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opencc-zeroJan 2024View details →

Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes

The datasets in this repository include plant community surveys, hyperspectral reflectance data at the leaf and canopy level, leaf trait data, and soil chemistry data collected at five sites along an elevation gradient of 2400m-3500m in the Chilean Andes (33°S, 70°W). The purpose of this study was to evaluate the environmental drivers of community assembly processes along the elevation gradient.

opencc-by-4.0Jan 2024View details →

Elevation differently shapes functional diversity patterns in understory forest communities when considering intraspecific and interspecific trait variability

Datasets used for the analysis done for the paper "Elevation differently shapes functional diversity patterns in understory forest communities when considering intraspecific and interspecific trait variability". Files present are: -Species x Plot (vegetation releveés). -Plot x Environment. -Plot x CWM_inter for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) using the traits fixed for species, i.e., holding traits constant as the species mean, thus incorporating only turnover. -Plot x CWM_intra for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) based on an individual by trait matrix, therefore incorporating both turnover and intraspecific trait variation. -Plot x SES-FD_inter (Standard Effect Size Functional Diversity) for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) using the traits fixed for species, i.e., holding traits constant as the species mean, thus incorporating only turnover. -Plot x SES-FD_intra (Standard Effect Size Functional Diversity) for Plant height, Leaf area, Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) based on an individual by trait matrix, therefore incorporating both turnover and intraspecific trait variation. All analysis were carried out using the software R version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria, <a href="http://www.R-project.org">http://www.R-project.org</a>) and can be consulted on GitHub https://github.com/AriannaFerrara/Elevation-and-Intraspecific-trait-variability.git

opencc-by-4.0Mar 2021View details →

Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna

The dataset associated with the manuscript "Fire promotes functional plant diversity and modifies soil carbon dynamics in tropical savanna" (Teixeira et al.) includes 6 different datasets, for which we provided one metadata.   Version 2 includes an update of the biomass data set, including the correct transformation to g/m2 on fine roots biomass data. Version 3 includes an update of the belowground traits data set based on correcting formatting errors in the belowground traits data.  Version 4 Sorry for the inconvenience. This version includes the correct updated belowground traits data file based on the correct formatting errors in the belowground trait data.  fluxes: it includes data related to net ecosystem C  and water exchange. NEE and ET from each plot were measured using the LiCOR 7500 infrared gas analyzer (Li-Cor Inc.). See the method section in the manuscript for full details. soil_carbon: it includes carbon soil data. biomass_v2: it includes data related to aboveground and belowground biomass. Aboveground data were collected in 0.5m2 subplot and belowground at 0.25m2 at 20cm depth both within 1m2 sampling plot. See the method section in the manuscript for full details. aboveground_traits: all aboveground functional traits from plant species. See the method section in the manuscript for full details. belowground_traitsv3: all roots functional traits from plant species. See the method section in the manuscript for full details. species_composition: plant community composition. See the method section in the manuscript for full details. Abstract Fire is an evolutionary environmental filter in tropical savanna ecosystems altering functional diversity and associated C pools in the biosphere and fluxes between the atmosphere and biosphere. Therefore, alterations in fire regimes (e.g. fire exclusion) will strongly influence ecosystem processes and associated dynamics. In those ecosystems, C dynamics and functions are underestimated by the fire-induced offset between C output and input. To determine how fire shapes ecosystem C pools and fluxes in an open savanna across recently burned and fire excluded areas, we measured the following metrics: (I) plant diversity including taxonomic (i.e. richness, evenness) and plant functional diversity (i.e. functional diversity, functional richness, functional dispersion and community weighted means); (II) structure (i.e. above- and below-ground biomass, litter accumulation); and (III) functions related to C balance (i.e. net ecosystem carbon dioxide (CO2) exchange (NEE), ecosystem transpiration (ET), soil respiration (soil CO2 efflux), ecosystem water use efficiency (eWUE) and total soil organic C (SOC). We found that fire promoted aboveground live and belowground biomass, including belowground organs, and coarse and fine root biomass, and contributed to higher biomass allocation belowground. Fire also increased both functional diversity and dispersion. NEE and total SOC were higher in burned plots compared to fire-excluded plots whereas soil respiration recorded lower values in burned areas. Both ET and eWUE were not affected by fire. Fire strongly favored functional diversity, fine root, and belowground organ biomass in piecewise SEM models but the role of both functional diversity and ecosystem structure to mediate the effect of fire on ecosystem functions remain unclear. Fire regime will impact C balance, and fire exclusion may lead to lower C input in open savanna ecosystems.

FIGURE 8 in Diversity and fossil record of larvae of three groups of lacewings with unusual ecology and functional morphology: Ithonidae, Coniopterygidae and Sisyridae

FIGURE 8. Comparison of the two beak larvae to a larva of Coniopterygidae. A. Larva (specimen 6802) from Haug et al. (2020b). B. New beak larva (specimen 6803, based on Figure 5B). C. Larva of Aleuropteryx loewi (based on several figures from Rousset, 1966).

FIGURE 6 in Diversity and fossil record of larvae of three groups of lacewings with unusual ecology and functional morphology: Ithonidae, Coniopterygidae and Sisyridae

FIGURE 6. Additional specimens of larvae of Sisyridae preserved in Baltic amber. Images kindly provided by Jonas Damzen (JD) and Marius Veta (RMV). A. JD 6345, specimen 6503, in ventral view, 3.5 mm long. B, C. RMV 2380, specimen 6504, 3 mm long. B. Dorsal view. C. Ventral view. D, E. JD 4198, specimen 6505, 4 mm long. D. Dorsal view. E. Ventral view. F, G. RMV 2793, specimen 6506, 2 mm long. F. Left lateral view. G. Right lateral view. H, I. RMV 2794, specimen 6507, 3.2 mm long. H. Lateral view. I. Dorsal view.

FIGURE 7 in Diversity and fossil record of larvae of three groups of lacewings with unusual ecology and functional morphology: Ithonidae, Coniopterygidae and Sisyridae

FIGURE 7. Scatterplot of PC2 vs. PC1. Note how the two beak larvae (specimens 6802 + 6803) plot together with larvae of Coniopterygidae.

FIGURE 4 in Diversity and fossil record of larvae of three groups of lacewings with unusual ecology and functional morphology: Ithonidae, Coniopterygidae and Sisyridae

FIGURE 4. Fossil larva, "beak larva" type 2, from Myanmar amber, PED 0596, specimen 6803. A. Dorsal view; posterior part not well accessible. B. Colour-marked version of A. Abbreviations: 1t = trunk appendage 1; at = antenna; hc = head capsule; ms = mesothorax; pl = palp; pt = prothorax.

FIGURE 5 in Diversity and fossil record of larvae of three groups of lacewings with unusual ecology and functional morphology: Ithonidae, Coniopterygidae and Sisyridae

FIGURE 5. Fossil larva, "beak larva" type 2, from Myanmar amber, PED 0596, specimen 6803, continued. A. Ventral view; details not well accessible; posterior end not inside the amber. B. Close-up on head in dorsal view. C. Close up on trunk appendage 1 in dorsal view. D. Same as C; estimated outline of appendage, visible through tergite outlined in red. Abbreviations: at = antenna; "b" = beak; pl = palp.

FIGURE 3 in Diversity and fossil record of larvae of three groups of lacewings with unusual ecology and functional morphology: Ithonidae, Coniopterygidae and Sisyridae

FIGURE 3. Fossil larva of Sisyridae from Baltic amber, CCGG 7122, specimen 6502. A. Dorsal view. B. Ventral view. C. Colour-marked version of B. D. Close-up of head in dorsal view. E. Colour-marked version of D. F. Close-up on gills. G. Close-up on trunk end. Abbreviations: at = antenna; hc = head capsule; ms = mesothorax; mt = metathorax; pt = prothorax; sy = stylet.

FIGURE 1 in Diversity and fossil record of larvae of three groups of lacewings with unusual ecology and functional morphology: Ithonidae, Coniopterygidae and Sisyridae

FIGURE 1. Fossil larva of Coniopterygidae from Baltic amber, CCHH 540-1, specimen 6301. A. Ventral view. B. Dorsal view. C. Colour-marked version of B. D. Close-up of head in dorsal view. E. Close-up of head in ventral view. F. Close-up on right hind leg; arrows mark claws. G. Close-up of hind leg; arrows mark claws. Abbreviations: a1–a8 = abdomen segment 1–8; at = antenna; cx = coxa; e1–3 = element 1–3; fe = femur; hc = head capsule; lp = labial palp; ms = mesothorax; mt = metathorax; pt = prothorax; st = stemmata; ta = tarsus; te = trunk end; ti = tibia; tr = trochanter.

FIGURE 2 in Diversity and fossil record of larvae of three groups of lacewings with unusual ecology and functional morphology: Ithonidae, Coniopterygidae and Sisyridae

FIGURE 2. Fossil larva of Sisyridae from Baltic amber, CCGG 1383, specimen 6501. A. Dorsal view. B. Ventral view. C. Colour-marked version of B. D. Close-up of head in dorsal view. E. Colour-marked version of D; arrows mark stemmata. F. Close-up on gills. G. Close-up on processes on trunk segments. Abbreviations: a3–a7 = abdomen segment 3–7; at = antenna; gi = gills; hc = head capsule; ms = mesothorax; mt = metathorax; pt = prothorax; sy = stylet; te = trunk end.

opencc-zeroApr 2024View details →

Scripts and data for: Integrating different facets of diversity into food web models: how adaptation among and within functional groups shape ecosystem functioning

Adaptation of communities to environmental fluctuations can emerge from different facets of biodiversity, which may impact ecosystem functioning differently. Previous work examined how ecosystem functions can be influenced by two sources of adaptive potential: sorting (i.e., changes in community composition due to fitness differences) can occur when multiple species or groups are present (richness), and trait adaptability (i.e., trait adjustments within species or functional groups) can emerge from genetic or phenotypic diversity. However, their effect is typically studied separately, and often in the context of only one trophic level. Therefore, we used a bitrophic trait-based model varying in richness and in the presence of trait adaptability at each trophic level, to investigate how sorting and trait adaptability, at one or two trophic levels, separately or jointly shape ecosystem functions. We found that the adaptive potential emerging from any facet of diversity-induced changes in trophic interactions, in turn, affects biomass distributions within and across trophic levels, dynamical behaviour, and synchrony of biomass dynamics within a trophic level. Particularly, sorting and trait adaptability could contribute to a similar degree and at a similar time to temporal changes in ecosystem functions, but their respective contribution depended on the speed of trait adaptation, the trait range between similar functional groups, and trophic interactions. We thus suggest to consider multiple facets of diversity and their corresponding sources of adaptive potential to deepen our mechanistic understanding of ecosystem functioning, especially in a context of rapid biodiversity change.

opencc-by-4.0Nov 2024View details →

UV radiation accelerates litter decomposition in a valley-type savanna by enhancing microbial community diversity and function

We present the data of the study by Gao et al. (2024): <a name="_Hlk179470571"></a><a name="OLE_LINK42"></a>UV radiation accelerates litter decomposition in a valley-type savanna by enhancing microbial community diversity and function. The excel file (Raw Data) includes the following sheets: 1- Radiation (w·m-2) variation of UV-A and UV-B during the experimental period. 2- Decay constants (K, yr−1) and changes in the mass loss rate of litter under different UV conditions during the experimental period. 3- The content of lignin, cellulose, C, N and P of litter under different UV conditions during the experimental period. 4- 16S ASVs under different UV conditions. 5- ITS ASVs under different UV conditions.

opencc-by-4.0Sep 2021View details →

Data for: Trap type affects dung beetle taxonomic and functional diversity in Bornean tropical forests

Dung beetle community composition data. Data was collected using either dung-baited pitfall traps or flight interception traps. Each row represents one trap, with the author/study information, name of study site, sampling period, trap type and habitat type. Dung beetle species and their abundances are listed. See "metadata" tab for more details. Paper abstract: Baited pitfall traps (BPTs) and flight intercept traps (FITs) are the most common methods employed for sampling dung beetle communities. These methods vary in their efficacy and are affected by factors such as the bait types used and the dispersal abilities of different dung beetle species. We present the first quantitative comparison of the taxonomic and functional diversity, and community composition of dung beetles caught in BPTs and FITs in Bornean tropical forests. We show that BPTs and FITs captured complementary communities with different functional traits, and that BPTs captured more functionally diverse communities. We therefore recommend using a combination of both baited BPTs and FITs for studies assessing the composition of dung beetles across habitat types. Our results also highlight that it is important to consider how trap type affects the trait composition of communities when relating dung beetle communities and functional traits to ecological functioning. We suggest modifications to FITs based on the design of harp traps to increase their effectiveness in capturing larger-bodied beetles.

opencc-by-4.0Mar 2022View details →

Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.

Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.: - Trait-based distances calculated for each pair of taxa; - CWM, CWM(LN) values, and their difference (CWMDIFF) Refer to the published articles for further details.

opencc-by-4.0Jul 2021View details →

Multi-taxa environmental DNA inventories reveal distinct taxonomic and functional diversity in urban tropical forest fragments

Urban expansion and associated habitat transformation drives shifts in biodiversity, with declines in taxonomic and functional diversity. Forests fragments within urban landscapes offer a number of ecosystem services, and help to maintain biodiversity and ecosystem functions. Here, we focus on a tropical forest environment, and on the soil biota. Using eDNA metabarcoding, we compare forest fragments within the city of Cayenne, French Guiana, with a neighbouring continuous undisturbed forest. We wished to determine if urban forest fragments conserve high levels of alpha and beta diversity as well as similar functional composition for plants, soil animals, fungi and bacteria. We found that alpha diversity is similar across habitats for plants and fungi, lower in urban forests for metazoans and higher for bacteria. We also found that urban forests communities differ from undisturbed forests in their taxonomic composition, with urban forests exhibiting greater turnover between fragments potentially caused by ecological drift and limited dispersal. However, their functional composition exhibited limited differences, with an enrichment of palms, arbuscular mycorrhizal fungi and bacteria and a depletion of climber plants and termites. Thus, although urban forest fragments do shelter soil biodiversity that differs from native forests, the losses of soil functions may be relatively limited. This study demonstrates the strong potential of a multi-taxa eDNA approach for rapid inventories across taxonomic kingdoms, in particular for cryptic soil diversity. It also demonstrates the key role of urban forest fragments in conserving biodiversity and ecosystem function, and points to a need for more systematic monitoring of these areas in urban management plans. For each of the 16 samples per plot, 15 g of soil was used for eDNA analyses. Extracellular DNA was extracted as described previously (Zinger et al., 2016; 2019), where each soil sample is added to 15ml of saturated phosphate buffer (Na2HPO4; 0.12m; pH ≈8) in 50ml Falcon tubes. This is placed in an agitator for 15 minutes, before a 2ml aliquot of the soil/phosphate buffer mixture is pipetted into an Eppendorf tube and centrifuged for five minutes at 13000 rcf. 500μL of the resulting supernatant is then recovered and used for the next extraction steps that are carried out with a commercial kit for soil DNA (NucleoSpin® Soil; Macherey-Nagel, Düren, Germany), skipping the lysis step and following manufacturer’s instructions. The DNA extract was recovered in 100 μL and diluted 10 times before being used as PCR template.  For each plot one DNA extraction negative control was performed adding up 17 extractions per plot. PCR amplifications were then conducted for four DNA molecular markers, with primers targeting either Viridiplantae (subsequently referred to as plants), Eukaryotes, Fungi or Bacteria (Table 1). For each marker, PCR amplification of samples occurred across 12 plates. Each PCR reaction was performed in a total volume of 20 μl and comprised 10 μl of AmpliTaq Gold Master Mix (Life Technologies, Carlsbad, CA, USA), 5.84 μl of Nuclease-Free Ambion Water (Thermo Fisher Scientific, Massachusetts, USA), 0.25 μM of each primer, 3.2 μg of BSA (Roche Diagnostic, Basel, Switzerland), and 2 μl of DNA template that was before 10-fold diluted to reduce the amounts of PCR inhibitors. Thermocycling conditions for each primer pair are indicated in Table 1.  A negative extraction control per site and a negative PCR control per PCR plate were amplified and sequenced in parallel with the regular samples. Positive controls were also included and consisted of mock communities of plants and fungi DNA (no mock communities were built for bacteria or eukaryotes here), which were used to guide choices in our data curation process. Two PCR replicates were performed for each sample and control. Amplification was conducted using a double indexing system strategy (Binladen et al. 2007) using a system of 32 by 36 octamers with at least five differences between them located at the 5’ end of each primer (Coissac 2012). In doing so, each PCR product had a unique combination of tags for both forward and reverse primers, allowing for the retrieval of sequence data for each sample. Ten wells per PCR plate were left empty to act as sequencing controls (non-used tag combinations) for downstream data curation (see below). PCR products were pooled and sequencing libraries were constructed using the Illumina TruSeq NanoPCRFree kit following the supplier’s instructions (Illumina Inc., San Diego, California, USA), except that the ligation product was not PCR amplified to limit tag-jump biases (Taberlet et al 2018). The libraries were then sequenced on different Illumina platforms (San Diego, CA, USA) depending on the marker considered (Table S1), using the paired-end technology. Bioinformatic analyses were performed on the GenoToul bioinformatics platform (Toulouse, France), with the OBITOOLS package (Boyer et al. 2016). First, ‘illuminapairedend’ was used to assemble paired-end reads. This algorithm is based on an exact alignment algorithm that considers the quality scores at all positions during the assembly process. Subsequently, we used the ‘ngsfilter’ command to identify and remove the primers and tags on each read, and assign reads to their respective samples. This program was used with its default parameters tolerating two mismatches for each of the two primers and no mismatch for the tags. Following this, sequencing reads were dereplicated using the ‘obiuniq’ command. Sequences of low quality (containing Ns or with paired-end alignment scores below 50) were excluded using the ‘obigrep’ command. The same command was used to exclude sequences represented by only one read (singletons) as they are more likely to be molecular artefacts (Taberlet et al. 2018). Sequences outside of the preset range were also discarded (Table 1). To remove PCR/sequencing errors as well as intraspecific variability, we built OTUs (Operational Taxonomic Units) using the ‘sumaclust’ clustering algorithm (Mercier et al. 2013), which considers the most abundant sequence of each cluster as the cluster representative.  OTUs were set at a sequence similarity threshold of 97% for eukaryotes, fungi and bacteria following the standards in microbial ecology, but this was lowered to 95% for plants since the eDNA target region is shorter (typically around 50 base pairs), where one mismatch inherently results in a lower percentage of similarity. To assign a taxon to plant and fungal OTUs, we built two reference sequence databases, one global, using the ecoPCR programme (Ficetola et al. 2010) and the plant / fungi specific markers on the European Molecular Biology Laboratory (EMBL; release 141), a second local, generated from specimens of fungi (Jaouen et al. 2019) and plants (see Zinger et al. 2019) collected in French Guiana. OTUs were then assigned a taxonomy, using OBITOOL’s ecotag programme (Boyer et al. 2016), which performs a global alignment of each OTU sequence (the query) against each reference. The reference taxon assigned to each OTU corresponds to the Last Common Ancestor of all the best-match sequences for the query. For taxonomic assignment of bacteria and eukaryote OTUs, the SILVA taxonomic database was used (version 1.3; Quast et al., 2012). Classification was performed by a local nucleotide BLAST search against the non-redundant version of the SILVA SSU Ref dataset (release 132; http://www.arb-silva.de) using blastn (version 2.2.30+; http://blast.ncbi.nlm.nih.gov/Blast.cgi) with standard settings (Camacho et al., 2009).  Eukaryote derived metazoan OTUs were then further assigned a taxonomy for Phyla identified at the Arthropoda, Annelida and Nematoda level using reference sequence databases built as above for these groups using the ecoPCR programme on EMBL release 141. Datasets were subsequently filtered to remove contaminants as well as artefacts such as PCR chimeras and remaining sequencing errors, following Zinger et al. (2019) and using routines now implemented in the metabaR R package (Zinger et al 2020b), in R version 3.6.1 (R Development Core Team, 2013). The filtering process consisted of four steps: (i) a negative control-based filtering. OTUs whose maximum abundance was found in extraction/PCR negative controls were removed from the dataset, as they were likely to be reagent/aerosol contaminants, better amplified in the absence of competing DNA fragments as it is the case in biological samples. (ii) a reference-based filtering. OTUs which are too dissimilar from sequences available in reference databases are potential chimeras generated during sequencing and amplification. In this study, we chose to set similarity thresholds at 95% for plants, 80% for bacteria and eukaryotes and due to the marker being more polymorphic, 65% for fungi. For plants and fungi, the remaining assignment was then verified with the local database, to confirm if assigned taxa also occurred in the local dataset, with preference given to local assignment. In addition, we removed all taxa that are not targeted by the primer used. (iii) an abundance-based filtering. This procedure targets incorrect assignment of a few numbers of sequences corresponding to true OTUs occurring to the wrong sample, a phenomenon called “tag-switching” (Esling et al. 2015), “tag jumps” (Schnell et al. 2015) or “cross-talk” (Edgar 2018). It consists in setting OTUs abundances to 0 in samples where their abundance represents < 0.03% of the total OTU abundance in the entire dataset. (iv) Finally, we conducted a PCR-based filtering by considering any PCR reaction that yielded less than 100 reads for plants, 1000 reads for fungi, bacteria and eukaryotes as non-functional, and removed them from the dataset. Data provided consists of 4 x OTU tables for each of the markers used to target different components of the soil biota, with rows representing each OTU, and columns the features of the OTU within the dataset, namely their id code, the number of read counts in the analysed dataset, their similarity score against the taxonomic dataset used to identify them, and when possible, a functional group assignment used in the manuscript. Details of these can be found above and in the manuscript and supplementary information. For each of the four datasets, we also provide a .rds file, corresponding to the processed dataset used in manuscript preparation. This is in the format of a metabaR list which includes PCR, Sample, Read count and the seperately provided OTU datasets. To facilitate interpretation, please refer to Zinger, L., Lionnet, C., Benoiston, A.S., Donald, J., Mercier, C. and Boyer, F., 2021. metabaR: an R package for the evaluation and improvement of DNA metabarcoding data quality. Methods in Ecology and Evolution, 12(4), pp.586-592. For the fungal (ITS) data, we also provide :  - the R1/R2 raw fastq files of the samples used in the paper + experimental controls - a tsv file containing the tag combinations corresponding to the samples/PCR replicates, to enable demultiplexing of data. - a csv file containing the description of each sample.

Dataset of Rainy years counteract negative effects of drought on taxonomic, functional, and phylogenetic diversity: resilience in annual plant communities

Data used in the article:  Rainy years counteract negative effects of drought on taxonomic, functional, and phylogenetic diversity: resilience in annual plant communities Abstract 1- Climate models forecast changes in the amounts and distribution of rain, which may affect ecosystems worldwide, especially in drylands where water is already the limiting factor for plant life. Annual plant communities are common in drylands where they can complete their entire life cycle during the rainy period while avoiding the dry season. Moreover, seed dormancy allows them to disperse over time by remaining in the seed bank for long periods. However, the extent to which these communities will be able to tolerate increasing drought is uncertain. 2- We performed a five-year rainfall reduction treatment under field conditions and determined its effects on annual plant communities in a Mediterranean gypsum ecosystem. We assessed the taxonomic, functional, and phylogenetic diversity of these communities each year for five years. 3-The taxonomic and functional diversity decreased under the rainfall reduction treatment whereas the phylogenetic diversity increased. Moreover, the relative importance of species with drought-resistant functional designs increased in the community assemblages. However, after a rainy season with above average rainfall, all of the diversity values recovered completely even under the rainfall reduction treatment. 4- Our results provide important insights into the responses of these plant communities under a climate change scenario, where they indicate high losses of diversity during drought events but rapid recovery in milder years. Synthesis Our findings highlight the great resilience of annual plant communities in drylands, which may allow them to tolerate increased drought under the present climate change scenario.

opencc-zeroJun 2022View details →

Trait functional diversity explains mixture effects on litter decomposition at the arid end of a climate gradient

Litter decomposition is controlled by climate, litter quality and decomposer communities. Because the decomposition of specific litter types is also influenced by the properties of adjacent types, mixing litter types may result in non-additive effects on overall decomposition rates. The strength of these effects seems to depend on the litter functional diversity. However, it is unclear which functional traits or combination of traits explain litter mixture effects and if these depend on the range of trait values and the ecosystems involved. These uncertainties hamper our ability to predict decomposition in plant communities. We aimed at understanding whether and how functional diversity (measured as functional dispersion, FDis) influences litter decomposition, and how this influence varies among different climates and across decomposition stages. We calculated FDis based on litter traits related to nutrient concentrations or to litter recalcitrance, and tested whether these diversity measures and climatic parameters (soil moisture and temperature) explained litter mixture effects on decomposition. Additive mixture effects (i.e. decomposition of mixtures equalling the mean decomposition of the single litter types) were common in most of the evaluated climates. Non-additive, negative effects were mainly restricted to the driest and warmest sites, and decreased with time. Non-additive effects increased in magnitude with the mixtures' FDis, with positive effects being related to FDis in nutrient traits and negative effects being related to FDis in recalcitrance traits. Synthesis: Litter mixing did not have strong effects on decomposition rates across the studied climatic gradient overall, and the direction and intensity of the mixture effects were context-dependent. The effects were stronger and more negative in the dryer ecosystems. Where effects were found, functional diversity calculated from selected groups of traits (related to nutrients or litter recalcitrance) predicted mixture effects, especially where trait ranges were broad, though much of the variation remains unexplained. We propose that functional diversity metrics based on litter traits that are mechanistically relevant, applied to diverse site-specific litter mixtures in different climates, can help to better understand under which conditions and in which direction litter diversity affects decomposition.

opencc-zeroJun 2022View details →

Functional diversity of macroinvertebrates as a tool to evaluate wetland restoration

Ecological restoration of aquatic ecosystems has become widespread in recent decades. Whereas the recovery of biodiversity in restored wetlands has been studied from a taxonomic perspective, our knowledge of how functional biodiversity recovers remains poorly understood. We studied the functional diversity of macroinvertebrate communities in 32 Mediterranean temporary ponds six to seven years after their creation during a restoration in South-West Spain, and compared them with 10 natural reference sites during two consecutive hydroperiods. We compared alpha functional diversity indices, and the individual contributions of new ponds and reference sites to the regional functional beta diversity, as well as to its turnover and nestedness components. We also investigated the influence of environmental and spatial variables on the dissimilarities of functional beta diversity and its components between new ponds and reference sites. Alpha functional diversity in new ponds was lower than in reference sites. Although the contribution of new ponds to the regional functional beta diversity was similar to that of reference sites, the latter contributed more to functional turnover, while new ponds contributed more to functional nestedness. Dispersal limitation coupled with environmental filtering structured the functional variation in communities between new ponds and reference sites, but their relative importance differed between beta components. New ponds can hold species with unique functional compositions, but their contribution to the regional functional beta diversity was mostly due to trait losses with respect to reference sites. Synthesis and applications. Considering different aspects of functional diversity of invertebrate communities can help elucidate the processes and mechanisms through which ecosystems recover following restoration. We encourage the use of trait-based approaches to identify trends in processes and patterns that can guide future wetland restoration projects.