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111 results for “Ecosystem processes”
Plant species percent cover data: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Soil nitrogen: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Soil carbon: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Local plant diversity and soybean biological control 2011 Harvest Measures:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Local plant diversity and soybean biological control 2012 Aphid Surveys:Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Whole plot plant tissue chemistry: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Soil Cation Exchange Capacity by the summation method CEC, Ca, Mg, K: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Datasets for the article "Emerging patterns of CO2:O2 dynamics in rivers and their link to ecosystem carbon processing "
<p>Datasets supporting the article "Emerging patterns of CO2:O2 dynamics in rivers and their link to ecosystem carbon processing". The datasets are analized using the acompanying repository in github (https://github.com/rocher-ros/O2_CO2_rivers).</p> <p> </p> <p>Currently the publication is under review, for further information and details on the analysis visit the github repository or the acompannying article. </p>
Process-based Modeling of Ecosystem-Level Monoterpene from a Japanese Larch (Larix Kaempferi) Forest
<p>Title ''Process-based Modeling of Ecosystem-Level Monoterpene from a Japanese Larch (Larix Kaempferi) Forest''<br>Zhanzhuo Chen 1,2, Tomomichi Kato 3, Akihiko Ito 4,5, Tatsuya Miyauchi 3, Yoshiyuki Takahashi 4, and Jing Tang 2</p> <p>1 Graduate School of Global Food Resources, Hokkaido University, Sapporo, Hokkaido, 060-0809, Japan<br>2 Center for Volatile Interactions (VOLT), Department of Biology, University of Copenhagen, DK-2100, Copenhagen, Denmark<br>3 Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan<br>4 Earth System Division, National Institute for Environmental Studies (NIES), Onogawa, Tsukuba, Ibaraki, 305-8506, Japan<br>5 Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan<br>Correspondence to: Tomomichi Kato (tkato@agr.hokudai.ac.jp)</p>
Termite abundance and ecosystem processes in Maliau Basin, 2015-2016 [HMTF]
<p><strong>Description: </strong></p> <p>This dataset consists of invertebrate abundance data and associated ecosystem measurements (Including leaf litter depth and mass, seedlings, soil moisture and nutrients, and rainfall) measured within an area of lowland, old growth dipterocarp rainforest in the Maliau Basin Conservation Area, Sabah, Malaysia between 2015 and 2016. Data were collected during a collaborative project which was included in the NERC Human-modified tropical forest (HMTF) programme.</p> <p><strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/54"><strong>Biodiversity and land-use impacts on tropical ecosystem function (BALI): Experimental manipulations of biodiversity at SAFE</strong></a></p> <p><strong>Funding: </strong>These data were collected as part of research funded by:</p> <ul> <li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NERC grant NE/L000016/1)</li> </ul> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <p> </p> <p><strong>Permits: </strong>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre (Research licence na)</li> </ul> <p> </p> <p><strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3265746">here</a></p> <p><strong>Files: </strong>This consists of 1 file: Termite_monitoring_maliau.xlsx</p> <p><strong>Termite_monitoring_maliau.xlsx</strong></p> <p>This file contains dataset metadata and 11 data tables:</p> <ol> <li> <p><strong>Leaf_litter_depth</strong> (described in worksheet Leaf_litter_depth)</p> <p>Description: summary of leaf litter measurements collected on experimental plots. An in situ assay of ecosystem-level decomposition was carried out by measuring leaf litter depth during the drought (March 2016) and non-drought (October 2016) periods. Forty leaf litter depth measurements were taken in total per plot in March 2016, with 10 measurements spaced every 3 m across four 30 m transect lines, with each transect being separated by 10 m. In October 2016, a total of sixty measurements were taken per plot, similarly spaced out across a total of six 30 m transect lines</p> <p>Number of fields: 5</p> <p>Number of data rows: 800</p> <p>Fields:</p> <ul> <li><strong>Date</strong>: The month and year in which leaf litter depth was recorded (Field type: Date)</li> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>LINE</strong>: The sampling line within each plot a leaf litter measurement was taken (Field type: ID)</li> <li><strong>Depth_cm</strong>: The depth of leaf litter measured at each point (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Leaf_litter_invertebrates</strong> (described in worksheet Leaf_litter_invertebrates)</p> <p>Description: In 2016 (two years after initial poisoning), fifteen 1 m2 leaf litter samples were collected from each plot. These were collected every 7m along a 100m transect. Sieved litter samples were suspended in Winkler bags for three days to extract invertebrates. All leaf litter invertebrates were identified to order and counted.</p> <p>Number of fields: 31</p> <p>Number of data rows: 120</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>Distance</strong>: Distance along the sampling transect in metres (Field type: ID)</li> <li><strong>Coleoptera_Adults</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Coleoptera_Larvae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Diptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Hemiptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Araneae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Opiliones</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Isopoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Oligochaeta</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Hymenoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Formicidae</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Mollusca</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Lepidoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Chilipoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Diplopoda</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Thysanoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Psocoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Dermaptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Orthoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Blattodea</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Leeches</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Plecoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Neuoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Trichoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Mecoptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Odonata</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Siphonaptera</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Termites</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> <li><strong>Pseudoscorpions</strong>: Frequency of inverts in leaf litter (Field type: Numeric trait)</li> </ul> </li> <li> <p><strong>Leaf_litter_mass</strong> (described in worksheet Leaf_litter_mass)</p> <p>Description: Decomposition rate was assessed using leaf litter decomposition bags. We collected freshly abscised Shorea johorensis leaf litter from trees close to our experimental plots for use in the leaf litter decomposition bags. The leaf litter was dried at 60 degrees Celsius until it reached a constant weight. We used 300-micron nylon mesh to produce macroinvertebrate exclusion bags, the closed-bag treatment, and created an open-bag treatment by cutting 10, 1 cm holes in each side of the 300-micron mesh bags to allow access to the material by termites and other macroinvertebrates. This approach avoided any unintentional bias due to the use of different mesh size. Each leaf litter bag contained on average 10.5 g ± 0.6 g of dried Shorea johorensis. We left litter bags on the forest floor for 112 days before collection. Bags were placed on plots at the beginning of the 2015 drought (August 2015) and again during the non-drought period (July 2016).</p> <p>Number of fields: 6</p> <p>Number of data rows: 87</p> <p>Fields:</p> <ul> <li><strong>Condition</strong>: The rainfall season in which leaf litter bags were deployed (Field type: Categorical)</li> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Bag_treatment</strong>: The treatment applied to each leaf litter bag - open = accessible to invertebrates, closed = inaccessible to invertebrates (Field type: Categorical)</li> <li><strong>Plot_treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>Mass_loss</strong>: total leaf litter mass loss from each bag in grams (Field type: Numeric)</li> <li><strong>Proportion</strong>: the proportion of leaf litter mass loss from each bag (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Non_target_inverts</strong> (described in worksheet Non_target_inverts)</p> <p>Description: non-termites were collected in 2014 (pre-drought and pre-suppression), 2015 (during the drought and the suppression) and 2016 (post-drought). We collected 1m2, leaf litter samples, sieved the leaf litter and extracted invertebrates with Winkler bags for three days.</p> <p>Number of fields: 5</p> <p>Number of data rows: 5040</p> <p>Fields:</p> <ul> <li><strong>Year</strong>: the year in which sampling occured (Field type: ID)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>variable</strong>: the order of non-target invertebrates samples (Field type: Taxa)</li> <li><strong>value</strong>: the number of individuals belonging to each order (Field type: Numeric)</li> <li><strong>log</strong>: the log of the number of individuals belonging to each order (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Seedling_survival_non_drought</strong> (described in worksheet Seedling_survival_non_drought)</p> <p>Description: Seedling mortality was assessed using a seedling transplant experiment. In July 2015, 200 individuals of a leguminous liana, Agelaea borneensis, were collected from the forest matrix surrounding our plots. Seedlings were selected from seedling mats resulting from a masting event in 2014. We selected individuals that had only their cotyledons and had not yet developed their first true leaves, and were roughly the same height. We are therefore confident that individuals were all of the same age and developmental stage and that we minimised confounding influences of genetic variability by using individuals from the same conspecific seedling mat. Seedlings were planted in the ground in July 2015 in the same grid of 25 used to assess soil moisture (n = 25 per plot), which was located within the central 50 m sampling area of experimental plots. Each seedling was separated by at least 5 m from the next closest seedling. To minimise the effect of stochastic disturbance-induced mortality as a result of transplantation shock, we used the number of individuals alive one month after the initial transplant as the baseline abundance. Survival of seedlings during the drought was assessed 11 months after transplantation, in June 2016. Following this assessment, the number of live individuals in June 2016 was used as a new baseline abundance. Survival during non-drought conditions was assessed 12 months later in June 2017</p> <p>Number of fields: 4</p> <p>Number of data rows: 64</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>alive_2016</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot (Field type: Categorical)</li> <li><strong>alive.2017</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Seedling_survival_drought</strong> (described in worksheet Seedling_survival_drought)</p> <p>Description: Tree seedlings survival during the drought</p> <p>Number of fields: 3</p> <p>Number of data rows: 274</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>alive_2016</strong>: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Soil_moisture</strong> (described in worksheet Soil_moisture)</p> <p>Description: Soil moisture was measured using a Delta-T Devices HH2 moisture metre in March and October 2016. Soil moisture was recorded at 25 points, spread evenly across each plot in a grid, with each sampling point separated by 5 m from the next point.</p> <p>Number of fields: 5</p> <p>Number of data rows: 400</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>soil_moisture</strong>: the % soil moisture measured at each point (Field type: Numeric)</li> <li><strong>condition</strong>: The rainfall season in which soil moisture measurements were taken (Field type: Categorical)</li> <li><strong>date</strong>: the month and year in which the soil moisture recording was taken (Field type: Date)</li> </ul> </li> <li> <p><strong>Soil_nutrients</strong> (described in worksheet Soil_nutrients)</p> <p>Description: We used Plant Root Simulator (PRS®) resin probes to assess mineralization rates of plant available soil nutrients (NO3-, NH4+, P, K, Ca, Mg, Mn, Al, Fe, Zn) over a two-week period, during drought and non-drought conditions. In March 2016, we buried two anion and cation probe pairs at a random subsample of 12 points within the 25 sampling grid used to measure soil moisture. In October 2016, four probe pairs were placed at each point of the complete 25 sampling grid. We buried the probe membranes to a depth of 10 cm and left them in situ for two weeks, after which they were removed from the soil, cleaned with de-ionized water and subsequently analysed by Western Ag Innovations, Saskatoon, Canada.</p> <p>Number of fields: 15</p> <p>Number of data rows: 292</p> <p>Fields:</p> <ul> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>Condition</strong>: the season in which the soil nutrient sampling occurred – drought (2015) or non-drought (2016) (Field type: Categorical)</li> <li><strong>NO3_N_micro_grams/10cm2/burial length</strong>: Soil NO3 at 10cm2 profile (Field type: Numeric)</li> <li><strong>NH4_N_micro_grams/10cm2/burial length</strong>: Soil NH4 at 10cm2 profile (Field type: Numeric)</li> <li><strong>Ca_micro_grams/10cm2/burial length</strong>: Soil Ca at 10cm2 profile (Field type: Numeric)</li> <li><strong>Mg_micro_grams/10cm2/burial length</strong>: Soil Mg at 10cm2 profile (Field type: Numeric)</li> <li><strong>K_micro_grams/10cm2/burial length</strong>: Soil K at 10cm2 profile (Field type: Numeric)</li> <li><strong>P_micro_grams/10cm2/burial length</strong>: Soil P at 10cm2 profile (Field type: Numeric)</li> <li><strong>Fe_micro_grams/10cm2/burial length</strong>: Soil Fe at 10cm2 profile (Field type: Numeric)</li> <li><strong>Mn_micro_grams/10cm2/burial length</strong>: Soil Mn at 10cm2 profile (Field type: Numeric)</li> <li><strong>Cu_micro_grams/10cm2/burial length</strong>: Soil Cu at 10cm2 profile (Field type: Numeric)</li> <li><strong>Zn_micro_grams/10cm2/burial length</strong>: Soil Zn at 10cm2 profile (Field type: Numeric)</li> <li><strong>B_micro_grams/10cm2/burial length</strong>: Soil B at 10cm2 profile (Field type: Numeric)</li> <li><strong>Al_micro_grams/10cm2/burial length</strong>: Soil Al at 10cm2 profile (Field type: Numeric)</li> </ul> </li> <li> <p><strong>Termite_cumulative_attacks</strong> (described in worksheet Termite_cummulative_attack)</p> <p>Description: We monitored termite feeding activity on the plots using untreated TPRs. Sixteen untreated TPRs were placed on each plot and were scored for termite attack on a 0 to 5 scale, where 0 is untouched and 5 is completely eaten. After one month, TPR were scored and replaced. Before they were replaced, we recorded the cumulative amount of TPR consumed on each plot and calculated the plot-level cumulative mean attack scores.</p> <p>Number of fields: 4</p> <p>Number of data rows: 120</p> <p>Fields:</p> <ul> <li><strong>month</strong>: The month in which termite attack scores were recorded (Field type: ID)</li> <li><strong>cumulative_consumption</strong>: The cumulative consumption rate for each plot (Field type: Numeric)</li> <li><strong>plot</strong>: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Termite_C_plot_SPI</strong> (described in worksheet Termite_C_plots_SPI)</p> <p>Description: To assess the relationship between rainfall and termite abundance, we carried out termite transects on control plots every 2 months from March 2016 to December 2016 and also at the beginning and the end of the experimental period in June 2015 and June 2017. Daily total rainfall was collected from Danum Valley forest reserve (4°57′53″ to 55″ N and 117°48′14″ to 30″E) from November 2010 to March 2017. Daily values were used to calculate total monthly rainfall in the region, and this was used to calculate 3-monthly Standardised Precipitation Index (SPI)[2] in the 'SPI' package in R. The SPI is a climatic proxy used to quantify and monitor drought; negative values indicate drier than average conditions, while positive values represent wetter than average conditions.</p> <p>Number of fields: 5</p> <p>Number of data rows: 32</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: the control plot on which samples were collected (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)</li> <li><strong>total</strong>: total number of termite hits recorded (Field type: Numeric)</li> <li><strong>date</strong>: the month in which sampling occurred (Field type: Date)</li> <li><strong>SPI</strong>: the standardized precipitation index number calculated for each time period from rainfall data collected at Danum Valley Field Station (Field type: Numeric)</li> <li><strong>Wet.dry</strong>: the rainfall conditions at the time of sampling (wet = 2017, dry = 2015) (Field type: Categorical)</li> </ul> </li> <li> <p><strong>Termite_hits_on_T_and_C_plot</strong> (described in worksheet Termite_hits_on_T_and_C_plots)</p> <p>Description: termite abundance data. In order to quantify the effect of the suppression treatment on termite community composition, we sampled termites on suppression and control plots in June 2015 and October 2016 using the Jones and Eggleton transect method</p> <p>Number of fields: 7</p> <p>Number of data rows: 192</p> <p>Fields:</p> <ul> <li><strong>Plot</strong>: The experimental plot that the data were collected from (Field type: Location)</li> <li><strong>Treatment</strong>: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)</li> <li><strong>date</strong>: The month and year in which the sampling occurred (Field type: Date)</li> <li><strong>genus</strong>: the genus to which each termite encounter belongs (Field type: Taxa)</li> <li><strong>hits</strong>: number of termite of hits on each plot (Field type: Numeric)</li> <li><strong>SPI</strong>: the standardized precipitation index at the time of each sampling occasion (Field type: Numeric)</li> <li><strong>Wet.dry</strong>: the season in which sampling occurred – wet = 2016, dry = 2015 (Field type: Categorical)</li> </ul> </li> </ol> <p><strong>Date range: </strong>2014-10-01 to 2017-07-30</p> <p><strong>Latitudinal extent: </strong>4.5000 to 5.0700</p> <p><strong>Longitudinal extent: </strong>116.7500 to 117.8200</p> <p><strong>Taxonomic coverage: </strong><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <p>Animalia<br>  - Annelida<br>  -  - Clitellata<br>  -  -  - [Oligochaeta]<br>  - Arthropoda<br>  -  - Arachnida<br>  -  -  - Araneae<br>  -  -  - Opiliones<br>  -  -  - Pseudoscorpiones<br>  -  - Chilopoda<br>  -  - Diplopoda<br>  -  - Insecta<br>  -  -  - Blattodea<br>  -  -  -  - [Termites]<br>  -  -  - Coleoptera<br>  -  -  - Dermaptera<br>  -  -  - Diptera<br>  -  -  - Hemiptera<br>  -  -  - Hymenoptera<br>  -  -  -  - Formicidae<br>  -  -  - Isoptera<br>  -  -  -  -  - <em>Procapritermes</em><br>  -  -  -  -  - <em>Prohamitermes</em><br>  -  -  -  - Rhinotermitidae<br>  -  -  -  -  - <em>Heterotermes</em><br>  -  -  -  -  - <em>Parrhinotermes</em><br>  -  -  -  -  - <em>Schedorhinotermes</em><br>  -  -  -  - Termitidae<br>  -  -  -  -  - <em>Bulbitermes</em><br>  -  -  -  -  - <em>Dicuspiditermes</em><br>  -  -  -  -  - <em>Globitermes</em><br>  -  -  -  -  - <em>Macrotermes</em><br>  -  -  -  -  - <em>Malaysiotermes</em><br>  -  -  -  -  - <em>Microcerotermes</em><br>  -  -  -  -  - <em>Odontotermes</em><br>  -  -  - Lepidoptera<br>  -  -  - Mecoptera<br>  -  -  - Neuroptera<br>  -  -  - Odonata<br>  -  -  - Orthoptera<br>  -  -  - Plecoptera<br>  -  -  - Psocodea<br>  -  -  - Siphonaptera<br>  -  -  - Thysanoptera<br>  -  -  - Trichoptera<br>  -  - Malacostraca<br>  -  -  - Isopoda<br>  - Mollusca</p> <p> </p>
Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing
<p>Lakes process both terrestrial and aquatic organic matter, and the relative contribution from each source is often measured via ecosystem metabolism and terrestrial resource use in the food web (i.e., consumer allochthony). Yet, ecosystem metabolism and consumer allochthony are rarely considered together, despite possible interactions and potential for them to respond to the same lake characteristics. In this study, we compiled global datasets of lake gross primary production (GPP), ecosystem respiration (ER), and zooplankton allochthony to compare the strength and shape of relationships with physicochemical characteristics across a broad set of lakes. GPP was positively related to total phosphorus (TP) in lakes with intermediate TP concentrations (11 - 75 μg L<sup>-1</sup>) and was highest in lakes with intermediate dissolved organic carbon (DOC) concentrations. While ER and GPP were strongly positively correlated, decoupling occurred at high DOC concentrations. Lastly, allochthony had a unimodal relationship with TP and related variably to DOC. By integrating metabolism and allochthony, we identified similar change points in GPP and zooplankton allochthony at intermediate DOC (4.5 - 10 mg L<sup>-1</sup>) and TP (8 - 20 μg L<sup>-1</sup>) concentrations, indicating that allochthony and GPP may be coupled and inversely related. The ratio of DOC : nutrients also helped to identify conditions where lake organic matter processing responded more to autochthonous or allochthonous organic matter sources. As lakes globally face eutrophication and browning, predicting how lake organic matter processing will respond requires an updated paradigm that incorporates nonlinear dynamics and interactions.</p>
Habitat isolation interacts with top-down and bottom-up processes in a seagrass ecosystem
<p>Habitat loss is accelerating at unprecedented rates, leading to the emergence of smaller, more isolated habitat remnants. Habitat isolation adversely affects many ecological processes independently, but little is known about how habitat isolation may interact with ecosystem processes such as top-down (consumer-driven) and bottom-up (resource-driven) effects. To investigate the interactive influence of habitat isolation, resource availability and consumer distribution and impact on community structure, we tested two hypotheses using invertebrate and algal epibionts on temperate seagrasses, an ecosystem of ecological and conservation importance. First, we hypothesized that habitat isolation will change the structure of the seagrass epibiont community, and isolated patches of seagrass will have lower epibiont biomass and different epibiont community composition. Second, we hypothesized that habitat isolation would mediate top-down (i.e., herbivory) and bottom-up (i.e., nutrient enrichment) control for algal epibionts. We used observational studies in natural seagrass patches, and experimental artificial seagrass to examine three levels of habitat isolation. We further manipulated top-down and bottom-up processes in artificial seagrass through consumer reductions and nutrient additions, respectively. We indeed found that habitat isolation of seagrass patches decreased epibiont biomass and modified epibiont community composition. This pattern was largely due to dispersal limitation of invertebrate epibionts that resulted in a decline in their abundance and richness in isolated patches. Further, habitat isolation reduced consumer abundances, weakening top-down control of algal epibionts in isolated seagrass patches. Nutrient additions, however, reversed this pattern, and allowed a top-down effect on algal richness to emerge in isolated habitats, demonstrating a complex interaction between patch isolation and top-down and bottom-up processes. Habitat isolation may therefore shape the relative importance of central processes in ecosystems, leading to changes in community composition and food web structure in marine habitats.</p>
Resource limitation of compensatory responses in ecosystem processes after biodiversity loss
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Habitat isolation interacts with top-down and bottom-up processes in a seagrass ecosystem
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Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing
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Is an ecosystem perspective sufficient to understand meta-ecosystem processes? A critical reflection
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Root carbon/nitrogen data: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Invasion strip soil nitrogen: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Invasion strip root biomass: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
Percent light penetration: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
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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.