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110 results for “ecological response”
warmXtrophic: plant community responses to the individual and interactive effects of climate warming and herbivory across multiple years at Kellogg Biological Station Long-Term Ecological Research Sites (KBS LTER), Michigan, USA, and University of Michigan Biological Station (UMBS), Michigan, USA.
Climate change has both direct and indirect effects on ecological communities. Whereas most climate change ecology experiments manipulate abiotic drivers to measure direct effects of climate on species or communities, fewer quantify the indirect effects through biotic interactions, especially over multiple sites and years. In this factorial experiment we manipulate temperature through open-top chambers, and the level of insect herbivory through insecticide. At two early successional field sites separated by 3 degrees of latitude and 3°C of mean annual temperature (University of Michigan Biological Station, Pellston, MI and Kellogg Biological Station, Hickory Corners, MI), 6 replicate 1-m2 plots per treatment were installed in May 2015. 12 plots per site are at ambient temperature, 12 are warmed with year-round non-UV filtering polycarbonate and wood frame construction OTCs for tall-stature plants (Welshofer et al. 2018 MEE). Insecticide reduces insect herbivory in half the plots (Welshofer et al. 2018 Oecologia). Over the course of the experiment, OTCs warmed the plant communities by 1.9°C-3.0°C on average over the growing season. Each year, through 2021, plant traits and community responses were measured at the species level: plant phenology (green-up, flowering, flowering duration, seed set); plant percent cover (aerial % cover of the 1m2 plot); plant traits (specific leaf area, C and N content), herbivory damage to leaves, and plant species biomass (only in 2021). Further methodological details are found within each response variable metadata. This experiment is ongoing and further data package updates are planned. L0 data is available upon request. R scripts can be found here: https://github.com/SpaCE-Lab-MSU/warmXtrophic. The biotic and abiotic community context and relative strengths of direct vs. indirect effects may yield ecological surprises under climate change unless addressed together. Large-scale experiments like this one can improve our ability to unde
Data and code from "No evidence of sex ratio manipulation by black-throated blue warblers in response to food availability" Kaiser et al. 2023 Behavioral Ecology and Sociobiology
This dataset is published in support of "No evidence of sex ratio manipulation by black-throated blue warblers in response to food availability" by Kaiser et al. 2023 in Behavioral Ecology and Sociobiology. Data and code to test the assumptions and key predictions of the Trivers-Willard hypothesis, which proposes that females produce more sons or daughters depending on food availability, in the black-throated blue warbler at the Hubbard Brook Experimental Forest, NH, 2007-2012. Datasets support analyses of sex ratio bias at both the nest and nestling levels. Data tables support the comparison of the ratio of variances in the scaled pre-fledging mass of male and female nestlings using an F test and reproduction of Figures 2a and 2b. Figures are those used in the published manuscript. Code supports the calculation of offspring sex ratio bias at the population level, and considering separately both low- and high-quality habitats, using the Neuhäuser test, statistical models testing the assumptions of the Trivers-Willard hypothesis, effects of food availability and parental provisioning on offspring sex ratio, and effects of food availability on pre-fledging nestling mass of sons and daughters, and a power analysis to determine the power to detect an effect of food supplementation on sex ratio. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the US Forest Service, Northern Research Station.
Relyea, R. A. 2001. Morphological and behavioral plasticity of larval anurans in response to different predators. Ecology 82:523-540.
Many organisms can adjust to a changing environment by developing alternative phenotypes that improve their fitness. Our understanding of phenotypic plasticity is largely based upon observations from single species responding to two different environments and measuring a single plastic trait. In this study, I examine predator-induced phenotypic plasticity in tadpoles by observing how six species of larval anurans respond to five different predator environments in 11 different traits (seven morphological traits, two behavioral traits, growth, and development). The results demonstrate that behavioral and morphological plasticity may be ubiquitous in larval anurans. The six prey species exhibited different responses to the same predator species, and each prey exhibited different responses to different predator species. This suggests that responses to a particular predator may not serve as general defense against all predators; rather, prey express predator-specific suites of responses. I also compared relative differences in plasticity among species and among traits. In contrast to earlier findings using only two predator environments, I found that different anurans possess similar degrees of plasticity for most of their traits when reared in a large number of environments. In addition, behavioral traits were always more plastic than morphological traits. Finally, I examined trait integration to address whether there were apparent trade-offs among traits and limits imposed by the abiotic environment. Trait integration, or the degree of correlated responses among traits across predator environments within a prey species, was very low. This further suggests that the suites of responses are predator specific and may be under independent directions of selection in different predator environments. Trait correlations across prey species indicated that there is an apparent trade-off between tail fin depth and body size. This relationship is supported by selection studies with
Relyea, R. A. 2001. The relationship between predation risk and anti-predator responses in larval anurans. Ecology 82:541-554.
Organisms that produce alternative, nondiscrete phenotypes in response to environmental conditions are expected to alter their phenotypes in relation to the degree of environmental change. This idea has been applied to the evolution of antipredator responses by prey, in which it has been hypothesized that prey should respond more strongly to predators that pose greater mortality risk. In a companion paper, I quantified predatorinduced behavioral and morphological responses in six species of larval anurans across five different predator environments and found that these responses were prey- and predatorspecific. In the present study, I addressed whether the responses were related to the level of predation risk posed by each of the predators. Within each prey species, I found that different predators posed different levels of predation risk; within each predator species, different prey species experienced different levels of risk. The differences in predation risk could be understood mechanistically after I quantified differences among predators in their ability to capture, handle, and consume prey and differences among prey in behavior and morphology. Using multivariate analyses, I found that predation risk had no significant effect on how a given prey responds to predators, although there were significant univariate behavioral effects; higher predation risk was related to greater decreases in activity and greater spatial avoidance. I also examined the relationship between risk and response across the six prey species within a predator treatment and found that higher predation risk across species leads to greater decreases in activity in the presence of Umbra and greater increases in tail depth in the presence of Anax. Thus, while previous studies have found relationships between predation risk and prey response when focusing on relatively few species, few predators, and a single trait, this more powerful test using 30 predator–prey combinations and nine traits sugge
Identifying the drivers and responses of abrupt changes across spatial and temporal scales in ecology: a review
Recently, the theoretical basis for understanding abrupt changes in ecosystems relative to regime shifts has emerged (Ratajczak et al. 2018). Abrupt changes are defined as, “substantial changes in the mean or variability of a system that occur in a short period of time relative to typical rates of change” (Ratajczak et al. 2018). Despite a driver-response framework to guide the environmental conditions under which abrupt changes are likely to occur coupled with many examples of unexpected changes from long-term ecological research, our theoretical basis of understanding of abrupt changes doesn’t include long-term scales, variability in drivers and responses, changes in the magnitude or direction of drivers, or the interactions among multiple drivers across spatiotemporal scales (sensu Ratajczak et al. 2018). Further, a critical review of the literature is lacking and essential to further understanding how common abrupt changes are detected and reported, as well as patterns and scales of drivers and responses of abrupt change in ecosystems. To address this knowledge gap, we searched the existing ecological literature for evidence and commonalities of abrupt change across ecosystems to identify commonalities and differences of abrupt change drivers and responses across terrestrial, freshwater, and marine ecosystems. We specifically asked the following questions: (1) How common are abrupt changes reported in the ecological literature? (2) How do driver and response temporal and spatial scales of abrupt changes compare and vary across terrestrial, freshwater, and marine ecosystem types? (3) Is there relative congruence between the temporal and spatial scale of drivers and responses? (3) What are common types of drivers and responses to abrupt changes, and how do they vary across ecosystem types? (4) What terms are most associated with drivers and responses of abrupt changes among ecosystem types?
Data for manuscript: Ecological lags govern the pace and outcome of plant community responses to 21st century climate change
<p>These data were used in the analyses reported in Block et al. "Ecological lags govern the pace and outcome of plant community responses to 21st century climate change".</p>
Ecological data for: Subsidy accessibility drives asymmetric food web responses
<p>Global change is fundamentally altering flows of natural and anthropogenic subsidies across space and time. After a pointed call for research on subsidies in the 1990s, an industry of empirical work has documented the ubiquitous role subsidies play in ecosystem structure, stability and function. Here, we argue that physical constraints (e.g., water temperature) and species traits can govern a species' accessibility to resource subsidies, which has been largely overlooked in the subsidy literature. We examined the input of a high quality, point-source anthropogenic subsidy (aquaculture feed) into a recipient freshwater lake food web. By using a combined bio-tracer approach, we detect a gradient in accessibility of the anthropogenic subsidy within the surrounding food web driven by the thermal preferences of three constituent species, effectively rewiring the recipient lake food web. Since aquaculture is predicted to increase significantly in coming decades to support growing human populations, and global change is altering temperature regimes, then this form of food web alteration may be expected to occur frequently. We argue that subsidy accessibility is a key characteristic of recipient food web interactions that must be considered when trying to understand the impacts of subsidies on ecosystem stability and function under continued global change.</p>
Evolutionary effects of nitrogen are not easily predicted from ecological responses
<p>Anthropogenic nitrogen (N) addition might alter the evolutionary trajectories of plant populations, in part because it alters the abiotic and biotic environment by increasing aboveground primary productivity, light asymmetry, and herbivory intensity, and reducing plant species diversity. Such evolutionary impacts could be caused by N altering patterns of natural selection (i.e., trait-fitness relationships) and the opportunity for selection (i.e., variance in relative fitness). Because at the community level N addition favors species with light acquisition strategies (e.g., tall species), we predict that N would also increase selection favoring those same traits. We also hypothesize that N could alter the opportunity for selection via its effects on mean fitness and/or competitive asymmetries.To investigate these evolutionary consequences of N, we quantified the strength of selection and the opportunity for selection in replicated populations of the annual grass Setaria faberi Herrm. (giant foxtail) growing in a long-term N addition experiment. We also correlated our measures of selection and opportunity for selection with light asymmetry, diversity, and herbivory intensity to identify the proximate causes of any N effects on evolutionary processes. N addition increased aboveground productivity, light asymmetry, and reduced species diversity. Contrary to expectations, N addition did not strengthen selection for trait values associated with higher light acquisition such as greater height and specific leaf area (SLA); rather, it strengthened selection favoring lower SLA. Increased light asymmetry was associated with stronger selection for lower SLA and lower species diversity was associated with stronger selection for greater height and lower SLA, suggesting a role for these factors in driving N-mediated selection. The opportunity for selection was not influenced by N addition (despite increased mean fitness) but was negatively associated with species diversity. Our results indicate that anthropogenic N enrichment can affect evolutionary processes, but that evolutionary changes in plant traits within populations are unlikely to parallel the shifts in plant traits observed at the community level.</p>
Survey questionnaire data on Perceptions, interactions, and responses to urban natural environments through ecological momentary assessment
<p>Data consist of a R file format and contain the cleaned survey quistionaire data where people were asked about their perceptions, interactions, and responses to urban natural environments. For more information please read the following report Published as part of the Regreen Horizon project. </p> <p>Panduro, T.E., Zandersen, M., Guell, C., Lovell, R., Garrett, J., Taylor, T., Fullam, J., Amegbor, P. (2024) Perceptions, interactions, and responses to urban natural environments through ecological momentary assessment (EMA). Deliverable D4.6. REGREEN - Fostering nature-based solutions for smart, green and healthy urban transitions in Europe and China. Horizon2020 Grant No. 821016. https://www.doi.org/10.5281/zenodo.10594764</p>
Data and scripts for Predictable Ecological Response to Rising CO2 of a Community of Marine Phytoplankton
<p>Rising atmospheric CO<sub>2</sub> and ocean acidification are fundamentally altering conditions for life of all marine organisms, including phytoplankton. Differences in CO<sub>2</sub> related physiology between major phytoplankton taxa lead to differences in their ability to take up and utilise CO<sub>2</sub>. These differences may cause predictable shifts in the composition of marine phytoplankton communities in response to rising atmospheric CO<sub>2</sub>. We report an experiment in which 7 species of marine phytoplankton, belonging to 4 major taxonomic groups (cyanobacteria, chlorophytes, diatoms and coccolithophores) were grown at both ambient (500 µatm) and future (1000 µatm) CO<sub>2</sub> levels. These phytoplankton were grown as individual species, as cultures of pairs of species and as a community assemblage of all seven species in two culture regimes (high-nitrogen batch cultures and lower-nitrogen semi-continuous cultures, though not under nitrogen limitation). All phytoplankton species tested in this study increased their growth rates under elevated CO<sub>2</sub> independent of the culture regime. We also find that, despite species-specific variation in growth response to high CO<sub>2</sub>, the identity of major taxonomic groups provides a good prediction of changes in population growth and competitive ability under high CO<sub>2</sub>. The CO<sub>2</sub>-induced growth response is a good predictor of CO<sub>2</sub>-induced changes in competition (R<sup>2</sup>>0.93) and community composition (R<sup>2</sup>>0.73). This study suggests that it may be possible to infer how marine phytoplankton communities respond to rising CO<sub>2</sub> levels from the knowledge of the physiology of major taxonomic groups, but that these predictions may require further characterisation of these traits across a diversity of growth conditions. These findings must be validated in the context of limitation by other nutrients. Also, in natural communities of phytoplankton, numerous other factors that may all respond to changes in CO2, including nitrogen fixation, grazing and variation in the limiting resource will likely complicate this prediction.</p>
Codes in R for spatial statistics analysis, ecological response models and spatial distribution models
<p>In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg <em>et al.</em>, 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).</p> <p>It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter <em>et al.</em>, 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (<em>e.g.</em>, Naoki <em>et al.</em>, 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:</p> <p>In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin <em>et al. </em>(2012) recommend the use of 10,000 background points when using regression methods (<em>e.g.</em>, Generalized Linear Model) or distance-based models (<em>e.g.</em>, DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (<em>e.g.</em>, bioclimatic, topographic, demographic, habitat) in function of presence and background points (<em>e.g.</em>, Hijmans and Elith, 2017).</p> <p>Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).</p> <p>After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (<em>e.g.</em>, Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.</p> <p>Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (<em>e.g.</em>, Awan <em>et al.</em>, 2021).</p> <p>Once the above codes were run, we uploaded the same subgroups (<em>i.e.</em>, presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the <em>p</em>-significance value of each variable (alpha ≤ 0.05); we selected the value one (<em>i.e.</em>, presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (<em>e.g.</em>, Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.</p> <p>On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).</p> <p>Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).</p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p>Validation set</p> </td> </tr> <tr> <td> <p>Model</p> </td> <td> <p>True</p> </td> <td> <p>False</p> </td> </tr> <tr> <td> <p>Presence</p> </td> <td> <p>A</p> </td> <td> <p>B</p> </td> </tr> <tr> <td> <p>Background</p> </td> <td> <p>C</p> </td> <td> <p>D</p> </td> </tr> </tbody> </table> <p>We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006).</p> <p>The last code (<em>i.e.</em>, <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter <em>et al.</em>, 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.</p> <p>Regarding the model evaluation and estimation, we selected the following estimators:</p> <p>1) partial ROC, which evaluates the approach between the curves of positive (<em>i.e.</em>, correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).</p> <p>2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long <em>et al.</em>, 1988).</p>
Data for: Simulated climate change causes asymmetric responses in insect life history timing potentially disrupting a classic ecological speciation system
<p>Climate change may alter phenology within populations with cascading consequences for community interactions and ongoing evolutionary processes. Here, we measured the response to climate change in two sympatric, recently diverged (~170 years) populations of <em>Rhagoletis</em> <em>pomonella</em> flies specialized on different host fruits (hawthorn and apple) and their parasitoid wasp communities. We tested whether warmer temperatures affect dormancy regulation and its consequences for synchrony across trophic levels and temporal isolation between divergent populations. Under warmer temperatures, both fly populations developed earlier. However, warming significantly increased the proportion of maladaptive pre-winter development in apple, but not hawthorn, flies. Parasitoid phenology was less affected, potentially generating ecological asynchrony. Observed shifts in fly phenology under warming may decrease temporal isolation, potentially limiting ongoing divergence. Our findings of complex sensitivity of life-history timing to changing temperatures predict that coming decades may see multifaceted ecological and evolutionary changes in temporal specialist communities.</p>
Ecological data for: Subsidy accessibility drives asymmetric food web responses
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Evolutionary effects of nitrogen are not easily predicted from ecological responses
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Data from: Ecological responses of <em>Orientallactaga sibirica</em>: Variations in body size and trophic niche across changing habitats
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Data for: Simulated climate change causes asymmetric responses in insect life history timing potentially disrupting a classic ecological speciation system
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Spatial distribution of benthic algae in the South China Sea: responses to gradually changing environmental factors and ecological impacts on coral communities
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Ecological network structure in response to community assembly processes over evolutionary time
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Data and R code for “Tree regeneration response to a shifting soil nutrient economy depends on mycorrhizal association and age”, Forest Ecology and Management, 2022
Atmospheric nitrogen (N) deposition has led to an increase in N cycling and N availability. This increase in inorganic N is likely to impact forest ecosystems, although the responses remain uncertain. Most tree species are associated with one of two mycorrhiza types – arbuscular mycorrhizae (AM) or ectomycorrhiza (ECM). Due to functional differences in their ability to access soil nutrients, we might expect that increased N cycling and availability of inorganic N would benefit AM associated species, with negative or neutral impact for ECM associated species. This study addresses how the abundance of the regeneration layer responds to those shifting soil conditions, based on their mycorrhizal association. We used a long-term experiment located in a temperate deciduous forest, where the native acidic soils are low in nutrients. Soil treatments began in 2009, by adding lime and/or phosphate to raise pH and increase the availability of N and P. All trees ≥ 6.0 cm in 2010 were tagged and have been monitored with annual censuses. To quantity the density of the regeneration layer, seedlings and saplings were recorded in the control and limed plots in 2019. Trees that had recruited into the canopy (DBH ≥ 6.0 cm) were measured in 2020 in all treatment plots. Seedlings older than one year responded to the treatments as predicted, as AM seedlings increased by 42% in the lime treatment (1.53 ± 0.38 individuals per m2 in control, to 2.17 ± 0.56 in lime) and ECM seedlings decreased by 49% in response to liming (0.61 ± 0.14 individuals per m2 in control, to 0.31 ± 0.04 in lime). AM saplings also responded positively to liming, increasing 254% from control to lime (from 0.013 ± 0.003 to 0.046 ± 0.018 individuals per m2), while ECM sapling abundance was neutral (0.063 ± 0.015 individuals per m2 in control, and 0.054 ± 0.009 in lime). The highest number of ECM recruits was found in the control plots (34.4%), followed by phosphate (25.8%), lime + phosphate (21.5%), then lime (18.3%).
Environmental, molecular, and life history data associated with ecological and evolutionary nematode responses to soil phosphorus availability, McMurdo Dry Valleys, Antarctica
Elemental stoichiometry is a useful theoretical framework for understanding the sources and controls on nutrient availability that can structure the composition, diversity, and life history of biotic communities. One such relationship, as postulated by the growth rate hypothesis (GRH), is that organismal development rate is positively linked to cellular phosphorus (P). To test the GRH as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) program, we examined the effects of phosphorus (P) availability both in situ and in vitro, on the evolution of growth and development of free-living soil nematodes (primarily Plectus murrayi) that occur in the McMurdo Dry Valleys of Antarctica. During the 2008-2009 austral summer, we collected soils from two glacial till sequences, the Ross Sea till and Taylor II till, occurring in the Lake Fryxell and Lake Bonney basins, respectively, of Taylor Valley. Through a variety of subsequent analyses, we generated the environmental, molecular, and life history trait data contained herein. In addition, this package contains body size and biomass data for nematodes isolated from soil samples collected during the 1999-2000 and 2004-2005 austral summers.
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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.