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430 results for “Forest Structure”
Data from: Massive structural redundancies in species composition patterns of floodplain forest moths
Terrestrial arthropod communities usually consist of very large species numbers. Data from experiments or long time-series would be required to ascertain the functional significance of individual species. Both are largely unavailable for species-rich natural communities. Recognising structural redundancies in species composition allows for an alternative approach to address how strong functional redundancy might be in natural assemblages, if structural and functional redundancies are related to each other. Determining structural redundancies is a regular topic in aquatic ecology, but has rarely been applied to terrestrial communities. We explored the extent of structural redundancy in species-rich terrestrial insect assemblages and whether structural redundancies are contingent to species abundances or functional group affiliations. We used the BVSTEP algorithm to determine structural redundancies in a large data set of moth species (32 181 individuals; 448 species) that had been sampled with light-traps in three different floodplain forests in eastern Austria. We partitioned the moth species into 12 functional types based on larval host-plant affiliations to test if moth species included in reduced subsets represent functional groups in the same proportions as they occur in the entire fauna. We observed far more massive structural redundancies in moth assemblages than previously found in aquatic communities. Subsets containing only 8–15 species (1.8–3.3% of all recorded species) were still highly representative for the overall data. Subsets selected by the BVSTEP procedure performed better than equally small subsets that were defined solely by species abundances or by functional group affiliations. Effective 'surrogate' subsets contained only 6–9 of the 12 functional moth types. High abundance only loosely corresponded with the frequency at which a moth species was included in the subsets. Thus, certain uncommon species contribute importantly to species composition patterns. Our results show unexpectedly extensive structural redundancies in complex floodplain forest moth communities, which may also indicate strong functional redundancies.
Data from: Forest structure and snow depth alter the movement patterns and subsequent expenditures of a forest carnivore, the Pacific marten
<p>Energetic balance is central to the survival and persistence of free-ranging animals. Quantifying expenditures and identifying factors that drive energetics informs our understanding of species' ecology and their responses to shifting environmental conditions. Approaches used to estimate energetic expenditures of free-ranging species, such as doubly-labelled water (DLW), are precise but difficult to implement. Global positioning system (GPS) collars and accelerometers have emerged as alternatives for estimating expenditures, but these techniques have few applications in terrestrial species and no applications in small-bodied (<5kg) terrestrial animals. Here, we estimated movement characteristics and field metabolic rates (FMR) of Pacific martens (Martes caurina), a small-bodied carnivore with relatively high energetic costs, in a heterogeneous landscape to explore the role of movement and landscape characteristics on energetics. We concurrently used DLW and GPS collars to investigate the relationship between movement characteristics and FMR. Movement velocity explained the greatest amount of variation in mass-specific FMR and we used this relationship to predict expenditures of previously collared martens. We found that predicted mass-specific FMR was highest among males and increased in open patches primarily as a result of increased velocity and more erratic movements. Additionally, martens moving through deep snow also exhibited increased FMR. Our work shows movement metrics can effectively explain variation in FMR and identify landscape features, like forest structure and snow depth, that influence movements with cascading effects on energetics for free-ranging mammals in rapidly changing systems.</p>
Data from: AFLP diversity and spatial structure of Calycophyllum candidissimum (Rubiaceae), a dominant tree species of Nicaragua's critically endangered seasonally dry forest
The Central American seasonally dry tropical (SDT) forest biome is one of the worlds' most endangered ecosystems, yet little is known about the genetic consequences of its recent fragmentation. A prominent constituent of this biome is Calycophyllum candidissimum, an insect-pollinated and wind-dispersed canopy tree of high socio-economic importance, particularly in Nicaragua. Here, we surveyed amplified fragment length polymorphisms across 13 populations of this species in Nicaragua to elucidate the relative roles of contemporary vs historical factors in shaping its genetic variation. Genetic diversity was low in all investigated populations (mean HE=0.125), and negatively correlated with latitude. Overall population differentiation was moderate (ΦST=0.109, P<0.001), and Bayesian analysis of population structure revealed two major latitudinal clusters (I: 'Pacific North'+'Central Highland'; II: 'Pacific South'), along with a genetic cline between I and II. Population-based cluster analyses indicated a strong pattern of 'isolation by distance' as confirmed by Mantel's test. Our results suggest that (1) the low genetic diversity of these populations reflects biogeographic/population history (colonisation from South America, Pleistocene range contractions) rather than recent human impact; whereas (2) the underlying process of their isolation by distance pattern, which is best explained by 'isolation by dispersal limitation', implies contemporary gene flow between neighbouring populations as likely facilitated by the species' efficient seed dispersal capacity. Overall, these results underscore that even tree species from highly decimated forest regions may be genetically resilient to habitat fragmentation due to species-typical dispersal characteristics, the necessity of broad-scale measures for their conservation notwithstanding.
Data from: Structure of the epiphyte community in a tropical montane forest in SW China
Vascular epiphytes are an understudied and particularly important component of tropical forest ecosystems. However, owing to the difficulties of access, little is known about the properties of epiphyte-host tree communities and the factors structuring them, especially in Asia. We investigated factors structuring the vascular epiphyte-host community and its network properties in a tropical montane forest in Xishuangbanna, SW China. Vascular epiphytes were surveyed in six plots located in mature forests. Six host and four micro-site environmental factors were investigated. Epiphyte diversity was strongly correlated with host size (DBH, diameter at breast height), while within hosts the highest epiphyte diversity was in the middle canopy and epiphyte diversity was significantly higher in sites with canopy soil or a moss mat than on bare bark. DBH, elevation and stem height explained 22% of the total variation in the epiphyte species assemblage among hosts, and DBH was the most important factor which alone explained 6% of the variation. Within hosts, 51% of the variation in epiphyte assemblage composition was explained by canopy position and substrate, and the most important single factor was substrate which accounted for 16% of the variation. Analysis of network properties indicated that the epiphyte host community was highly nested, with a low level of epiphyte specialization, and an almost even interaction strength between epiphytes and host trees. Together, these results indicate that large trees harbor a substantial proportion of the epiphyte community in this forest.
Data from: Geographic population structure of the African malaria vector Anopheles gambiae suggests a role for the forest-savannah biome transition as a barrier to gene flow
The primary Afrotropical malaria mosquito vector Anopheles gambiae sensu stricto has a complex population structure. In western Africa, this species is split into two molecular forms and displays local and regional variation in chromosomal arrangements and behaviours. To investigate patterns of macro-geographic population substructure, 25 An. gambiae samples from 12 African countries were genotyped at 13 microsatellite loci. This analysis detected the presence of additional population structuring, with the M-form being subdivided into distinct west, central and southern African genetic clusters. These clusters are coincident with the central African rainforest belt and northern and southern savannah biomes, which suggests restrictions to gene flow associated with the transition between these biomes. By contrast geographically patterned population substructure appears much weaker within the S-form.
Data from: Hydrological niche segregation defines forest structure and drought tolerance strategies in a seasonal Amazon forest
1) Understanding if and how trees coordinate rooting depth and aboveground hydraulic traits to define drought-resistance strategies in seasonal Amazon forests is a major gap to model parametrization aimed at predicting the effects of climate change in these ecosystems. 2) We assessed the rooting depth of 12 dominant tree species (representing ~ 42% of the forest basal area) in a seasonal Amazon forest, using the stable isotope ratios (δ18O and δ²H) of water collected from tree xylem and soils from a range of depths. We took advantage of a major ENSO-related drought in 2015/2016 that caused substantial evaporative isotope enrichment on soil. We measured the minimum dry-season leaf water potential both in a normal year (2014; Ψnon-ENSO) and in an extreme drought year (2015; ΨENSO). Furthermore, we measured xylem hydraulic traits that indicate the range of water potentials that trees tolerate without risking hydraulic failure (P50 and P88). 3) We demonstrate that coexisting trees are largely segregated along a single hydrological niche axis defined by root depth differences, access to light, and tolerance of low water potential. These differences in rooting depth were strongly related to tree size; diameter at breast height (DBH) explained 72% of the variation in the δ18Oxylem. Additionally, δ18Oxylem explained 49% of the variation in P50 and 70% of P88, with higher tolerance of low water potential in shallow-rooted species, while δ18O of xylem water explained 47% and 77% of the variation of minimum Ψnon-ENSO and ΨENSO. 4) We propose a new formulation to estimate an effective functional rooting depth, i.e., the likely soil depth from which roots can sustain water uptake for physiological functions, using DBH as predictor of root depth at this site. Based on these estimates, we conclude that a number of families, genera and species are restricted to drawing water from shallow to deep soil in a large area of the Tapajós forest. 5) Our results support the theory of hydrological niche segregation and its underlying trade-off related to drought resistance, which also affect the dominance structure of trees in this seasonal eastern Amazon forest.
Data from: Forest structure provides the income for reproductive success in a southern population of Canada lynx
Understanding intrinsic and extrinsic drivers of reproductive success is central to advancing animal ecology and characterizing critical habitat. Unfortunately, much of the work examining drivers of reproductive success is biased toward particular groups of organisms (e.g., colonial birds, large herbivores, capital breeders). Long-lived mammalian carnivores that are of conservation concern, solitary, and territorial present an excellent situation to examine intrinsic and extrinsic drivers of reproductive success, yet they have received little attention. Here, we used a Canada lynx (Lynx canadensis) dataset, from the southern periphery of their range, to determine if reproductive success in a solitary carnivore was consistent with capital or income breeding. We radio-marked and monitored 36 female Canada lynx for 98 lynx years. We evaluated how maternal characteristics and indices of food supply (via forest structure) in core areas influenced variation in body condition and reproductive success. We characterized body condition as mass/length and reproductive success as whether a female produced a litter of kittens for a given breeding season. Consistent with life-history theory, we documented a positive effect of maternal age on body condition and reproductive success. In contrast to predictions of capital breeding, we observed no effect of pre-pregnancy body condition on reproductive success in Canada lynx. However, we demonstrated statistical effects of forest structure on reproductive success in Canada lynx, consistent with predictions of income breeding. The forest characteristics that defined high success included (1) abundant and connected mature forest and (2) intermediate amounts of small-diameter regenerating forest. These attributes are consistent with providing abundant, temporally stable, and accessible prey resources (i.e., snowshoe hares; Lepus americanus) for lynx and reinforce the bottom-up mechanisms influencing Canada lynx populations. Collectively, our results suggest that lynx on the southern range periphery exhibit an income breeding strategy and that forest structure supplies the income important for successful reproduction. More broadly, our insights advance the understanding of carnivore ecology and serve as an important example on integrating long-term field studies with ecological theory to advance landscape management.
Data from: What shapes cerambycid beetle communities in a tropical forest mosaic? Assessing the effects of host tree identity, forest structure, and vertical stratification
Due to anthropogenic activities, tropical rain forests face many challenges in sustaining biodiversity and maintaining global climates. This study explores how forest successional stage, tree composition, and stratum affect communities of saproxylic cerambycid beetles—concealed feeders that play important roles in forest nutrient cycling. Forty trees in five families (Fabaceae, Lecythidaceae, Malvaceae, Moraceae, and Sapotaceae) were sampled in a mosaic of old-growth and secondary forest on the Osa Peninsula, Costa Rica. Bait branches yielded 3549 cerambycid individuals in 49 species. Species richness was almost identical in old-growth and secondary forest, and both yielded specialists, but abundance was higher in old-growth forest. Overall community structure was most strongly influenced by host plant species; within most plant families it was also impacted by forest successional status. Moraceae was the exception, presumably because the focal tree species was abundant in both old-growth and secondary forest. Several host and old-growth specialist species reached high densities within patches of old-growth forest, but seldom colonized apparently suitable trees within secondary forest. This suggests that even small areas of old-growth forest can act as refuges, but that secondary forest may act as a barrier to dispersal. The vulnerability of specialized saproxylic insects to land use change will be linked to the ability of their preferred hosts to disperse to and persist in successional habitats; rearing studies may provide the most accurate method to monitor community changes over time.
Data from: Stepping-stone expansion and habitat loss explain a peculiar genetic structure and distribution of a forest insect
It is challenging to unravel the history of organisms with highly scattered populations. Such species may have fragmented distributions because extant populations are remnants of a previously more continuous range, or because the species has narrow habitat requirements in combination with good dispersal capacity (naturally or vector borne). The northern pine processionary moth Thaumetopoea pinivora has a scattered distribution with fragmented populations in two separate regions, northern and south-western Europe. The aims of this study were to explore the glacial and postglacial history of T. pinivora, and add to the understanding of its current distribution and level of contemporary gene flow. We surveyed published records of its occurrence and analysed individuals from a representative subset of populations across the range. A 633 bp long fragment of the mtDNA COI gene was sequenced and nine polymorphic microsatellite loci were genotyped. Only nine nucleotide sites were polymorphic in the COI gene and 90% of the individuals from across its whole range shared the same haplotype. The microsatellite diversity gradually declined towards the north, and unique alleles were found in only three of the northern and three of southern sites. Genetic structuring did not indicate complete isolation among regions, but an increase of genetic isolation by geographic distance. Approximate Bayesian model choice suggested recent divergence during the postglacial period, but glacial refugia remain unidentified. The progressive reduction of suitable habitats is suggested to explain the genetic structure of the populations and we suggest that T. pinivora is a cold-tolerant relict species, with situation-dependent dispersal.
Data from: Spatial structure of above-ground biomass limits accuracy of carbon mapping in rainforest but large scale forest inventories can help to overcome
Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.
Data from: Comparing forest structure and biodiversity on private and public land: secondary tropical dry forests in Costa Rica
Secondary forests constitute a substantial proportion of tropical forestlands. These forests occur on both public and private lands and different underlying environmental variables and management regimes may affect post‐abandonment successional processes and resultant forest structure and biodiversity. We examined whether differences in ownership led to differences in forest structure, tree diversity, and tree species composition across a gradient of soil fertility and forest age. We collected soil samples and surveyed all trees in 82 public and 66 private 0.1‐ha forest plots arrayed across forest age and soil gradients in Guanacaste, Costa Rica. We found that soil fertility appeared to drive the spatial structure of public vs. private ownership; public conservation lands appeared to be non‐randomly located on areas of lower soil fertility. On private lands, areas of crops/pasture appeared to be non‐randomly located on higher soil fertility areas while forests occupied areas of lower soil fertility. We found that forest structure and tree species diversity did not differ significantly between public and private ownership. However, public and private forests differed in tree species composition: 11 percent were more prevalent in public forest and 7 percent were more prevalent in private forest. Swietenia macrophylla, Cedrela odorata, and Astronium graveolens were more prevalent in public forests likely because public forests provide stronger protection for these highly prized timber species. Guazuma ulmifolia was the most abundant tree in private forests likely because this species is widely consumed and dispersed by cattle. Furthermore, some compositional differences appear to result from soil fertility differences due to non‐random placement of public and private land holdings with respect to soil fertility. Land ownership creates a distinctive species composition signature that is likely the result of differences in soil fertility and management between the ownership types. Both biophysical and social variables should be considered to advance understanding of tropical secondary forest structure and biodiversity.
Data from: Effects of fire regime on the population genetics of natural pine stands, in Genetic structure of forest trees in biodiversity hotspots at different spatial scales (Ph.D. thesis).
The recurrence of wildfires is predicted to increase worldwide due to climate change, resulting in severe impacts on biodiversity and ecosystem functioning. We used simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers to examine the effects of fire regime on genetic diversity, demographic history and fine-scale spatial genetic structure (SGS) of Pinus pinaster and P. halepensis, two conifers with similar adaptations to fire in the eastern Iberian Peninsula. Stands growing under high (HiFi) or low (LoFi) frequency of crown fires had similar levels of genetic diversity and similar demographic history, with bottlenecks detected in all stands in both species. HiFi populations were not genetically depleted, suggesting that adaptations such as a diverse canopy seed bank due to serotinous cones, an early age of first flowering and high gene flow buffer against possible reductions of genetic diversity. Significantly stronger SGS at SNPs in HiFi than LoFi stands of P. halepensis suggested fire-related altered dispersal possibly combined with microenvironmental selection in this fire-sensitive "seeder" species. In contrast, SGS at SNP markers was unrelated to fire regime in P. pinaster. This could be a consequence of more pronounced fire-resistance in this species enabling some adults to survive fire, hence causing a lower dependence on post-fire regeneration. Our results highlight that the impact of fire differs in species with similar life-history traits. Therefore, species-specific studies are needed to understand the role of wildfires for the evolution of future forests
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>
Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists
<p><strong>### Update 03/02/2025: the most up to date version of the processing pipeline presented here, also working on Linux, is available on github: https://github.com/fischer-fjd/GCA/tree/main, and a worked example with open data from the Dutch AHN surveys is available on Zenodo: https://zenodo.org/records/14722001 ###<br></strong></p> <p>This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It accompanies the article "Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists" (accepted in Methods in Ecology and Evolution on 25/08/2024). </p> <p>In the article, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to point cloud degradation (pulse density thinning, large scan angles, loss of higher-order returns) and how uncertainties and biases propagate to commonly used forest structure metrics. In addition, we provide a standardized processing pipeline in R to convert point clouds into CHMs. </p> <p>The main data source for this study are ALS point clouds from nine Australian research sites belonging to the Terrestrial Ecosystem Research Network (TERN, 5 km x 5 km extent each). The underlying data can be found here: https://portal.tern.org.au/metadata/TERN/4ff0b4c9-cfa0-4d09-9520-b5402adc583f. For one site (Robson Creek), we also used field data to assess the sensitivity of aboveground biomass estimates to ALS point cloud characteristics. Data are available here: https://portal.tern.org.au/metadata/supersite.174. </p> <p>To characterize climatic/environmental differences between sites, we used climatic data from the CHELSA/BIOCLIM+ climatology 1981-2010 (Brun et al. 2022: Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data, 14(12), 5573–5603. https://doi.org/10.5194/essd-14-5573-2022; Karger et al. 2017: Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4(1), 170122. https://doi.org/10.1038/sdata.2017.122). </p> <p>We note that the enormous size of the full set of manipulated point clouds (original + thinned + individual flightlines: ~400 GB) and the derived raster products (~200 GB) by far exceeds limits on data storage in Zenodo. However, all analyses can be recreated from scratch from the openly available data and the R code in this repository. In addition, we include derived products for the nine study sites that allow to replicate results in the main text without any point cloud processing (CHMs and other rasters across thinned point clouds + summary statistics). </p> <p>The different data layers are:</p> <p><strong>01_rscripts.zip:</strong></p> <ul> <li>contains a sample script to test the processing pipeline (<em>test.processing.R</em>) as well as a collection of helper functions (<em>ALS_processing_helperfunctions_v40.R</em>); the script can be run directly after unzipping the folder, but an installation of LAStools (https://rapidlasso.de) is necessary (path_lastools = "PATH/TO/LASTOOLS/BIN"); we note that the script was developed on Windows PCs, its application with the recent Linux distribution of LAStools has not yet been tested</li> <li>contains the full set of scripts necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data (<em>create.CHMs.R)</em> as well as the overall robustness analysis (<em>analyze.CHMs.R</em>); to replicate the processing of the raw point clouds step by step, .laz files should be downloaded from the TERN repository (cf. citation above) and placed in a "data" folder, with subfolders for each site and with the same naming conventions as in this repository (e.g., "/data/Alice Mulga")</li> </ul> <p><strong>02_reference.zip</strong></p> <ul> <li>contains reference digital surface models (DSMs), canopy height models (CHMs) and digital terrain models (DTMs) for all nine TERN sites, based on the original ALS point clouds</li> <li>note that these reference layers are produced with the "CHMhighest" algorithm, which provides an easily interpretable canopy description as long as pulse densities are high (>= 20 shots per squaremetre)</li> </ul> <p><strong>03_climate.zip</strong></p> <ul> <li>contains site coordinates</li> <li>contains the climate layers from the CHELSA climatology (cf. citation above, only used to evaluate climatic ranges of sites)</li> </ul> <p><strong>04_robson_additional.zip</strong></p> <ul> <li>contains biomass estimates for Robson Creek</li> <li>contains shapefiles for large trees at Robson Creek (only used for visualization purposes)</li> </ul> <p><strong>05_downsampling_pulse_[Site name].zip</strong></p> <ul> <li>[Site name] is a stand-in for the nine TERN sites (e.g., "Alice Mulga.zip", "Credo.zip", etc.)</li> <li>contains the data necessary to reproduce results in the main text of the study, i.e. DSMs, CHMs, and DTMs for all nine TERN sites, and at different pulse density levels (from 16 down to 0.5 laser shots per squaremetre)</li> <li>also contains calculated summary statistics for each site</li> </ul> <p>All zip files should be extracted into the same folder, except for 05_downsampling_pulse_[Site name].zip which should all be moved to a subfolder called "downsampling_pulse".</p>
Data for: The long-term impacts of deer herbivory in determining temperate forest stand and canopy structural complexity
<p>1. Ungulates place immense consumptive pressure on forest vegetation globally, leaving legacies of reduced biodiversity and simplified vegetative structure. However, what remains unresolved is whether browse-induced changes occurring early in succession ultimately manifest themselves in the developed forest canopy. Understanding the development and persistence of these legacies is critical as canopy structure is an important determinant of forest ecosystem functions like carbon sequestration and wildlife habitat.</p> <p>2. We measured how white-tailed deer (Odocoileus virginianus) browse during stand initiation affected canopy structure, tree species richness, diversity, stem density, and basal area on Pennsylvania's Allegheny Plateau using a portable canopy LiDAR system. We capitalized on an historic deer enclosure experiment where forests were subjected to four deer densities (4, 8, 15, and 25 deer/km2) for ten years following stand initiation.</p> <p>3. Deer browsing impacts on the forest canopy are apparent nearly four decades since stand initiation. The highest deer density treatment experienced a significant reduction in tree species diversity, density, and basal area with stands becoming dominated by black cherry (Prunus serotina). Reductions in overstory diversity and tree density resulted in a more open canopy with low leaf area and high horizontal leaf variability. Canopies were tallest at the lowest and highest deer densities.</p> <p><i>4. Synthesis and Applications</i>: Using a portable canopy LiDAR system and a former deer enclosure experiment, we show that high deer browsing pressure during stand initiation can have a decades-long impact on stand and canopy structure. High deer densities led to stands with lower species diversity and tree density, which resulted in canopies that were taller and less dense. Managers should consider the lasting legacy of ungulate herbivory on canopy structure, as canopy structure influences several important management goals, such as forest carbon sequestration, maintenance of diverse understory communities, and creation of wildlife habitat.</p>
Differences in feather structure between urban and forest great tits – constraint or adaptation?
<p>Urbanization is one of the strongest habitat transforming processes today that has resulted in changes in the ecological conditions for wild populations. In birds, the limitation of natural food sources and a warmer microclimate in cities can potentially influence the development and functioning of the plumage that may have important fitness consequences. Despite its potential significance, the plumage structure of urban birds are largely unexplored and it is unclear whether and how they respond to urban ecological processes such as different constraints and selection pressures. In this study, we compared several structural properties of contour, primary and tail feathers between two forest and two urban great tit <em>(Parus major)</em> populations. Our results show that the urban environment affects only a few structural properties of feathers and only in the plumage of first-year birds. We found that both the plumulaceous and the pennaceous parts of their contour feathers are longer and have lower barb density in the urban than in the forest habitat. We also found that the primaries of first-year birds have narrower rachis and higher barbule density in the cities than in the forests, but there were no differences in other wing feather traits and in any tail feather traits between habitats. We did not find differences in the feather structure of urban versus forest adult birds. The habitat differences in first-year birds may indicate nutritional constraints or the effects of the warmer microclimate of the urban environment. These differences seem to disappear completely in adulthood that can be explained by the selective mortality of first-year birds, or by adults being less sensitive than first-year birds to environmental effects during their molt.</p>
Tropical montane forest in South Asia: Composition, structure and dieback in relation to soils and topography
<p>We evaluated the composition, structure and dieback of a montane forest in relation to soils and physiography of an important biogeographic region that has been sparsely studied. Our objectives were to: 1. Describe the forest composition and structure; 2. Assess the current extent of dieback; and 3. Relate tree composition, structure, and dieback proneness to edaphic and physiographic measures. We enumerated all live and dead standing plants ≥ 3 cm diameter at breast height (DBH), in thirty 20×15 m<sup>2</sup> over story plots. We measured all regeneration <u><</u> 1m height in subplots, sampled soils and measures of physiography, and visually rated the proportion of crown die-back, and recorded standing dead trees.</p>
Data from: Irregular forest structures originating after fire: an opportunity to promote alternatives to even-aged management in boreal forests
<p><span>1. Even-aged silviculture based on short-rotation clearcuts had severely altered boreal forests. Silvicultural alternatives (e.g., continuous cover or retention forestry) has the potential to restore and protect the habitats and functions of boreal forests. These alternatives are however often restricted to structurally complex old-growth forest, which are particularly threatened by anthropogenic disturbances. Increasing the use of alternatives to even-aged silviculture in early-successional stands could help recruit more structurally complex forests, with characteristics closer to the old-growth. In this article, we therefore evaluate the potential for silvicultural alternatives to even-aged management in boreal forests that burned less than a century ago.</span></p> <p><span>2. We analyzed 1085 field plots in a 243000 km<sup>2</sup> area situated in the boreal forest of eastern Canada. These plots burned 30 to 100 years before the survey and had not been subjected to previous or subsequent anthropogenic disturbance; they hence represent young primary forests. The main patterns of tree diameter distribution variation within the plots were identified using k-means clustering. Stand structure, tree species composition, and environmental variables that most explained the differences among the clusters were identified with a random forest model, and then compared using Kruskal-Wallis and Fisher's exact tests.</span></p> <p><span>3.</span> <span>The majority (>75 %) of the plots presented an irregular structure of stem diameters (i.e., non-normally distributed, with many small diameter trees). The understorey was generally dominated by black spruce (<em>Picea mariana</em> [Mill.] BSP), a shade-tolerant species. Irregular structures were observed in both forests of high and low productivity, implying that different processes (e.g., early regeneration, variable tree growth) can lead to observed early irregular structure. Regular structures were generally characterized by a higher productivity and abundance in hardwood species compared to the irregular structures. </span></p> <p><span>4. <em>Synthesis and application</em>: Many boreal forests of eastern Canada progress towards an irregular structure in the decades following the last stand-replacing fire. A substantial part of these early-successional forests may be suitable for alternatives to even-aged silviculture that better maintains habitats and functions of preindustrial boreal forests. </span></p>
NEON forest and woodland plots: diversity, structure and climate
<p>We combined climate variables with field measurements and airborne lidar from all forest and woodland plots in the National Ecological Observatory Network (NEON) to characterize the role of climate in constraining biodiversity – forest structure relationships across the United States. </p>
Data for: The formation of "mega‐flocks" depends on vegetation structure in montane coniferous forests of Taiwan
<p>A mixed-species bird flock is a social assemblage where two or more bird species are moving together while foraging and might benefit from increased foraging efficiency and antipredator vigilance. A "mega-flock," which includes flocking species from different vegetation layers, often exhibits high species diversity. Mechanisms for the formation of mega-flocks have not yet been explored. In this study, we evaluated the influence of vegetation structure and bird species diversity/richness in driving the occurrence of mega-flocks. We investigated the composition of mixed-species flocks, local bird communities, and vegetation structure in five vegetation types of two high-elevation sites in central Taiwan. (For more details, please see the paper which has been published in Ecology and Evolution, entitled "The formation of "mega-flocks" depends on vegetation structure in montane coniferous forests of Taiwan." Doi: https://doi.org/10.1002/ece3.8608)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.