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132 results for “Ecological network”
Maps of ecosystem multifunctionality and ecological connectivity for identifying Green Infrastructure networks in the European Alps
<p>High resolution raster datasets (20 meters) containing the results of an ecological connectivity and an ecosystem multifunctionality assessment for identifying Green Infrastructure networks in 10 pilot regions of the European Alps, modelled as part of the LUIGI Interreg Alpine Space project. Pilot regions include: department of Isère (FR), departments of Savoie and Haute-Savoie (FR), Munich Metropolitan Region (DE), Central Area of Salzburg (AT), South Burgenland (AT), Goriška region (SI), South Tyrol (IT), canton of Grisons (CH), Metropolitan City of Milan (IT), and Metropolitan City of Turin (IT). For a preview of the data and the results available for each pilot region <a href="https://www.alpine-space.org/projects/luigi/en/project-results/d.t1.2.1-pilot-regions-policy-briefs">click here</a></p> <p>Further information on the LUIGI project is available at: <a href="https://www.alpine-space.org/projects/luigi/en/home">https://www.alpine-space.org/projects/luigi/en/home</a></p> <p><a href="https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf">https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf </a></p> <p>The datasets include:</p> <ul> <li>a map for ecosystem service-based multifunctionality calculated out of the average of 11 standardized ecosystem service indicators: water provision, crop potential, timber production, fodder provision, pollination potential, carbon sequestration, nitrogen retention, natural hazard mitigation, runoff retention, outdoor recreation, and landscape aesthetics.</li> <li>a map of the modelled Ecological Network composed of core areas and ecological corridors. Corridors are modelled for medium-large forest mammal species and represent least-cost pathways connecting core areas. Different classes indicate areas with different levels of current ecological connectivity starting from core areas to areas in cities or anthropized land with no connectivity. Modeled corridors are presented in two classes to mirror different levels of prioritization and management actions.</li> <li>a map of the resistance of the landscape to the movement of forest mammal species. The landscape resistance raster has been developed by reclassifying and aggregating a high resolution (5m) land use and land cover map. Resistance values have been determined in relation to the naturalness of different land use and land cover classes. In this context, land use or landscape resistance is intended as the opposite of habitat suitability.</li> </ul>
Fig. 2 in Networks and the ecology of parasite transmission: A framework for wildlife parasitology
Fig. 2. How do we use networks to understand the ecology of parasite transmission? Networks allow us to describe how the behaviour of individuals collectively affects the transmission of parasites within wildlife populations. They provide a flexible framework that enables analysis at three different levels; individual (panel A), dyadic (pair-wise associations) (panel B) and the network (population) level (panel C). Within each level of analysis, there are different metrics and analytical approaches that can be used to explore the ecology of parasite transmission.
Fig. 1 in Networks and the ecology of parasite transmission: A framework for wildlife parasitology
Fig. 1. What is a network? A network in its most elementary form is an adjacency matrix, where row and column labels represent the individuals in the network, and the remaining cells represent the pair-wise associations among individuals in the network (panel A). These associations can be weighted, as below (panel A), where stronger relationships are assigned a higher value (for example, the duration or frequency of contact). They can also be directed, to reflect the direction of the association; in this instance, the direction of possible parasite transmission. In this case, rows represent donor nodes, and columns represent recipient nodes (e.g., in panel A: from node C (donor) to node D (recipient), there is a score of 1). The matrix can be visualised as a network diagram (panel B), consisting of nodes, which represent the epidemiological unit of interest (usually individuals) connected together by a series of edges representing the measure of association (the potential for parasite transmission). In context of understanding the ecology of parasite transmission, edges represent a 'contact' between two hosts that provides an opportunity for parasite transfer. The weighting of edges represents the likelihood of parasite transmission (e.g., the frequency or intensity of contact among hosts). The definition of a contact will depend on the type of parasite considered, and how it is passed from one host to another.
Fig. 5. Parsimony splits network constructed from a per and ITS2 concatenated sequence data set. Heterozygous specimens are indicated with A and B in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism
Fig. 5. Parsimony splits network constructed from a per and ITS2 concatenated sequence data set. Heterozygous specimens are indicated with A and B. 'bufonivora_EUROPE_A' represents a consistent haplotype present in all 12 samples from Europe (Table 1), of which just two were heterozygous ('bufonivora_frog' and 'bufonivora_NLWi'). 'bufonivora_CAN' and 'elongata_CAN' are represented by two samples each, none of which were heterozygous. Scale bar represents expected changes per site.
Data from: Quantifying and estimating ecological network diversity based on incomplete sampling data
<p>An ecological network refers to the ecological interactions among sets of species. Quantification of ecological network diversity and related sampling/estimation challenges have explicit analogues in species diversity research. A unified framework based on Hill numbers and their generalizations was developed to quantify taxonomic, phylogenetic, and functional diversity. Drawing on this unified framework, we propose three dimensions of network diversity that incorporate the frequency (or strength) of interactions, species' phylogenies and traits. As with surveys in species inventories, nearly all network studies are based on sampling data and thus also suffer from under-sampling effects. Adapting the sampling/estimation theory and the iNEXT (interpolation/extrapolation) standardization developed for species diversity research, we propose the iNEXT.link method to analyze network sampling data. The proposed method integrates the following four inference procedures: (i) Assessment of sample completeness of networks, (ii) asymptotic analysis via estimating the true network diversity, (iii) non-asymptotic analysis based on standardizing sample completeness via rarefaction and extrapolation with network diversity, and (iv) estimation of the degree of unevenness or specialization in networks based on standardized diversity. Interaction data between European trees and saproxylic beetles are used for illustrating the proposed procedures. The software iNEXT.link is developed to facilitate all computations and graphics.</p>
Implementing social network analysis to understand the socio-ecology of wildlife co-occurrence and joint interactions with humans in anthropogenic environments
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Code for: A century of wild bee sampling: historical data and neural network analysis reveal ecological traits associated with species loss
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Code from: Spatial resource heterogeneity stabilizes local and regional predator-prey dynamics in ecologically-realistic networks
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Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model
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Data from: Quantifying and estimating ecological network diversity based on incomplete sampling data
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Data from: Network ecology in dynamic landscapes
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R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys
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Ecological network structure in response to community assembly processes over evolutionary time
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International Long-Term Ecological Research Network (ILTER) Atmospheric Deposition and Stream Nitrogen Synthesis
We identified variables controlling stream nitrogen concentrations and fluxes, and how they have changed over time, by synthesizing 20 time series ranging from 5 to 51 years of data collected from forest and grassland dominated watersheds across Europe, North America, and East Asia and across four climate types (tropical, temperate, Mediterranean, and boreal) using the International Long-Term Ecological Research Network. We found declining trends in bulk ammonium and nitrate deposition, with ammonium contributing significantly more to atmospheric nitrogen deposition over time. Among sites, there were significant positive relationships between (1) precipitation and stream ammonium and nitrate fluxes and (2) atmospheric nitrogen inputs and stream nitrogen concentrations and fluxes. There were no significant relationships between air temperature and stream nitrogen export. Our long-term data shows that although nitrogen deposition is declining over time, atmospheric nitrogen inputs and precipitation remain the main predictors for nitrogen exported from forested and grassland watersheds. Overall, we also demonstrate that long-term monitoring provides understanding of ecosystems and biogeochemical cycling that would not be possible with short-term studies alone. Acknowledgements: We thank the organizers and funders of the ILTER Nitrogen Initiative Training Course and Workshop in Hokkaido, Japan in June 2016, which brought together many of the participants in this project. Templer was supported by a US National Science Foundation LTER grant NSF DEB 1637685. McDowell was supported by US National Science Foundation LTER grant NSF DEB 1831592. We are grateful to the EU Horizon 2020 funded eLTER PLUS project (Grand Agreement No. 871128) for financial support to Haase and Dirnböck. Dirnböck was also funded by the LTER-CWN project (FFG project number 858024). This study was partly supported by the Research Initiative Grants of the ILTER, Grants-in-Aid for Scientific Research (1
temporalNEON: Repository containing raw and cleaned-up organismal data from the National Ecological Observatory Network (NEON) useful for evaluating the links between change in biodiversity and ecosystem stability
Organismal data include the following taxonomic groups: small mammals, fish, ground beetles, and aquatic macroinvertebrates. Data were retrieved from the National Ecological Observatory Network (NEON) database in November 2020. We submit both raw data retrieved from NEON as .rds files, R code used to process these data, as well as processed data as .csv files.
Data from: Does biological intimacy shape ecological network structure? A test using a brood pollination mutualism on continental and oceanic islands
Biological intimacy—the degree of physical proximity or integration of partner taxa during their life cycles—is thought to promote the evolution of reciprocal specialization and modularity in the networks formed by co‐occurring mutualistic species, but this hypothesis has rarely been tested. Here, we test this "biological intimacy hypothesis" by comparing the network architecture of brood pollination mutualisms, in which specialized insects are simultaneously parasites (as larvae) and pollinators (as adults) of their host plants to that of other mutualisms which vary in their biological intimacy (including ant‐myrmecophyte, ant‐extrafloral nectary, plant‐pollinator and plant‐seed disperser assemblages). We use a novel dataset sampled from leafflower trees (Phyllanthaceae: Phyllanthus s. l. [Glochidion]) and their pollinating leafflower moths (Lepidoptera: Epicephala) on three oceanic islands (French Polynesia) and compare it to equivalent published data from congeners on continental islands (Japan). We infer taxonomic diversity of leafflower moths using multilocus molecular phylogenetic analysis and examine several network structural properties: modularity (compartmentalization), reciprocality (symmetry) of specialization and algebraic connectivity. We find that most leafflower‐moth networks are reciprocally specialized and modular, as hypothesized. However, we also find that two oceanic island networks differ in their modularity and reciprocal specialization from the others, as a result of a supergeneralist moth taxon which interacts with nine of 10 available hosts. Our results generally support the biological intimacy hypothesis, finding that leafflower‐moth networks (usually) share a reciprocally specialized and modular structure with other intimate mutualisms such as ant‐myrmecophyte symbioses, but unlike nonintimate mutualisms such as seed dispersal and nonintimate pollination. Additionally, we show that generalists—common in nonintimate mutualisms—can also evolve in intimate mutualisms, and that their effect is similar in both types of assemblages: once generalists emerge they reshape the network organization by connecting otherwise isolated modules.
Data from: Ecological network inference from long-term presence-absence data
Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbial systems. In this study, we test the suitability of these methods for inferring ecological interactions by constructing networks using Dynamic Bayesian Networks, Lasso regression, and Pear-son's correlation coefficient, then comparing the model networks to empirical trophic and nontrophic webs in two ecological systems. We find that although each model significantly replicates the structure of at least one empirical network, no model significantly predicts network structure in both systems, and no model is clearly superior to the others. We also find that networks inferred for the Tatoosh intertidal match the nontrophic network much more closely than the trophic one, possibly due to the challenges of identifying trophic interactions from presence-absence data. Our findings suggest that although these methods hold some promise for ecological network inference, presence-absence data does not provide enough signal for models to consistently identify interactions, and networks inferred from these data should be interpreted with caution.
Ecological mechanisms explaining interactions within plant-hummingbird networks: morphological matching increases towards lower latitudes
<p><a name="ab4">Interactions between species are influenced by different ecological mechanisms, such as morphological matching, phenological overlap, and species abundances</a>. <a name="Ab1"></a><a name="ab5">How</a> these mechanisms explain interaction frequencies across environmental gradients remains poorly understood. Consequently, we also know little about the mechanisms that drive the geographical patterns in network structure, such as complementary specialization and modularity. Here, we use data on morphologies, phenologies and abundances to explain interaction frequencies between hummingbirds and plants at a large geographic scale. <a name="ab2">For 24 quantitative networks sampled throughout the Americas, we found that</a> the tendency of species to interact with morphologically matching partners contributed to specialized and modular network structures. Morphological matching best explained interaction frequencies in networks found closer to the equator and in areas with low temperature seasonality. When comparing the three ecological mechanisms within networks, we found that both morphological matching and phenological overlap generally outperformed abundances in the explanation of interaction frequencies. Together, these findings provide insights into the ecological mechanisms that underlie geographical patterns in resource specialization. Notably, our results highlight morphological constraints on interactions as a potential explanation for increasing resource specialization towards lower latitudes.</p>
Data supplementing the article "Assessing ecological status with diatoms DNA metabarcoding : scaling-up on a WFD monitoring network (Mayotte island, France)" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal
<p>These data supplement the article"Assessing ecological status with DNA metabarcoding or microscopy? Comparison using benthic diatoms in tropical rivers" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal.</p> <p>The directory contains the following files:</p> <p><strong>80 PGM sequencing libraries (raw data, fastq files).rar </strong>- contains the 80 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>80 fastq files information.xlsx</strong> - contains the information relative to the 80 samples including: the ID used in Mothur analyses (corresponding to the name of the fastq files), the sample name, the sampling site code, the name of the river, the monitoring network to which rivers belong, the year of sampling and the GPS coordinates of sampling sites.</p> <p><strong>OTU (95 percent of similarity) list of 80 Mayotte samples.xlsx</strong> - contains the final OTU list obtained after applying all the bioinformatics treatments (trimming, clustering,...): OTUs created at 95% of similarity, the number of DNA reads per sample was normalized at 5710 reads (the smallest values obtained in one sample). A DNA representative sequence and the taxonomic assignment determined using Mothur (using classify.otu command) are also provided for each OTU.</p>
Understanding trophic interactions in a warming world by bridging foraging ecology and biomechanics with network science
<p><strong><em><span>Background</span></em></strong></p> <p><span>Leaf-cutter ants (<em>Atta</em> spp. and <em>Acromyrmex </em>spp.) are the principal insect pest and a major ecosystem engineer throughout the Neotropics (Leal et al., 2014; Wirth et al., 2003). They harvest plant matter in the surroundings of their colonies to grow a fungus as crop, and in doing so they cut plant matter on an almost industrial scale: about 15 % of the foliar biomass in the Neotropics, or about every sixth leaf, is consumed by leaf-cutter ant colonies (Costa et al., 2008; Fowler et al., 1989; Herz et al., 2007; Wirth et al., 2003), and more than half of all woody species are attacked by them (Cherrett, 1968; Rockwood, 1976). Leaf-cutter ants are perhaps the most voracious and polyphagous herbivorous insects (Lugo et al., 1973; Wirth et al., 2003), and their foraging activity is affected by a variety of environmental conditions, including wind (Alma et al., 2016b), precipitation (Steadman et al., 2020) and barometric pressure (Sujimoto et al., 2020), all of which will be subject to variation due to climate change. </span></p> <p><span>Although leaf-cutter foraging is clearly a complex, multi-factorial behaviour, it has at its core a biomechanical interaction between ant consumer and plant food resource: the force the ants can apply must exceed the force required to drag the mandible through the tissue (Püffel, Roces, et al., 2023; Püffel, Walthaus, et al., 2023). The magnitude of the available bite force is determined by worker size, and the magnitude of the minimum required cutting force is determined by structural and mechanical properties of the plant leaf; consumer and resource properties interact. This mechanical competition has resulted in extraordinary adaptations in both the anatomy and physiology of the leaf-cutter ant bite apparatus: their disproportionately large heads are filled to the rim with optimally packed mandible closer muscles (Püffel et al., 2021). Both their muscle stress and size-specific bite forces are among the highest measured for any animal (Püffel, Johnston, et al., 2023; Püffel, Roces, et al., 2023), and their mandibles are close to “ideally sharp” (Püffel, Walthaus, et al., 2023). As a result, the vast majority of worker sizes can cut the majority of tropical leafs; without these adaptations, and a bite performance commensurate with their body size, only the largest workers would be able to perform this crucial mechanical task (Püffel, Roces, et al., 2023). How will a warming climate affect resource accessibility for the leaf-cutters?</span></p> <p><span>Temperature increases have various implications for the trophic interactions of ants, including altered search behaviour <span>(Frizzi, 2018),</span> and foraging site selection (Spicer et al., 2017; Traniello et al., 1984). An increase in average temperatures can also drive body size decreases in insects (Tseng et al., 2018), including ants (Molet et al., 2017)<a href="https://www.zotero.org/google-docs/?broken=QmLD4C"><span>,</span></a> concomitantly reducing their available bite force (Püffel, Roces, et al., 2023; Rühr et al., 2022). Since leaf-cutter mandibles are so sharp that they already cut with a force close to the minimum dictated by cutting mechanics, the force required to cut leaves will likely be unaffected (Püffel, Walthaus, et al., 2023), and any change in body size will therefore only significantly impact bite forces. Because the relationship between bite forces and body size in the leaf-cutter is well understood mechanistically (Püffel, Roces, et al., 2023), it is possible to predict how these changes will impact trophic networks. A very rough estimate of the change in network structure serves to illustrate how network science can integrate biomechanics and foraging ecology to study the effect of climate change on trophic interactions. </span></p> <p><span>To demonstrate the potential of network science to integrate biomechanical and foraging data within the context of climate change, we constructed and analysed hypothetical plant-ant networks across six hypothetical temperatures. </span></p> <p> </p> <p><strong><em><span>Datasets and methods</span></em></strong></p> <p><span>All analysis was performed in R version 4.3.1 (R Core Team, 2023), and data processed reproducibly via the ‘tidyverse’ package (Wickham et al., 2019). We compiled two datasets and some additional contextual information. Leaf-cutter ant biomass (a proxy for body size) and bite force data were taken from <span>Püffel et al. (2023)</span> for 248 individual ants across three colonies. Required cutting forces for 1197 individual plants representing 868 taxa available to leaf-cutter ants were taken from <span>Onoda et al. (2011)</span>. Insect temperature-body size relationships were taken from <span>Tseng et al. (2018)</span>; specifically, a body size decrease of 1.56 % per degree Celsius increase for museum specimens, to represent gradual long-term change. Based on these data, edgelists (i.e., pairwise lists of consumers and resources) were generated for ants and plants in which binary interaction weights were applied; where bite forces exceeded the force required to cut leaves, a weighting of 1 was given, and 0 otherwise. This edgelist was then replicated for incremental increases of 1 °C up to a 5 °C increase by adjusting bite forces based on incremental body size decreases of 1.56 %. In order to estimate the change of bite force with body mass, we used direct bite force measurements from Püffel et al. (2023), which suggest that maximum bite force in <em>Atta vollenweideri</em> varies with body mass <em>m</em> as <em>T ~ m^0.9</em>. Thus, if body size decreases by a factor of 0.9844 (i.e., 1.56 % decrease) with every degree Celsius temperature increase, then the maximum bite force decreases by a factor of 0.9844<em><sup>0.9</sup></em>. Consequently, adjusted bite forces were calculated, and new binary edgelist weightings generated based on whether the adjusted bite force was greater than the required cutting force.</span></p> <p><span>Bipartite networks were constructed with consumer nodes and resource nodes representing the three ant colonies and the 868 plant taxa, respectively. All six networks were visualised using ‘ggnetwork’ (Briatte, 2021) via ‘igraph’ (Csardi & Nepusz, 2006) in a single network diagram to highlight persistence of links across temperatures using scaled red colours. Network metrics, specifically consumer degree (the number of plants ants were deemed able to interact with) and generality (the total range of plants accessible across all ants), were generated via the ‘bipartite’ package (Dormann et al., 2008) and visually compared via ‘ggplot2’ (Wickham, 2016).</span></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.