Termite abundance and ecosystem processes in Maliau Basin, 2015-2016 [HMTF]

Description:

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.

Project: This dataset was collected as part of the following SAFE research project: Biodiversity and land-use impacts on tropical ecosystem function (BALI): Experimental manipulations of biodiversity at SAFE

Funding: These data were collected as part of research funded by:

• UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant, NERC grant NE/L000016/1)

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.

Permits: These data were collected under permit from the following authorities:

• Sabah Biodiversity Centre (Research licence na)

XML metadata: GEMINI compliant metadata for this dataset is available here

Files: This consists of 1 file: Termite_monitoring_maliau.xlsx

Termite_monitoring_maliau.xlsx

This file contains dataset metadata and 11 data tables:

1. Leaf_litter_depth (described in worksheet Leaf_litter_depth)

   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

   Number of fields: 5

   Number of data rows: 800

   Fields:

   • Date: The month and year in which leaf litter depth was recorded (Field type: Date)
   • Plot: The experimental plot that the data were collected from (Field type: Location)
   • LINE: The sampling line within each plot a leaf litter measurement was taken (Field type: ID)
   • Depth_cm: The depth of leaf litter measured at each point (Field type: Numeric)
   • Treatment: The experimental treatment that was applied to the plot (Field type: Categorical)

2. Leaf_litter_invertebrates (described in worksheet Leaf_litter_invertebrates)

   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.

   Number of fields: 31

   Number of data rows: 120

   Fields:

   • Plot: The experimental plot that the data were collected from (Field type: Location)
   • Treatment: The experimental treatment that was applied to the plot (Field type: Categorical)
   • Distance: Distance along the sampling transect in metres (Field type: ID)
   • Coleoptera_Adults: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Coleoptera_Larvae: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Diptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Hemiptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Araneae: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Opiliones: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Isopoda: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Oligochaeta: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Hymenoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Formicidae: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Mollusca: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Lepidoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Chilipoda: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Diplopoda: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Thysanoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Psocoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Dermaptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Orthoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Blattodea: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Leeches: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Plecoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Neuoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Trichoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Mecoptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Odonata: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Siphonaptera: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Termites: Frequency of inverts in leaf litter (Field type: Numeric trait)
   • Pseudoscorpions: Frequency of inverts in leaf litter (Field type: Numeric trait)

3. Leaf_litter_mass (described in worksheet Leaf_litter_mass)

   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).

   Number of fields: 6

   Number of data rows: 87

   Fields:

   • Condition: The rainfall season in which leaf litter bags were deployed (Field type: Categorical)
   • Plot: The experimental plot that the data were collected from (Field type: Location)
   • Bag_treatment: The treatment applied to each leaf litter bag - open = accessible to invertebrates, closed = inaccessible to invertebrates (Field type: Categorical)
   • Plot_treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)
   • Mass_loss: total leaf litter mass loss from each bag in grams (Field type: Numeric)
   • Proportion: the proportion of leaf litter mass loss from each bag (Field type: Numeric)

4. Non_target_inverts (described in worksheet Non_target_inverts)

   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.

   Number of fields: 5

   Number of data rows: 5040

   Fields:

   • Year: the year in which sampling occured (Field type: ID)
   • Treatment: The experimental treatment that was applied to the plot (Field type: Categorical)
   • variable: the order of non-target invertebrates samples (Field type: Taxa)
   • value: the number of individuals belonging to each order (Field type: Numeric)
   • log: the log of the number of individuals belonging to each order (Field type: Numeric)

5. Seedling_survival_non_drought (described in worksheet Seedling_survival_non_drought)

   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

   Number of fields: 4

   Number of data rows: 64

   Fields:

   • plot: The experimental plot that the data were collected from (Field type: Location)
   • alive_2016: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)
   • Treatment: The experimental treatment that was applied to the plot (Field type: Categorical)
   • alive.2017: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)

6. Seedling_survival_drought (described in worksheet Seedling_survival_drought)

   Description: Tree seedlings survival during the drought

   Number of fields: 3

   Number of data rows: 274

   Fields:

   • Plot: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
   • alive_2016: whether or not each seedling was alive at the time of inspection - 1 = alive, 0 = dead (Field type: Numeric)
   • Treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)

7. Soil_moisture (described in worksheet Soil_moisture)

   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.

   Number of fields: 5

   Number of data rows: 400

   Fields:

   • plot: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
   • treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)
   • soil_moisture: the % soil moisture measured at each point (Field type: Numeric)
   • condition: The rainfall season in which soil moisture measurements were taken (Field type: Categorical)
   • date: the month and year in which the soil moisture recording was taken (Field type: Date)

8. Soil_nutrients (described in worksheet Soil_nutrients)

   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.

   Number of fields: 15

   Number of data rows: 292

   Fields:

   • plot: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
   • Treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)
   • Condition: the season in which the soil nutrient sampling occurred – drought (2015) or non-drought (2016) (Field type: Categorical)
   • NO3_N_micro_grams/10cm2/burial length: Soil NO3 at 10cm2 profile (Field type: Numeric)
   • NH4_N_micro_grams/10cm2/burial length: Soil NH4 at 10cm2 profile (Field type: Numeric)
   • Ca_micro_grams/10cm2/burial length: Soil Ca at 10cm2 profile (Field type: Numeric)
   • Mg_micro_grams/10cm2/burial length: Soil Mg at 10cm2 profile (Field type: Numeric)
   • K_micro_grams/10cm2/burial length: Soil K at 10cm2 profile (Field type: Numeric)
   • P_micro_grams/10cm2/burial length: Soil P at 10cm2 profile (Field type: Numeric)
   • Fe_micro_grams/10cm2/burial length: Soil Fe at 10cm2 profile (Field type: Numeric)
   • Mn_micro_grams/10cm2/burial length: Soil Mn at 10cm2 profile (Field type: Numeric)
   • Cu_micro_grams/10cm2/burial length: Soil Cu at 10cm2 profile (Field type: Numeric)
   • Zn_micro_grams/10cm2/burial length: Soil Zn at 10cm2 profile (Field type: Numeric)
   • B_micro_grams/10cm2/burial length: Soil B at 10cm2 profile (Field type: Numeric)
   • Al_micro_grams/10cm2/burial length: Soil Al at 10cm2 profile (Field type: Numeric)

9. Termite_cumulative_attacks (described in worksheet Termite_cummulative_attack)

   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.

   Number of fields: 4

   Number of data rows: 120

   Fields:

   • month: The month in which termite attack scores were recorded (Field type: ID)
   • cumulative_consumption: The cumulative consumption rate for each plot (Field type: Numeric)
   • plot: The experimental plot that the data were collected from (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
   • treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)

10. Termite_C_plot_SPI (described in worksheet Termite_C_plots_SPI)

    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.

    Number of fields: 5

    Number of data rows: 32

    Fields:

    • Plot: the control plot on which samples were collected (CC= Carbon Control, GC = Gully Control, KC = Knowledge Control, DC = Distant Control) (Field type: Location)
    • total: total number of termite hits recorded (Field type: Numeric)
    • date: the month in which sampling occurred (Field type: Date)
    • SPI: the standardized precipitation index number calculated for each time period from rainfall data collected at Danum Valley Field Station (Field type: Numeric)
    • Wet.dry: the rainfall conditions at the time of sampling (wet = 2017, dry = 2015) (Field type: Categorical)

11. Termite_hits_on_T_and_C_plot (described in worksheet Termite_hits_on_T_and_C_plots)

    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

    Number of fields: 7

    Number of data rows: 192

    Fields:

    • Plot: The experimental plot that the data were collected from (Field type: Location)
    • Treatment: The experimental treatment that was applied to the plot, Termite = termite suppression plot; C = Control plot (Field type: Categorical)
    • date: The month and year in which the sampling occurred (Field type: Date)
    • genus: the genus to which each termite encounter belongs (Field type: Taxa)
    • hits: number of termite of hits on each plot (Field type: Numeric)
    • SPI: the standardized precipitation index at the time of each sampling occasion (Field type: Numeric)
    • Wet.dry: the season in which sampling occurred – wet = 2016, dry = 2015 (Field type: Categorical)

Date range: 2014-10-01 to 2017-07-30

Latitudinal extent: 4.5000 to 5.0700

Longitudinal extent: 116.7500 to 117.8200

Taxonomic coverage:
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.

Animalia
 - Annelida
 -  - Clitellata
 -  -  - [Oligochaeta]
 - Arthropoda
 -  - Arachnida
 -  -  - Araneae
 -  -  - Opiliones
 -  -  - Pseudoscorpiones
 -  - Chilopoda
 -  - Diplopoda
 -  - Insecta
 -  -  - Blattodea
 -  -  -  - [Termites]
 -  -  - Coleoptera
 -  -  - Dermaptera
 -  -  - Diptera
 -  -  - Hemiptera
 -  -  - Hymenoptera
 -  -  -  - Formicidae
 -  -  - Isoptera
 -  -  -  -  - Procapritermes
 -  -  -  -  - Prohamitermes
 -  -  -  - Rhinotermitidae
 -  -  -  -  - Heterotermes
 -  -  -  -  - Parrhinotermes
 -  -  -  -  - Schedorhinotermes
 -  -  -  - Termitidae
 -  -  -  -  - Bulbitermes
 -  -  -  -  - Dicuspiditermes
 -  -  -  -  - Globitermes
 -  -  -  -  - Macrotermes
 -  -  -  -  - Malaysiotermes
 -  -  -  -  - Microcerotermes
 -  -  -  -  - Odontotermes
 -  -  - Lepidoptera
 -  -  - Mecoptera
 -  -  - Neuroptera
 -  -  - Odonata
 -  -  - Orthoptera
 -  -  - Plecoptera
 -  -  - Psocodea
 -  -  - Siphonaptera
 -  -  - Thysanoptera
 -  -  - Trichoptera
 -  - Malacostraca
 -  -  - Isopoda
 - Mollusca