xi + 263.64, and yi = 919.96xi + 201.64; here, yi represents the area below the absorbance curve, xi represents the concentration of the secondary metabolite, and i = 1, 2, 3, and 4 for 4-HBA, CGA, QUE, and SA, respectively. SA was not present in any of the samples tested (Table S1 online31).We used a logistic regression model to test the effect of the sexual form, month of study, cladode age category, cladode size, the number of cladodes above a given cladode, and the cladode order above the soil level, on the probability of detecting secondary metabolites in the cladodes. Since the latter data were ordinal, the sexual form and month were considered as discrete variables and treated the other traits as continuous variables60. We applied the generalized linear mixed model (GLMM) with a logit link function [ln(P/(1-P)], where P indicated the probability of detecting a given metabolite, binomial response distribution, maximum likelihood estimation technique, Newton–Raphson optimisation algorithm, and Person Chi-Square/df fit criterion. We used the GLIMMIX procedure in SAS statistical software61 (Methods S1).We used generalized linear models (GLMs) in R47 to determine the relationship between cladode length, width, thickness, months, age, cladode order from the soil, and cladodes above a given cladode, and the concentrations of the different secondary metabolites. Since many concentrations were null, we analysed only the positive concentrations (Methods S1).Comparison of damage between female and hermaphrodite individualsWe used the same plants as those used for relative growth rate analysis. We analysed the extent of damage caused by herbivorous insects on both sexes of O. robusta from March–June 2017. We selected two branches, one with flowers and the other with cladodes, from each plant. We estimated two types of damage caused by herbivores using image analysis, to determine the total percentage of tissue removed and other types of damage, such as scars or necrosis. We acquired photographs of one randomly selected face of each structure, using a Nikon D3200 with an AF-S DX NIKKOR 18–55 mm f/3.5–5.6G VR lens (Nikon Corporation, Tokyo, Japan) mounted on a tripod, using a 1-cm piece of millimetre paper as a reference for size. We analysed all images using ImageJ62 to estimate the total proportion of damaged areas.We analysed data on herbivore damage and other damages using a GLM procedure with the Gaussian error distribution and identity link function47 in R. The response variables were the logit transformed proportion of damage (ln[P/(1-P)]), where P represents the proportion of tissue damaged. In our statistical models, the transformation improved the distribution of residuals. The explanatory variables were sex, type of structure, and their interactions. We performed partial regressions using the ggeffects package in R48.Comparison of the occurrence/concentrations of secondary metabolites between younger and older vegetative tissuesWe named the oldest cladodes (closest to the soil) as ‘first-order cladodes,’ those growing on the oldest cladodes as ‘second-order cladodes’ etc. We selected each plant branch with the largest number of cladodes. We measured the length, width, and thickness of each cladode. We sampled vegetative tissues from plants belonging to each of the eight groups; the first group on the 10th March, the second group on the 12th April and so on, through the 10th May, 14th June, 12th July, 10th August, 13th September, and 11th October 2014. We measured the length and width of each cladode to the nearest 0.5 cm, using a measuring tape, and their thickness to the nearest 0.01 mm, using a calliper. We conducted the latter measurement in the apical part of the cladodes in the case of apical cladodes, or at the point of ramification of the daughter cladode when it grew on its apex.During eight years of observations prior to the commencement of this study, we observed that the age of the cladodes in the studied zone could be estimated by examining the following colour patterns of their spines: 1—yellowish, 2—yellow, white base, 3—white-yellowish, 4—white, 5—greyish, 6—black, with ‘1’ being the youngest, and ‘6’ being the oldest. We assigned each cladode to one of the classes. We used the HPLC procedure described by Janczur and González Camarena59 to determine the concentrations of different secondary metabolites in the plant tissues.To test whether different estimators of cladode age were parallel (to test whether younger cladodes were mostly apical, and thus bore fewer cladodes above), we examined the relationship between the cladode order from the soil or cladode number above a given cladode and cladode age, using ordinary least squares regression (OLS). We used a numerical algorithm applied to the SMATR software for R63. We included a test for the determination of the effects of cladode age estimators on the SMSs occurrence/concentration in the same GLM models, as described in the previous section.Trade-off between investments in defence, growth, and reproductionWe tested the relationship between cladode length and cladode order or cladode age to determine whether cladode size was parallel to cladode age. We performed OLS analysis and slope comparison between sexual forms using the Wald test (WT—test statistic) and tested the significance of differences between the intercepts. We used a numerical algorithm applied in the SMATR software63. To estimate the relative investment in growth and reproduction, we counted the number of flower and cladode buds on parental cladodes of the same plants used in the study performed by Sandoval and Janczur (Dataset online29). We used generalized linear models in R, with a negative binomial error distribution and log link function47,55,56, to test the effects of sexual form on the average number of flower and cladode buds. Significant differences between the number of flowers and cladodes for certain sexual forms implies a higher relative reproductive investment.We used the same method of quantification for the standardized major axis and GLM models for intersexual comparisons, as described in the previous Sect. 59. For example, larger relative allocations for reproduction and secondary metabolites together with lower allocation to growth in one sexual form, compared to lower allocations for reproduction and secondary metabolites, and higher allocations for growth in the other sexual form imply that the production of secondary metabolites does not compete with either growth or reproduction; rather, growth competes with reproduction, and allocation to the production of secondary metabolites is an outcome of the gain in terms of fitness from such an allocation.Effects of the existence of trade-offs between different secondary metabolites on the predictions of the plant defence hypothesisWe used ordinary least squares regression (OLS), coefficient of determination, and t-tests to determine the existence of possible trade-offs in the proportion of cladodes harbouring different secondary metabolites. We performed the t-test to determine the significance of correlation between cladode order and cladode age64.Ethics statementThis research did not involve any human or animal measurements. We obtained permission from the head of the Singuilucan municipality, State of Hidalgo, Mexico, to conduct research activities at the selected sites of the municipality. The owners of the lands permitted us to conduct the study and were informed of the permission granted by the municipality. MKJ obtained a permit (09,448/14) from the Ministry of Environment and Natural Resources of the United States of Mexico (SEMARNAT), which stated that no permission is necessary to conduct field studies on plants belonging to the genus Opuntia. The study site was not considered to be a protected area65, and O. robusta was not considered to be an endangered species66. During this study, we did not affect or involve any endangered species. As we did not sample all plants, we did not deposit specimens in a public herbarium. No plant was killed or severely damaged as a result of our research activity; the plant material used for this study was sampled at a limited scale, and therefore, the sampling presented with negligible effects on the functions of the broader ecosystem. All the methods were carried out in accordance to relevant guidelines and regulations. More

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    Author Correction: Mature Andean forests as globally important carbon sinks and future carbon refuges

    Departamento de Ciencias Forestales, Universidad Nacional de Colombia Sede Medellín, Medellín, ColombiaAlvaro Duque, Miguel A. Peña & Sebastián González-CaroGrupo de Investigación en Biodiversidad, Medio Ambiente y Salud -BIOMAS – Universidad de Las Américas (UDLA), Quito, EcuadorFrancisco Cuesta, Marco Calderón-Loor & Esteban PintoDepartment of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN, USAPeter KennedySchool of Geography, University of Leeds, Leeds, UKOliver L. PhillipsCentre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, VIC, AustraliaMarco Calderón-LoorInstituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, ArgentinaCecilia Blundo, Julieta Carilla, Ricardo Grau, Agustina Malizia & Oriana Osinaga-AcostaHerbario Nacional de Bolivia (LPB), La Paz, BoliviaLeslie Cayola, Alfredo Fuentes & María I. Loza-RiveraMissouri Botanical Garden, St. Louis, MO, USALeslie Cayola, Alfredo Fuentes & María I. Loza-RiveraCenter for Conservation and Sustainable Development, Missouri Botanical Garden, St. Louis, MO, USAWilliam Farfán-Ríos, María I. Loza-Rivera & J. Sebastián TelloLiving Earth Collaborative, Washington University in Saint Louis, St. Louis, MO, USAWilliam Farfán-RíosPlant Ecology and Ecosystems Research, University of Gottingen, Gottingen, GermanyJürgen HomeierCentre of Biodiversity and Sustainable Land Use (CBL), University of Gottingen, Gottingen, GermanyJürgen HomeierEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UKYadvinder MalhiFacultad de Ciencias Agrarias, Universidad Nacional de Jujuy, Jujuy, ArgentinaLucio MaliziaUniversité du Quebec a Montreal, Montreal, QC, CanadaJohanna A. Martínez-VillaDepartment of Biology, Washington University in St. Louis, St. Louis, MO, USAJonathan A. MyersConsorcio para el Desarrollo Sostenible de la Ecorregión Andina (CONDESAN), Quito, EcuadorManuel PeralvoColumbus State University, University System of Georgia, Columbus, GA, USAEsteban PintoCarbon Cycle and Ecosystems, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USASassan SaatchiCenter for Energy, Environment and Sustainability, Winston-Salem, NC, USAMiles SilmanCentro Jambatú de Investigación y Conservación de Anfibios, Quito, EcuadorAndrea Terán-ValdezBiology Department, University of Miami, Coral Gables, FL, USAKenneth J. Feeley More

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    Nutrients cause consolidation of soil carbon flux to small proportion of bacterial community

    Sample collection and incubationThree replicates of soil samples were collected from the top 10 cm in of plant-free patches in four ecosystems along the C. Hart Merriam elevation gradient in Northern Arizona25 beginning at high desert grassland (1760 m), and followed at higher elevations by piñon-pine juniper woodland (2020 m), ponderosa pine forest (2344 m), and mixed conifer forest (2620 m). Soils were air-dried for 24 h at room temperature, homogenized, and passed through a 2 mm sieve before being stored at 4 °C for another 24 h. Soil incubations were performed on soils with mass of 20 g of dry soil for measurements of CO2 and microbial biomass carbon (MBC), while 2 g of dry soil aliquots were incubated separately (but under equivalent conditions) for quantitative stable isotope probing (qSIP). We applied three treatments to these soils through the addition of water (up to 70% water-holding capacity): water alone (control), with glucose (C treatment; 1000 µg C g−1 dry soil), or with glucose and nitrogen (C + N treatment; [NH4]2SO4 at 100 µg N g−1 dry soil). All samples for qSIP were incubated with 18O-enriched water (97 atom%) and matching controls necessary to calculate the change in 18O enrichment across the microbial community. We applied water at natural abundance (i.e., no 18O-enriched water) to the larger soil samples prepared for measurement of carbon flux. All soils were incubated in the dark for one week. Following incubation, soils were frozen at −80 °C for 1 week prior to DNA extraction.Soil, CO2, and microbial biomass measurementsWe analyzed headspace gas of soils for CO2 concentration and δ13CO2 three times during the week-long incubation using a LI-Cor 6262 (LI-Cor Biosciences Inc. Lincoln, NE, USA) and a Picarro G2201 (Picarro Inc., Sunnyvale, CA, USA), respectively. Prior to incubation we analyzed soil MBC using the chloroform-fumigation extraction method on 10 g of soil. One sub-sample was immediately extracted with 25 ml of a 0.05 M K2SO4 solution, while a second sub-sample was first fumigated with chloroform (for 5 days), after which it was similarly extracted. Following K2SO4 addition, we agitated soils for 1 h, filtered the extract through a Whatman #3 filter paper, and dried the filtered solution (60 °C, 4 days). Salts with extracted C were ground and analyzed for total C using an elemental analyzer coupled to a mass spectrometer. MBC was calculated as the difference between the fumigated and immediately extracted samples’ soil C using an extraction efficiency of 0.45 (as per Liu et al.26).Quantitative stable isotope probingWe performed DNA extraction and 16S amplicon sequencing on 18O-incubated qSIP soils11,12,13. The procedures targeted the V4 region of the 16S gene as specified by the Earth Microbiome Project (EMP, http://www.earthmicrobiome.org) standard protocols27,28. We used PowerSoil DNA extraction kits following manufacture instructions to isolate DNA from soil (MoBio laboratories, Carlsbad, CA, USA). We quantified extracted DNA using the Qubit dsDNA High-Sensitivity assay kit and a Qubit 2.0 Fluorometer (Invitrogen, Eugene, OR, USA). To quantify the degree of 18O isotope incorporation into bacterial DNA, we performed density fractionation and sequenced 15–18 fractions separately following methods modified from the canonical publication7. We added 1 µg of DNA to 2.6 mL of saturated CsCl solution in combination with a gradient buffer (200 mM Tris, 200 mM KCL, 2 mM EDTA) in a 3.3 mL OptiSeal ultracentrifuge tube (Beckman Coulter, Fullerton, CA, USA). The solution was centrifuged to produce a gradient of increasingly labeled (heavier) DNA in an Optima Max bench top ultracentrifuge (Beckman Coulter, Brea, CA, USA) with a Beckman TLN-100 rotor (127,000 × g for 72 h) at 18 °C. We separated each sample from the continuous gradient into approximately 20 fractions (150 µL) using a modified fraction recovery system (Beckman Coulter). We then measured the density of each separate fraction with a Reichart AR200 digital refractometer (Reichert Analytical Instruments, Depew, NY, USA) and retained fractions with densities between 1.640 and 1.735 g cm−3. We cleaned and purified DNA in these fractions using isopropanol precipitation, quantified DNA using the Quant-IT PicoGreen dsDNA assay (Invitrogen) and a BioTek Synergy HT plate reader (BioTek Instruments Inc., Winooski, VT, USA), and quantified bacterial 16S gene copies using qPCR (primers: Supplementary Table 1) in triplicate. We used 8 µL reactions consisting of 0.2 mM of each primer, 0.01 U µL−1 Phusion HotStart II Polymerase (Thermo Fisher Scientific, Waltham, MA), 1× Phusion HF buffer (Thermo Fisher Scientific), 3.0 mM MgCl2, 6% glycerol, and 200 µL of dNTPs. We amplified DNA using a Bio-Rad CFX384 Touch real-time PCR detection system (Bio-Rad, Hercules, CA, USA) with the following cycling conditions: 95 °C at 1 min and 44 cycles of 95 °C (30 s), 64.5 °C (30 s), and 72 °C (1 min).We sequenced the 16S V4 region (primers: EMP standard 515F—806R; Supplementary Table 1) on an Illumina MiSeq (Illumina, Inc., San Diego, CA, USA). Sequences were amplified using the same reaction mix as qPCR amplification but cycling at 95 °C for 2 min followed by 15 cycles of 95 °C (30 s), 55 °C (30 s), and 60 °C (4 min). In addition to post-incubation soils, we extracted, amplified, and sequenced DNA of the bacterial community at the start of the incubation.Sequence processing and qSIP analysisThe raw sequence data of forward and reverse reads (FASTQ) were processed within the QIIME 2 environment (release 2018.6)29,30, denoising sequences with the available DADA2 pipeline31. We clustered the remaining sequences into amplicon sequence variants or ASVs (at 100% sequence identity) against the SILVA 132 database32 using an open-reference Naïve Bayes feature classifier33. We removed global singletons and doubleton ASVs, non-bacterial lineages, and samples with less than 4000 sequence reads. Removal of global singletons and doubletons resulted in the removal of 2241 unique ASVs from the feature table yielding 115,647 out of 117,888 (a retention of 98% of all ASVs) as well as the loss of 4018 sequences leaving 37,765,678 (a retention >99% of all sequences). We combined taxonomic information and ASV sequence counts with per-fraction qPCR and density measurements using the phyloseq package (version 1.24.2), in R (version 3.5.1)34. Because high-throughput sequencing produces relativized measures of abundance, we converted ASV sequencing abundances in each fraction to the number of 16S rRNA gene copies per g dry soil based on the known amount of dry soil added and the amount of DNA in each soil sample. All data and analytical code have been made publicly accessible35.To perform qSIP analysis and calculate per-capita growth rates of each ASV, we used our in-house qsip package (https://github.com/bramstone/qsip) based on previously published research7,10. Because rare and infrequent taxa are more likely to be lost in samples with poor sequencing depth with their absences affecting DNA density changes, we invoked a presence or absence-based filtering criteria on ASVs prior to calculation of per-capita growth rates. Within each ecosystem, we kept only ASVs that appeared in two of the three replicates of a treatment (18O, C, and C + N) and at that appeared in at least five of the fractions within each of those two replicates. ASVs filtered out of one treatment were allowed to appear in another if they met the frequency threshold.For all remaining ASVs (1081 representing less than 1% of all ASVs but 58% of all sequence reads), we calculated per-capita gross growth (i.e., cell division) rates observed in each replicate using an exponential growth model10. We applied these per-capita rates to the number of 16S rRNA gene copies to estimate the production of new 16S rRNA gene copies of each ASV per g dry soil per week using the following equation:$$frac{{rm{d}}{N}_{{rm{i}}}}{{{rm{d}}t}}={N}_{{rm{i,t}}}-{N}_{{rm{i,t}}}{e}^{-{g}_{{rm{i}}}t},$$
    (1)
    Where Ni,t is the number of 16S rRNA gene copies of taxon i at time t (here after 7 days) and gi represents the per-capita growth rate (calculated as a daily rate). See Supplementary Fig. 3 for results on the production of 16S gene copies.Calculation of 16S rRNA gene copy numbers and cell massIn parallel to taxonomic assignment, we compared quality-filtered 16S sequences against a database of 12,415 complete prokaryote genomes obtained from GenBank. From these genomes, we extracted data on 16S rRNA gene copy number, total genome size, and 16S gene sequence. We used BLAST to find matches against this database to the ASVs generated from QIIME 2 to make per-taxon assignments of 16S rRNA gene copy number and total genome size13. For ASVs that did not find an exact match, we assigned 16S rRNA gene copy number values and genome sizes based on the median values observed in the most specific possible taxonomic rank. We estimated the mass of individual cells for each population using published allometric scaling relationships between genome length and cellular mass from West and Brown:36$${{{log }}}_{10}({M}_{{rm{i}}})=frac{{{{log }}}_{10}left({G}_{{rm{i}}}right)-9.4}{0.24},$$
    (2)
    where Mi indicates cellular mass (g) and Gi indicates genome length (bp) for taxon i. We obtained this relationship by digitizing Fig. 436 using DataThief III and re-fitting the trend line in log–log space. We estimated that 20% of the cellular mass was carbon37. To validate this approach, cellular mass estimates and initial 16S copy number measurements were used to estimate population-level biomass C values which were summed and compared to initial community-level MBC. We found that these values overestimated initial MBC by an order of magnitude. As such, cellular carbon mass was divided by 10 in our final calculations. We applied cellular mass and 16S copy number estimates to the production of 16S copies to estimate the production of biomass carbon for each taxon during the incubation period (t):$${P}_{{rm{i}}}=frac{{rm{d}}{N}_{{rm{i}}}/{{rm{d}}t}}{C_{{rm{i}}}}cdot {M}_{{rm{i}}}cdot 0.2,$$
    (3)
    where Pi indicates production of biomass carbon (µg C g dry soil−1 week−1) and Ci indicates 16S copy number per cell for taxon i. The 0.2 coefficient represents an estimate that 20% of cellular mass is composed of carbon.Efficiency and respiration modelingWe estimated rates of respiration using qSIP-informed growth rates and community-level carbon use efficiency (CUE). CUE estimates were based on the incorporation of 18O-water into DNA as a measure of gross biomass production38,39 and measured CO2 in headspace gas from soil incubations. We calculated the production of 18O-labeled biomass carbon (18P) at the community-level for each sample by summing the products of per-taxon 18O enrichment (excess atom fraction, EAF) and relative abundance:$${, }^{18}{P}=mathop{sum }limits_{i=1}^{n}({,}^{18}{{{rm{EAF}}}}_{{rm{i}}}cdot {y}_{{rm{i}}})cdot {rm{DN}}{rm{A}}_{0}cdot fleft({{rm{MB}}}{rm{C}}_{0} sim {rm{DN}}{rm{A}}_{0}right),$$
    (4)
    where 18P indicates the gross production of 18O-labeled microbial biomass carbon per gram of dry soil per week, 18EAFi indicates the enrichment of DNA of taxon i and yi indicates its relative abundance, DNA0 indicates the concentration of DNA per gram of dry soil prior to incubation, and MBC0 indicates the microbial biomass carbon per gram of dry soil prior to incubation. Here, the MBC0 ~ DNA0 function indicates the linear relationship between MBC and DNA concentration. We used the output from Eq. 4 to calculate community CUE for each sample:$${{rm{CUE}}}=frac{{,}^{18}{{P}}}{(!{,}^{18}P+R)},$$
    (5)
    where R indicates the total CO2 respired per gram dry soil per week.We used the community CUE values from each sample (Eq. 5) to constrain/as upper and lower limits our estimates of per-taxon CUE. For a group of three replicates from a given ecosystem and treatment, we used the minimum and maximum observed community-level CUE values as the acceptable range of per-taxon CUE values. These constraints were used to control the shape of the function of per-taxon CUE and growth rate, though functions were modeled both with and without constraints (i.e., per-taxon CUE values were bounded only by 0 and 0.7). The range of community-level CUE values for each treatment were 0.18–0.53 for control soils, 0.04–0.13 for carbon amended soils and 0.03–0.08 for carbon and nitrogen amended soils and did not vary much between ecosystems. As a result of uncertainty in the literature about the relationship between growth rate and CUE14, several different relationships were postulated to model per-taxon CUE as a function of per-taxon growth rate: linear increase, linear decrease, exponential decrease, unimodal with peak CUE at growth rate of 0.5, and unimodal with peak CUE at a growth rate of 0.05 (the median of all per-taxon growth rates in the data). Comparisons between functions were made by calculating AIC values from per-taxon respiration, summed, and regressing against measured respiration values. Likewise, for each function, we tested how well per-taxon CUE estimates reconstructed community-level CUE by weighting the CUE value of each taxon by its relative abundance, summing, and regressing against community-level CUE. To select the best per-taxon CUE function, AIC values from both scaling efforts were combined. To make AIC values comparable, all respiration and CUE terms were z-transformed prior to regression scaling. To reflect our priority of estimating per-taxon respiration, AIC values from the respiration scaling regression models were multiplied by two and summed with AIC values from CUE scaling such that AICTotal = 2(AICResp) + AICCUE. Across these comparisons, the best estimate of per-taxon CUE was the unimodal function of growth rate, constrained by community-level CUE and peaking at growth rates of 0.5 (Table 1), such that:$${{rm{CUE}}}_{{rm{i}}}=-4({{rm{CUE}}}_{{rm{E}}{rm{:}}{rm{T}}{rm{:}}{{rm{range}}}})cdot {left({g}_{{rm{i}}}-0.5right)}^{2}+({{rm{CUE}}}_{{rm{E}}{rm{:}}{rm{T}}{rm{:}}{max }}),$$
    (6)
    where CUEi indicates per-taxon CUE, CUEE:T:max indicates the maximum CUE values observed for a group of replicates within a given ecosystem and treatment (E:T). With this function, higher per-capita growth rate values were parameterized to produce higher CUE values initially and then decrease reflecting a growth-CUE tradeoff14, here bound by the difference in maximum and minimum CUE values. We applied per-taxon CUE estimates from Eq. 6 to per-taxon growth rates to yield estimates of per-taxon respiration:$${r}_{{rm{i}}}={r}_{{rm{g,i}}}+{r}_{{rm{m,i}}}=left(frac{{g}_{{rm{i}}}}{{{rm{CUE}}}_{{rm{i}}}}-{g}_{{rm{i}}}right)+left(frac{{g}_{{rm{i}}}}{{{rm{CUE}}}_{{rm{i}}}}-{g}_{{rm{i}}}right)cdot beta,$$
    (7)
    where ri indicates per-capita respiration for taxon i, rg,i indicates growth-related respiration, rm,i indicates maintenance-related respiration, and β is a constant of 0.01 that represents the maintenance requirements as a proportion of total energy use40. We used these values of per-taxon, per-capita respiration rates to estimate per-taxon respiration per gram of dry soil per week:$${R}_{{rm{i}}}={P}_{{rm{i}}}cdot {r}_{{{rm{g,i}}}}+{P}_{{rm{i}}}cdot {r}_{{{rm{m,i}}}},$$
    (8)
    where Ri indicates respiration of CO2–C (µg C g dry soil−1 week−1) for taxon i.In addition to per-taxon respiration estimates based on 18O enrichment, we used another model for comparison. Here, respiration was calculated based on 16S abundance alone:$${R}_{{rm{i}}}={N}_{{rm{i}}}cdot f(R sim N+0),$$
    (9)
    where Ni indicates final 16S abundance for taxon i, R indicates microbial respiration of CO2-C (µg C g dry soil−1 week−1) and N indicates total 16S abundance at the end of the incubation. Here, the R ~ N function indicates the linear relationship, with an intercept of 0, between CO2 respiration and 16S gene concentration across all samples.Diversity, compositional, and statistical analysisFor patterns of evenness in bacterial carbon use and relative abundance, we used Pielou’s evenness which is the quotient of Shannon’s diversity and the observed richness. For each sample, we applied Pielou’s evenness to bacterial abundances as well as bacterial carbon use (relativized to sum to one, in both cases).We created a linear mixed model to test the relationship between the carbon use (the sum of biomass production and respiration) and relative abundance of bacterial genera from the dominant phyla, which accounted for >90% of all C flux. Here, we averaged carbon use and relative abundance for all replicates in a given ecosystem and treatment. We used the lme4 R package (version 1.1-20)41 and obtained p-values using the Satterthwaite method in the lmerTest R package (version 3.1-0)42. To limit pseudo-replication, we accounted for differences in carbon use across ecosystems and due to bacterial Genus by implementing random intercepts. We selected for the optimal random and fixed components by dropping individual terms and comparing models with likelihood ratio tests, disregarding models that failed to converge. Our final model fit was:$${{{log }}}_{10}({C}_{{rm{i}}}) sim {{{log }}}_{10}left({y}_{{rm{i}}}right)ast T+left(1|Eright)+(1|{{rm{Genus}}}),$$
    (10)
    where Ci indicates the relativized carbon use for taxon i (averaged across all three replicates in a given ecosystem and treatment), yi indicates the relative abundance of taxon i (averaged across all three replicates), T indicates soil treatment, and E indicates ecosystem.For differences in composition, we created species abundance tables using the traditional abundances, as well as measures of carbon use (growth and maintenance respiration) of each ASV in each sample. To account for differences in absolute abundances and flux rates between sites, we relativized all abundance tables. We summarized compositional differences using Bray–Curtis dissimilarities then identified multivariate centroids for all replicates in a site by treatment group. We tested the effect of site and nutrient amendment on the resulting group centroids using PERMANOVA tests implemented with the adonis function in the vegan package (version 2.5-3)43. We related compositional shifts in relative abundance to those in relativized growth and maintenance using Mantel tests with the mantel function in vegan.To test for changes in the type of soil C preferred by microbial genera (either 13C-labeled glucose or 12C soil carbon) in response to nitrogen addition, we used Levene’s test with the car package (version 3.0-10)44. Specifically, we analyzed the relationship between 13C use and 12C use (both relativized) on bacterial genera across all replicates and in C and C + N treatments using a linear model. We then extracted model residuals and tested whether variance was significantly different across treatments by focusing on the interaction between individual replicates and treatment. This produced a significance test describing treatment-level differences in 13C–12C use.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More