Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

Model outputs from a NEMO-PISCES configuration of the tropical Atlantic Ocean

Model outputs from a NEMO-PISCES configuration of the tropical Atlantic Ocean (35°S-35°N, 100°W-20°E), from 1998 to 2007. It contains monthly climatologies (10-years averages) of several ocean and biogeochemical variables, as well as monthly Chlorophyll, for 4 sensitivity experiments. These 4 simulations are described and analyzed in the paper: "On the importance of riverine organic matter for the Amazon plume: a modeling study" by Gévaudan et al., under review in JGR: Oceans. A preprint is available here: https://doi.org/10.22541/essoar.172081327.75230541/v2.

opencc-by-4.0Feb 2024View details →

Love thy neighbour? Tropical tree growth and its response to climate anomalies is mediated by neighbourhood hierarchy and dissimilarity in carbon and water related traits

<div> <div>Data and R code to reproduce all analyses, figures and tables for Krebber et al. 2024, Ecology Letters: Love thy neighbour? Tropical tree growth and its response to climate anomalies is mediated by neighbourhood hierarchy and dissimilarity in carbon and water related traits. </div> <div> </div> <div>All analyses have been conducted and produced in the R environment (R version 4.1.2; R Core Team, 2021; RStudio Team, 2020). Bayesian hierarchical models have been run using the R package brms (Version 2.19.0; Bürkner, 2017). The data and R files containing the code to reproduce the results, figures and tables are described in the README file and code in more detailed is described in RCode_xxx files. Please note that the analyses are highly computational intensive and the scripts should be run on a high performance cluster (models and scripts running models and handeling model outputs presented here have been run with 16 cpus and with 50 - 100 GB of RAM). Packages needed are loaded at the beginning of each script. Please ensure that these and all their dependencies have been previously installed. The R code in the files has been carefully commented.</div> </div>

opencc-by-4.0Sep 2024View details →

Liana-induced tree height reduction limits carbon storage and growth in a tropical forest in Panama

Subset of data and script used in the paper titled 'Lianas reduce tree height with negative consequences for carbon storage and growth estimates'

opencc-by-4.0Sep 2024View details →

Data and models for "Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery" by Lagerquist et al.

The file geocenter_models.tar contains all models comprising the GeoCenter ensemble: 3 convolutional neural networks (CNN), 3 isotonic-regression files (one for correcting each CNN’s mean estimate), and 3 more isotonic-regression files (one for correcting each CNN’s ensemble spread). Every model is found in a subdirectory whose names indicate which infrared (IR) wavelengths are used as input to the CNN. For example: <ul> <li> wavelengths-microns=3.900-7.340-13.300/model.weights.h5: An HDF5 file containing the trained CNN that uses data from bands 7, 10, 16 (corresponding to 3.9, 7.34, and 13.3 microns on the GOES ABI imager). The trained CNN can always be read by neural_net_utils.read_model() in the ml4tccf library (https://doi.org/10.5281/zenodo.15116854). </li> <li> wavelengths-microns=3.900-7.340-13.300/model_metadata.p: A Pickle file containing metadata for the trained CNN. This file is needed to read the CNN itself with neural_net_utils.read_model(). Otherwise, you will probably never need to access this metafile directly. </li> <li> wavelengths-microns=3.900-7.340-13.300/isotonic_regression/isotonic_regression.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble mean from the same CNN. The trained isotonic-regression models can always be read by scalar_isotonic_regression.read_file() in the ml4tccf library. Note that there are technically two isotonic-regression models for every CNN’s ensemble mean: one that bias-corrects the x-coordinate of the TC-center, another that bias-corrects the y-coordinate. </li> <li> wavelengths-microns=3.900-7.340-13.300/uncertainty_calibration/uncertainty_calibration.dill: A Dill file containing isotonic-regression models used to bias-correct the ensemble spread from the same CNN. In the ml4tccf code, I make a distinction between “isotonic_regression” (correcting the ensemble mean) and “uncertainty_calibration” (correcting the ensemble spread), but note that both models are isotonic regression and use the sklearn.isotonic.IsotonicRegression class. The trained uncertainty-calibration models can always be read by scalar_uncertainty_calibration.read_file() in the ml4tccf library. Again, note that there are technically two uncertainty-calibration models per CNN: one for spread in the x-coordinate, one for spread in the y-coordinate. </li> </ul>   As mentioned above, every trained CNN can be read by neural_net_utils.read_model(). Also, every trained CNN can be applied to new data (inference mode) by neural_net_utils.apply_model(). The input argument model_object should be the object returned by neural_net_utils.read_model(), and I suggest setting num_examples_per_batch = 10 to avoid out-of-memory errors. The only other input argument is predictor_matrices, which is a list of two numpy arrays. The first numpy array contains IR imagery centered at the first-guess TC center, and the second numpy array contains ATCF scalars. The first numpy array should have dimensions S (number of TC samples) x 300 (grid rows) x 300 (grid columns) x 9 (lag times) x 3 (wavelengths). Lag times should be in the following order: 240, 210, 180, 150, 120, 90, 60, 30, 0 min ago.  Wavelengths should be in the order indicated by the subdirectory name.  The numpy array itself should contain normalized brightness temperatures at the given lag times and wavelengths, following the grid specifications laid out in the journal paper (a plate carrée grid with 2-km spacing). The original IR data (brightness temperatures) must be normalized to z-scores using the same normalization parameters as in the journal paper, i.e., those based on the training data. See details below. The second numpy array in predictor_matrices should have dimensions S (number of TC samples) x 9 (variables). The variables must in the order: absolute latitude, cosine of longitude, sine of longitude, TC intensity, minimum central pressure, tropical flag, subtropical flag, extratropical flag, disturbance flag. The journal paper contains details on all these variables in one table. These variables must come from A-deck files at the second-most recent synoptic time. Like the IR data, these ATCF scalars must be normalized to z-scores using the same normalization parameters as in the journal paper. See details below.   Once you have predictions (estimated TC-center locations) from a CNN, you can bias-correct these predictions. To read the isotonic-regression model for the given CNN’s ensemble mean, use scalar_isotonic_regression.read_file() in the ml4tccf library. To apply the same model, use scalar_isotonic_regression.apply_models(). For the CNN’s ensemble spread, use scalar_uncertainty_calibration.read_file() and scalar_uncertainty_calibration.apply_models().   To normalize the IR data, you will need the file ir_satellite_normalization_params.tar included with this dataset. Within the tar file is a single zarr file. You can read the zarr file with normalization.read_file() in the ml4tccf library; then you can normalize new data with normalization.normalize_data().   To normalize the ATCF data, you will need the file a_deck_normalization_params.nc included with this dataset. This is a NetCDF file, containing the full set of training values for all 5 ATCF variables that are normalized (the binary storm-type flags are not normalized). You can read this file using any of the standard Python methods for reading NetCDF files, such as xarray.open_dataset(). To normalize new ATCF data, you can use the method normalization._normalize_one_variable(), where the argument actual_values_training is the list of training values from a_deck_normalization_params.nc for the given variable, while actual_values_new is the list of values to be normalized (currently in physical units, to be converted to z-score units).

opencc-by-4.0Oct 2021View details →

Data-base for : 'Partitioning carbon sources between wetland and well-drained ecosystems to a tropical first-order stream - Implications to carbon cycling at the watershed scale (Nyong, Cameroon)'

Dataset of carbon (pCO2, TA, DIC, DOC, POC) and ancillary parameters (water temperature, oxygen saturation, pH, specific conducitivity) in ground and surface waters of the Nyong watershed (Cameroon). The dataset covers one entire year (in 2016) and thus allows describing the varability of carbon and ancillary paramaters concentrations induced by seasons.

opencc-zeroOct 2021View details →

Trophic rewilding benefits a tropical community through direct and indirect network effects

Species reintroductions can be used as a conservation strategy to restore ecological interactions and the functionality of impoverished ecosystems. The ecological effects of reintroductions go beyond restoring pairwise interactions, because reintroductions can change how extant species are indirectly linked to each other in an ecological community. These indirect pathways, in turn, may shape a myriad of ecological and evolutionary processes operating in ecological systems. Here, we investigated how reintroductions may affect the direct and indirect pathways connecting species in ecological networks. We modeled the potential effects of the reintroduction of four frugivore species (channel-billed toucans, red-humped agoutis, brown howler monkeys and yellow-footed tortoises) to the local seed dispersal network in an Atlantic Forest site, the Tijuca National Park (Tijuca, Brazil). We used a seed dispersal interaction dataset together with data on species occurrences in Tijuca to build network models. Then, we calculated how network structure and the total amount of indirect effects varied across simulated networks with and without the reintroduced species. Using random reintroduction simulations, we tested if the observed network changes were expected merely from the increase in species richness. The reintroduction of the frugivore species increased network connectance, nestedness, robustness, number of pathways and total amount of indirect effects in all simulated networks. The increase in number of pathways with the addition of the four reintroduced species was greater than the sum of isolated effects for each species, as some interaction pathways contained several reintroduced species. These changes in network metrics were significantly greater than if a randomly chosen set of four species were reintroduced. Furthermore, our results indicate that multiple reintroductions in the same area, known as refaunation, may have an even greater restoration effect than single species reintroductions through increased indirect connections in the network.

opencc-by-4.0Apr 2021View details →

Vertical profiles of leaf photosynthesis and leaf traits, and soil nutrients in two tropical rainforests in French Guiana before and after a three-year nitrogen and phosphorus addition experiment

We provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important leaf traits, including leaf N and P concentrations, from two three-year, large-scale fertilisation experiments conducted in two tropical rainforests in French Guiana. These data present a unique source of information to further improve model representations of the roles of N, P, and other leaf nutrients, in photosynthesis in tropical forests. To further facilitate the use of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon (C)-nutrient cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability interacts with C uptake in tropical forests.

Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia

Aim: Environmental DNA (eDNA) metabarcoding has demonstrated its applicability as a highly sensitive biomonitoring tool across small spatial and temporal scales in marine ecosystems. However, it has rarely been tested across large spatial scales, or biogeographical barriers. Here, we scale up marine eDNA metabarcoding, test its ability to detect a major marine biogeographic break, and evaluate its use as a regional biomonitoring tool in Australia. Location: North-western Australia (NWA) Methods: We applied metabarcoding assays targeting the mitochondrial 16S rRNA and CO1 genes to 284 surface seawater eDNA samples collected from 71 mid-shelf, inshore, coastal and nearshore estuarine sites over 700 km of the NWA coastline. Results: Metabarcoding detected a wide range of bony fish (404 taxa), elasmobranchs (44) and aquatic reptiles (5). We detected bioregional and depth differentiation within inshore bony fish communities. These findings support the presence of a marine biogeographic break, which is purported to occur in the vicinity of Cape Leveque, demarcating the border between the Kimberley and Canning bioregions. Inshore bony fish and elasmobranch communities, as well as coastal bony assemblages, were additionally found to differ between the South and North Kimberley regions suggesting previously unrecognised subregional differentiation among these taxa. The overall compositional data has been used to update distribution information for a number of endangered, elusive and data-deficient taxa, including sawfish (family: Pristidae), northern river shark (Glyphis garricki) and wedgefish (genus: Rhynchobatus). Main conclusions: eDNA metabarcoding demonstrated a high level of sensitivity that was able to discern fine-scale patterns across the large-scale, remote and oceanographically complex region of North-western Australia. Importantly, this study highlights the potential of integrating broad-scale eDNA metabarcoding alongside other baseline surveys and long-term monitoring approaches, which are crucial for the sustainable management and conservation of marine biodiversity in this unique marine region.

opencc-by-4.0Nov 2021View details →

Phylogenetic α- and β-diversities jointly reveal leaf-litter ant community assembly mechanisms along a tropical elevational gradient

This study was conducted along the eastern slope of the Cofre de Perote mountain, in Veracruz, Mexico. This region is located at the junction of the Trans-Mexican volcanic belt and the Sierra Madre Oriental. We selected eight study sites spanning an elevational gradient of 3500 meters of altitude. Regardless of the geographical distance, all sites were systematically separated with an elevational difference of 500 meters on average between each other. We placed our study sites at the following elevations above sea level: 30-50 m, 610-670 m, 900-1010 m, 1470-1650 m, 2020-2230 m, 2470-2600 m, 3070-3160 m and 3480-3540 m, however, for simplicity, we will refer to each site as discrete units (i.e. 0, 600, 1000, 1500, 2100, 2500, 3100, 3500 m). Sampling sites were old-growth forests characterized by no obvious forest use and highly dominance of mature forests, except in the case of the lowest site (i.e., La Mancha), where most of its original vegetation has been transformed. To overcome the effect of perturbation in the studied patterns, we sampled La Mancha in a secondary forest with up to 30 years of regeneration. All sampling sites were closed-canopy forests in which a leaf-litter layer could be guaranteed. During the rainy season (July-September) of 2018 one 300-m transversal transect was located at each one of the eight study locations where we established 10 equidistantly sampling points (i.e., 30 meters between each other). Two independent 1-m2 samples were taken perpendicularly to each sampling point: one 10 meters on the right side and the other 10 meters from the left side. This procedure was repeated in a second transect placed during the dry season (March-May) of 2019 to increase community characterization as well as reduce any seasonality effect on our diversity patterns. Transects within an elevational site were separated at least 1 km away from each other. Thus, we obtained 320 m2 leaf-litter samples characterized the whole mountain (8 study sites x 20 m2 per transect x 2 transects = 320 m2). In each 1-m2 quadrat, we collected the leaf litter inside and sifted it through a coarse mesh screen of 1-cm grid size to remove the largest fragments and concentrate the fine litter. The concentrated fine litter from each sample was suspended in independent mini-Winkler sacks for 3 days in the laboratory. Falling arthropods were collected into a container with 95% ethanol. Ant workers were removed from each container for identification. When possible, specimens were identified at the species level. If not, we assigned a morphospecies number.   Phylogenetic tree constructions Ideally, one would use a complete, species-level phylogeny of all ant species present in your study area to calculate phylogenetic diversity, yet our current understanding of ant relationships is still limited. As an alternative, we built a genus-level phylogeny based on the tree by Moreau & Bell, (2013), but using the phylogenetic relationships and divergence times within Myrmicinae from Ward et al. (2015). This phylogeny was then pruned to keep only a single species per genus to generate a genus-level phylogeny. To maximize taxonomic coverage, we replaced genera that were missing from those studies by closely-related lineages that were not present in our dataset using other phylogenetic studies (Borowiec, 2016; Lapolla et al., 2010; Schmidt & Shattuck, 2014). We then used the list of species (Supporting Information Table S1) in our dataset to simulate a species-level phylogeny in which the relationships within genera were obtained from a Yule (pure-birth) process using the genus.to.species.tree function in the “phytools” package (Revell, 2012). A total of 1000 simulated trees were obtained to account for phylogenetic uncertainty [see Arnan et al. (2018) and Divieso et al. (2020) for similar approach]. Additionally, we constructed a maximum clade credibility tree (hereafter MCC tree) which was used to summarize the uncertainty of the 1000 simulated trees. The MCC tree was constructed from the sample of the 1000 trees with the maxCladeCred function incorporated in the “ape” package (Paradis et al., 2019). Both the 1000 hypothetical trees and the MCC tree were used in downstream analyses (Supporting Information Fig. 1).

opencc-by-4.0Nov 2021View details →

Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux

This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021) Followings are the explanation of data file. *** File naming rule ***     {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc         data_group_name             - atm             - track         experiment_name             - Wind             - Wave             - SlabO         tropical_cyclone_case_number             - 001             - 002                 ...             - 099             - 100 *** Description on each data group ***          atm: three dimentional atmospheric velocity data         - level: pressure levels for vertical atmospheric data     - longitude: Longitude         - latitude:  Latitude         - velocity_u_component: averaged atmospheric eastward velocity     track: data around tropical cyclone track         - time: UTC time (YYYYMMDDHH)         - longitude_center: Longitude of typhoon center         - latitude_center: Latitude of typhoon center         - central_pressure: typhoon central pressure         - maximum_surface_wind: typhoon maximum surface wind speed         - longitude_sfc: Longitude for surface data around typhoon center         - latitude_sfc: Latitude for surface data around typhoon center         - surface_wind_u_component: surface eastward wind around typhoon         - surface_wind_v_component: surface northward wind around typhoon         - sea_level_pressure: sea level pressure around typhoon         - latent_heat_flux: surface upward latent heat flux         - sensible_heat_flux: surface upward sensible heat flux         - time_atm: UTC time (YYYYMMDDHH) for atmospheric data         - level: pressure levels for atmospheric data         - longitude_atm: Longitude for atmospheric data around typhoon center         - latitude_atm: Latitude for atmospheric data around typhoon center         - velocity_u_component: 3d eastward velocity around typhoon         - velocity_v_component: 3d northward velocity around typhoon

opencc-by-4.0Nov 2011View details →

Fig. 2 in Dynamics of fish assemblages on a continuous rocky reef and adjacent unconsolidated habitats at Fernando de Noronha Archipelago, tropical western Atlantic

Fig. 2. Mean values (±SE) of temporal fluctuation and temporal stability.

UCE phylogenomics of New World Cryptopone (Hymenoptera: Formicidae) elucidates genus boundaries, species boundaries, and the vicariant history of a temperate-tropical disjunction

The genus Cryptopone Emery contains 25 species of litter and soil ants, 5 of which occur in the Americas. Cryptoponegilva occurs in the southeastern U.S.A. and cloud forests of Mesoamerica, exhibiting an uncommon biogeographic disjunction observed most often in plants. We used phylogenomic data from ultraconserved elements (UCEs), as well as mitogenomes and legacy markers, to investigate phylogenetic relationships, species boundaries, and divergence dates among New World Cryptopone. Species delimitation was conducted using a standard approach and then tested using model-based molecular methods (SNAPP, BPP, SODA, and bPTP). We found that Cryptopone as currently constituted is polyphyletic, and that all the South American species belong to Wadeura Weber, a separate genus unrelated to Cryptopone. A single clade of true Cryptopone occurs in the Americas, restricted to North and Central America. This clade is composed of four species that originated ~4.2 million years ago. One species from the mountains of Guatemala is sister to the other three, favoring a vicariance hypothesis of diversification. The taxonomy of the New World Cryptoponeand Wadeura are revised. Taxonomic changes are: Wadeura Weber is resurrected, with new combinations W. guianensisWeber, W. holmgreni (Wheeler), and W. pauli (Fernandes & Delabie); C. guatemalensis (Forel) (rev. stat.) is raised to species, and includes C. obsoleta (Menozzi) (syn. nov.). The following new species are described: Cryptopone gilvagrande, C. gilvatumida, and Wadeura holmgrenita. Cryptopone hartwigi is transferred to Fisheropone (n. comb.). Cryptopone mirabilis (Mackay & Mackay 2010) is a junior synonym of Centromyrmex brachycola (Roger) (syn. nov.).

Data from: Landscape heterogeneity filters functional traits of rice arthropods in tropical agroecosystems

Biological control services of agroecosystems depend on the functional diversity of species traits. However, the relationship between arthropod traits and landscape heterogeneity is still poorly understood, especially in tropical rice agroecosystems which harbour a high diversity of often specialized species. We investigated how landscape heterogeneity, measured by three metrics of landscape composition and configuration, influenced body size, functional group composition, dispersal ability and vertical distribution of rice-arthropods in the Philippines. We found that landscape composition and configuration acted to filter arthropod traits in tropical rice agroecosystems. Landscape diversity and rice habitat fragmentation were the two main gradients influencing rice-arthropod traits, indicating that different rice-arthropods have distinct habitat requirements. Whereas small parasitoids and species mostly present in the rice-canopy were favoured in landscapes with high compositional heterogeneity, predators and medium-sized species occupying the base of the rice plant, including planthoppers, mostly occurred in highly fragmented rice habitats. We demonstrate the importance of landscape heterogeneity as an ecological filter for rice-arthropods, identifying how the different components of landscape heterogeneity selected for or against specific functional traits. However, the contrasting effects of landscape parameters on different groups of natural enemies indicate that not all beneficial rice-arthropods can be promoted at the same time when using a single land management strategy. Increasing compositional heterogeneity in rice landscapes can promote parasitoids but may also negatively affect predators. Future research should focus on identifying trade-offs between fragmented rice habitats and structurally diverse landscapes to maximize the presence of multiple groups of beneficial arthropods.

Soil nitrogen mediates the effect of distance from climatic center on herbivory rates in a tropical herb

The center-periphery hypothesis predicts that species are most abundant at the center of their range. Differential herbivory rates between center and periphery populations can explain this variation in species abundance. However, if the geographic center of a species distribution coincides with its ecological optimum, the resource availability hypothesis predicts higher herbivory rates and tolerances at the center compared to the periphery. Biogeographic studies on herbivory have treated these two mechanisms separately, limiting our mechanistic understanding of the role of herbivory in shaping species range limits. We analyzed the role of resource availability on herbivory variation from center to periphery using data collected across the distribution of Thunberbia atacorensis, a range limited species of West Africa. We used two types of distances: geographic distance (the distance from each plot to the geographic center of all populations) and climatic distance (the distance from each plot to the preferendum of the species). We found no increase in herbivory toward the periphery of the climatic and geographic ranges. However, herbivory rates increased with soil nitrogen. Nitrogen in soil decrease from center to periphery of climatic range. Phylogenetic diversity and surrounding plants' competition did not affect herbivory rates. Our study provides insights into how nutrient limitation can shape species center-periphery distribution by altering spatial variation in herbivory rates.

Not all trees can make a forest: tree species composition and competition control forest encroachment in a tropical savanna

Forest encroachment into savannas is a widespread phenomenon, the rate of which may depend on soil conditions, species composition, or changes in stand structure. As savanna specialist trees are replaced by generalist species, rates of stand development may increase. Because generalists can persist in forests, they are likely to grow more quickly and survive longer in dense stands, compared to savanna specialists. Furthermore, the faster growth rates of generalists may allow them to overtop and outcompete savanna specialists, causing rapid species turnover. We measured growth and survival of 6147 individuals of 112 species of savanna and generalist tree species over a period of 10 years in an ecological reserve in Assis, São Paulo State, Brazil. We modeled growth and mortality as a function of soil texture and nutrients, tree size, competitive neighborhood, and membership in savanna or generalist (species which can persist in forests and savannas) functional groups. Tree growth and survival was strongly influenced by competition, as estimated by the basal area of trees taller than a focal tree. At the stand level, savanna species are unable to contribute basal area growth in closed stands, while generalist species continue to increase in basal area even at high stand basal area. This phenomenon is driven by differences in growth and mortality. Generalists grew faster than savanna species, both in height and diameter. This difference in growth rates led to savanna species becoming suppressed more rapidly than generalists. When suppressed, savanna species were more than twice as likely to die than were generalists. Soils had inconsistent and mostly weak effects which were difficult to separate from gradients of stand structure. Synthesis: We demonstrate that the presence of generalist trees accelerates rates of basal area accumulation due to their greater growth rates and tolerance of shading. Generalists outcompete savanna trees by growing faster in the open and overtopping savanna specialists. Due to the slow growth and high mortality of savanna species in the shade, they are unable to form closed-canopy stands. Accounting for differences among functional types and development of vegetation structure is critical for modeling forest encroachment.

Tree dynamic response and survival in a category-5 tropical cyclone: The case of super typhoon Trami

In the future with climate change, we expect more forest and tree damage due to the increasing strength and changing trajectories of tropical cyclones (TCs). However, to date, we have limited information to estimate likely damage levels, and nobody has ever measured exactly how forest trees behave mechanically during a TC. In 2018, a category-5 TC destroyed trees in our ongoing research plots, in which we were measuring tree movement and wind speed in two different tree spacing plots. We found damaged trees in only the wider spaced plot. Here, we present how trees dynamically respond to strong winds during a TC. Sustained strong winds obviously trigger the damage to trees and forests but inter-tree spacing is also a key factor because the level of support from neighboring trees modifies the effective "stiffness" against the wind both at the single tree and whole forest stand level.

Lianas and trees exhibit divergent intrinsic water-use efficiency along elevational gradients in South American and African tropical forests

Elevational gradients provide excellent opportunities to explore long-term morphological and physiological responses of plants to environmental change. We determined the difference in the elevational pattern of foliar carbon isotope composition (δ13C) between lianas and trees, and assessed whether this difference arises from changes in photosynthesis or stomatal conductance. We also explored the pattern of nutrient limitations with the elevation of these two growth forms. We conducted inventories of lianas and trees using standardized techniques along elevational gradients in Ecuador and Rwanda. We determined the values of several foliar traits including δ13C and chemical traits in dominant liana and tree species. We set up Bayesian linear mixed-effect models to quantify the effects of elevation and these two growth forms, and the difference of the effect of elevation between the two growth forms on each of the foliar traits. We found consistent growth form specific divergences in foliar δ13C and carbon to nitrogen ratio (C:N) responses to elevation. While we noted a meaningful increase in foliar δ13C and C:N with elevation for trees, lianas did not exhibit such a trend. Foliar δ13C and C:N remained relatively constant for lianas along the transects. The physiological processes at the basis of foliar carbon isotope fractionation shift differently in lianas and trees along elevation. Lianas operate at relatively constant intrinsic water- and nitrogen- use efficiencies with elevation as opposed to trees. Altogether, the study suggests the existence of a functional divergence of water and nutrient use strategies between lianas and trees along tropical elevational transects.

Data for: The interplay of environmental cues and wood density in the vegetative and reproductive phenology of seasonally dry tropical forest trees

The great phenological diversification characteristic of seasonally dry tropical forests (SDTF) suggests that these patterns result from a complex interplay between exogenous (e.g., climatic) and endogenous (e.g., morphological, physiological, anatomical) factors. Based on the well-established relationships of wood density with water-storing capacity and cavitation vulnerability in woody plants, we hypothesized differential vegetative and reproductive phenological responses to environmental cues for hardwood and softwood species. To test this hypothesis, we compared phenological patterns of pairs of conspecific populations of 10 species differing in wood density, occurring in two localities with slightly different climatic regimes, and evaluated the influence of three environmental variables (rainfall, photoperiod, temperature) on them. Our results, based on the assessment of the overlap of the phenological curves of conspecific populations occurring in different sites and on linear modeling, showed different effects of the environmental factors on phenophase attributes, depending on wood density of the study species, thus supporting our hypothesis. Leaf out in softwood species took place in the dry season, they shed the foliage at the first signs of drought, and once leafless, they flowered and fruited shortly after. By contrast, hardwood species bore leaves and flowers in the rainy season, shed their leaves several months after the rain ceased, and produced fruits during the dry season. We conclude that the role of environmental variables in cueing growth and reproduction cycles in SDTF tree species is interrelated with their wood density, a key endogenous factor crucially linked to plant hydraulics in these water-limited ecosystems.

opencc-zeroFeb 2022View details →

No evidence for trade-offs between bird diversity, yield and water table depth on oil palm smallholdings: implications for tropical peatland landscape restoration

Tropical peat swamp forests retain large carbon stocks and support unique biodiversity, but clearance and drainage for agriculture have resulted in fires, carbon emissions and biodiversity losses. Initiatives to re-wet cultivated peatlands may benefit biodiversity if this protects remaining forests from fire and agricultural encroachment, but there are concerns that re-wetting could reduce yields and damage livelihoods, as relationships between drainage, on-farm biodiversity, and crop yields have not been studied. We examined oil palm fruit yields and bird diversity on 41 smallholder farms in Jambi (Sumatra, Indonesia), which varied in drainage intensity (12-month mean water table per plot from August 2018 to August 2019: -52 to -3 cm below ground). We also compared farm bird diversity with a neighbouring area of protected forest (11,000 ha, 21 plots; mean water table per plot -3 to +15 cm). Bird species richness (3-18 species per plot), species composition, and oil palm yields (4.5-19.2 t fresh fruit bunch ha-1 yr-1) varied among farms, but were not detectably affected by water table depth, although ground-level vegetation was more complex on wetter farms. Bird richness in oil palm (mean = 10.3 species per plot) was <50% of that in forest (26 species per plot), and only three out of 35 conservation-priority species found in forest were recorded in oil palm. Synthesis & applications: Tropical peatlands in Indonesia have been drained to allow farmer access and improve farm yields, but we found no trade-offs between drainage depth, yields or bird diversity on smallholder oil palm farms in our study landscape. Current restoration initiatives to re-wet peat may benefit farmers by reducing fire risk, without affecting yields. Wetter farms had increased understorey vegetation complexity, but this did not affect bird diversity, so we find no evidence that re-wetting improves on-farm biodiversity within the studied range of drainage depths. However, on-farm fire reduction efforts in cultivated peatlands, including re-wetting, will be vital for reducing the risk of fires escaping into nearby forests, which contain unique and diverse bird species assemblages. Protection of remaining peatland forests from fire and clearance is key for biodiversity conservation, and for providing a source of seed dispersers and genetic material for future forest and landscape restoration efforts. Restoration of more biodiversity-friendly land covers will improve landscape permeability and help conserve species and the ecosystem services they deliver.