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883 results for “termite”
Termite trait data from: Continental-scale shifts in termite diversity and nesting and feeding strategies
<p>Typically, termites are treated as a single guild, which ignores important internal diversity, including diverse feeding and nesting traits. These termite traits are crucial for both ecosystem-level fluxes and trophic webs, with implications for vertebrate species. Despite their ecological importance, the large-scale distribution of termite feeding and nesting traits and the relationship with termite diversity is largely unknown. We investigated whether functional diversity, species richness, and feeding (wood, litter, grass, dung) and nesting trait (aboveground mound, belowground nest, inside tree or outside tree nest) distributions of termites were climatically control. To address this gap, we assembled a continental-scale database of termite traits and occurrence in Australia and modelled termite nesting and feeding traits in response to macroclimate. Functional richness and evenness increased primarily with temperature. Australia showed multiple hotspots of termite diversity with each hotspot showing a distinct guild composition. The large-scale distribution of nesting traits showed that aboveground nesting species were the most common nesting guild in the dry and wet tropics while belowground nesting dominated in seasonally cold arid environments, demonstrating a strong climatic control on nesting strategy. Given their large biomass and many interactions with other species, the macro-ecology of termite traits may be especially important in predicting shifts in other species' distributions at continental and global scales.</p>
Analyses on the fungus-farming termite, Macrotermes natalensis
<p>Kings and queens of eusocial termites can live for decades, while queens sustain a nearly maximal fertility. To investigate the molecular mechanisms underlying their long lifespan, we carried out transcriptomics, lipidomics and metabolomics in <i>Macrotermes natalensis</i> on sterile short-lived workers, long-lived kings and five stages spanning twenty years of adult queen maturation. Reproductives share gene expression differences from workers in agreement with a reduction of several aging-related processes, involving upregulation of DNA damage repair and mitochondrial functions. Anti-oxidant gene expression is downregulated, while peroxidability of membranes in queens decreases. Against expectations, we observed an upregulated gene expression in fat bodies of reproductives of several components of the IIS pathway, including an insulin-like peptide, Ilp9. This pattern does not lead to deleterious fat storage in physogastric queens, while simple sugars dominate in their hemolymph and large amounts of resources are allocated towards oogenesis. Our findings support the notion that all processes causing aging need to be addressed simultaneously in order to prevent it.</p>
Molecular phylogeny reveals the past transoceanic voyages of drywood termites (Isoptera, Kalotermitidae)
<p><span>Termites are major decomposers in terrestrial ecosystems and the second most diverse lineage of social insects. The Kalotermitidae form the second-largest termite family and are distributed across tropical and subtropical ecosystems, where they typically live in small colonies confined to single wood items inhabited by individuals with no foraging abilities. How the Kalotermitidae have acquired their global distribution patterns remains unresolved. Similarly, it is unclear whether foraging is ancestral to Kalotermitidae or was secondarily acquired in a few species. These questions can be addressed in a phylogenetic framework. We inferred time-calibrated phylogenetic trees of Kalotermitidae using mitochondrial genomes of ~120 species, about 27% of kalotermitid diversity, including representatives of 21 of the 23 kalotermitid genera. Our mitochondrial genome phylogenetic trees were corroborated by phylogenies inferred from nuclear ultraconserved elements derived from a subset of 28 species. We found that extant kalotermitids shared a common ancestor 84 Mya (75–93 Mya 95% HPD), indicating that a few disjunctions among early-diverging kalotermitid lineages may predate Gondwana breakup. However, most of the ~40 disjunctions among biogeographic realms were dated at less than 50 Mya, indicating that transoceanic dispersals, and more recently human-mediated dispersals, have been the major drivers of the global distribution of Kalotermitidae. Our phylogeny also revealed that the capacity to forage is often found in early-diverging kalotermitid lineages, implying the ancestors of Kalotermitidae were able to forage among multiple wood pieces. Our phylogenetic estimates provide a platform for critical taxonomic revision and future comparative analyses of Kalotermitidae.</span></p>
Termite dispersal is influenced by their diet
<p>Termites feed on vegetal matter at various stages of decomposition. Lineages of wood- and soil-feeding termites are distributed across terrestrial ecosystems located between 45°N and 45°S of latitude, a distribution they acquired through many transoceanic dispersal events. While wood-feeding termites often live in the wood on which they feed and are efficient at dispersing across oceans by rafting, soil-feeders are believed to be poor dispersers. Therefore, their distribution across multiple continents requires an explanation. Here, we reconstructed the historical biogeography and the ancestral diet of termites using mitochondrial genomes and δ13C and δ15N stable isotope measurements obtained from 324 termite samples collected in five biogeographic realms. Our biogeographic models showed that wood-feeders are better at dispersing across oceans than soil-feeders, further corroborated by the presence of wood-feeders on remote islands devoid of soil-feeders. However, our ancestral range reconstructions identified 33 dispersal events among biogeographic realms, 18 of which were performed by soil-feeders. Therefore, despite their lower dispersal ability, soil-feeders performed several transoceanic dispersals that shaped the distribution of modern termites.</p>
Termite diversity is resilient to land-use change
<p>Cocoa is an important crop for Ghana's economy, contributing 25% of Gross Domestic Product (GDP). The crop, however, is mainly cultivated on forest-derived soils and is a major cause of land-use change. Termites are an important biological component of tropical ecosystems providing numerous ecosystem services. Previous studies have indicated that termites are sensitive to forest disturbance and decrease in richness and abundance across land-use intensification gradients, with consequences for the essential services that they provide. Native shade trees are often used to improve cocoa cultivation and may reduce the detrimental effects of land-use change on some aspects of biodiversity. The aim of this study was therefore to explore how termites respond to land-use change along a shade-tree gradient in Kakum National Park and surrounding cocoa farms in Ghana (from forest at 80% tree cover to cocoa with no shade cover, to the extreme of cultivated arable crop land). It was predicted that termite richness and abundance would decrease with decreasing shade cover, and with increasing distance from the forest edge. Thirty-four species from 29 genera were sampled, with Ancistrotermes crucifer being found in all the locations (47% of all encounters). Species richness and abundance differed marginally across the land-use gradient, as well as the distance from the forest edge, however, species richness did not show any significance with distance. All the same, termite communities were robust to the disturbance. Our findings suggest that though site influenced species richness and abundance, cocoa trees can play a crucial role in maintaining biodiversity and environmental quality in an agricultural landscape by providing a habitat for forest species that are not found in pastures or farm fields. However, we caution that the relatively low forest baseline of existing forest diversity may inflate the value of cocoa land, with those forests no longer representing undisturbed natural habitats: this highlights that shifting baselines may need to be accounted for when interpreting findings in the Anthropocene.</p>
Species-level termite methane production rates
<p>Termites consume substantial amounts of plant material across tropical and subtropical ecosystems. During the process of lignocellulose digestion, the symbiotic methanogenesis within termites' guts produces the potent greenhouse gas methane (CH4). Termites contribute an estimated 1-5% of global CH4 emissions, with these estimates derived from the product of termite biomass and termite CH4 production rate per unit of termite biomass. However, termite CH4 production rates vary significantly across species, genus, family, and feeding group, yet our understanding of this variation remains poor. Here, we reviewed papers published from 1975 to 2021 to create a single consistently derived list of species-level termite CH4 production rates. We searched Google Scholar using two key words: termite AND methane. We only included studies that had measured termite CH4 production rates using the incubation method. For each eligible study, we extracted and tabulated termite CH4 production rates and other relevant variables (e.g., feeding groups). We used μg CH4 g-1(termite) h-1 as the standardized unit, and if other units were presented, we converted them into this standardized unit. Overall, These data include 134 termite species from 65 genera and 5 families. Termite CH4 production rates ranged from 0 to 25.26 μg CH4 g-1(termite) h-1, with an average rate of 3.74 (standard deviation = 4.08, n = 251). Reported CH4 production rates were largely concentrated in the family Termitidae. Across feeding groups, soil feeders tended to have higher CH4 production rates than wood feeders. However, published data represent fewer than 5% of described termite species, and therefore we hope that our study will initiate a community-wide effort to fill data gaps and advance our understanding of the role of termites in critical biogeochemical cycles and other ecosystem processes.</p>
Nutritional composition of adult African winged termite and bonga shad
<p>Cheap sources of protein and essential amino acids are among the major concern in aquaculture due to the unbearable cost of fishmeal. There is current research effort on use of insect meal as dietary substitute for fish meal in animal nutrition. As first step for a successful trial, knowledge of the nutritional composition of the experimental insect is important. It was observed that bonga shad showed higher levels of crude protein and ash while African winged termite was higher in crude lipid and dry matter. There was similarity in the levels of essential amino acids in both animals. However, there were considerable differences in the level of fatty acids of the two experimental animals in favour of the insect.</p>
Occurrence datasets, model outputs, and R script for 12 termite species used for niche modeling
<p>The advent of citizen-science databases in conjunction with museum specimen locality information has exponentially increased the power and accuracy of ecological niche modeling (ENM). Increased occurrence data has provided colossal potential to understand the distributions of lesser known or endangered species, including arthropods. Although niche modeling of termites has been conducted in the context of invasive and pest species, few studies have been performed to understand the distribution of basal termite genera. Using specimen records from the American Museum of Natural History (AMNH) as well as locality databases, we generated ecological niche models for 12 basal termite species belonging to six genera and three families. We extracted environmental data from the Worldclim 19 bioclimatic dataset v2, along with SoilGrids datasets and generated models using MaxEnt. We chose Optimal models based on partial Receiving Operating characteristic (pROC) and omission rate criterion and determined variable importance using permutation analysis. We also calculated response curves to understand changes in suitability with changes in environmental variables. Optimal models for our 12 termite species ranged in complexity, but no discernible pattern was noted among genera, families, or geographic range. Permutation analysis revealed that habitat suitability is affected predominantly by seasonal or monthly temperature and precipitation variation. Our findings not only highlight the efficacy of largely citizen-science and museum-based datasets, but our models provide a baseline for predictions of future abundance of lesser-known arthropod species in the face of habitat destruction and climate change.</p>
Data for: Marking through moults: An evaluation of visible implant elastomer to permanently mark individuals in a lower termite species
<p class="FirstParagraph">1. Advances in individual marking methods have facilitated detailed studies of animal populations and behaviour as they allow tracking of individuals through time and space. Hemimetabolous insects, representing a wide range of commonly-used model organisms, present a unique challenge to individual marking as they are not only generally small-bodied, but also moult throughout development, meaning that traditional surface marks are not persistent.</p> <p class="FirstParagraph">2. Visible implant elastomer (VIE) offers a potential solution as small amounts of the inert polymer can be implanted under the skin or cuticle of an animal. VIE has proved useful for individually marking fish, crustaceans and amphibians in both field and laboratory studies, and has recently been successfully trialled in laboratory populations of worms and fly larvae. We trialled VIE in the single-piece nesting termite <i>Zootermopsis angusticollis</i>, a small hemimetabolous insect.</p> <p class="FirstParagraph">3. We found that there was no effect of VIE on survival and that marks persisted following moulting. However, we found some evidence that marked termites performed less allogrooming and trophallaxis than controls, although effect sizes were very small.</p> <p class="FirstParagraph">4. Our study suggests that VIE is an effective technique for marking small hemimetabolous insects like termites but we advocate that caution is applied, particularly when behavioural observation is important.</p>
Differential effects of vegetation and climate on termite diversity and damage
<p><span>Species diversity shapes ecosystem services. Despite the advantages that this relationship has for pest management, few studies have investigated the links between infrastructure damage (i.e. the percentage amount of infrastructures infested by termites), species richness and the environment. Moreover, it is not clear that which proportion of species richness (total/functional-dominant/common/rare) contributes most to infrastructure damage.</span></p> <p><span>We correlated termite species richness with termite infestation throughout 83 cities in Zhejiang Province, eastern China. Species were classified according to whether or not they fed on wood, and based upon their distributional range, whether they were common or rare. We analyzed the relative importance and the direct/indirect effects of climate, vegetation, anthropogenic activities, and the species richness of four functional categories of termites on the damage levels of eight infrastructure types in populated (i.e. urban and rural building, green space and sea wall) and remote areas (i.e. ancient building, large-old tree, agroforest and reservoir dam). </span></p> <p><span>Common species favoured populated areas, whereas rare species favoured remote areas. Common species, with preferences for deciduous vegetation, caused more damage to the infrastructures of populated areas. Rare species, with preferences for evergreen vegetation, caused more damage in remote areas. Reforestation project which emphasised evergreen trees increased the number of rare species but reduced the number of common species. Elevation and drought risk were positively correlated with rare species richness but neutrally with common species richness.</span></p> <p><span>Structural equation models showed that vegetation predominantly influenced infrastructure damage in populated areas via altering common species richness, whereas climate predominantly and directly influenced infrastructure damage in remote areas. Notably, elevation and drought risk were positively correlated with infrastructure damage especially in remote areas.</span></p> <p><span>Synthesis and applications</span><span>. </span> <span>Termites cause global economic losses of 15~40 billion dollars per year. Our study reveals that managing city forests and green space, for example increasing the proportion of evergreen trees, is a sustainable means of suppressing common termites and thereby reducing infrastructure damage in populated areas. Conservation strategies, supported by regular inspections, will become increasingly important as climate change not only threatens the survival of </span>less harmful <span>rare </span><span>species,</span><span> but also increases infrastructure damage in remote areas.</span></p>
Eusociality and the transition from biparental to alloparental care in termites
<ol> <li>In eusocial organisms, cooperative brood care within a colony represents a situation where the ancestral parental care duties have shifted away from the reproductive parent(s) towards their own offspring. The shift to alloparental care was often instrumental in the initial emergence of eusociality, as it ultimately contributed to the establishment of reproductive division of labor.</li> <li>Remarkably, eusocial taxa such as ants and termites, which still display an ancestral independent colony foundation phase, must go through an obligatory parental care period, as a temporary subsocial family unit. In termites specifically, an incipient colony inherently remains a woodroach family unit until alloparental care is established. Colony foundation success can then be limited by a series of factors that may include environmental, behavioral, symbiotic, and physiological constraints.</li> <li>In this study, 450 incipient termite colonies (<em>Coptotermes gestroi</em>) were established to investigate the timing of physiological changes in founders during the transition from biparental to alloparental care. Results showed that the finite initial internal nutritional resources that alates carry during the dispersal flight are a primary limiting factor for successful colony establishment. The <em>Coptotermes</em> queen and king must rapidly establish (< 150 d) their first cohort of offspring to reach alloparental care or simply run out of resources and die. Alates, therefore, carry just enough internal resources to produce the first few alloparents (< 15 workers) to prime the system toward colony ergonomic growth, with a definitive shift to solely reproductive functions.</li> <li>Eusocial insect primary reproductive traits were optimized for three successive functions within the life cycle of a colony: alate dispersal (sexual reproduction), colony foundation (parental care), and colony growth (increased egg production toward colony maturity). However, results suggest that trade-offs involving these functions appear to primarily favor dispersal ones (quantity vs. quality of alates), as founder(s) carry minimal resources and have no room for parental care inefficiency, and as they then fully rely on their alloparents for further reproductive output.</li> </ol> <p>The transition toward alloparental care during colony foundation of eusocial insects may therefore reflect on the initial evolutionary transition from ancestral subsociality to eusociality.</p>
Data from: Caste-biased patterns of brain investment in the subterranean termite Reticulitermes flavipes
<p>Investment into neural tissue is expected to reflect the specific sensory and behavioral capabilities of a particular organism. Termites are eusocial insects that exhibit a caste system in which individuals can develop into one of several morphologically and behaviorally distinct castes. However, it is unclear to what extent these differences between castes are reflected in the anatomy of the brain. To address this question, we used deformation-based morphometry to conduct pairwise comparisons between the brains of different castes in the eastern subterranean termite, <em>Reticulitermes flavipes</em>. The dataset presented here consists of the confocal images of all the brains used in our analysis, separated by caste. Brains from five castes are presented - workers, soldiers, ergatoids, nymphs, and alates - which are further divided by sex.</p>
Fig. 4 in Aparatermes thornatus (Isoptera: Termitidae: Apicotermitinae), a new species of soldierless termite from northern Amazonia
Fig. 4. Distribution of Aparatermes from University of Florida Termite Collection records.
FIG. 3 in Bryophytes associated with termite mounds on the northeastern Nigerian highlands
FIG. 3. — Bryophyte mats at the base of termite mound. Mambilla Plateau, Nigeria.
FIG. 2 in Bryophytes associated with termite mounds on the northeastern Nigerian highlands
FIG. 2. — Termite mound with bryophyte mats at the base. Mambilla Plateau, Nigeria.
Ant and termite assemblages along a tropical forest disturbance gradient in Sabah, Malaysia: A study of co-variation and trophic interactions
<b>Description: </b><p>Termite community composition from soil pits and deadwood</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/103"><b>Ant and termite assemblages along a tropical forest disturbance gradient in Sabah, Malaysia: A study of co-variation and trophic interactions</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=38">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Functional traits</b> (Worksheet Function)</p><p>Dimensions: 36 rows by 3 columns</p><p>Description: Functional traits associated with each genus</p><p>Fields: </p><ul><li><b>Genus</b>: Genus ID (Field type: Taxa)</li><li><b>Functional.group</b>: Humification gradient (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Soil pit data</b> (Worksheet SoilPits)</p><p>Dimensions: 954 rows by 35 columns</p><p>Description: Termite community composition from soil pits</p><p>Fields: </p><ul><li><b>2nd.order.point</b>: SAFE Project sample site (Field type: Location)</li><li><b>Quadrat.number.(in.my.study)</b>: Quadrat number (Field type: ID)</li><li><b>Date</b>: Date of sample collection (Field type: Date)</li><li><b>Pit.number</b>: Pit number within the plot (Field type: Replicate)</li><li><b>Time</b>: Time samples were collected (Field type: Time)</li><li><b>No..of.termites</b>: Number adult termites (Field type: Abundance)</li><li><b>No.juvenile.termites</b>: Number juvenile termites (Field type: Abundance)</li><li><b>Unknown</b>: Number of damaged individuals or individuals that can't definitively be assigned to genera (Field type: Abundance)</li><li><b>Dicuspiditermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Schedorhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Prohamitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Malaysiotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Mirocapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Hypotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Procapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homatermes.undescribed.genus</b>: Number of individuals (Field type: Abundance)</li><li><b>Termes</b>: Number of individuals (Field type: Abundance)</li><li><b>Syncapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pericapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Microcerotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Macrotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Globitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Lacessititermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pseudocapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homallotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Oriencapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Oriensublitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Labritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Euramitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Nasutitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Bulbitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Odontotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Heterotermes</b>: Number of individuals (Field type: Abundance)</li></ul><br></li><li><p><b>Deadwood data</b> (Worksheet Deadwood)</p><p>Dimensions: 138 rows by 23 columns</p><p>Description: Termite community composition from deadwood</p><p>Fields: </p><ul><li><b>2nd.order.point</b>: SAFE Project sample site (Field type: Location)</li><li><b>Quadrat.number.(in.my.study)</b>: Quadrat number (Field type: ID)</li><li><b>Date</b>: Date of sample collection (Field type: Date)</li><li><b>Wood.sample</b>: Wood piece within Quadrat (Field type: Replicate)</li><li><b>Time</b>: Time samples were collected (Field type: Time)</li><li><b>No..of.termites</b>: Number adult termites (Field type: Abundance)</li><li><b>No.juvenile.termites</b>: Number juvenile termites (Field type: Abundance)</li><li><b>Unknown</b>: Number of damaged individuals or individuals that can't definitively be assigned to genera (Field type: Abundance)</li><li><b>Dicuspiditermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Schedorhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Homallotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Bulbitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Macrotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Globitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Nasutitermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Heterotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Syncapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Malaysiotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Parrhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Pericapritermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Rhinotermes</b>: Number of individuals (Field type: Abundance)</li><li><b>Aciculitermes</b>: Number of individuals (Field type: Abundance)</li></ul><br></li></ol><p><b>Date range: </b>2010-04-21 to 2010-05-25</p><p><b>Latitudinal extent: </b>4.6353 to 4.7520</p><p><b>Longitudinal extent: </b>116.9542 to 117.6288</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>Aciculitermes</i><br> -  -  -  -  - [<i>Euramitermes</i>]<br> -  -  -  -  - <i>Homallotermes</i><br> -  -  -  -  - <i>Hypotermes</i><br> -  -  -  -  - <i>Mirocapritermes</i><br> -  -  -  -  - <i>Procapritermes</i><br> -  -  -  -  - <i>Prohamitermes</i><br> -  -  -  -  - <i>Pseudocapritermes</i><br> -  -  -  - Rhinotermitidae<br> -  -  -  -  - <i>Heterotermes</i><br> -  -  -  -  - <i>Parrhinotermes</i><br> -  -  -  -  - <i>Rhinotermes</i><br> -  -  -  -  - <i>Schedorhinotermes</i><br> -  -  -  - Termitidae<br> -  -  -  -  - <i>Bulbitermes</i><br> -  -  -  -  - <i>Dicuspiditermes</i><br> -  -  -  -  - <i>Globitermes</i><br> -  -  -  -  - <i>Labritermes</i><br> -  -  -  -  - [<i>Lacessititermes</i>]<br> -  -  -  -  - <i>Macrotermes</i><br> -  -  -  -  - <i>Malaysiotermes</i><br> -  -  -  -  - <i>Microcerotermes</i><br> -  -  -  -  - <i>Nasutitermes</i><br> -  -  -  -  - <i>Odontotermes</i><br> -  -  -  -  - <i>Oriencapritermes</i><br> -  -  -  -  - <i>Oriensubulitermes</i><br> -  -  -  -  - <i>Pericapritermes</i><br> -  -  -  -  - <i>Syncapritermes</i><br> -  -  -  -  - <i>Termes</i><br> -  -  -  -  - <i>Hodotermes</i><br> -  - [Homatermes.undescribed.genus]<br></div><p></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>
Traffic flow formation in termites
<p>The material shared here is part of the research project on the mechanism of traffic flow formation. We use termites as a biological model and their natural foraging activity as a method. Here, we present some video fragments extracted from the recordings of the full foraging process of the termites <em>Constrictotermes </em><em>cyphergaster</em> at laboratory conditions. This research project is supported by the Brazilian Government through the Minas Gerais State Agency for Research (FAPEMIG), the Brazilian Council for Research (CNPq) and the Coordination for the Improvement of Higher Education Personnel (CAPES).</p> <p>Details of the videos: Each file recording is named with the ID of the nest used for each test. A test consisted of the passage of the termites from the box containing the entire nest to the foraging box through a bridge. The width and the form of the bridge were modified between tests. In the fragment, 2018-IX-07-RCB-N09 we tested a bridge with a constant width of 7.5 cm, while in the fragment 2018-IX-07-RCB-N18 we tested a "bottleneck" bridge with a hybrid width of 2.5 cm - 1.5 cm - 2.5cm.</p> <p>For aditional information, please contact us: Julieth Castiblanco (castiblancoq.j@gmail.com), Og DeSouza (og.souza@ufv.br).</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>
Fig. 5 in Methods for collecting large numbers of exuviae from Coptotermes (Blattodea: Rhinotermitidae) termite colonies
Fig. 5. (A) Coptotermes gestroi exuviae (approximately 550); (B) close up of exuviae.
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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.