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Crossett Experimental Forest site, station Diameter limit logging stands at Crossett Experimental Forest, study of production volume of pine in units of cubicMetersPerHectare on a yearly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Crossett Experimental Forest (CRO) contains production volume of pine measurements in cubicMetersPerHectare units and were aggregated to a yearly timescale.

Crossett Experimental Forest site, station Heavy seedtree logging stands at Crossett Experimental Forest, study of pine tree merchantable volume in units of cubicMetersPerHectare on a yearly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Crossett Experimental Forest (CRO) contains pine tree merchantable volume measurements in cubicMetersPerHectare units and were aggregated to a yearly timescale.

Crossett Experimental Forest site, station Heavy seedtree logging stands at Crossett Experimental Forest, study of production volume of pine in units of cubicMetersPerHectare on a yearly timescale

Crossett Experimental Forest site, station Selection logging stands at Crossett Experimental Forest, study of pine tree merchantable volume in units of cubicMetersPerHectare on a yearly timescale

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Crossett Experimental Forest (CRO) contains pine tree merchantable volume measurements in cubicMetersPerHectare units and were aggregated to a yearly timescale.

Crossett Experimental Forest site, station Selection logging stands at Crossett Experimental Forest, study of production volume of pine in units of cubicMetersPerHectare on a yearly timescale

Data from: Fine-root exploitation strategies differ in tropical old-growth and logged-over forests in Ghana

Understanding the changes in root exploitation strategies during post-logging recovery is important for predicting forest productivity and carbon dynamics in tropical forests. We sampled fine (diameter < 2 mm) roots using the soil-core method to quantify fine-root biomass, and architectural and morphological traits to determine root exploitation strategies in an old-growth forest and in a 54-year-old logged-over forest influenced by similar parent material and climate. Seven root traits were considered: four associated with resource exploitation potential or an 'extensive' strategy (fine-root biomass, length, surface area and volume); and three traits which reflect exploitation efficiency or an 'intensive' strategy (specific root area, specific root length and root tissue density). We found that total fine-root biomass, length, surface area, volume, and fine-root tissue density were higher in the logged-over forest, whereas the old-growth forest had higher total specific root length and specific root surface area than the logged-over forest. The results suggest different root exploitation strategies between the forests. Plants in the old-growth forest invest root biomass more efficiently to maximize soil volume explored, whereas plants in the logged-over forest increase the spatial distribution of roots resulting in the expansion of the rhizosphere.

opencc-zeroDec 2015View details →

Data from: Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species

Selective logging is one of the major drivers of tropical forest degradation, causing important shifts in species composition. Whether such changes modify interactions between species and the networks in which they are embedded remain fundamental questions to assess the 'health' and ecosystem functionality of logged forests. We focus on interactions between lianas and their tree hosts within primary and selectively logged forests in the biodiversity hotspot of Malaysian Borneo. We found that lianas were more abundant, had higher species richness and different species compositions in logged than in primary forests. Logged forests showed heavier liana loads disparately affecting slow-growth tree species, which could exacerbate the loss of timber value and carbon storage already associated to logging. Moreover, simulation scenarios of host tree local species loss indicated that logging might decrease the robustness of liana-tree interaction networks if heavily infested trees (i.e. the most connected ones) are more likely to disappear. This effect is partially mitigated in the short term by the colonization of host trees by a greater diversity of liana species within logged forests, yet this might not compensate for the loss of preferred tree hosts in the long term. As a consequence, species interaction networks may show a lagged response to disturbance, which may trigger sudden collapses in species richness and ecosystem function in response to additional disturbances, representing a new type of "extinction debt".

opencc-zeroDec 2014View details →

Data from: Mating system and genetic diversity of progenies before and after logging: a case study of Bagassa guianensis (Moraceae), a low-density dioecious tree of the Amazonian forest

The logging of large trees in tropical forests causes a decrease in the density of reproductive individuals, which likely affects the pattern of pollen dispersal and the mating system of the remaining trees in the population. Here, we investigate the impact of logging on mating system and genetic diversity of the low-density, thrip-pollinated, dioecious tree Bagassa guianensis within a 500-ha plot at Tapajós National Forest, Pará State, Brazil. Mating system parameters of the logged population were estimated using mixed-mating model. Six microsatellite loci were used to genotype 232 seeds from ten remnant female trees over three seasons (2006, 2007, and 2008). The data were compared with the mating system of the unlogged population of B. guianensis (18 female trees, 488 seeds) in the same plot. The overall number of alleles found in the open-pollinated progenies decreased after logging (K = 71 before logging and K = 57 after logging, considering the three after logging seasons pooled), as well as the average number of alleles per locus (A = 11.8 and 9.5, respectively). Similarly, the number of private alleles, which is defined in the context of this study as the number of alleles observed in the population exclusively before or after logging, also decreased after logging (15 and 1, respectively). However, the average number of alleles per locus and the observed and expected heterozygosities were not significantly higher before logging than after logging. Logging also did not affect the inbreeding of progenies in the population. Somewhat unexpectedly, the effective number of pollen donors was consistently higher after (N ep = 14.5 in 2006, 8.1 in 2007, and 6.3 in 2008) than before logging (N ep = 3.4), suggesting that the loss of alleles in the population was compensated by the higher heterogeneity in the pollen pool after the removal of the largest trees by logging. Potential causes explaining the patterns found here include the maintenance of large patches of forest around the logged plot and the species' ability to perform long-distance pollination by airborne thrips. Maintenance of landscape integrity as logged and unlogged forests around the exploited areas is recommended to enhance pollen migration and to avoid long-term losses in genetic diversity.

Data from: Selective logging intensity in an East African rain forest predicts reductions in ant diversity

As natural forest ecosystems increasingly face pressure from deforestation, it is ever more important to understand the impacts of habitat fragmentation and degradation on biodiversity. Most studies of anthropogenic change in the tropics come from Southeast Asia and South America, and impacts of habitat modification are often taxon-specific. Here we empirically assessed the impact of habitat fragmentation and recent (within 25 years) and historic (>25 years ago) selective logging on the diversity of ants in the Kakamega rain forest in western Kenya, and asked whether these forms of degradation interact as multiple stressors. We found that the severity of recent selective logging was negatively related to overall species richness and abundance as well as the richness and abundance of forest specialists, but found no detrimental effect of past selective logging or habitat fragmentation on ant diversity, although habitat fragment size was correlated with estimated species richness. There was also no effect of any form of habitat degradation on the richness or abundance of open habitat specialists, even though these species often exploit niches created in disturbed environments. Ultimately, this study reveals the detrimental impact of even moderate forms of habitat degradation on insect biodiversity in the understudied African rain forests.

opencc-zeroDec 2016View details →

Data from: Log moisture capacity does not predict epixylic bryophyte growth under thinned and unthinned forest canopies

1. Coarse woody debris (CWD) serves as habitat for diverse and rare taxa in forest systems. Because the abundance of mosses appears to be correlated with log size and stage of decay, many have suggested that CWD serves as a moisture reservoir, ensuring a humid microclimate and facilitating moss growth, but no one has tested this connection. Intact forest canopies are also thought to maintain humid conditions that benefit moss growth. If microclimatic regulation is the primary mechanism contributing to high moss abundance on CWD, then epixylic moss growth should increase with the capacity of the log moisture reservoir, and the importance of the reservoir size should increase with canopy opening. 2. Three types of synthetic logs, identical in size and shape but differing in moisture capacity, and two natural substrates, well-decayed birch and Thuja logs, were used to test the effects of log moisture capacity on growth of Dicranum flagellare under thinned and intact canopies of a spruce plantation over 20 months. 3. Surface humidity was positively associated with moisture capacity, but did not reach the water compensation point outside of precipitation events. Under a closed forest canopy, moss growth was negligible across all log types. Under an open forest canopy, moss growth was greater on natural substrates than synthetic ones, and negatively related to moisture capacity. CWD facilitates a sufficiently humid surface for only a short time after precipitation, presumably when a film of liquid water is maintained near the surface. 4. For Dicranum flagellare, canopy condition is a more accurate predictor of growth than CWD-moisture capacity; any beneficial properties of CWD appear to be lessened by a dense forest canopy (as in silvicultural plantations), probably because it reduces access to liquid water from precipitation. 5. We propose that the surface moisture availability of CWD depends on optimal depth of a "resisting layer" below the log surface, representing a tradeoff between water retention and loss (via percolation or runoff at extremely deep vs. shallow resisting layer depth). CWD may possess microclimate-regulating traits that benefit moss growth, but it does not appear to act as a moisture capacitor.

opencc-zeroAug 2019View details →

Data from: Spatio-temporal effects of logging and fire on tall, wet temperate eucalypt forest birds

Forests globally are subject to multiple disturbances such as logging and fire that create complex temporal variation in spatial patterns of forest cover and stand age. However, investigations that quantify temporal changes in biodiversity in response to multiple forms of disturbance in space and time are relatively uncommon. Over a 10-year period, we investigated the response of bird species to spatio-temporal changes in cover associated with logging and wildfire in the Mountain Ash (Eucalyptus regnans) forests of south-eastern Australia. Specifically, we examined how bird occurrence changed with shifts in the proportion of area burnt or logged in a 4.5km radius surrounding each of 87 field survey sites. Overall species richness was greatest in older forest patches. Bird species richness declined as the amount of fire around each site increased. At the individual species level, most species were more likely to be found in old growth than younger forest. Twenty-five of 36 bird species we modeled, exhibited a negative response to the amount of fire in the surrounding landscape (while two species responded positively to fire). Only nine species exhibited signs of post-fire recovery. Ten species were more likely to be recorded as the proportion of logged forest surrounding a site increased, suggesting a possible "concentration effect" with displaced birds moving into unlogged areas following harvesting of adjacent areas. In contrast to predictions from the disturbance-congruence hypothesis, no bird species exhibited similar responses to fire and logging in the landscape surrounding our sites. Similarly, no bird life history traits were associated with burned or logged forest, although insectivorous birds were more likely to be found in old growth forests and increased over time. Birds in Mountain Ash forests are strongly associated with old growth stands and exhibit complex, time-dependent and species-specific responses to landscape disturbance. Despite logging and fire both being high-severity perturbations, species responses to one kind of landscape-scale disturbance are not readily predictable based on an understanding of the responses to another kind of (albeit superficially similar) disturbance.

Data from: Multiple stages of tree seedling recruitment are altered in tropical forests degraded by selective logging

Tropical forest degradation is a global environmental issue. In degraded forests, seedling recruitment of canopy trees is vital for forest regeneration and recovery. We investigated how selective logging, a pervasive driver of tropical forest degradation, impacts canopy tree seedling recruitment, focusing on an endemic dipterocarp Dryobalanops lanceolata in Sabah, Borneo. During a mast-fruiting event in intensively logged and nearby unlogged forest, we examined four stages of the seedling recruitment process: seed production, seed predation, and negative density-dependent germination and seedling survival. Our results suggest that each stage of the seedling recruitment process is altered in logged forest. The seed crop of D. lanceolata trees in logged forest was one-third smaller than that produced by trees in unlogged forest. The functional role of vertebrates in seed predation increased in logged forest while that of non-vertebrates declined. Seeds in logged forest were less likely to germinate than those in unlogged forest. Germination increased with local-scale conspecific seed density in unlogged forest, but seedling survival tended to decline. However, both germination and seedling survival increased with local-scale conspecific seed density in logged forest. Notably, seed crop size, germination and seedling survival tended to increase for larger trees in both unlogged and logged forests, suggesting that sustainable timber extraction and silvicultural practices designed to minimize damage to the residual stand are important to prevent seedling recruitment failure. Overall, these impacts sustained by several aspects of seedling recruitment in a mast-fruiting year suggest that intensive selective logging may affect long-term population dynamics of D. lanceolata. It is necessary to establish if other dipterocarp species, many of which are threatened by the timber trade, are similarly affected in tropical forests degraded by intensive selective logging.

Data from: Temporal dynamics in animal community assembly during post-logging succession in boreal forest

Species assemblages can result from deterministic processes, such as niche differentiation and interspecific interactions, and from stochastic processes, such as random colonisation and extinction events. Although changes in animal communities following disturbances have been widely examined, few studies have investigated the mechanisms structuring communities during ecological succession. We assessed the impact of logging on small mammal and beetle assemblages in landscapes dominated by old-growth boreal forests. Our objectives were to 1) characterize variations in communities during the first 66 years of post-harvest forest succession, 2) determine if there are non-random patterns of species co-occurrence (i.e., deterministic processes), and if there are, 3) establish whether non-random co-occurrences are best explained by habitat attributes or by interspecific interactions. We captured small mammals and beetles along a gradient of forest succession (5-66 years) and in old-growth forest, and characterized key vegetation attributes. First, we tested whether community compositions in clear-cut stands became similar to those in natural stands after 66 years. We then used null models, which were either unconstrained or constrained by habitat attributes, to address the last two objectives and distinguish effects of vegetation attributes from interspecific interactions on community assembly. We showed that beetle assemblages differed in stands 21-30 years post-harvest compared to old-growth forests. In contrast, harvesting did not influence the composition of small mammal communities. Overall, our results suggest that community assembly during forest succession is driven by both stochastic and deterministic processes, the latter being linked to interspecific interactions more strongly than to vegetation attributes.

opencc-zeroDec 2014View details →

Data from: Reconciling timber extraction with biodiversity conservation in tropical forests using reduced-impact logging

1. Over 20% of the world's tropical forests have been selectively logged, and large expanses are allocated for future timber extraction. Reduced-impact logging (RIL) is being promoted as best practice forestry that increases sustainability and lowers CO2 emissions from logging, by reducing collateral damage associated with timber extraction. RIL is also expected to minimize the impacts of selective logging on biodiversity, although this is yet to be thoroughly tested. 2. We undertake the most comprehensive study to date to investigate the biodiversity impacts of RIL across multiple taxonomic groups. We quantified birds, bats and large mammal assemblage structures, using a before-after control-impact (BACI) design across 20 sample sites over a 5-year period. Faunal surveys utilized point counts, mist nets and line transects and yielded >250 species. We examined assemblage responses to logging, as well as partitions of feeding guild and strata (understorey vs. canopy), and then tested for relationships with logging intensity to assess the primary determinants of community composition. 3. Community analysis revealed little effect of RIL on overall assemblages, as structure and composition were similar before and after logging, and between logging and control sites. Variation in bird assemblages was explained by natural rates of change over time, and not logging intensity. However, when partitioned by feeding guild and strata, the frugivorous and canopy bird ensembles changed as a result of RIL, although the latter was also associated with change over time. Bats exhibited variable changes post-logging that were not related to logging, whereas large mammals showed no change at all. 4. Indicator species analysis and correlations with logging intensities revealed that some species exhibited idiosyncratic responses to RIL, whilst abundance change of most others was associated with time. 5. Synthesis and applications. Our study demonstrates the relatively benign effect of reduced-impact logging (RIL) on birds, bats and large mammals in a neotropical forest context, and therefore, we propose that forest managers should improve timber extraction techniques more widely. If RIL is extensively adopted, forestry concessions could represent sizeable and important additions to the global conservation estate – over 4 million km2.

opencc-zeroDec 2015View details →

Data from: How persistent are the impacts of logging roads on Central African forest vegetation?

1. Logging roads can trigger tropical forest degradation by reducing the integrity of the ecosystem and providing access for encroachment. Therefore, road-management is crucial in reconciling selective logging and biodiversity conservation. Most logging roads are abandoned after timber harvesting, however little is known about their long-term impacts on forest vegetation and accessibility, especially in Central Africa. 2. In 11 logging concessions in the Congo Basin we field-sampled a chronosequence of roads that, judged by satellite images, had been abandoned between 1985 and 2015. We assessed recovery of timber resources, tree diversity and above-ground biomass in three zones: the road track, the road edge (where forest had been cleared during road construction) and the adjacent logged forest. 3. The density of commercial timber species ≤ 15 cm DBH was almost three times higher in the road track (327 individuals ha-1) and edge (278) than in the logged adjacent forest (111). Over time, tree species diversity converged to a comparable level between roads and adjacent forests, along with decreasing soil compaction and increasing canopy closure. 4. The average width of forest clearing for road construction was 20 m, covering a total 0.76% of the forest area inside concessions. After 15 years following abandonment, road tracks had recovered 24 Mg ha-1 of above-ground woody biomass, which was 6% of that in the adjacent forest, while road edges had accumulated 167 Mg ha-1 (42%). Ten years after abandonment, roads were no longer penetrable by poachers on motorcycles. Exotic herb species were fully replaced by dominant Marantaceae that have even higher abundance in the adjacent forest. 5. Synthesis and applications. Our evidence of vegetation recovery suggests that logging roads are mostly transient elements in forest landscapes. However, given the slow recovery of biomass on abandoned road tracks, we advocate both reducing the width of forest clearing for road construction and reopening old logging roads for future harvests, rather than building new roads in intact forests. Road edges seem suitable for post-logging silviculture which needs to be assisted by removing dominant herbs during the early years after abandonment while the road track is still accessible

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Data analysis scripts for Marsh et al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'

<p>Data analysis scripts for the manuscript <strong>Marsh<em> </em>et<em> </em>al. 2024 'Tropical forest clearance impacts biodiversity and function whereas logging changes structure'</strong></p> <p><strong>Update for Version 2:</strong> The calculation of confidence intervals around the mean effects in Figure 2 has been updated to use the <code>marginaleffects</code> package (many thanks to Biao Wang and Shuang Zhang for pointing out an error in the original code). Using the Satterthwaite method for determining degrees of freedom, the updated confidence intervals are around 32% smaller than our original estimates (MLF = 32.0%, HLF = 32.1%, OP = 21.6%). Note, this change is only relevant to fig. 2 and figs. S2-4; the mean effect sizes and trends along the disturbance gradient, all statistical comparisons, and the constrast analyses in fig. 3 remain unaffected. The updated figures S2-4 and Table S6 can be seen in the file 'Updated figures S2-4 with recalculated confidence intervals.pdf'.</p> <p>In the zip file 'BALI_synthesis_analysis.zip' there are outputs from RMarkdown scripts that include all steps of the analysis for each dataset, including R code, incorporating data visualisation, exploration and standardisation, model building and evaluation, and visualisation of results. Fig. 2b can be regenerated using code in the zip file 'Marsh_etal_2024_Science_fig1b_chm_and_canopy_profiles-main.zip'.</p> <p>Each dataset presented in the manuscript has an html file within the folder 'Analyses'. For datasets involving bat, bird, dung beetle and tree traits additional markdown documents are available for steps take during data preparation in the folder 'Data preparation'.</p> <p>In the zip file 'BALI_synthesis_data.zip' are .rds data files that have been cleaned, prepared and z-score standardised following the procedures outlined in the respective markdown files.</p> <p>To repeat any given analysis, follow the respective rmarkdown document, excluding the data manipulation steps:</p> <ol> <li>Read in the data file as described above: dd <- readRDS(paste0("path/to/rds/file/", "name_of_file.rds"))</li> <li>Run the code at the top of the markdown workflow (sections "Data information" and "Load in necessary libraries")</li> <li>Do not run the sections "Read in data" through to "Visual inspection of the data"</li> <li>Continue the analysis from the 'Modelling' section</li> </ol> <div> <h3> </h3> <h3>Level 1 - Structure & Environment</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Above-ground carbon</td> <td>Above ground carbon</td> <td>Above_ground_carbon</td> <td>Terhi Riutta</td> </tr> <tr> <td>Leaf-area index</td> <td>Leaf-area index</td> <td>Leaf_area_index</td> <td>Terhi Riutta</td> </tr> <tr> <td>Soil temperature</td> <td>Soil temp.</td> <td>Soil_temperature</td> <td>Terhi Riutta</td> </tr> <tr> <td>Soil moisture</td> <td>Soil moisture</td> <td>Soil_moisture</td> <td>Dafydd Elias</td> </tr> <tr> <td>Air temperature: Minimum</td> <td>Air temp.: Min.</td> <td>Air_temperature_minimum</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Air temperature: Mean</td> <td>Air temp.: Mean</td> <td>Air_temperature_mean</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Air temperature: Maximum</td> <td>Air temp.: Max.</td> <td>Air_temperature_maximum</td> <td>Benjamin Blonder</td> </tr> <tr> <td>Soil bulk density</td> <td>Soil bulk density</td> <td>Soil_bulk_density</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil horizon depth</td> <td>Soil horizon depth</td> <td>Soil_horizon_depth</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil pH</td> <td>Soil pH</td> <td>Soil_pH</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon</td> <td>Soil nutrients (C)</td> <td>Soil_nutrients_C</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Nitrogen</td> <td>Soil nutrients (N)</td> <td>Soil_nutrients_N</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Inorganic Phosporous</td> <td>Soil nutrients (Inorganic P)</td> <td>Soil_nutrients_Inorganic_P</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon:Phosphorous</td> <td>Soil nutrients (C:P)</td> <td>Soil_nutrients_C_P</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil nutrients: Carbon:Nitrogen</td> <td>Soil nutrients (C:N)</td> <td>Soil_nutrients_C_N</td> <td>Dafydd Elias</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 2 - Tree traits</h3> </div> <p>All tree traits were collected as part of the following study (details in this table have been extracted from table S1 of that publication): S. Both, T. Riutta, C.E.T. Paine, D.M.O. Elias, R.S. Cruz, A. Jain, D. Johnson, U.H. Kritzler, M. Kuntz, N. Majalap-Lee, N. Mielke, M.X. Montoya Pillco, N.J. Ostle, Y. Arn Teh, Y. Malhi, D.F.R.P. Burslem (2019) Logging and soil nutrients independently explain plant trait expression in tropical forests. New Phytologist. 221:4, 1853–1865.</p> <p> </p> <p><em><strong>Photosynthesis Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated photosynthesis traits</td> <td>Photosyn. traits</td> <td>Photosynthesis traits</td> </tr> <tr> <td>δ<sup>13</sup>C</td> <td>δ<sup>13</sup>C</td> <td>Traits_13C</td> </tr> <tr> <td>Light-saturated photosynthetic rate</td> <td>Photosyn. rate: A<sub>sat</sub></td> <td>Traits_Asat</td> </tr> <tr> <td>Maximum photosynthetic rate</td> <td>Photosyn. rate: A<sub>max</sub></td> <td>Traits_Amax</td> </tr> <tr> <td>Maximum photosynthetic rate: Nitrogen concentration</td> <td>Max. photosyn. rate: N(%)</td> <td>Traits_N_conc</td> </tr> <tr> <td>Maximum photosynthetic rate: Phosphorous mass (area)</td> <td>Max. photosyn. rate: P(mass)</td> <td>Traits_Phos_area</td> </tr> <tr> <td>Dark respiration (Rd)</td> <td>Dark respiration</td> <td>Traits_Dark_resp</td> </tr> <tr> <td>Specific leaf area (SLA)</td> <td>Specific leaf area</td> <td>Traits_SLA</td> </tr> <tr> <td>Carotenoids (area)</td> <td>Carotenoids: Area</td> <td>Traits_Carot_area</td> </tr> <tr> <td>Carotenoids (mass)</td> <td>Carotenoids: Mass</td> <td>Traits_Carot_mass</td> </tr> <tr> <td>Chlorophyll a (area)</td> <td>Chlorophyll a: Area</td> <td>Traits_Chl_a_area</td> </tr> <tr> <td>Chlorophyll a (mass)</td> <td>Chlorophyll a: Mass</td> <td>Traits_Chl_a_mass</td> </tr> <tr> <td>Chlorophyll b (area)</td> <td>Chlorophyll b: Area</td> <td>Traits_Chl_b_area</td> </tr> <tr> <td>Chlorophyll b (mass)</td> <td>Chlorophyll b: Mass</td> <td>Traits_Chl_b_mass</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Nutrient Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated nutrient traits</td> <td>Nutrient traits</td> <td>Nutrient_traits</td> </tr> <tr> <td>δ<sup>15</sup>N</td> <td>δ<sup>15</sup>N</td> <td>Traits_15N</td> </tr> <tr> <td>Carbon concentration</td> <td>Carbon conc.</td> <td>Traits_Carbon_conc</td> </tr> <tr> <td>Nitrogen concentration</td> <td>Max. photosyn. rate: N(%)</td> <td>Traits_N_perc</td> </tr> <tr> <td>Phosphorous concentration</td> <td>Max. photosyn. rate: P(mass)</td> <td>Traits_Phos_mass</td> </tr> <tr> <td>Magnesium concentration</td> <td>Regulat. nutrients: Total Mg</td> <td>Traits_Total_Mg</td> </tr> <tr> <td>Potassium concentration</td> <td>Regulat. nutrients: Total K</td> <td>Traits_Total_K</td> </tr> <tr> <td>Calcium concentration</td> <td>Regulat. nutrients: Total Ca</td> <td>Traits_Total_Ca</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Structural Traits</strong></em></p> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> </tr> </tbody> <tbody> <tr> <td>Aggregated structural traits</td> <td>Structural traits</td> <td>Structural_traits</td> </tr> <tr> <td>Branch specific density</td> <td>Branch wood density</td> <td>Traits_Branch_WD</td> </tr> <tr> <td>Leaf cellulose concentration</td> <td>Leaf fibre conc.: Cellul.</td> <td>Traits_Cellulose</td> </tr> <tr> <td>Leaf lignin concentration</td> <td>Leaf fibre conc.: Lignin</td> <td>Traits_Lignin</td> </tr> <tr> <td>Leaf hemicellulose concentration</td> <td>Leaf fibre conc.: Hemicel.</td> <td>Traits_Hemicellulose</td> </tr> <tr> <td>Leaf area</td> <td>Leaf size: Area</td> <td>Traits_Leaf_area</td> </tr> <tr> <td>Leaf dry weight</td> <td>Leaf size: Dry wgt</td> <td>Traits_Dry_weight</td> </tr> <tr> <td>Leaf force to punch</td> <td>Leaf strength: Tough.</td> <td>Traits_Leaf_toughness</td> </tr> <tr> <td>Leaf thickness</td> <td>Leaf strength: Thick.</td> <td>Traits_Leaf_thickness</td> </tr> <tr> <td>Leaf dry matter content</td> <td>Leaf strength: Dry mat.</td> <td>Traits_LDMC</td> </tr> <tr> <td>Total phenol concentration</td> <td>Leaf defence: Phenol</td> <td>Traits_Phenol</td> </tr> <tr> <td>Total tannin concentration</td> <td>Leaf defenct: Tannin</td> <td>Traits_Tannin</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 3 - Biodiversity</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Soil bacterial richness</td> <td>Soil microbial richness: Bacteria</td> <td>Soil_richness_Bacteria</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil protist richness</td> <td>Soil microbial richness: Protists</td> <td>Soil_richness_Protist</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil ectomycorrhizal richness</td> <td>Soil fungal richness: Ectomycorrhiza</td> <td>Soil_richness_Ectomycorrhiza</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil fungal richness</td> <td>Soil fungal richness: Fungi</td> <td>Soil_richness_Fungi</td> <td>Dafydd Elias</td> </tr> <tr> <td>Soil arbuscular mycorrhizal richness</td> <td>Soil fungal richness: Arbuscular mycorrhiza</td> <td>Soil_richness_Arbuscular_mycorrhizal</td> <td>Dafydd Elias</td> </tr> <tr> <td>Leaf spectral diversity</td> <td>Spectral diversity</td> <td>Spectral_diversity</td> <td>Matheus Nunes</td> </tr> <tr> <td>Liana abundance</td> <td>Liana abundance</td> <td>Liana_abundance</td> <td>Boris Bongalov</td> </tr> <tr> <td>Dung beetle abundance</td> <td>Dung beetle abund.</td> <td>Dung_beetle_abundance</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: richness</td> <td>Dung beetle diversity: q=0</td> <td>Dung_beetle_diversity_q=0</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: Shannon diversity</td> <td>Dung beetle diversity: q=1</td> <td>Dung_beetle_diversity_q=1</td> <td>Eleanor Slade</td> </tr> <tr> <td>Dung beetle diversity: Simpson diversity</td> <td>Dung beetle diversity: q=2</td> <td>Dung_beetle_diversity_q=2</td> <td>Eleanor Slade</td> </tr> <tr> <td>Bird abundance</td> <td>Bird abund.</td> <td>Bird_abundance</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: richness</td> <td>Bird diversity: q=0</td> <td>Bird_diversity_q=0</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: Shannon diversity</td> <td>Bird diversity: q=1</td> <td>Bird_diversity_q=1</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bird diversity: Simpsons diversity</td> <td>Bird diversity: q=2</td> <td>Bird_diversity_q=2</td> <td>Simon Mitchell</td> </tr> <tr> <td>Bat abundance</td> <td>Bat abund.</td> <td>Bat_abundance</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (small scale)</td> <td>Bat diversity (sm scale)</td> <td>Bat_diversity_small_scale</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): richness</td> <td>Bat diversity (lg scale): q=0</td> <td>Bat_diversity_large_scale_q=0</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): Shannon diversity</td> <td>Bat diversity (lg scale): q=1</td> <td>Bat_diversity_large_scale_q=1</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat diversity (large scale): Simpson diversity</td> <td>Bat diversity (lg scale): q=2</td> <td>Bat_diversity_large_scale_q=2</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Nestedness</td> <td>Bat β-diversity: Nested.</td> <td>Bat_beta_diversity_Nestedness</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Turnover</td> <td>Bat β-diversity: Turn.</td> <td>Bat_beta_diversity_Turnover</td> <td>David Hemprich-Bennett</td> </tr> <tr> <td>Bat β-diversity: Total</td> <td>Bat β-diversity: Total</td> <td>Bat_beta_diversity_Total</td> <td>David Hemprich-Bennett</td> </tr> </tbody> </table> <div> <h3> </h3> <h3>Level 4 - Functioning</h3> </div> <table> <tbody> <tr> <th>Dataset</th> <th>Label</th> <th>Filename</th> <th>Collector</th> </tr> </tbody> <tbody> <tr> <td>Soil respiration</td> <td>Respiration: Soil</td> <td>Soil_respiration</td> <td>Terhi Riutta</td> </tr> <tr> <td>Stem respiration</td> <td>Respiration: Stem</td> <td>Stem_respiration</td> <td>Terhi Riutta</td> </tr> <tr> <td>Net primary productivity</td> <td>NPP</td> <td>NPP</td> <td>Terhi Riutta</td> </tr> <tr> <td>Litterfall</td> <td>Litterfall</td> <td>Litterfall</td> <td>Terhi Riutta</td> </tr> <tr> <td>Leaf litter decomposition</td> <td>Litter decomposition</td> <td>Litter_decomposition</td> <td>Sabine Both</td> </tr> <tr> <td>Soil mycelial production</td> <td>Mycelial production</td> <td>Hyphal_length</td> <td>Samuel Robinson</td> </tr> <tr> <td>Dung removal</td> <td>Dung removal</td> <td>Dung_removal</td> <td>Eleanor Slade</td> </tr> </tbody> </table> <p> </p> <h2>Funding</h2> <p>Analyses were carried out, and data were collected, as part of the BALI (Biodiversity And Land-use Impacts on tropical ecosystem function) and LOMBOK (Land-use Options for Maintaining BiOdiversity & eKosystem functions) projects using the following funding:</p> <ul> <li>NERC Human-modified Tropical Forests Programme (NE/K016377/1, NE/K016261/1, NE/K016148/1, NE/K016407/1);</li> <li>NERC grant (NE/I028068/1);</li> <li>British Ecological Society Small Ecological Project Grant (No.: 3256/4035);</li> <li>Varley-Gradwell Travelling Fellowship in Insect Ecology;</li> <li>Bat Conservation International Student Research Scholarship;</li> <li>NOMIS Foundation;</li> <li>ERC European Union's Horizon 2020 research and innovation programme (grant agreement No 865403);</li> <li>ERC Advanced Investigator Grant, GEM-TRAIT (321131);</li> <li>The SAFE Project is funded by the Sime Darby Foundation.</li> </ul>

opencc-by-4.0Aug 2024View details →

zenodo32/100

The first comprehensive revision of all the species attributed to Melomys led J. I. Menzies in 1996 to resurrect the genus Paramelomys and to redefine its morphologicallimits and species content. Menzies created P. gressitti as a new species belonging to a group displaying morphological similarities and including also P. lorentzii and P. moncktoni. Monotypic Distribution. E New Guinea. Descriptive notes. Head-body 135-162 mm, hindfoot 30-34 mm; no specific data are available for body weight. Gressitt's Mosaic-tailed Rat is a medium-sized Paramelomys with a soft, thick and woolly pelage, a long narrow foot, and a tail with three hairs per scale. It exhibits a medium-sepia dorsal pelage and a gray-buff ventral one. Tail is slightly shorter (99%) than head-body length. The skull has a narrow zygomatic plate. Habitat. Moist tropical mountain forest between 2300 m and 2400 m. Food and Feeding. No information. Breeding. No information. Activity patterns. Gressitt's Mosaic-tailed Rat is terrestrial. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List owing to its small geographic range (less than 3500 km?*) and the destruction ofits habitat by mining and logging activities. The major threat to Gressitt's Mosaic-tailed Rat is ongoing habitat degradation caused by nearby human populations; habitat on Mount Kandy has been destroyed by gold-miners and wood-cutters. Bibliography. Menzies (1996). in Muridae

The first comprehensive revision of all the species attributed to Melomys led J. I. Menzies in 1996 to resurrect the genus Paramelomys and to redefine its morphologicallimits and species content. Menzies created P. gressitti as a new species belonging to a group displaying morphological similarities and including also P. lorentzii and P. moncktoni. Monotypic Distribution. E New Guinea. Descriptive notes. Head-body 135-162 mm, hindfoot 30-34 mm; no specific data are available for body weight. Gressitt's Mosaic-tailed Rat is a medium-sized Paramelomys with a soft, thick and woolly pelage, a long narrow foot, and a tail with three hairs per scale. It exhibits a medium-sepia dorsal pelage and a gray-buff ventral one. Tail is slightly shorter (99%) than head-body length. The skull has a narrow zygomatic plate. Habitat. Moist tropical mountain forest between 2300 m and 2400 m. Food and Feeding. No information. Breeding. No information. Activity patterns. Gressitt's Mosaic-tailed Rat is terrestrial. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List owing to its small geographic range (less than 3500 km?*) and the destruction ofits habitat by mining and logging activities. The major threat to Gressitt's Mosaic-tailed Rat is ongoing habitat degradation caused by nearby human populations; habitat on Mount Kandy has been destroyed by gold-miners and wood-cutters. Bibliography. Menzies (1996).

opennotspecifiedNov 2017View details →

publicFeb 2016View details →

Data from: Reduced hornbill abundance associated with low seed arrival and altered recruitment in a hunted and logged tropical forest

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publicMar 2015View details →

Data from: Reconciling timber extraction with biodiversity conservation in tropical forests using reduced-impact logging

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