Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
440
datasets available to search
ShareScore release 0.9.0
Dataset results
440 results for “community changes”
Data from: Small but mighty: how overlooked small species maintain community structure through middle Eocene climate change
<p>Understanding current and future biodiversity responses to changing climate is pivotal as anthropogenic climate change continues. This understanding is complicated though by the multitude of available metrics to quantify dynamics, and by biased sampling protocols. Here, we investigate the impact of sampling protocol strategies using a data-rich fossil record to calculate effective diversity using Hill numbers for the first time on Paleogene planktonic foraminifera. We sample 22,830 individual tests, in two different size classes, across a seven-million-year time slice of the Middle Eocene featuring a major transient warming event, the Middle Eocene Climatic Optimum (MECO; ~40 million years ago (Ma)), at study sites in the mid-latitude North Atlantic. Using Generalized Additive Models (GAMs), we investigate community responses to climatic fluctuations. After correcting for any effects of fossil fragmentation, we show a peak in generic diversity in the early and mid-stages of the MECO as well as divergent trajectories between the typical size-selected community (> 180 µm) and a broader selection including smaller genera (> 63 µm). Assemblages featuring smaller genera are more resilient to the climatic fluctuations of the MECO than those assemblages that feature only larger genera, maintaining their community structure at the reference Hill numbers for Shannon's and Simpson's Index. These results raise fundamental questions about how communities respond to climate excursions. In addition, our results emphasise the need to design studies with the aim of collecting the most inclusive data possible, to allow detection of community changes and determine which species are likely to dominate future environments. </p>
Data for: Social-ecological vulnerability of fishing communities to climate change: a U.S. West Coast case study
<p><span>Climate change is already impacting coastal communities, and ongoing and future </span><span>shifts in fisheries species productivity from climate change have implications for the </span><span>livelihoods and cultures of coastal communities. Harvested marine species in the </span><span>California Current Large Marine Ecosystem support U.S. West Coast communities </span><span>economically, socially, and culturally. Ecological vulnerability assessments exist for </span><span>individual species in the California Current but ecological and human vulnerability are </span><span>linked and vulnerability is expected to vary by community. Here, we present </span><span>automatable, reproducible methods for assessing the vulnerability of U.S. West Coast </span><span>fishing-dependent communities to climate change within a social-ecological </span><span>vulnerability framework. We first assessed the ecological risk of marine resources, on </span><span>which fishing communities rely, to 50 years of climate change projections. We then </span><span>combined this with the adaptive capacity of fishing communities, based on social </span><span>indicators, to assess the potential ability of communities to cope with future changes. </span><span>Specific communities (particularly in Washington state) were determined to be at risk to </span><span>climate change mainly due to economic reliance on at risk marine fisheries species, </span><span>like salmon, hake, or sea urchins. But, due to higher social adaptive capacity, these </span><span>communities were often not found to be the most vulnerable overall. Conversely, </span><span>certain communities that were not the most at risk, ecologically and economically, </span><span>ranked in the category of highly vulnerable communities due to low adaptive capacity </span><span>based on social indicators (particularly in Southern California). Certain communities </span><span>were both ecologically at risk due to catch composition and socially vulnerable (low </span><span>adaptive capacity) leading to the highest tier of vulnerability. The integration of climatic, </span><span>ecological, economic, and societal data reveals that factors underlying vulnerability are </span><span>variable across fishing communities on the U.S West Coast, and suggests the need to </span><span>develop a variety of well-aligned strategies to adapt to the ecological impacts of climate </span><span>change.</span></p>
Data from: Changes in the foliar fungal community between oak leaf flushes along a latitudinal gradient in Europe
<p><strong>Aim:</strong> To advance our understanding of how foliar fungal communities are structured and assembled, we assessed to what extent leaf flush and latitude can explain the within- and among-tree variation in the foliar fungal community in a foundation tree species, the pedunculate oak <em>Quercus robur</em>.</p> <p><strong>Location:</strong> A latitudinal gradient spanning <em>c</em>. 20 degrees in latitude in Europe.</p> <p><strong>Major taxa studied:</strong> The foliar fungal community on <em>Quercus robur.</em></p> <p><strong>Methods:</strong> We examined the main and interactive effects of leaf flush and latitude on the foliar fungal community by sampling 20 populations of the pedunculate oak <em>Quercus robur</em> across the tree's range, spanning <em>c</em>. 20 degrees in latitude.</p> <p><strong>Results:</strong> Species composition, but not species richness, differed between leaf flushes. Across the latitudinal gradient, species richness was highest in the central part of the oak's distributional range, and foliar fungal community composition shifted along the latitudinal gradient. Among fungal guilds, the relative abundance of plant pathogens and mycoparasites was lower on the first leaf flush, and the relative abundance of plant pathogens and saprotrophs decreased with latitude.</p> <p><strong>Conclusions: </strong>Changes in community composition between leaf flushes and along the latitudinal gradient were mostly a result of species turnover. Overall, our findings demonstrate that leaf flush and latitude explain 5-22% of the small- and large-scale spatial variation in the foliar fungal community on a foundation tree within the temperate region, with major consequences for plant health, species interactions and ecosystem dynamics.</p>
Long-term changes in taxonomic and functional composition of European marine fish communities
<p>Evidence of large-scale biodiversity degradation in marine ecosystems has been reported worldwide, yet most research has focused on few species of interest or on limited spatiotemporal scales. Here we assessed the spatial and temporal changes in the taxonomic and functional composition of fish communities in European seas over the last 25 years (1994-2019). We then explored how these community changes were linked to environmental gradients and fishing pressure. We show that the spatial variation in fish species composition is more than two times higher than the temporal variation, with a marked spatial continuum in taxonomic composition and a more homogenous pattern in functional composition. The regions warming the fastest are experiencing an increasing dominance and total abundance of r-strategy fish species (lower age of maturity). Conversely, regions warming more slowly show an increasing dominance and total abundance of K-strategy species (high trophic level and late reproduction). Among the considered environmental variables, sea surface temperature, surface salinity, and chlorophyll-a most consistently influenced communities' spatial patterns, while bottom temperature and oxygen had the most consistent influence on temporal patterns. Changes in communities' functional composition were more closely related to environmental conditions than taxonomic changes. Our study demonstrates the importance of integrating community-level species traits across multi-decadal scales and across a large region to better capture and understand ecosystem-wide responses and provides a different lens on community dynamics that could be used to support sustainable fisheries management.</p>
Investigating the resilience of termite communities to logging and climate change in Borneo
<b>Description: </b><p>This project set out to quantify the tolerance of termite communities to climate change, or more specifically, temperature and humidity change, two climatic variables that have been hypothesised to drive species distributions (particularly for small ectotherms such as termites). The data presented here are the tolerances of termites to increasing temperatures, and decreasing humidities. <br><br>The thermal data was recorded by inserting termites into individual glass vials, placing those sealed vials into a water bath, and increasing the temperature until they could no longer function. This temperature was recorded, and taken as CTmax (Critical Thermal Maximum), for each individual termite. These data can be found in the TemperatureData worksheet. <br><br>The humidity data was recorded slightly differently. Groups of termites (of the same genus) were weighed and placed in one of two types of glass vial. Dessicated vials also contained silica gel (and a barrier to prevent termite interaction with the gel) which reduced the humidity to an average of 30%. Control vials did not contain any silica gel and had an average humidity of 85%. These vials were removed at one of 5 time points, and the termites were weighed again, and weight change was recorded. This weight change was attributed to water loss. <br><br>The body water data was used to calculate the proportion of body mass that was water, for multiple termite genera. This was done so that percentage of body water lost could be calculated for the humidity experiment, rather than an absolute value of water loss (as termites vary in size, using absolute values would cause false conclusions). </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/31"><b>Investigating the resilience of termite communities to logging and climate change in Borneo</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=33">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Thermal tolerance data</b> (Worksheet TemperatureData)</p><p>Dimensions: 1256 rows by 17 columns</p><p>Description: Thermal tolerance data of termites</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that each termite was taken from (Field type: ID)</li><li><b>Day</b>: The day on which the experiment took place (Field type: ID)</li><li><b>Termite_no</b>: The unique termite number, missing numbers are due to non-experimental deaths (Field type: ID)</li><li><b>Experiment</b>: Whether it was the first or second experiment from the same colony (Field type: Replicate)</li><li><b>Family</b>: The family of the termite (Field type: ID)</li><li><b>Genus</b>: The genus of the termite (Field type: ID)</li><li><b>Species</b>: The species (where known) of the termite (Field type: ID)</li><li><b>Name</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>CTmax</b>: The critical thermal maximum of the termite, or the temperature that it died at (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Nest_type</b>: The type of nest that the termite builds (Field type: Categorical Trait)</li><li><b>Nest_Layer</b>: The layer within the forest that the nest is built (Field type: Categorical Trait)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Humidity tolerance data</b> (Worksheet HumidityData)</p><p>Dimensions: 169 rows by 16 columns</p><p>Description: Humidity tolerance data of 4 termite genera</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tube</b>: The unique tube number that the termites were placed in (Field type: ID)</li><li><b>Taxa</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Time</b>: The five time points that the tubes were removed at (Field type: Numeric)</li><li><b>Treatment</b>: Whether the termites were placed in a control or desiccated tube (Field type: Categorical)</li><li><b>Initial</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Finish</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage_lost</b>: Proportion of body mass change (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>No_termites</b>: Number of termites placed in the tube (Field type: Numeric)</li><li><b>No_dead</b>: Number of termites that were dead at the point of the second weighing (Field type: Numeric)</li><li><b>Percentage_Dead</b>: Percentage of termites that are dead at point of second weighing (Field type: Numeric)</li></ul><br></li><li><p><b>Termite total body water data</b> (Worksheet BodyWaterData)</p><p>Dimensions: 38 rows by 12 columns</p><p>Description: Data calculating the total body water of 4 termite genera, this data was used in the humidity data to calculate the percentage of body water lost during the experiment</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tray_no</b>: The unique tray number that the termites were placed in (Field type: ID)</li><li><b>Genus</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Weight_start</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Weight_end</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage</b>: Percentage of body mass that is water (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li></ol><p><b>Date range: </b>2016-02-01 to 2016-07-01</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><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><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Isoptera<br> -  -  -  -  - <i>Homallotermes</i><br> -  -  -  -  -  - [<i>Homallotermes foraminifer</i>]<br> -  -  -  - Kalotermitidae<br> -  -  -  -  - <i>Glyptotermes</i><br> -  -  -  -  -  - [Glyptotermes sp.]<br> -  -  -  - Rhinotermitidae<br> -  -  -  -  - <i>Coptotermes</i><br> -  -  -  -  -  - [Coptotermes sp.]<br> -  -  -  -  - <i>Parrhinotermes</i><br> -  -  -  -  -  - [<i>Parrhinotermes pygmaeus</i>]<br> -  -  -  -  - <i>Schedorhinotermes</i><br> -  -  -  -  -  - [<i>Schedorhinotermes sarawakensis</i>]<br> -  -  -  -  -  - [Schedorhinotermes sp.]<br> -  -  -  - Termitidae<br> -  -  -  -  - <i>Bulbitermes</i><br> -  -  -  -  -  - [Bulbitermes sp.]<br> -  -  -  -  - <i>Dicuspiditermes</i><br> -  -  -  -  -  - [Dicuspiditermes sp.]<br> -  -  -  -  - <i>Globitermes</i><br> -  -  -  -  -  - [<i>Globitermes globosus</i>]<br> -  -  -  -  - <i>Hospitalitermes</i><br> -  -  -  -  -  - [<i>Hospitalitermes hospitalis</i>]<br> -  -  -  -  -  - [<i>Hospitalitermes bicolour</i>]<br> -  -  -  -  - [Lacessitermes sp.]<br> -  -  -  -  - <i>Longipeditermes</i><br> -  -  -  -  -  - [Longipeditermes sp.]<br> -  -  -  -  - <i>Macrotermes</i><br> -  -  -  -  -  - [<i>Macrotermes gilvus</i>]<br> -  -  -  -  - <i>Microcerotermes</i><br> -  -  -  -  -  - [Microcerotermes sp.]<br> -  -  -  -  - <i>Nasutitermes</i><br> -  -  -  -  -  - [<i>Nasutitermes havilandi</i>]<br> -  -  -  -  -  - [Nasutitermes sp.]<br> -  -  -  -  - <i>Odontotermes</i><br> -  -  -  -  -  - [Odontotermes sp.]<br></div><p></p>
Figure 1 in Changes in abundance and community structure of the zooplankton population during the 2008 mucilage event in the northeastern Marmara Sea
Figure 1. Study area.
Dataset for Resistance of Australian fish communities to drought and flood: implications for climate change and adaptations
<p>Data and code analyzing fish response to extreme drought and flood in Australia's Murray Darling Basin. Original fish data can be found here: <a href="https://www.gbif.org/dataset/f38cc07c-49a8-4ca4-8882-5cdde1733c12"><u>https://www.gbif.org/dataset/f38cc07c-49a8-4ca4-8882-5cdde1733c12</u></a>. Original rainfall data can be found here: http://www.bom.gov.au/climate/maps/rainfall/?variable=rainfall&map=totals&period=month®ion=nat&year=2004&month=01&day=31 </p>
Table 2 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel
<p><b>Table 2</b> Mean values (± SD) of soil physical and chemical parameters at different treatment sites at the Safari Zoological Center, Israel, December 2013. SM = soil moisture, OM = organic matter, pH = soil pH, SEC = soil electrical conductivity, SD = soil density, WHC = water-holding capacity. OE = open places under enclosure conditions, OT = open places under trampling conditions; EE <i>E.</i> = <i>camaldulensis</i> canopy habitat under enclosure conditions, ET = <i>E</i>. <i>camaldulensis</i> canopy habitat under trampling conditions, TE <i>T</i> =. <i>aphylla</i> canopy habitat under enclosure conditions, TT = <i>T. aphylla</i> canopy habitat under trampling conditions, CE = <i>C</i>. <i>sempervirens</i> canopy habitat under enclosure conditions, CT = <i>C. sempervirens</i> canopy habitat under trampling conditions. Different letters in the same column represent significant difference <i>p</i> at <0.05.</p><table><tbody><tr><th></th><th>SM (%)</th><th>OM (%)</th><th>pH</th><th>SEC (µ -1) cm</th><th>SD (g -3) cm</th><th>WHC (%)</th></tr></tbody><tbody><tr><th>OE</th><td>25.6±3.4a</td><td>1.1±0.2b</td><td>7.5±0.2b</td><td>87.6±17.5d</td><td>1.1±0.0b</td><td>53.6±0.9ab</td></tr><tr><th>OT</th><td>7.8±1.7c</td><td>0.2±0.0e</td><td>7.6±0.0b</td><td>150.3±51.3c</td><td>1.6±0.0a</td><td>25.6±1.0c</td></tr><tr><th>EE</th><td>16.6±1.6b</td><td>1.3±0.2b</td><td>7.6±0.0b</td><td>130.2±9.4cd</td><td>1.0±0.1c</td><td>52.6±11.1ab</td></tr><tr><th>ET</th><td>23.2±3.8a</td><td>2.0±0.3a</td><td>7.6±0.0b</td><td>255.3±39.6ab</td><td>1.1±0.0bc</td><td>31.9±3.5c</td></tr><tr><th>TE</th><td>21.9±4.4ab</td><td>0.4±0.1d</td><td>7.9±0.0a</td><td>152.0±17.8c</td><td>1.0±0.1c</td><td>52.1±14.6ab</td></tr><tr><th>TT</th><td>14.1±5.2b</td><td>1.1±0.1b</td><td>7.6±0.1b</td><td>254.1±48.0ab</td><td>1.0±0.1c</td><td>43.3±6.2b</td></tr><tr><th>CE</th><td>26.9±3.4a</td><td>0.8±0.3c</td><td>7.6±0.1b</td><td>209.9±22.5b</td><td>1.0±0.1c</td><td>56.3±5.8a</td></tr><tr><th>CT</th><td>22.0±6.0ab</td><td>0.5±0.2d</td><td>7.8±0.1a</td><td>275.9±21.9a</td><td>1.0±0.0c</td><td>43.0±3.9b</td></tr></tbody></table>
Table 3 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel
<p><b>Table 3</b> Effects of sampling habitat (“Habitat”), trampling management (“Trampling”), and their interaction on soil parameters, abundance of soil microarthropods, and diversity indices of soil Acari at the Safari Zoological Center, Israel, December 2013 (General linear model, α = 0.05). * <i>p</i> <0.05, ** <i>p</i> <0.01, *** <i>p</i> <0.001.</p><table><tbody><tr><th><b>Microarthropods</b></th><th><i>d</i> <i>f</i></th><th><i>F</i></th><th><b>Soil parameters</b></th><th><i>d</i> <i>f</i></th><th><i>F</i></th></tr></tbody><tbody><tr><th><b>Total microarthropod abundance</b></th><td></td><td></td><td><b>Soil moisture</b></td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>15.85***</td><td>Model</td><td>8</td><td>109.38***</td></tr><tr><th>Trampling</th><td>1</td><td>39.13***</td><td>Trampling</td><td>1</td><td>18.46***</td></tr><tr><th>Habitat</th><td>3</td><td>1.65</td><td>Habitat</td><td>3</td><td>5.90**</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>4.89**</td><td>Trampling * Habitat</td><td>3</td><td>12.84***</td></tr><tr><th><b>Collembola abundance</b></th><td></td><td></td><td><b>Organic matter</b></td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>4.90**</td><td>Model</td><td>8</td><td>116.32***</td></tr><tr><th>Trampling</th><td>1</td><td>11.18**</td><td>Trampling</td><td>1</td><td>0.05</td></tr><tr><th>Habitat</th><td>3</td><td>2.29</td><td>Habitat</td><td>3</td><td>48.99***</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>2.56</td><td>Trampling * Habitat</td><td>3</td><td>32.99***</td></tr><tr><th><b>Other arthropod abundance</b></th><td></td><td></td><td><b>Soil pH</b></td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>1</td><td>Model</td><td>8</td><td>35120.21***</td></tr><tr><th>Trampling</th><td>1</td><td>1.8</td><td>Trampling</td><td>1</td><td>0.5</td></tr><tr><th>Habitat</th><td>3</td><td>0.73</td><td>Habitat</td><td>3</td><td>4.69*</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>0.73</td><td>Trampling * Habitat</td><td>3</td><td>13.93***</td></tr><tr><th><b>Soil Acari abundance</b></th><td></td><td></td><td><b>Electrical conductivity</b></td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>18.85***</td><td>Model</td><td>8</td><td>156.31***</td></tr><tr><th>Trampling</th><td>1</td><td>43.09***</td><td>Trampling</td><td>1</td><td>61.80***</td></tr><tr><th>Habitat</th><td>3</td><td>0.88</td><td>Habitat</td><td>3</td><td>20.89***</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>3.60*</td><td>Trampling * Habitat</td><td>3</td><td>1.76</td></tr><tr><th><b>Taxon richness of soil Acari</b></th><td></td><td></td><td><b>Soil density</b></td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>16.11***</td><td>Model</td><td>8</td><td>1769.86***</td></tr><tr><th>Trampling</th><td>1</td><td>34.68***</td><td>Trampling</td><td>1</td><td>57.12***</td></tr><tr><th>Habitat</th><td>3</td><td>3.17*</td><td>Habitat</td><td>3</td><td>82.99***</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>1.23</td><td>Trampling * Habitat</td><td>3</td><td>36.99***</td></tr><tr><th><b>Shannon index of soil Acari</b></th><td></td><td></td><td><b>Water-holding capacity</b></td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>18.88***</td><td>Model</td><td>8</td><td>155.49***</td></tr><tr><th>Trampling</th><td>1</td><td>51.07***</td><td>Trampling</td><td>1</td><td>45.98***</td></tr><tr><th>Habitat</th><td>3</td><td>4.03*</td><td>Habitat</td><td>3</td><td>3.22*</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>0.78</td><td>Trampling * Habitat</td><td>3</td><td>2.6</td></tr><tr><th><b>Simpson index of soil Acari</b></th></tr><tr><th>Model</th><td>8</td><td>13.42***</td><td></td><td></td><td></td></tr><tr><th>Trampling</th><td>1</td><td>0</td><td></td><td></td><td></td></tr><tr><th>Habitat</th><td>3</td><td>7.47**</td><td></td><td></td><td></td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>5.22**</td><td></td><td></td><td></td></tr><tr><th><b>Evenness index of soil Acari</b></th></tr><tr><th>Model</th><td>8</td><td>42.11***</td><td></td><td></td><td></td></tr><tr><th>Trampling</th><td>1</td><td>120.61***</td><td></td><td></td><td></td></tr><tr><th>Habitat</th><td>3</td><td>3.68*</td><td></td><td></td><td></td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>1.82</td><td></td><td></td><td></td></tr></tbody></table>
Table 1 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel
<p><b>Table 1</b> Sampling design (replication = 4) for sites at the Safari Zoological Center, Israel, December 2013. Herbaceous ground cover: +++ patchy; + a few plants, – no plants. OE = open places under enclosure conditions, OT = open places under trampling conditions; EE <i>E</i> =. <i>camaldulensis</i> canopy habitat under enclosure conditions, ET = <i>E. camaldulensis</i> canopy habitat under trampling conditions, TE <i>T</i> =. aphylla canopy habitat under enclosure conditions, TT <i>T</i> =. <i>aphylla</i> canopy habitat under trampling conditions, CE = <i>C</i>. <i>sempervirens</i> canopy habitat under enclosure conditions, CT = <i>C</i>. <i>sempervirens</i> canopy habitat under trampling conditions.</p><table><tbody><tr><th>Habitat</th><th>Code</th><th>Treatment</th><th>Tree height (m)</th><th>Tree canopy crown (m2)</th><th>Herbaceous vegetation</th><th>Soil physical/biological top layer</th><th>Litter layer (cm)</th></tr></tbody><tbody><tr><th>Open spaces</th><td>OT OE</td><td>Trampling Enclosure</td><td>- -</td><td>- -</td><td>No +++</td><td>No Physical top layer</td><td>No No</td></tr><tr><th><i>E. camaldulensis</i></th><td>ET EE</td><td>Trampling Enclosure</td><td>10-13</td><td>6×8</td><td>No +</td><td>No Biological top layer</td><td>No 2-3</td></tr><tr><th><i>T. aphylla</i></th><td>TT TE</td><td>Trampling Enclosure</td><td>14-16</td><td>8×8</td><td>No +++</td><td>No Physical top layer</td><td>No Few</td></tr><tr><th><i>C. sempervirens</i></th><td>CT CE</td><td>Trampling Enclosure</td><td>14-16</td><td>7×9</td><td>No +</td><td>No Biological layer</td><td>No 1-2</td></tr></tbody></table>
Table 4 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel
<p><b>Table 4</b> Correlation coefficients (Pearson correlation, <i>r</i>) between the abundance of microarthropods, diversity indices of soil Acari, and soil parameters at the Safari Zoological Center, Israel, December 2013. SM = soil moisture, OM = organic matter, pH = soil pH, SEC = soil electrical conductivity, SD = soil density, WHC = water-holding capacity <i>p</i>. <*0.05, ** <i>p</i> <0.01, *** <i>p</i> <0.001.</p><table><tbody><tr><th><b>Index</b></th><th></th><th><b>SM</b></th><th><b>OM</b></th><th><b>pH</b></th><th><b>SEC</b></th><th><b>SD</b></th><th><b>WHC</b></th></tr></tbody><tbody><tr><th></th><td>Acari</td><td>0.349*</td><td>0.068</td><td>-0.198</td><td>-0.506**</td><td>-0.299</td><td>0.571***</td></tr><tr><th>Abundance</th><td>Collembola Other soil arthropods</td><td>0.153 0.202</td><td>0.210 0.098</td><td>-0.486** -0.294</td><td>-0.506** -0.209</td><td>-0.107 -0.065</td><td>0.207 0.245</td></tr><tr><th></th><td>Total microarthropod</td><td>0.292</td><td>0.158</td><td>-0.403*</td><td>-0.574***</td><td>-0.233</td><td>0.445*</td></tr><tr><th>Diversity indices of Acari</th><td>Taxon richness Shannon index Simpson index</td><td>0.253 0.285 -0.006</td><td>0.023 0.032 -0.175</td><td>-0.098 -0.120 0.165</td><td>-0.392* -0.455** 0.240</td><td>-0.316 -0.293 -0.475**</td><td>0.561*** 0.585*** 0.336</td></tr><tr><th></th><td>Evenness index</td><td>0.387*</td><td>0.009</td><td>-0.203</td><td>-0.466**</td><td>-0.411*</td><td>0.739***</td></tr></tbody></table>
The conflict between hosts and non-hosts changes the severity of diseases in degraded grassland plant communities
<p>load: disease severity of plant community</p> <p>richnes: species richness</p> <p>Nonhost: Non-hosts richness</p> <p>coverage: relative coverage</p> <p>RHost: relative hosts richness</p> <p>PD: Faith's phylogenetic distance</p> <p>beta: beta diversity of plant community</p> <p>SLA: CWM SLA</p> <p>LN: CWM leaf N content</p> <p>LP:CWM leaf P content</p> <p>NP: CWM leaf N:P</p> <p>group: degree of grassland degradation</p> <p>Shannon: Shannon index of plant community</p> <p>Simpson: Simpsion index of plant community</p> <p>Pielou: Pielou index of plant community</p> <p>marglef: Marglef index of plant community</p>
Data from: Plant community responses to long-term fertilization: changes in functional group abundance drive changes in species richness
Declines in species richness due to fertilization are typically rapid and associated with increases in aboveground production. However, in a long-term experiment examining the impacts of fertilization in an early successional community, we found it took 14 years for plant species richness to significantly decline in fertilized plots, despite fertilization causing a rapid increase in aboveground production. To determine what accounted for this lag in the species richness response, we examined several potential mechanisms. We found evidence suggesting the abundance of one functional group—tall species with long-distance (runner) clonality—drove changes in species richness, and we found little support for other mechanisms. Tall runner species initially increased in abundance due to fertilization, then declined dramatically and were not abundant again until later in the experiment, when species richness and the combined biomass of all other functional groups (non-tall runner) declined. Over 86 % of the species found throughout the course of our study are non-tall runner, and there is a strong negative relationship between non-tall runner and tall runner biomass. We therefore suggest that declines in species richness in the fertilized treatment are due to high tall runner abundance that decreases the abundance and richness of non-tall runner species. By identifying the functional group that drives declines in richness due to fertilization, our results help to elucidate how fertilization decreases plant richness and also suggest that declines in richness due to fertilization can be lessened by controlling the abundance of species with a tall runner growth form.
Understanding the local drivers of beta-diversity patterns under climate change: The case of seaweed communities in Galicia, North West of the Iberian Peninsula
<p>Aim: To understand spatial-temporal changes (beta-diversity) in coastal communities and their drivers in the context of climate change. Coastal ecosystems are extremely exposed and dynamic, where changes in seaweed assemblages have been associated with changing water temperatures. However, at the local scale, the effects of changes in the upwelling events and related stressors seek further exploration.<br> <br> Location: Galicia rías, North West of the Iberian Peninsula<br> <br> Methods: Using data collected in 42 sampling localities in Galicia rias and over two time periods (1998 and 2014), we analyzed changes in the seaweed community's composition through time and space. We calculated the temporal beta diversity index and spatial beta diversity as the pairwise composition differences between sampling localities. We use generalized dissimilarity models, to identify local environmental drivers of spatial and temporal beta-diversity.<br> <br> Results: We found a significant change in seaweed communities of Galicia rias, between 1998 and 2014 (temporal beta-diversity). They were mostly related to species loss rather than to species replacement. The dissimilarity among localities (spatial beta diversity) was significantly higher in 2014 than 1998. Nitrate concentration was consistently predicted as the main driver of both temporal and spatial beta-diversity patterns.<br> <br> Main conclusions: Unlike other studies in marine ecosystems, our results suggest that observed changes in the structure of perennial seaweed assemblages in Galicia Rias might lead to a local biotic heterogenization, indirectly linked to climate change through changes in nutrients availability and the upwelling intensity. Changes in Galicia seaweed communities call scientific attention to the importance of local stressors in climate change studies.</p>
Functional traits reveal the dominant drivers of long‐term community change across a North American Great Lake
<p>The datasets here were used to determine annual sentinel fish species and trait composition in Lake Erie's western and central basins during 1969–2018 in relation to multiple anthropogenic stressors. Here, we provide three datasets, which are used in the paper by Sinclair et al. titled: "Functional traits reveal the dominant drivers of long-term community change across a North American Great Lake". Each dataset is provided as a separate tab in a single Excel worksheet. The first dataset ("Western basin") provides the annual catch-per-unit-effort (CPUE; individuals per trawl minute) for all sentinel fish species caught in Lake Erie's western basin during 1969–2018 in fall (September-October) trawl surveys conducted by the Ohio Department of Natural Resources - Division of Wildlife. The second dataset ("Central basin") provides the CPUE values for Lake Erie's central basin. The third dataset ("Environmental stressors") provides the annual values for nutrient inputs, water transparency, temperature, white perch abundances, and goby abundances for the western and central basins. A summary and explanation of each variable are also provided in the "Info" tab. Further information on how fish CPUE and stressor values were calculated is provided in the methods and supporting information of the associated article.</p>
Eco-evolutionary contributions to community trait change in floating aquatic plants
<p>An entire community of organisms may become modified when its environment changes. These modifications can happen through physiological process (plasticity), evolutionary processes (adaptation) or shifts in species composition (sorting). The outcome of these three sources of change constitutes the community's phenotypic response, but how they combine to drive community trait dynamics is not currently well understood. We have conducted a community selection experiment in which communities of short-lived floating aquatic plants were grown in a range of stressful conditions, and measured changes in their body size. Determinants of phenotypic change were assessed with a full community reciprocal transplant which led to estimates of the contributions of plasticity, adaptation, and sorting. Species were modified during the experiment by both plasticity and adaptation, but in either case the magnitude and direction of change differed among species. Sorting and adaptation were of equal magnitude, but tended to act in opposite directions: in conditions where species with large fronds prevailed, each species evolved smaller fronds, and vice versa. We conclude that community trait dynamics cannot be understood simply by extrapolating the adaptive response of any single species to the whole community.</p>
Macrosystem community change in lake phytoplankton and its implications for diversity and function
<p><span><strong>Aim</strong>: </span><span>We use lake phytoplankton community data to quantify the spatio-temporal and scale-dependent impacts of eutrophication, land-use, and climate change</span><span> </span><span>on species niches and community assembly processes while accounting for species traits and phylogenetic constraints.</span></p> <p><span><strong>Location</strong>: </span><span>Finland</span></p> <p><span><strong>Time period</strong>: </span><span>1977-2017</span></p> <p><span><strong>Major taxa</strong>: </span><span>Phytoplankton</span></p> <p><span><strong>Methods</strong>: </span><span>We use Hierarchical Modelling of Species Communities (HMSC) to</span><span> model meta-community trajectories at 853 lakes over four decades of environmental change, including a hierarchical spatial structure to account for scale-dependent processes. Using a 'region of common profile' approach, we evaluate compositional changes of species communities and trait profiles and investigate their temporal development. </span></p> <p><span><strong>Results</strong>: </span><span>We demonstrate the emergence of novel and widespread community composition clusters in previously more compositional homogeneous communities, with cluster-specific community trait profiles, indicating functional differences. A strong phylogenetic signal of species' responses to the environment implies similar responses among closely related taxa. Community cluster-specific species prevalence point to lower taxonomic dispersion within the current dominant clusters compared to the historically dominant cluster and overall higher prevalence of smaller species sizes within communities. Our findings denote profound spatio-temporal structuring of species co-occurrence patterns and highlight</span><span> </span><span>functional differences of lake phytoplankton communities.</span></p> <p><span><strong>Main conclusions</strong>: </span><span>Diverging community trajectories have led to a nationwide reshuffling of lake phytoplankton communities. At regional and national scales, lakes are not single entities but metacommunity hubs in an interconnected waterscape. The assembly mechanisms of phytoplankton communities are strongly structured by spatio-temporal dynamics, which have led to novel community types but only a minor part of this reshuffling could be linked to temporal environmental change.</span></p>
Changes in plant community assembly from patchy degradation of grasslands and grazing by different-sized herbivores
<p>Grassland degradation caused by increases in livestock grazing threatens a variety of ecosystem services. Understanding changes in plant community assembly during the process of grassland degradation in the presence of grazing is important to help restore degraded grasslands worldwide but has received little attention thus far. The grassland degradation process is typified by heterogeneous degradation, i.e., gradual formation of degraded patches (hereafter "patchy degradation"). Here, we experimentally examined the effects of herbivore grazing and patchy degradation on plant community assembly using nine pairs of non-degraded (intact) and patch-degraded (fragmented) grasslands subject to grazing by different-sized herbivores (i.e., NG, no grazing; SG, sheep grazing; CG, cattle grazing) over four years. Using a null-model approach, we estimated the relative magnitude of deterministic processes of community assembly by comparing the observed and expected β-diversity. We found that in the absence of herbivore grazing, deterministic processes played a greater role in community assembly, regardless of whether patchy degradation had occurred. However, the deterministic processes resulted in plant communities being more spatially similar in non-degraded grasslands while being more dissimilar in patchy degraded grasslands. Compared with non-degraded grasslands, species with strong competitive abilities (i.e., Leymus chinensis) were less dominant in patchy degraded grasslands, indicating relaxed competition and a reduced role of species interactions over plant communities. Instead, patchy degradation added the role of environmental variables over plant communities. Sheep grazing consistently promoted more stochastic plant community assembly in both non-degraded and patch-degraded grasslands, while cattle grazing promoted more stochastic plant community assembly only in the non-degraded state, having no effect in the patch-degraded state. Our study offers important insights into changes in plant community assembly during ongoing patch-degradation of grasslands, indicating the role of increased environmental filtering of soil and reduced species interactions in driving plant community dynamics with increasing grassland patchy degradation. We also uncovered an herbivore species-specific effect on plant community assembly during the process of grassland degradation, which will better inform and improve future grassland restoration planning efforts.</p>
dmurraystoker/OGC-biomass: Long-term comparison of invertebrate communities in a blackwater river reveals taxon-specific biomass change
<p>Data, metadata, and R code for "Long-term comparison of invertebrate communities in a blackwater river reveals taxon-specific biomass change."</p> <p>Abstract</p> <p>1. Around the world, researchers are reporting declines in insect fauna. Though uncommonly evaluated in high-profile studies of insect declines, the community context of population trends can facilitate interpretation of the causes and consequences of such losses. Here, we aimed to explore the shifts in a well-studied invertebrate community of a blackwater river and identify potential catalysts of such change.</p> <p>2. We compared the density, biomass, and community structure of freshwater invertebrate assemblages separated by more than 30 years in the Ogeechee River, in the southeastern US, and found biomass declines. We also evaluated long-term trends in river discharge, water temperature, and precipitation.</p> <p>3. Overall, the biomass in the 2010s was approximately 60% of the total in the 1980s. Community analyses indicated that this decline was driven by reduced densities of large-bodied, filter-feeding insects, particularly Hydropsychidae caddisflies (Trichoptera). Conversely, predators and small-bodied primary consumers increased in density, though their contributions to overall biomass were minimal and their increased density was not sufficient to compensate for biomass declines. Seasonal shifts in both invertebrate populations and environmental parameters were evident, especially when focusing on discharge and dissolved organic carbon.</p> <p>4. Through a combination of direct analysis and the use of established research on the metabolic dynamics of the study site, we determined that the overall decline of freshwater invertebrate biomass may be driven by climate-related changes in flood dynamics: seasonal flooding that facilitates delivery of floodplain carbon to filter-feeding consumers has decreased over several decades. Water temperature had also increased and has likely had effects on the invertebrate assemblages.</p> <p>5. Whole-community evaluations such as this one, in contrast to single-taxon and abundance-based studies, provide critical information to elucidate the dynamics of freshwater impairment and insect loss in the Anthropocene.</p>
Data from: Initial ecological change in plant and arthropod community composition after wildfires in designated areas of upland peatland
<p>This dataset is published in conjunction with the publication of : Kelly, R., Montgomery, W. I., & Reid, N. (2022) Initial ecological change in plant and arthropod community composition after wildfires in designated areas of upland peatland. <em>Ecology and Evolution, </em>and contains the environmental, botanical and entomological datasets underpinning the analysis contained there in. Additionally, it contains datasets on seedbanks and bird species which were collected as part of the Northern Ireland Environment Agency funded project 'Quantifying the impact of wildfires in the Northern Ireland' from 2012 to 2015 (see: <a href="https://www.daera-ni.gov.uk/publications/quantifying-impact-wildfires-northern-ireland-final-report-2016">https://www.daera-ni.gov.uk/publications/quantifying-impact-wildfires-northern-ireland-final-report-2016</a>)</p> <p>This data underpins the following publications which discuss the impacts of wildfires at these key upland sites in Northern Ireland: </p> <ul> <li>Kelly, R., Boston, E., Montgomery, W. I., & Reid, N. (2016). The role of the seed bank in recovery of temperate heath and blanket bog following wildfires. Applied Vegetation Science, 19(4), 620-633. <a href="https://doi.org/10.1111/avsc.12242">https://doi.org/10.1111/avsc.12242</a> </li> <li>Kelly, R., Montgomery, W. I., & Reid, N. (2018). Differences in soil chemistry remain following wildfires on temperate heath and blanket bog sites of conservation concern. Geoderma, 315, 20-26. - <a href="https://doi.org/10.1016/j.geoderma.2017.11.016" rel="noreferrer noopener" title="Persistent link using digital object identifier">https://doi.org/10.1016/j.geoderma.2017.11.016</a> </li> <li>Reid, N., Kelly, R. & Montgomery, W.I. (2022) Impact of wildfires on ecosystems and bird communities on designated areas of blanket bog and heath. Bird Study (In press)</li> <li>Kelly, R., Montgomery, W. I., & Reid, N. (2022) Initial ecological change in plant and arthropod community composition after wildfires in designated areas of upland peatland. Ecology and Evolution. (In press)</li> </ul> <p>Data were collected and collated by Ruth Kelly (ruth.kelly@afbini.gov.uk/ruthkelly123@gmail.com) in collaboration with Dr. Neil Reid, Dr. Emma Boston, Prof. W.I. Montgomery (Queen's University Belfast) and Dr. Damian Mc Ferran (National Museums of Northern Ireland). We are also very grateful to Roy Anderson, Orla McLaughlin, Anna Hart, Rachel Hamill, Adam Mantell, Tony Smith, Joanne Denyer and Richard Weyl for their expertise and assistance in species identification, and to Gillian Riddell, Caroline Finlay, Florentine Spaans, Jeremy Adelard, Marion Chapalain, Alice McPherson, Claire McVeigh, Amber Woods and Rachel Hamill for assistance and expertise in the laboratory and field. </p>
ScienceDex guides
Understand access before you commit
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.