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FIGURE 5 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 5. Non-stationary associations between ecological diversity (ED) and net primary productivity (NPP). The maps show the spatial variation in local beta coefficients (b) for NPP as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 4 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 4. Non-stationary associations between ecological diversity (ED) and mean annual temperature (TEMP). The maps show the spatial variation in local beta coefficients (b) for TEMP as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of tropics in the Northern and Southern Hemispheres.

FIGURE 8 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 8. Non-stationary associations between ecological diversity (ED) and coefficient of variation in annual precipitation (PRECcv). The maps show the spatial variation in local beta coefficients (b) for PRECcv as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 7 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 7. Non-stationary associations between ecological diversity (ED) and annual range in temperature (TEMPr). The maps show the spatial variation in local beta coefficients (b) for TEMPr as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equalarea projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 1 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 1. Spatial patterns of variation in the ecological diversity (ED) of different mammal groups over the Americas. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 14 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 14. Non-stationary associations between phylogenetic diversity (AvPD) and coefficient of variation in annual precipitation (PRECcv). The maps show the spatial variation in local beta coefficients (b) for PRECcv as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 6 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 6. Non-stationary associations between ecological diversity (ED) and annual precipitation (PREC). The maps show the spatial variation in local beta coefficients (b) for PREC as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of tropics in the Northern and Southern Hemispheres.

FIGURE 13 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 13. Non-stationary associations between phylogenetic diversity (AvPD) and annual range in temperature (TEMPr). The maps show the spatial variation in local beta coefficients (b) for TEMPr as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 12 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 12. Non-stationary associations between phylogenetic diversity (AvPD) and annual precipitation (PREC). The maps show the spatial variation in local beta coefficients (b) for PREC as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 10 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 10. Non-stationary associations between phylogenetic diversity (AvPD) and mean annual temperature (TEMP). The maps show the spatial variation in local beta coefficients (b) for TEMP as predictor of AvPD, obtained from the full model, i.e., including all environmental predictors, after application of geographically weighted regression separately on data for each mammal group. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 2 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 2. Spatial patterns of variation in the phylogenetic diversity (AvPD) of different mammal groups over the Americas. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

FIGURE 3 in Biogeographical affinity shapes relationships between ecological and phylogenetic mammal diversity and associations with their environmental correlates in the Americas

FIGURE 3. Non-stationary associations between ecological diversity (ED) and phylogenetic diversity (AvPD). The maps show the local beta coefficients (b) for AvPD as predictor of ED, obtained from the full model, i.e., including all environmental predictors and species richness (TR), after application of geographically weighted regression separately on data for each mammal group. Following (Matthews & Yang 2012) non-significant values (p> 0.05) are excluded from the maps to optimize the visualization of patterns. Regions of (+) and negative (-) associations are indicated. Maps are in Mollweide equal-area projection. Dash lines on each map indicate the location of the tropics in the Northern and Southern Hemispheres.

opencc-zeroDec 2021View details →

Data: Environmentally associated chromosomal structural variation influences fine-scale population structure of Atlantic Salmon

Chromosomal rearrangements (e.g., inversions, fusions, and translocations) have long been associated with environmental variation in wild populations. New genomic tools provide the opportunity to examine the role of these structural variants in shaping adaptive differences within and among wild populations of non-model organisms. In Atlantic Salmon (Salmo salar), variations in chromosomal rearrangements exist across the species natural range, yet the role and importance of these structural variants in maintaining adaptive differences among wild populations remains poorly understood. We genotyped Atlantic Salmon (n = 1429) from 26 populations within a highly genetically structured region of southern Newfoundland, Canada with a 220K SNP array. Multivariate analysis, across two independent years, consistently identified variation in a structural variant (translocation between chromosomes Ssa01 and Ssa23), previously associated with evidence of trans-Atlantic secondary contact, as the dominant factor influencing population structure in the region. Redundancy analysis suggested that variation in the Ssa01/Ssa23 chromosomal translocation is strongly correlated with temperature. Our analyses suggest environmentally mediated selection acting on standing genetic variation in genomic architecture introduced through secondary contact may underpin fine-scale local adaptation in Placentia Bay, Newfoundland, Canada, a large and deep embayment, highlighting the importance of chromosomal structural variation as a driver of contemporary adaptive divergence.

opencc-zeroJun 2022View details →

The Bug in a teacup – Monitoring arthropod-plant associations with environmental DNA from dried plant material

Environmental DNA analysis has revolutionized the field of biomonitoring in the past years. Various sources have been shown to contain eDNA of diverse organisms, for example water, soil, gut content and plant surfaces. Here we show that dried plant material is a highly promising source for arthropod community eDNA. We designed a metabarcoding assay to enrich diverse arthropod communities, while preventing amplification of plant DNA. Using this assay, we analyzed various commercially produced teas and herbs. These samples recovered ecologically and taxonomically diverse arthropod communities, a total of over a thousand species in more than 20 orders, many of them specific to their host plant and its geographic origin. Atypically for eDNA, arthropod DNA in dried plants shows a very high temporal stability, opening up plant archives as a source for historical arthropod eDNA. Considering these results, dried plant material appears excellently suited as a novel tool to monitor arthropods and arthropod-plant interactions, detect agricultural pests, and identify the geographic origin of imported plant material. The ability to detect highly diverse arthropod communities from all over the world in tea bags also highlights the utility of our approach for outreach purposes and to raise awareness about biodiversity.

opencc-zeroAug 2022View details →

Caulerpa-associated bacterial 16S rRNA in response to environmental stress

This dataset contains data from algal-associated bacteria from the green macroalgae, Caulerpa, from the paper " Morrissey, K.L. et al. (2021) Impacts of environmental stress on resistance and resilience of algal-associated bacterial communities. Ecology and Evolution". The experiment investigates the effects of a factorial combination of nutrient and temperature stress on the bacterial communities. We have also assessed the resistance and resilience of the algal-associated microbiota to environmental stress, using community dissimilarity metrics. Bacteria were characterised using the 16S rRNA gene and the community compositions were compared between different parts of the algal thallus (endo-, epi- and rhizomicrobiome). The results of this study provide evidence that nutrient enrichment has a significant influence on the taxonomic and functional structure of the epimicrobiota, with a low community resistance index observed for both. Temperature and nutrient stress had a significant effect on the rhizomicrobiota taxonomic composition, exhibiting the lowest overall resistance to change. The functional performance of the rhizomicrobiota had low resilience to the combination of stressors, indicating potential additive effects. Interestingly, the endomicrobiota had the highest overall resistance, yet the lowest overall resilience to environmental stress. This further contributes to our understanding of algal microbiome dynamics in response to environmental changes.

opencc-zeroDec 2012View details →

Data from: Host species and environmental effects on bacterial communities associated with Drosophila in the laboratory and in the natural environment

The fruit fly Drosophila is a classic model organism to study adaptation as well as the relationship between genetic variation and phenotypes. Although associated bacterial communities might be important for many aspects of Drosophila biology, knowledge about their diversity, composition, and factors shaping them is limited. We used 454-based sequencing of a variable region of the bacterial 16S ribosomal RNA gene to characterize the bacterial communities associated with wild and laboratory Drosophila isolates. In order to specifically investigate effects of food source and host species on bacterial communities, we analyzed samples from wild Drosophila melanogaster and D. simulans collected from a variety of natural substrates, as well as from adults and larvae of nine laboratory-reared Drosophila species. We find no evidence for host species effects in lab-reared flies; instead, lab of origin and stochastic effects, which could influence studies of Drosophila phenotypes, are pronounced. In contrast, the natural Drosophila–associated microbiota appears to be predominantly shaped by food substrate with an additional but smaller effect of host species identity. We identify a core member of this natural microbiota that belongs to the genus Gluconobacter and is common to all wild-caught flies in this study, but absent from the laboratory. This makes it a strong candidate for being part of what could be a natural D. melanogaster and D. simulans core microbiome. Furthermore, we were able to identify candidate pathogens in natural fly isolates.