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156 results for “biotic interactions”

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dryad32/100

Data from: Quantifying the role of colonization history and biotic interactions in shaping communities –a community transplant approach

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publicJun 2016View details →
dryad32/100

Data from: Effect of distance to edge and edge interaction on seedling regeneration and biotic damage in tropical rainforest fragments: a long‐term experiment

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publicApr 2019View details →
dryad32/100

Data from: Plant interactions as biotic drivers of plasticity in leaf litter traits and decomposability of Quercus petraea

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publicJan 2017View details →
dryad32/100

Data from: Past tree influence and prescribed fire mediate biotic interactions and community reassembly in a grassland-restoration experiment

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publicNov 2016View details →
dryad32/100

Data from: Antagonistic biotic interactions mitigate the positive effects of warming on wood decomposition

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publicDec 2024View details →
dryad28/100

Links between prey assemblages and poison frog toxins: a landscape ecology approach to assess how biotic interactions affect species phenotypes

<p>Ecological studies of species pairs showed that biotic interactions promote phenotypic change and eco-evolutionary feedbacks. However, it is unclear how phenotypes respond to synergistic interactions with multiple taxa. We investigate whether interactions with multiple prey species explain spatially structured variation in the skin toxins of the Neotropical poison frog <i>Oophaga pumilio</i>. Specifically, we assess how dissimilarity (i.e., beta diversity) of alkaloid-bearing arthropod prey assemblages (68 ant species) and evolutionary divergence between frog populations (from a neutral genetic marker) contribute to frog poison dissimilarity (toxin profiles composed of 230 different lipophilic alkaloids sampled from 934 frogs at 46 sites). We find that models that incorporate spatial turnover in the composition of ant assemblages explain part of the frog alkaloid variation, and we infer unique alkaloid combinations across the range of <i>O. pumilio</i>. Moreover, we find that alkaloid variation increases weakly with the evolutionary divergence between frog populations. The analytical framework proposed here can be extended to other multi-trophic systems, coevolutionary mosaics, microbial assemblages, and ecosystem services.</p>

opencc-zeroNov 2020View details →
dryad28/100

Data from: Predicting biotic interactions and their variability in a changing environment

Global environmental change is altering the patterns of biodiversity worldwide. Observation and theory suggest that species' distributions and abundances depend on a suite of processes, notably abiotic filtering and biotic interactions, both of which are constrained by species' phylogenetic history. Models predicting species distribution have historically mostly considered abiotic filtering and are only starting to integrate biotic interaction. However, using information on present interactions to forecast the future of biodiversity supposes that biotic interactions will not change when species are confronted with new environments. Using bacterial microcosms, we illustrate how biotic interactions can vary along an environmental gradient and how this variability can depend on the phylogenetic distance between interacting species.

opencc-zeroDec 2015View details →
dryad28/100

Data from: Process-based species pools reveal the hidden signature of biotic interactions amid the influence of temperature filtering

A persistent challenge in ecology is to tease apart the influence of multiple processes acting simultaneously and interacting in complex ways to shape the structure of species assemblages. We implement a heuristic approach that relies on explicitly defining species pools and permits assessment of the relative influence of the main processes thought to shape assemblage structure: environmental filtering, dispersal limitations and biotic interactions. We illustrate our approach using data on the assemblage composition and geographic distribution of hummingbirds, a comprehensive phylogeny and morphological traits. The implementation of several process-based species pool definitions in null models suggests that temperature, but not precipitation or dispersal limitation, acts as the main regional filter of assemblage structure. Incorporating this environmental filter directly into the definition of assemblage-specific species pools revealed an otherwise hidden pattern of phylogenetic evenness, indicating that biotic interactions might further influence hummingbird assemblage structure. Such hidden patterns of assemblage structure call for a reexamination of a multitude of phylogenetic- and trait-based studies that did not explicitly consider potentially important processes in their definition of the species pool. Our heuristic approach provides a transparent way to explore patterns and refine interpretations of the underlying causes of assemblage structure.

opencc-zeroDec 2014View details →
dryad28/100

Data from: The interplay of nested biotic interactions and the abiotic environment regulates populations of a hypersymbiont

1. The role of biotic interactions in shaping distribution and abundance of species should be particularly pronounced in symbionts. Indeed, symbionts have a dual niche composed of traits of their individual hosts and the abiotic environment external to the host, and often combine active dispersal at finer scales with host-mediated dispersal at broader scales. The biotic complexity in the determinants of species distribution and abundance should be even more pronounced for hypersymbionts (symbionts of other symbionts). 2. We use a chain of symbiosis to explore the relative influence of nested biotic interactions and the abiotic environment on occupancy and abundance of a hypersymbiont. 3. Our empirical system is the epibiont ciliate Lagenophrys discoidea, which attaches to an ostracod that is itself ectosymbiotic on crayfish (the basal host). We applied multimodel selection and variance partitioning for GLMM to assess the relative importance of: 1) traits of symbiotic hosts (ostracod sex and abundance), 2) traits of basal hosts (crayfish body weight, abundance and intermoult stage), 3) the abiotic environment (water chemistry and climate), and 4) geospatial autocorrelation patterns (capturing potential effects of crayfish dispersal among localities). 4. Our models explained about half of the variation in prevalence and abundance of the hypersymbiont. Variation in prevalence was partly explained, in decreasing order of importance (18-4%) by shared effects of symbiotic host traits and the abiotic environment, pure fixed effects of symbiotic hosts, abiotic environment and geospatial patterns (traits of basal hosts were not relevant). Hypersymbiont abundance was most strongly explained by random effects of host traits (mainly the symbiotic host), in addition to weaker fixed effects (mostly abiotic environment). 5. Our results highlight the major role of the interplay between abundance of symbiotic hosts and water physico-chemistry in regulating populations of a hypersymbiotic ciliate, which is likely critical for dispersal dynamics, availability of attachment resources and suitability of on-host living conditions for the ciliate. We also found moderate signal of regulation by the basal host, for which we propose three mechanisms: 1) modulation of microhabitat suitability (crayfish-created water currents); 2) concentration of symbiotic hosts within crayfish; and 3) dispersal mediated by crayfish. 30-Jul-2019

opencc-zeroAug 2019View details →
zenodo28/100

Raw data associated to the paper "Analysis of species pair associations combined with functional traits reveals the effects of environmental filtering and biotic interactions in an unmanaged temperate forest"

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opencc-by-4.0Nov 2023View details →
zenodo28/100

Global Biotic Interactions: Elton Dataset Cache for Hurlbert Lab's Avian Diet Database

<p>Global Biotic Interactions: Elton Dataset Cache</p> <p>The intended use of this archive/cache is to allow for offline-enabled access versions of existing species interaction datasets. The program &quot;Elton&quot; (https://doi.org/10.5281/zenodo.998263) was used to populate the content of elton-datasets.tar.gz . The same program can be used to extract information from the cache archive also. Global Biotic Interactions (https://globalbioticinteractions.org,&nbsp;https://doi.org/10.1016/j.ecoinf.2014.08.005) also uses these archives to create derived species interaction data archives, search indexes&nbsp;and APIs.</p> <p>Please note that due to size considerations, offline-enabled access to an elton dataset cache of iNaturalist interaction data has been excluded from this publications and moved into a separate Zenodo publication at https://doi.org/10.5281/zenodo.3950546 .</p> <p>Contents<br> --------</p> <p>README:<br> this file</p> <p>elton-datasets.tar.gz:<br> versioned archive with species interaction datasets</p> <p>elton-datasets.tar.sha256:<br> content signature of elton-datasets.tar</p> <p>elton-datasets.tsv:<br> list of included datasets</p> <p>elton.jar:<br> commandline program to help access the species interaction datasets</p> <p>Usage<br> -----</p> <p>To install, extract elton-datasets.tar.gz into a directory of choice using:</p> <p>tar xfz elton-dataset.tar.gz</p> <p>To use, download elton.jar included&nbsp;this publication and execute the following to get a list of available datasets:</p> <p>java -Xmx4G -jar elton.jar datasets</p> <p>on a system that has java v8+ installed.</p> <p>If all goes well, you should be able to regenerate the included file elton-dataset.tsv .</p> <p>For more information on how to use elton.jar, execute:</p> <p>java -jar elton.jar usage</p> <p>or visit https://github.com/globalbioticinteractions/elton for more available commands.</p> <p>Alternatively, without using Elton, you can access the data by inspecting the access.tsv files in the various directories of the datasets directory.</p> <p>When using these datasets in a publication or product, please cite the *original* data providers and publications. You can find the citations in the data.</p> <p>Included datasets:</p> <p>hurlbertlab/dietdatabase&nbsp;&nbsp; &nbsp;Allen Hurlbert. 2017. Avian Diet Database.&nbsp;&nbsp; &nbsp;https://zenodo.org/record/5557741/files/hurlbertlab/dietdatabase-v.1.0.7.zip&nbsp;&nbsp; &nbsp;2021-11-13T04:21:32.951Z&nbsp;&nbsp; &nbsp;6200e01b57d2d71c6235dec12e4e16f7b63f3a02c6d295be8115e5e83f6707e6&nbsp;&nbsp; &nbsp;0.12.2</p> <p>Associated content ids:</p> <p>hash://sha256/0a35187e80af8fd338b60b5436672694226aeb322f29ed8caf553e8f84db3a91<br> hash://sha256/898789535052ddff268097cde3666c470516b0713b0a3d807eee054a6e0c57a7<br> hash://sha256/c904e29c9c3f145fbcf4844297a2059e59b33cd40042a217e9ac957df773e494<br> hash://sha256/27c32d5a051eee84c2f03035cc88358150c2bf558f04734864301e8a1fbe5dd2<br> hash://sha256/2e21c326fd7813d754ed45d87420d91846a97854441d137442d5ab04ca67a533<br> hash://sha256/e1bdb9dfbf88669480e20d506ae0fb04e428401038752d4a40969f6a1dd98036<br> hash://sha256/b107405cc9b872500d0cf82e2ad7f052a44ed0eab59eb34691492b596af9f1ef<br> hash://sha256/2bfd8b345b16deb4044137c00c0109c6449cc4f07cc0e21026e4aac7b16f2f01<br> 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opencc-zeroNov 2021View details →
dryad28/100

Data for: Effects of biotic interactions on plant fecundity depend on spatial and functional structure of communities and time since disturbance

<p><span>Biotic interactions in plant communities affect individual fitness and community dynamics. Interactions between plants vary in space, over time and with organisational complexity. Yet it is challenging to quantify temporal, spatial and functional determinants of different types of interactions between long-lived perennial plant species and their effect on lifetime fecundity. We studied how plant-plant, pollinator- and seed predator-mediated interactions affect year-to-year variation in three fecundity components (cone production, seed set and seed survival) during post-fire recovery. Age-stratified data on the three fecundity components were collected in 19 even-aged communities comprising 20 serotinous <em>Protea </em>shrub species in the South African Fynbos. We analyse data on these fecundity components with neighbourhood models to infer the sign and strength of interactions throughout post-disturbance recovery, the neighbour plant traits that shape them and the spatial scale at which interactions take place. For each fecundity component, these models describe how neighbourhood effects change over time and with spatial distance between plants. For each focal plant, we then predicted neighbourhood effects on individual fecundity components and cumulative reproductive output at different post-fire stand ages. Competitive effects on cone production and seed set increased with post-fire stand age as biomass and floral resources for pollinators build up. In contrast, neighbourhood effects on seed survival were weak throughout post-disturbance recovery. Plant-plant interactions were shaped by neighbour traits related to resource acquisition, whereas animal-mediated interactions depended on neighbour traits related to resource availability for pollinators and seed predators. The spatial scale of the interactions increased from plant-plant over predator-mediated to pollinator-mediated interactions. The joint effect of these interactions on cumulative reproductive output caused the proportion of focal plants experiencing competition to increase with time since fire. We show that temporal changes in biotic interactions throughout post-disturbance recovery of perennial plant communities depend on functional traits and can be integrated to neighbourhood effects on lifetime fecundity. Studying the temporal, spatial and functional determinants of neighbourhood effects on lifetime fecundity is important for predicting not only individual plant fitness, but also population and community dynamics in changing environments. </span></p>

opencc-zeroOct 2022View details →
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Supplementary material 1 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Detailed design of the Field Experiment

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Supplementary material 2 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Brief description of the Field Experiment

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Figure 1 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Figure 1 A. Conceptual diagram of the mechanistic approach of the planned Research Unit. B. Conceptual scheme of the proposed evolutionary niche shifts in plant monocultures and mixtures. This idea feeds into our understanding of how evolutionary history influences the ecological interactions of species that compete for growth factors, ultimately defining biotope space (gray rectangle; Hutchinson 1978). Graphically depicted, species (ellipses) in mixture will show increasing niche differentiation over time due to competition (niche overlap). Thus, history of selection in diverse communities is expected to result in greater interspecific differences (less overlap of ellipses) and more specialization (smaller ellipses) than a history of isolation (monocultures). In monocultures, species will experience strong selection pressure by accumulating soil-borne pathogens, and species may invest energy in chemical and morphological defense traits (depicted by ellipses shifting towards the same corner of the habitat space). Plants in mixtures together may exploit more available biotope space than single monocultures, causing increasing diversity effects on ecosystem functions over time. However, there is limited support for this assumption for traits related to light (e.g., Lipowsky et al. 2015, Roscher et al. 2015) and resource use (Jesch et al. 2018) so far.

opencc-by-4.0Oct 2019View details →
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Figure 4 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Figure 4 Experimental design and hypotheses of the Ecotron Experiment. Briefly, four treatments will be established based on monoliths from a selection of the 9-year old Trait-Based Experiment (TBE; Ebeling et al. 2014) and from bare ground plots of the Jena Experiment as well as two seed sources: the respective plots and the original seed material that was used for the set-up of the TBE. (1) With plot-specific plant history and with plot-specific soil history; (2) without plot-specific plant history and with plot-specific soil history; (3) with plot-specific plant history and without plot-specific soil history; and (4) without plot-specific plant history and without plot-specific soil history. We expect the biodiversity–ecosystem function relationships to differ among the four treatments (see main text for details).

opencc-by-4.0Oct 2019View details →
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Figure 3 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Figure 3 Hypothesized slope of BEF relationships in the different treatments of the Field Experiment (see main text for details). Note that the 'with plant history, with soil history' only serves as a control in the Field Experiment, and effects of plant history can only be tested in the planned Ecotron Experiment. Redrawn after Vogel et al. (2019). '+', with; '-', without.

opencc-by-4.0Oct 2019View details →
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Supplementary material 4 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Detailed design of the Ecotron Experiment

opencc-zeroOct 2019View details →
zenodo28/100

Supplementary material 5 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Brief description of the Ecotron Experiment

opencc-zeroOct 2019View details →
zenodo28/100

Figure 2 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042

Figure 2 Structure of the proposed Research Unit. Three complementary experimental approaches are envisaged to study long-term biodiversity-ecosystem function (BEF) relationships, and how these are influenced by plant history and soil history. BEF patterns are studied in the Field Experiment with long-term plant diversity plots and manipulations of soil-history effects. BEF mechanisms are studied in the Ecotron Experiment and in Microcosm Experiments. In the Ecotron Experiment, plant history and soil history are independently crossed and detailed process measurements are possible. The Microcosm Experiments zoom in on focal interactions. In the Field Experiment and in the Ecotron Experiment, studies are conducted at the community level as well as at the plant individual level (magnifier; see detailed design of studies in the Appendices). Subprojects' (SPs') participation in experiments are illustrated with lines. The SPs of the proposed Research Unit fall into two tightly linked main categories (in gray) with two research areas each that aim at exploring variation in community assembly processes, micro-evolutionary changes, and resulting differences in biotic interactions as determinants of the long-term BEF relationship. Subprojects under "Microbial community assembly" (blue) and "Assembly and functions of animal communities" (red) mostly focus on plant diversity effects on the assembly of communities and their feedback effects on biotic interactions and ecosystem functions, while subprojects under "Mediators of plant-biotic interactions" (orange) and "Intraspecific diversity and micro-evolutionary changes" (green) mostly focus on plant diversity effects on plant trait expression and micro-evolution. PIs with requested personnel are underlined.

opencc-by-4.0Oct 2019View details →

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International Brain Laboratory public data

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OpenNeuro

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Last verified 2026-04-29Open record