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opencc-by-4.0Mar 2024View details →

Data and scripts for: Airborne DNA reveals predictable spatial and seasonal dynamics of fungi

Fungi are among the most diverse and ecologically important kingdoms of life. However, the distributional ranges of fungi remain largely unknown, as do the ecological mechanisms that shape their distributions. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardised aerial sampling of fungal spores. The vast majority of OTUs were detected only within one climatic zone, and the spatio-temporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal, and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating only for a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with microbial lifestyles follow the global biodiversity paradigms known for macro-organisms. The analyses presented in the paper can be reproduced with the R-script pipeline provided here. The starting point for the scripts is the datafile allData.RData that was published originally by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). The datafile allData.RData is provided also here for convenience, and it includes the following three objects: metadata, taxonomy, and otu.table (see Ovaskainen et al. for details). The script pipeline consists of the following elements (for deltails, see the Methods of the paper): <ul> <li>Scripts S01: data preparation <ul> <li>S01.1_download_clim_data.R. This script downloads daily climatic data for the entire world.</li> <li>S01.2_select_and_preprocess_clim_data.R. This script selects the data relevant for the study locations and preprocesses it.</li> <li>S01.3_add_climatic_data_to_metadata.R. This script adds the preprocessed climatic data to the metadata.</li> <li>S01.4_otu_guild_assignment.R. This script performs the guild assignment to the OTUs. It utilizes the datafiles Fung_LifeStyle_Data.RDS and funguild_db.rds provided here, and it utilizes the taxonomy of ProtaxFungi provided by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). Note that while the paper presents analyses and results only for the trait database of Aguilar-Trugueros et al., the scipts repeat the trait analyses also for the FunGuild database. The reason for not showing the results for the FunGuild database in the paper was that the database of Aguilar-Trugueros et al. contains FunGuild as one of the data sources, and that the results were highly coherent between the two databases.</li> <li>S01.5_add_trait_data_to_taxonomy_and_metadata.R. This script adds the guild data and spore size data to taxonomy (taxon-specific traits) as well as to metadata (community-weighted mean traits). It utilizes the datafile Spore_data_12Nov21.RDS provided here. This script can also be used to generated simulated contamination to the OTU table by setting contaminate=TRUE.</li> </ul> </li> <li>Scripts S02: exploratory analyses <ul> <li>S02.1_show_descriptive_statistics.R. This script outputs basic desriptive statistics from the data.</li> <li>S02.2_make_study_design_maps.R. This script plots the study design map shown in the paper.</li> <li>S02.3_compute_site_and_biome_profiles.R. This script computes site_profiles (needed in ordinations) and biome_profiles (needed to create Venn diagrams).</li> <li>S02.4_make_venns.R. This script produces Venn diagrams.</li> </ul> </li> <li>Scripts S03: ordination analyses <ul> <li>S03.1_make_ordination_maps.R. This script makes the ordination analyses.</li> </ul> </li> <li>Scripts S04: univariate analyses <ul> <li>S04.1_conceptualize_univariate_models.R. This script produces a figure that illustrates conceptually the differenent model variants. </li> <li>S04.2_make_univariate_analysis.R. This script implements the univariate analyses.</li> <li>S04.3_show_univariate_results.R. This script summarizes the results of the univariate analyses by producing tables of AIC and R2.</li> <li>S04.4_plot_univariate_results.R. This script plots the univariate models.</li> <li>S04.5_compute_temporal_turnover.R. This script computes site-specific indices of temporal turnover.</li> <li>S04.6_show_temporal_turnover.R. This script generates a plot illustrating temporal turnover.</li> </ul> </li> <li>Scripts S05: Hmsc analyses <ul> <li>S05.1_define_Hmsc_models.R. This script defines the Hmsc models. It utilizes the R-function as.phylo.formula provided here.</li> <li>S05.2_export_Hmsc_models_for_fitting.R. This script exports the unfitted Hmsc-models for fitting with Hmsc-HPC that operates on python/tensorflow.</li> <li>S05.3_import_fitted_Hmsc_models.R. This script imports the fitted Hmsc-models back to Hmsc-R.</li> <li>S05.4_postprocess_Hmsc_results.R. This script postprocesses the results of the fitted Hmsc model.</li> <li>S05.5_show_Hmsc_results.R. This script generates a plot that illustrates the postprocessed results.</li> </ul> </li> </ul>

opencc-zeroMay 2022View details →

Data from: Hierarchical variation in phenotypic flexibility across timescales and associated survival selection shape the dynamics of partial seasonal migration

Population responses to environmental variation ultimately depend on within-individual and among-individual variation in labile phenotypic traits that affect fitness, and resulting episodes of selection. Yet, complex patterns of individual phenotypic variation arising within and between time periods, and associated variation in selection, have not been fully conceptualised or quantified. We highlight how structured patterns of phenotypic variation in dichotomous threshold traits can theoretically arise and experience varying forms of selection, shaping overall phenotypic dynamics. We then fit novel multistate models to ten years of band-resighting data from European shags to quantify phenotypic variation and selection in a key threshold trait underlying spatio-seasonal population dynamics: seasonal migration versus residence. First, we demonstrate substantial among-individual variation alongside substantial between-year individual repeatability in within-year phenotypic variation ('flexibility'), with weak sexual dimorphism. Second, we demonstrate that between-year individual variation in within-year phenotypes ('supraflexibility') is structured and directional, consistent with the threshold trait model. Third, we demonstrate strong survival selection on within-year phenotypes, and hence on flexibility, that varies across years and sexes, including episodes of disruptive selection representing costs of flexibility. By quantitatively combining these results, we show how supraflexibility and survival selection on migratory flexibility jointly shape population-wide phenotypic dynamics of seasonal movement.

opencc-zeroJul 2022View details →

Seasonal dynamics of the wild rodent faecal virome

Viral discovery studies in wild animals often rely on cross‐sectional surveys at a single time point. As a result, our understanding of the temporal stability of wild animal viromes remains poorly resolved. While studies of single host–virus systems indicate that host and environmental factors influence seasonal virus transmission dynamics, comparable insights for whole viral communities in multiple hosts are lacking. Utilizing noninvasive faecal samples from a long‐term wild rodent study, we characterized viral communities of three common European rodent species (Apodemus sylvaticus, A. flavicollis and Myodes glareolus) living in temperate woodland over a single year. Our findings indicate that a substantial fraction of the rodent virome is seasonally transient and associated with vertebrate or bacteria hosts. Further analyses of one of the most common virus families, Picornaviridae, show pronounced temporal changes in viral richness and evenness, which were associated with concurrent and up to ~3‐month lags in host density, ambient temperature, rainfall and humidity, suggesting complex feedbacks from the host and environmental factors on virus transmission and shedding in seasonal habitats. Overall, this study emphasizes the importance of understanding the seasonal dynamics of wild animal viromes in order to better predict and mitigate zoonotic risks.

opencc-zeroMay 2024View details →

Seasonal dynamics in the mammalian microbiome between disparate environments

Host-associated bacterial microbiomes can facilitate host acclimation to seasonal environmental change and are hypothesized to help hosts cope with recent anthropogenic environmental perturbations (e.g., landscape modification). However, it is unclear how recurrent and recent forms of environmental change interact to shape variation in the microbiome. The majority of wildlife microbiome research occurs within a single seasonal context. Meanwhile, the few studies of seasonal variation in the microbiome often restrict focus to a single environmental context. By sampling urban and exurban eastern grey squirrel populations in the spring, summer, autumn, and winter, we explored whether seasonal rhythms in the grey squirrel gut microbiome differed across environments using a 16S amplicon sequencing approach. Differences in the microbiome between urban and exurban squirrels persisted across most of the year, which we hypothesize is linked to anthropogenic food consumption; but we also observed similarities in the urban and exurban grey squirrel microbiome during the autumn, which we attribute to engrained seed caching instincts in preparation for the winter. Host behaviour and diet selection may therefore be capable of maintaining similarities in microbiome structure between disparate environments. However, the depletion of an obligate host mucin glycan specialist (Akkermansia) during the winter in both urban and exurban squirrels was among the strongest differential abundance patterns we observed. In summary, urban grey squirrels showed different seasonal patterns in their microbiome than squirrels from exurban forests, however, in some instances host behaviour and physiological responses might be capable of maintaining similar microbiome responses across seasons.

opencc-zeroMay 2024View details →

Data from: Seabird presence and seasonality influence nutrient dynamics of atoll habitats

In this study, we investigate nutrient dynamics linked to seabird presence and seasonality. We compare seabird colonies of different taxa nesting on separate islands of Farquhar Atoll, Seychelles. We first quantify guano nutrient input and guano nitrogen isotopes for each seabird species. We assess the transfer of seabird-derived nutrients in terrestrial and marine habitats by comparing nitrogen isotope values in soil, plants, and seagrass on each seabird island and a control island, during the wet and dry seasons. We then assess the influence of seabirds by comparing nutrient levels in soil, plants, and seagrass on each seabird island and a control island, during the wet and dry seasons. For plants and seagrass, we also assess and compare nutrient ratios and carbon stable isotope ratios on each seabird island and a control island, during the wet and dry seasons.

opencc-by-4.0Jun 2024View details →

Data supporting "Seasonal dynamics and punctuated carbon sink reduction suggest photosynthetic capacity of boreal silver birch is reduced by the accumulation of hexose"

Data supporting our New Phytologist publication: "Seasonal dynamics and punctuated carbon sink reduction suggest photosynthetic capacity of boreal silver birch is reduced by the accumulation of hexose". The file present the data produced during long-term (three consecutive years 2019-2021) field observations and short-term girdling manipulation of top-crown shoots in three mature Betula pendula trees as details of our manuscript. Values of leaf gas-exchange, environmental variables, leaf water and nitrogen status, and concentrations of sucrose, hexoses (glucose and fructose), starch, total sugar, and total non-structural carbohydrate in leaves are listed.

opencc-by-4.0Jun 2024View details →

Data from: Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests

This dataset supports the analysis about Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests

Fig. 8 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 8: Biomass of gillnet landings.

Fig. 17 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 17: Total value and selling price per unit weight of gillnet landings.

Fig. 12 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 12: Trammel net landing biomass per vessel for the main target species.

Fig. 4 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 4: Number of active vessels using traps.

Fig. 3 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 3: Number of active vessels using trammel nets.

Fig. 1 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 1: Study area: the Adriatic Sea subdivided into GFCM Geographical Sub-Area (GSA) 17 and 18.

Fig. 2 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 2: Number of active vessels using gillnets.

Fig. 11 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 11: Gillnet landing biomass per vessel for the main target species.

Fig. 19 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 19: Total value and selling price per unit weight of traps landings.

Fig. 10 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 10: Biomass of traps landings.

Fig. 13 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 13: Trap landing biomass per vessel for S. officinalis.

Fig. 9 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 9: Biomass of trammel net landings.

Fig. 18 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea

Fig. 18: Total value and selling price per unit weight of trammel net landings.