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558 results for “Species interactions”
Species interactions during succession in the western Cascade Range of Oregon, 1990 to present
The factors that contribute to plant species establishment and decline following disturbance determine the rates and patterns of successional change of a system. In this long-term field experiment, we test the commonly held assumption that competition for space or resources by dominant species determines the outcome of succession. Specifically, we examine the population- and community-level consequences of removing one or more potentially dominant species from the post-disturbance community after clearcut logging and burning of a mature/old-growth Douglas-fir forest. Experimental treatments include: (1) removal of early-seral annual, Senecio sylvaticus, or perennial, Epilobium angustifolium—or both—to test the influences of these early-seral dominants on subsequent community development; (2) removal of all species except Senecio or Epilobium, to test whether the decline of these early-seral dominants is driven by competitive displacement; or (3) removal of shade-tolerant forest species that dominate subsequent stages of succession—Rubus ursinus or Berberis nervosa plus Gaultheria shallon—to test the influences of these long-lived perennials on understory development. The experiment is a randomized block design comprising eight removal treatments plus a control replicated in each of 25 blocks. Removal (reduction in competition) is achieved by removing seedlings or vegetative stems annually from a treatment area of 2.5 x 2.5 m. Sample plots (1 x 1 m) centered within these are used to estimate cover of all vascular plant species and, for the first 8 yr of the experiment, stem density and height, facilitating estimates of above-ground biomass. Pre-harvest data were collected in 1990, logging/burning occurred in 1991, and removal treatments and post-treatment sampling were initiated in 1992. Six of the nine experimental treatments were terminated between 1996 and 1998, with loss of early-seral Senecio and Epilobium from the system. The remaining three treatments (removal
Interactions between plants and fungi and their roles in decay rates and CO2 release in five tropical leaf species
A microcosm experiment was used to test for the effects of interactions between particular plant and fungal decomposer species on rates of leaf decomposition. Each microcosm contained one species of leaf that was sterilized with gamma irradiation and then inoculated with a single fungus. Five plant species and ten fungal species (two dominants from each of the litter types) were used in all possible combinations. Plant species were selected for pair-wise comparisons based on phylogenetic relationships and litter quality characteristics. Decomposition was measured by both mass loss and CO2 release. Differences in weight loss and CO2 evolution were highly significant for plants, fungal species, and their interactions. Mass loss was positively correlated with CO2 evolution. Contrary to our hypotheses, however, microfungal dominants did not decompose their source leaves faster than microfungal dominants from other leaf species, nor were responses to other types of specificity detected. Matching of fungi to leaf substrates by their source, by phylogenetic relationships, or by chemical, physical and structural characteristics was not associated with consistent increases in decomposition. Although previously documented differences in microfungal species composition and dominance among decomposing leaves of different trees were confirmed in this study, such differences apparently do not directly affect the rates of ecosystem processes. The presence in a few of the microcosms of a generalist basidiomycete that had ligninolytic enzymes, Melanotus eccentricus, significantly accelerated the rate of decomposition. Non-specific basidiomycetes may therefore have a stronger effect on early stages of leaf litter decomposition than host-selective microfungi. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Pue
Protein Protein Interaction Prdiction Datasets of H.Pylori and S.cerevisiae Species
<p><br> Protein protein interaction prediction datasets related to 2 different species. These datasets have been comprehensively used in published literature to assess the performance of protein-protein interaction predictors.<br> </p>
Geographic range size and species morphology determines the organization of sponge host-guest interaction networks across tropical coral reefs (Raw data)
<p>Datasets for the analysis developed in the Article "<em><strong>Geographic range size and species morphology determines the organization of sponge host-guest interaction networks across tropical coral reefs</strong></em>". For more information, please refer to the original publication.</p> <p>Network_Structural_Index_&_SpogeTraits.csv <- Structural Index for the sponge-dwelling fauna network, sponge accumulated area and sponges’ morphology.</p> <p>NWTA_CoralReefs_Sponges_ interactions.csv <- Relationship between host sponges and guest fauna in the Northwester Atlantic coral reefs</p> <p>NWTA_CoralReefs_Sponge_reacords.csv <- Sponge species incidence records in the Northwester Atlantic coral reefs</p> <p>sponges_morphological_description.csv <- Sponge morphological standardization</p> <p>Network.html <- Interactive sponge-dwelling fauna network</p> <p>Enjoy!<br> </p>
Soil nitrogen and phosphorus effects on plant virus density, transmission, and species interactions
This data package includes data and code from an experiment testing the effects of nitrogen and phosphorus addition on interactions between two grass viruses (BYDV-PAV and CYDV-RPV). Data include virus density within oats (Avena sativa) and transmission of viruses to a second set of oats. Data were collected by Amy E. Kendig and collaborators between February 2014 and August 2014 at the University of Minnesota in St. Paul Minnesota, USA. Experiments were performed in growth chambers, virus density data were obtained using one-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and transmission data were obtained using RT-PCR. The code includes statistical analyses and figures. Model objects created through statistical analyses are also included. The code was run using R (version 3.5.2).
Behavioral data and analyses of competitive interactions between invasive and native ant species [from Cordonnier et al. 2021, Animals]
<p>This README accompanies the files "data_Cordonnier_Animals.txt" & "script_Cordonnier_Animals.txt"</p> <p> </p> <p>Associated publication : </p> <p>The native ant <em>Lasius niger</em> can limit the access to resources of the invasive Argentine ant</p> <p>M. Cordonnier, O. Blight, E. Angulo, and F. Courchamp</p> <p>Published in <em>Animals</em></p> <p> <br> ********************************** CONTENTS *****************************<br> The data are in table form with TABs as variables field delimiters so they can be readily imported in any statistical package or spreadsheet program. Please, contact me if you need the file formatted otherwise. </p> <p> </p> <p>*******************************************************************************<br> Variable names and descriptions</p> <p> </p> <p>Status_Lh status of Linepithema humile (Colonizer or Resident) </p> <p>opp species of the opponent</p> <p>combirc combination of status and species interacting</p> <p>temp temperature during the test</p> <p>hygro hygrometry during the test</p> <p>categ interacting species combination</p> <p>n_deadtot_opp total number of dead opponent workers</p> <p>t_50dead_opp time when 50% of the opponent mortality load have been diagnosed</p> <p>t_interact time of the first interaction between L. humile and opponent workers</p> <p>t_maxfights time when the maximal number of simultaneous fights occurs</p> <p>ET_fights standard deviation of the numbers of fights over time</p> <p>mean_fights mean number of simultaneous fights during the contest</p> <p>n_deadtot_Lh total number of dead workers of L. humile</p> <p>t_50dead_Lh time when 50% of the L. humile mortality load have been diagnosed</p> <p>t_arena_opp time of the opponent entrance in the arena</p> <p>t_bait_opp time of opponent resources’ discovery</p> <p>t_maxarena_opp time when the max. number of opponent workers occurs in the arena</p> <p>mean_arena_opp mean number of opponent workers simultaneously present in the whole arena</p> <p>t_maxbait_opp time when the maximal number of opponent workers on the bait occurs</p> <p>mean_bait_opp mean number of opponent workers on the bait</p> <p>t_arena_Lh time of the entrance in the arena of L. humile</p> <p>t_maxarena_Lh time when the max. number of workers of L. humile occurs in the arena</p> <p>mean_arena_Lh mean number of L. humile workers simultaneously present in the whole arena</p> <p>n_totprey_Lh total number of preys brought by L. humile</p> <p>t_bait_Lh time of resources’ discovery by L. humile</p> <p>t_maxbait_Lh time when the maximal number of L. humile individuals on the bait occurs</p> <p>ETbait_Lh standard deviation of the numbers of L. humile workers on the bait over time</p> <p>mean_bait_Lh mean number of L. humile workers on the bait</p> <p>t_50prey_Lh time when 50% of the final prey load</p> <p> </p> <p>******************************** CONTACT *********************************<br> Please contact me at:</p> <p>Marion Cordonnier<br> e-mail: marion.cordonnier@hotmail.com</p> <p>*******************************************************************************</p> <p> </p>
Data and code corresponding to the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities"
<p>This upload contains the Datasets and code to generate the results of the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities".</p><p>The database comprises two files containing the abundances of plants and pollinators, and one containing the interaction networks among plants and pollinators. </p><p>The code folder contains the code to generate the results, and to generate the figures of the manuscript. </p>
Distinguishing cophylogenetic signal from phylogenetic congruence clarifies the interplay between evolutionary history and species interactions
<p>Interspecific interactions, including host-symbiont associations, can profoundly affect the evolution of the interacting species. Given the phylogenies of host and symbiont clades and knowledge of which host species interact with which symbiont, two questions are often asked: "Do closely related hosts interact with closely related symbionts?" and "Do host and symbiont phylogenies mirror one another?". These questions are intertwined and can even collapse under specific situations, such that they are often confused one with the other. However, in most situations, a positive answer to the first question, hereafter referred to as "cophylogenetic signal", does not imply a close match between the host and symbiont phylogenies. It suggests only that past evolutionary history has contributed to shaping present-day interactions, which can arise, for example, through present-day trait matching, or from a single ancient vicariance event that increases the probability that closely related species overlap geographically. A positive answer to the second, referred to as "phylogenetic congruence", is more restrictive as it suggests a close match between the two phylogenies, which may happen, for example, if symbiont diversification tracks host diversification or if the diversifications of the two clades were subject to the same succession of vicariance events. Here we apply a set of methods (ParaFit, PACo, and eMPRess), which significance is often interpreted as evidence for phylogenetic congruence, to simulations under three biologically realistic scenarios of trait matching, a single ancient vicariance event, and phylogenetic tracking. The latter is the only scenario that generates phylogenetic congruence, whereas the first two generate a cophylogenetic signal in the absence of phylogenetic congruence. We find that tests of global-fit methods (ParaFit and PACo) are significant under the three scenarios, whereas tests of event-based methods (eMPRess) are only significant under the scenario of phylogenetic tracking. Therefore, significant results from global-fit methods should be interpreted in terms of cophylogenetic signal and not phylogenetic congruence; such significant results can arise under scenarios when hosts and symbionts had independent evolutionary histories. Conversely, significant results from event-based methods suggest a strong form of dependency between hosts and symbionts evolutionary histories. Clarifying the patterns detected by different cophylogenetic methods is key to understanding how interspecific interactions shape and are shaped by evolution.</p>
Stronger negative species interactions in the tropics supported by a global analysis of nest predation in songbirds
<p>Original data, phylogeny and list of studies from: "Stronger negative species interactions in the tropics supported by a global analysis of nest predation in songbirds"</p>
Data for: The interaction between metabolic rate, habitat choice, and resource use in a polymorphic freshwater species
<p>Raw respirometry data and respirometry code</p> <p>Data.xlsx is the data about each fish that was used for all analyses including Stable Isotope values, length, weight, sex, and habitat. This is the data that is used in the R code. </p> <p>Example code of the models used in our analyses</p> <p>TEF_metabolism.xlsx is data on the fish that were kept in the lab for almost a year. </p> <p> </p>
Fig. 1 in Interspecific Interactions as a Factor of Limitation of Geographical Distribution: Evidence Obtained by Modeling Home Ranges of Vole Twin Species Microtus Arvalis – M. Levis (Rodentia, Microtidae)
Fig. 1. Potential distribution of the Common vole Microtus arvalis. White circles are georeferenced occurrences of genetically identified individuals; black indicates areas of maximum habitat suitability, white are areas of lowest suitability.
Stable species and interactions in plant-pollinator networks deviate from core position in fragmented habitats
<p><span>S</span><span>pecies</span><span> and their interactions are more dynamic over time and space</span> <span>in</span><span> fragmented habitats </span><span>than</span><span> in continuous habitats</span><span>.</span> <span>In fragmented habitats,</span><span> the</span> <span>low </span><span>nestedness</span> <span>of </span><span>mutualistic</span><span> networks may be related to the</span> <span>position</span><span> change</span> <span>of stable (high persistence over time/space) species and interactions in </span><span>the</span><span> network</span><span>s.</span><span> Previous studies</span> <span>have shown that </span><span>s</span><span>table species </span><span>and</span><span> interactions tend to </span><span>be in</span><span> the core position </span><span>of</span> <span>mutualistic</span><span> networks</span><span>. </span><span>H</span><span>owever</span><span>, </span><span>in fragmented habitats</span><span>, </span><span>it remains unknown whether </span><span>stable species or interactions still </span><span>tend to </span><span>be in</span><span> the core position.</span><span> </span><span>To address this gap,</span> <span>here</span><span> we evaluated </span><span>the correlation between the position of proximity to the network core and the temporal/spatial stability of </span><span>species and interactions</span><span>, </span><span>using</span> <span>the </span><span>observation of 42 plant-pollinator networks conducted in a fragmented island landscape over 3 years</span><span>.</span> <span>We showed that temporally/spatially </span><span>stable </span><span>species </span><span>and</span><span> interactions </span><span>deviated from the network core</span><span> to varying degrees</span><span>. Temporally stable plants</span><span> were</span> <span>most likely to deviate from the network core, followed by</span> <span>pollinators and</span> <span>interactions</span><span>, while only </span><span>spatially stable </span><span>pollinators</span><span> tend to </span><span>deviate from the network core</span><span>. </span><span>When unstable species (</span><span>present in few time/space points</span><span>, </span><span>typically specialists) and interactions occupy the network core,</span> <span>they cannot interact with most species in the network </span><span>as</span><span> generalists</span> <span>do</span><span>, </span><span>result</span><span>ing</span> <span>in</span> <span>the</span> <span>decrease of network nestedness. Therefore, from the perspective of</span><span> position and stability,</span><span> s</span><span>table species and interactions </span><span>deviate from the network core</span> <span>in</span> <span>fragmented habitats</span><span>, which </span><span>is an important reason for</span><span> the</span><span> decrease of</span><span> nestedness in </span><span>mutualistic</span><span> networks</span><span>.</span><span> </span><span>Our study</span><span> suggests that protecting</span> <span>plants that</span><span> occupy the core in large plant-pollinator networks is </span><span>essential for</span> <span>maintaining the network persistence in fragmented habitats.</span></p>
Gut microbiota inter-species interactions shape the response of Clostridioides difficile to clinically relevant antibiotics
<p>In the human gut, the growth of <em>Clostridioides difficile </em>is impacted by a complex web of inter-species interactions with members of human gut microbiota. We investigate the contribution of inter-species interactions on the antibiotic response of <em>C. difficile </em>to clinically relevant antibiotics using bottom-up assembly of human gut communities. We discover two classes of microbial interactions that alter <em>C. </em>difficile’s antibiotic susceptibility: infrequent increases in tolerance at high antibiotic concentrations and frequent growth enhancements at low antibiotic concentrations. Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species <em>Desulfovibrio piger </em>increases metronidazole tolerance of <em>C. difficile</em>. Competition with species that display higher sensitivity to the antibiotic than <em>C. difficile </em>leads to enhanced growth of <em>C. difficile </em>at low antibiotic concentrations. A dynamic computational model identifies the ecological design principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping <em>C. difficile</em>’s response to antibiotics, which could inform therapeutic interventions.</p>
Gut microbiota inter-species interactions shape the response of Clostridioides difficile to clinically relevant antibiotics
<p>In the human gut, the growth of <em>Clostridioides difficile </em>is impacted by a complex web of inter-species interactions with members of human gut microbiota. We investigate the contribution of inter-species interactions on the antibiotic response of <em>C. difficile </em>to clinically relevant antibiotics using bottom-up assembly of human gut communities. We discover two classes of microbial interactions that alter <em>C. </em>difficile’s antibiotic susceptibility: infrequent increases in tolerance at high antibiotic concentrations and frequent growth enhancements at low antibiotic concentrations. Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species <em>Desulfovibrio piger </em>increases metronidazole tolerance of <em>C. difficile</em>. Competition with species that display higher sensitivity to the antibiotic than <em>C. difficile </em>leads to enhanced growth of <em>C. difficile </em>at low antibiotic concentrations. A dynamic computational model identifies the ecological design principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping <em>C. difficile</em>’s response to antibiotics, which could inform therapeutic interventions.</p>
Deciphering the interactions between plant species and their main fungal root pathogens in mixed grassland communities
<p>1. Plant diversity can reduce the risk of plant disease, but positive, and neutral effects have also been reported. These contrasting relationships suggest that plant community composition, rather than diversity per se, affects disease risk. Here, we investigated how diversity and composition of plant communities drive root-associated pathogen accumulation belowground.</p> <p>2. In a temperate grassland biodiversity experiment, containing 16 plant species (forbs and grasses), we determined the abundance of root-associated fungal pathogens in individual plant species growing in monocultures and in 4-species mixtures through Illumina MiSeq amplicon sequencing.</p> <p>3. In the plant monocultures, we identified three major fungal pathogens that differed in host range: <em>Paraphoma chrysanthemicola</em>, associated with roots of forb species of the Asteraceae family, <em>Slopeiomyces cylindrosporus</em>, associated with grass species, and <em>Rhizoctonia solani</em>, associated with multiple forb and grass species. In mixtures, there was no significant reduction in relative abundance of these pathogens in their host species as compared to monocultures. However, in mixtures, there was a significant increase in relative abundance of each pathogen in several non-host and host plant species. Across mixtures, plant community composition affected pathogen relative abundance in individual plant species. This effect was driven by the presence of a particular neighbouring plant species (depending on the pathogen), rather than functional group composition (i.e. grass/forb ratio) or averaged pathogen pressure (based on monocultures) of all neighbours. Specifically, the presence of neighbour host species <em>Achillea millefolium</em> significantly increased <em>P. chrysanthemicola</em>, but decreased <em>R. solani</em> relative abundance in several host and non-host plant species in mixtures.</p> <p>4. Synthesis: Our results indicate that interactions between different plant species – both host and non-hosts – and fungal pathogens underlie effects of plant diversity on root pathogen abundance. Non-host species may act as pathogen reservoirs in diverse plant communities, as they harboured certain pathogens in mixtures, but not in monocultures. Additionally, particular host species can strongly affect pathogen abundance in other (host and non-host) plant species in plant mixtures, suggesting clear effects of species identity in the diversity-disease relationship. Belowground disease risk thus depends on plant community composition rather than diversity per se, via specific interactions between plant species and their root-associated pathogens.</p>
Simulation Data & R scripts for: "Introducing recurrent events analyses to assess species interactions based on camera trap data: a comparison with time-to-first-event approaches"
<p><strong>Files descriptions:</strong></p> <p>All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration. <br>"results_perfect_detection.csv" refers to the results from the first simulation part with all the observations.<br>"results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.</p> <p>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>PAMM30: p-value of the PAMM running on the 30-days survey.<br>PAMM7: p-value of the PAMM running on the 7-days survey.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p>"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations.<br>"results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.<br>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p><strong>Scripts files description:</strong><br>1_Functions: R script containing the functions:<br> - MRPP from Karanth et al. (2017) adapted here for time efficiency.<br> - MRPP from Murphy et al. (2021) adapted here for time efficiency.<br> - Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets.<br> - The simulation() function used to simulate two species observations with reciprocal effect on each other.<br>2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.<br>3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.<br>3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.<br>4_Graphs: R script containing the code for plotting results from the simulation part and appendices.<br>5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.<br>5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.</p>
Data and code for Reeb, R.A. & Kuebbing, S.E. (2024). Phenology mediates direct and indirect interactions among co-occurring invasive plant species. Ecology, e4446.
<p>Data and analysis code for:</p> <p>Reeb, R.A. & Kuebbing, S.E. (2024). Phenology mediates direct and indirect interactions among co-occurring invasive plant species. Ecology, e4446. <a href="https://doi.org/10.1002/ecy.4446">https://doi.org/10.1002/ecy.4446</a></p> <p>Repository contains R markdown analysis code, datasets, and the associated metadata file.</p>
Abundance-mediated species interactions between coyote, fisher, and marten in Northeastern US
<p>Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, contemporary approaches for modelling species interactions predominantly centre around occupancy states. Here, we use simulations to illuminate the inadequacies of modelling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to bias in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an occupancy-abundance model designed explicitly for modelling abundance-mediated species interactions involving two or more species. When modelling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modelling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.</p>
Figure 1 in Invasions of two estuarine gobiid species interactively induced from water diversion and saltwater intrusion
Figure 1. The East Route of South-to-North Water Transfer Project, showing the five major lakes along the route (shadow areas) as storages, the Grand Canal as conveyance, and geographic relationships of the major rivers (i.e., the Yangtze River, the Huai River, and the Yellow River) with the route. The Nansi Lake is separated into the Lower Nansi Lake and Upper Nansi Lake by the Erji Dam. The year of the first record of the two invasive species, Taenioides cirratus and Tridentiger bifasciatus, in each of these lakes was indicated to show their invasion patterns.
Fig. 1 in Interactions of selected species of stink bugs (Hemiptera: Heteroptera: Pentatomidae) from leguminous crops with plants in the Neotropics
Fig. 1. Total records of plants associated with different species of stink bugs pests of legumes (Fabaceae) in the neotropics based on literature review. The dark line links the different values as follows: (A) = number of plant species on where each stink bug species was observed; (B) = number of plant families on where each species of stink bug was observed; and (C) = number of reproductive hosts (plants on which bug can complete development) on where each species of stink bug was observed. Note that the area for total plant species in (A) is much greater that the one for reproductive hosts in (C), indicating that on the majority of the plants the bugs are observed they do not reproduce. NV = Nezara viridula; PG = Piezodorus guildinii; EH = Euschistus heros; EM = Edessa meditabunda; DF = Dichelops furcatus; DM = Dichelops melacanthus; and TP = Thyanta perditor.
ScienceDex guides
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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.