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ispartof: FRONTIERS IN ECOLOGY AND EVOLUTION vol:11 status: published

Significance. Primates from the Americas and Madagascar are predominantly arboreal but occasionally descend to the ground. This increased ground use was associated with multiple ecological drivers, including increased temperature and a decrease in canopy cover, as well as species-specific traits, including a dietary shift away from fruits and larger group size. As anthropogenic impacts to habitats and climate worsen, our results suggest that diurnal species already inhabiting hot, sparsely canopied sites, and exhibiting more generalized diets, are more likely to shift toward greater ground use., Among mammals, the order Primates is exceptional in having a high taxonomic richness in which the taxa are arboreal, semiterrestrial, or terrestrial. Although habitual terrestriality is pervasive among the apes and African and Asian monkeys (catarrhines), it is largely absent among monkeys of the Americas (platyrrhines), as well as galagos, lemurs, and lorises (strepsirrhines), which are mostly arboreal. Numerous ecological drivers and species-specific factors are suggested to set the conditions for an evolutionary shift from arboreality to terrestriality, and current environmental conditions may provide analogous scenarios to those transitional periods. Therefore, we investigated predominantly arboreal, diurnal primate genera from the Americas and Madagascar that lack fully terrestrial taxa, to determine whether ecological drivers (habitat canopy cover, predation risk, maximum temperature, precipitation, primate species richness, human population density, and distance to roads) or species-specific traits (body mass, group size, and degree of frugivory) associate with increased terrestriality. We collated 150,961 observation hours across 2,227 months from 47 species at 20 sites in Madagascar and 48 sites in the Americas. Multiple factors were associated with ground use in these otherwise arboreal species, including increased temperature, a decrease in canopy cover, a dietary shift away from frugivory, and larger group size. These factors mostly explain intraspecific differences in terrestriality. As humanity modifies habitats and causes climate change, our results suggest that species already inhabiting hot, sparsely canopied sites, and exhibiting more generalized diets, are more likely to shift toward greater ground use.

Among mammals, the order Primates is exceptional in having a high taxonomic richness in which the taxa are arboreal, semiterrestrial, or terrestrial. Although habitual terrestriality is pervasive among the apes and African and Asian monkeys (catarrhines), it is largely absent among monkeys of the Americas (platyrrhines), as well as galagos, lemurs, and lorises (strepsirrhines), which are mostly arboreal. Numerous ecological drivers and species-specific factors are suggested to set the conditions for an evolutionary shift from arboreality to terrestriality, and current environmental conditions may provide analogous scenarios to those transitional periods. Therefore, we investigated predominantly arboreal, diurnal primate genera from the Americas and Madagascar that lack fully terrestrial taxa, to determine whether ecological drivers (habitat canopy cover, predation risk, maximum temperature, precipitation, primate species richness, human population density, and distance to roads) or species-specific traits (bodymass, group size, and degree of frugivory) associate with increased terrestriality. We collated 150,961 observation hours across 2,227 months from 47 species at 20 sites in Madagascar and 48 sites in the Americas. Multiple factors were associated with ground use in these otherwise arboreal species, including increased temperature, a decrease in canopy cover, a dietary shift away from frugivory, and larger group size. These factors mostly explain intraspecific differences in terrestriality. As humanity modifies habitats and causes climate change, our results suggest that species already inhabiting hot, sparsely canopied sites, and exhibiting more generalized diets, are more likely to shift toward greater ground use.

Additional file 2: Supplemental Figure 1. Taxonomic composition of the gelada gut microbiome at the phylum and family levels. Relative abundance (A) of all bacterial phyla and (B) of the 24 most abundant families (mean relative abundance>0.02%) in the gelada feces. The y-axis is on a log10 scale to better represent the variation across samples. The median and median absolute deviation (error limit) are represented in orange. Supplemental Figure 2. Genus composition of the gelada gut. Relative abundance of the 38 most abundant genera (mean relative abundance>0.01%) in the gelada feces. The tick marks on the y-axis are spaced on a log10 scale. The median and median absolute deviation (error limit) are represented in orange. Supplemental Figure 3. Rainfall is not associated with Observed richness and Faith’s phylogenetic diversity. Partial residual plot of (A) Observed richness and (B) Faith’s phylogenetic diversity (PD) according to cumulative rainfall (in mm). Black dots represent the partial residuals from the LMM (i.e. showing the association between cumulative rainfall and alpha diversity, while controlling for all other predictors). The blue line and confidence intervals come from a linear regression (for representation only). One and 5 outlier samples (with a particularly low diversity) were omitted for panel A and B respectively for clarity of representation. Supplemental Figure 4. Bacterial functional pathways that significantly associated with cumulative rainfall at KO level 3. The estimate of the cumulative rainfall effect comes from a LMM fitted on the relative abundance of each pathway per sample. Only pathways with pBH < 0.05 were considered significant. For ease of representation, only pathways with effect sizes > |0.006| are represented. The full list can be found in Table S9. Classification of KO level 3 pathways in broader categories were based on their KO level 2 assignment, with a few changes made for clarity of representation. Level 3 pathways from Metabolism of Other Amino Acids (level 2) were reclassified in the “Amino Acid Metabolism”, Translation proteins and Replication, recombination and repair proteins (both level 3 and initially in Genetic Information Processing at level 2) were reclassified in “Transcription & Translation" and "Replication and Repair" respectively. The category “Membrane Transport & Cellular Signalling" regroups pathways from “Membrane Transport” and the other pathways from “Cellular Processes and Signaling”. Supplemental Figure 5. Rainfall predicts the functional profile of the gut microbiome. Relative abundance of ten functional pathways (at KO level 3) that are enhanced (A) during the wet season and (B) during the dry season. Note that the tick marks on the y-axis are spaced on a log10 scale. The blue line and confidence intervals come from a linear regression (for representation only). The significance of the rainfall effect effects per pathway have been estimated using LMMs including individual and unit membership as random effects. Supplemental Figure 6. Small effect of ambient temperature on the gelada gut microbiome. (A) Partial residual plots of the three alpha diversity indices (Shannon index, Observed richness and Faith’s phylogenetic diversity) according to the average minimum temperature in the previous month of sample collection (in oC). Black dots represent the partial residuals from the LMM (i.e. showing the association between temperature and alpha diversity, while controlling for all other predictors). The blue line and confidence intervals come from a linear regression (for representation only). For clarity of representation, 9, 1, and 5 outlier samples (with a particularly low diversity) were omitted for Shannon, richness and Faith’s PD, respectively. (B) Visualization of between-sample dissimilarity (based on Aitchison distance) on the first and second principal component according to minimum temperature. (C) Compositional barplot of the five most abundant phyla in the cold (i.e. 8oC in the past month, N=567) samples (minimum temperature was converted to a categorical variable for representation purposes). Supplemental Figure 7. Genera that significantly associated with average minimum temperature. The estimate for the effect of temperature for each taxa comes from a negative binomial GLMM controlling for sample sequencing depth as an offset factor, and including individual and unit membership as random effects. Only taxa with pBH < 0.05 were considered significant. The full list can be found in Table S7. Supplemental Figure 8. Bacterial pathways that are differentially abundant according to average minimum temperature at KO (A) level 2 and (B) level 3. The estimate comes from a LMM fitted on the relative abundance of each pathway per sample. Only pathways with pBH < 0.05 were considered significant. For ease of representation on panel B, only pathways with effect size > |0.002| were represented. The full list can be found in Table S8. Supplemental Figure 9. Effect of sex on the gelada gut microbiome. (A) Partial residual plots of the three alpha diversity indices (Shannon index, Observed richness and Faith’s phylogenetic diversity) according to the sex of the sampled individual. Black dots represent partial residuals of the LMM. The median and median absolute deviation (error limit) of the distribution are represented in orange. Ten, 1 and 5 outlier samples (with a particularly low diversity) were omitted for Shannon, richness and Faith’s PD respectively for clarity of representation. (B) Visualization of between-sample dissimilarity (based on Aitchison distance) on the first and second principal component according to sex. (C) Compositional barplot of the five most abundant phyla in male (N=138) and female (N=620) samples. Supplemental Figure 10. Effect of female reproductive state on the gelada gut microbiome. (A) Partial residual plots of the three alpha diversity indices (Shannon index, Observed richness and Faith’s phylogenetic diversity) according to the reproductive state of the sampled female. Black dots represent partial residuals of the LMM. The median and median absolute deviation (error limit) of the distribution are represented in orange. Ten, 1 and 5 outlier samples (with a particularly low diversity) were omitted respectively for clarity of representation. (B) Visualization of between-sample dissimilarity (based on Aitchison distance) on the first and second principal component according to reproductive state. (C) Compositional barplot of the five most abundant phyla in pregnant (N=61), lactating (N=346) and cycling (N=158) female samples. Supplemental Figure 11. Effect of age on the gelada gut microbiome. (A) Partial residual plots of the three alpha diversity indices (Shannon index, Observed richness and Faith’s phylogenetic diversity) according to the age of individuals at the date of sample collection (in years). Black dots represent the partial residuals from the GLMM (i.e. showing the association between age and alpha diversity, while controlling for all other predictors). The blue line and confidence intervals come from a linear regression (for representation only). Nine, 1 and 5 outlier samples (with a particularly low diversity) were omitted respectively for clarity of representation. (B) Visualization of between-sample dissimilarity (based on Aitchison distance) on the first and second principal component according to age. (C) Compositional barplot of the five most abundant phyla between young (17 years old, N=123 samples) individuals (age was converted to a categorical variable for representation purposes only). Supplemental Figure 12. Rarefaction curves of samples. Only samples that had at least 20000 reads were included in this study. Supplemental Figure 13. 16S sequencing and dataset characteristics. (A) Distribution of the total number of reads per sample (the tick marks on the x-axis are spaced on a log10 scale). (B) Distribution of the total number of ASVs per sample. Supplemental Figure 14. Visualization of differences in the gut microbiome composition according to sequencing depth of the samples based on Aitchison distance dissimilarity matrix. Points represent individual samples.

Background Habitat disturbance affects the biology and health of animals globally. Understanding the factors that contribute to the differential responses of animals to habitat disturbance is critical for conservation. The gut microbiota represents a potential pathway through which host responses to habitat disturbance might be mediated. However, a lack of quantitative environmental data in many gut microbiome (GM) studies of wild animals limits our ability to pinpoint mechanisms through which habitat disturbance affects the GM. Here, we examine the impact of anthropogenic habitat disturbance on the diet and GM of the Critically Endangered black-and-white ruffed lemur (Varecia variegata editorum). We collected fecal samples and behavioral data from Varecia occupying habitats qualitatively categorized as primary forest, moderately disturbed forest, and heavily disturbed forest. Results Varecia diet and GM composition differed substantially across sites. Dietary richness predicted GM richness across sites, and overall GM composition was strongly correlated to diet composition. Additionally, the consumption of three specific food items positively correlated to the relative abundances of five microbial strains and one microbial genus across sites. However, diet did not explain all of the GM variation in our dataset, and differences in the GM were detected that were not correlated with diet, as measured. Conclusions Our data suggest that diet is an important influence on the Varecia GM across habitats and thus could be leveraged in novel conservation efforts in the future. However, other factors such as contact with humans should also be accounted for. Overall, we demonstrate that quantitative data describing host habitats must be paired with GM data to better target the specific mechanisms through which environmental change affects the GM.

Our growing awareness of the microbial world's importance and diversity contrasts starkly with our limited understanding of its fundamental structure. Despite recent advances in DNA sequencing, a lack of standardized protocols and common analytical frameworks impedes comparisons among studies, hindering the development of global inferences about microbial life on Earth. Here we present a meta-analysis of microbial community samples collected by hundreds of researchers for the Earth Microbiome Project. Coordinated protocols and new analytical methods, particularly the use of exact sequences instead of clustered operational taxonomic units, enable bacterial and archaeal ribosomal RNA gene sequences to be followed across multiple studies and allow us to explore patterns of diversity at an unprecedented scale. The result is both a reference database giving global context to DNA sequence data and a framework for incorporating data from future studies, fostering increasingly complete characterization of Earth's microbial diversity. Fil: Thompson, Luke R.. University of California at San Diego; Estados Unidos. University of Southern Mississippi; Estados Unidos. National Oceanic and Atmospheric Administration; Estados Unidos Fil: Sanders, Jon G.. University of California at San Diego; Estados Unidos Fil: McDonald, Daniel. University of California at San Diego; Estados Unidos Fil: Amir, Amnon. University of California at San Diego; Estados Unidos Fil: Ladau, Joshua. University of California San Francisco; Estados Unidos Fil: Locey, Kenneth J.. Indiana University; Estados Unidos Fil: Prill, Robert J.. IBM Almaden Research Center; Estados Unidos Fil: Tripathi, Anupriya. University of California at San Diego; Estados Unidos Fil: Gibbons, Sean M.. Massachusetts Institute of Technology; Estados Unidos Fil: Ackermann, Gail. University of California San Diego; Estados Unidos Fil: Navas Molina, Jose A.. University of California at San Diego; Estados Unidos Fil: Janssen, Stefan. University of California at San Diego; Estados Unidos Fil: Kopylova, Evguenia. University of California at San Diego; Estados Unidos Fil: Vázquez Baeza, Yoshiki. University of California at San Diego; Estados Unidos Fil: González, Antonio. University of California at San Diego; Estados Unidos Fil: Morton, James T.. University of California at San Diego; Estados Unidos Fil: Mirarab, Siavash. University of California at San Diego; Estados Unidos Fil: Xu, Zhenjiang Zech. University of California at San Diego; Estados Unidos Fil: Jiang, Lingjing. University of California at San Diego; Estados Unidos Fil: Haroon, Mohamed F.. Harvard University; Estados Unidos Fil: Kanbar, Jad. University of California at San Diego; Estados Unidos Fil: Zhu, Qiyun. University of California at San Diego; Estados Unidos Fil: Song, Se Jin. University of California at San Diego; Estados Unidos Fil: Kosciolek, Tomasz. University of California at San Diego; Estados Unidos Fil: Bokulich, Nicholas A.. Northern Arizona University; Estados Unidos Fil: Lefler, Joshua. University of California at San Diego; Estados Unidos Fil: Brislawn, Colin J.. Earth and Biological Sciences Directorate; Estados Unidos Fil: Humphrey, Gregory. University of California at San Diego; Estados Unidos Fil: Owens, Sarah M.. Argonne National Laboratory; Estados Unidos Fil: Hampton Marcell, Jarrad. Argonne National Laboratory; Estados Unidos. University of Illinois at Chicago; Estados Unidos Fil: Berg Lyons, Donna. University of Colorado; Estados Unidos Fil: McKenzie, Valerie. University of Colorado; Estados Unidos Fil: Fierer, Noah. University of Colorado; Estados Unidos Fil: Fuhrman, Jed A.. University of Colorado; Estados Unidos Fil: Clauset, Aaron. University of Colorado; Estados Unidos Fil: Stevens, Rick L.. Argonne National Laboratory; Estados Unidos. University of Chicago; Estados Unidos Fil: Shade, Ashley. Michigan State University; Estados Unidos Fil: Pollard, Katherine S.. The Gladstone Institutes; Estados Unidos. University of California San Francisco; Estados Unidos Fil: Goodwin, Kelly D.. National Oceanic and Atmospheric Administration; Estados Unidos Fil: Jansson, Janet K.. Earth and Biological Sciences Directorate; Estados Unidos Fil: Gilbert, Jack A.. Argonne National Laboratory; Estados Unidos. University of Chicago; Estados Unidos Fil: Knight, Rob. University of California San Diego; Estados Unidos Fil: Dionisi, Hebe Monica. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina Fil: Lozada, Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Centro para el Estudio de Sistemas Marinos; Argentina Fil: Mac Cormack, Walter Patricio. Universidad de Buenos Aires; Argentina. Ministerio de Relaciones Exteriores, Comercio Interno y Culto. Dirección Nacional del Antártico. Instituto Antártico Argentino; Argentina