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FIGURES 7–12 in Three new species of the genus Trachelas (Araneae: Trachelidae) from an oak forest inside the Mesoamerican biodiversity hotspot in Mexico
FIGURES 7–12. Trachelas crassus sp. n., female. 7 Prosoma, anterior view; 8 Habitus, dorsal view; 9 Epigynum, ventral view; 10 Habitus, lateral view; 11 Epigynum, dorsal view; 12 Habitus, ventral view. Scale bars: 7, 9 = 0.5 mm; 8, 10, 12 = 1.0 mm; 11 = 0.2 mm.
FIGURES 13–17 in Three new species of the genus Trachelas (Araneae: Trachelidae) from an oak forest inside the Mesoamerican biodiversity hotspot in Mexico
FIGURES 13–17. Trachelas crassus sp. n., genitalia. 13 Epigynum, cleared ventral view; 14 Same, dorsal view; 15 Palp, prolateral view; 16 Same, retrolateral view; 17 Same, ventral view. Scale bars: 13, 14 = 0.1 mm; 15–17 = 0.3 mm.
FIGURES 1–6 in Three new species of the genus Trachelas (Araneae: Trachelidae) from an oak forest inside the Mesoamerican biodiversity hotspot in Mexico
FIGURES 1–6. Trachelas crassus sp. n., male. 1 Prosoma, anterior view; 2 Habitus, dorsal view; 3 Palp, retrolateral view; 4 Habitus, lateral view; 5 Palp, ventral view; 6 Habitus, ventral view. Scale bars: 1, 2, 4, 6 = 1.0 mm; 3, 5 = 0.5 mm.
FIGURE 52 in Three new species of the genus Trachelas (Araneae: Trachelidae) from an oak forest inside the Mesoamerican biodiversity hotspot in Mexico
FIGURE 52. Map of the locality where T. crassus, T. ductonuda and T. odoreus were collected near Pico de Orizaba (Veracruz) marked with an X. Scale bar = 200 km.
FIGURES 47–51 in Three new species of the genus Trachelas (Araneae: Trachelidae) from an oak forest inside the Mesoamerican biodiversity hotspot in Mexico
FIGURES 47–51. Trachelas odoreus sp. n., genitalia. 47 Epigynum, cleared ventral view; 48 Same, dorsal view; 49 Palp, prolateral view; 50 Same, retrolateral view; 51 Same, ventral view. Scale bars: 47, 48 = 0.1 mm; 49–51 = 0.2 mm.
Figure 2. from: Tree Diversity and Dynamics of the Forest of Seu Nico, Viçosa, Minas Gerais, Brazil - Biodiversity Data Journal 3: e5425 (31 July 2015) https://doi.org/10.3897/BDJ.3.e5425
Figure 2. - Croqui of the 100 x 100 m FSN Dynamics Plot (Suppl. material 1).
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>
Restoration priorities for Caatinga dry forests: landscape resilience, connectivity and biodiversity value
<p>1. Restoration actions can halt biodiversity loss and rescue its services. However, in order to be effective, priority areas for restoration should be chosen based on objective large-scale restoration planning. Here, a multi-criteria graph theory (GT) framework is proposed to indicate priority areas for active restoration, based on landscape resilience, landscape connectivity, and biodiversity conservation value, focusing on threatened endemic plant species.</p> <p>2. We applied this GT framework to 10,406 catchment basins of the Brazilian Caatinga, the largest seasonally dry tropical forest of the New World. Vegetation cover and within-catchment connectivity were used to identify catchments of intermediate landscape resilience, which in principle offer more effective opportunities for restoration. Then, such catchments were independently classified into (i) three classes according their value for between-catchment connectivity and (ii) three classes of biodiversity conservation value, based on richness of threatened, endemic plant species. By the integration of landscape resilience, landscape connectivity and biodiversity conservation values, three priority classes for restoration were generated.</p> <p>3. The multi-criteria framework generated several restoration priority cut-offs. Prioritization based on landscape resilience selected 36% of the Caatinga catchments as high priority for restoration. By independently adding landscape connectivity and biodiversity conservation value, only 12% and 3% of the catchments, respectively, were considered high priority. By combining all three criteria, 9% of the catchments were selected as high priority and less than 1% as top priority for restoration.</p> <p>4. Synthesis and applications: The multicriteria GT framework for restoration prioritization, which maximizes the effectiveness of restoration actions, landscape connectivity for climate change adaptation and conservation of threatened species, can be applied worldwide under different budged limitations and spatial scales, being useful for private, state, and federal initiatives.</p>
Figure 4 in Faunistic analysis of longhorn beetles (Cerambycidae: Coleoptera) in Cerrado and Atlantic Forest areas: biodiversity hotspots of Brazil
Figure 4. Diversity estimates based on Hill's series (1973), for the phytophysiognomies freshwater swamp forests (MDB), cerradão (CER) and semidecidual seasonal forest (FES), present in the Municipal Botanical Garden of Bauru, SP (p <0.05). q = 0: species richness; q = 1: estimate of abundant species; q = 2: estimate of dominant species.
Figure 6 in Faunistic analysis of longhorn beetles (Cerambycidae: Coleoptera) in Cerrado and Atlantic Forest areas: biodiversity hotspots of Brazil
Figure 6. Cumulative richness of Cerambycidae species collected in the phytophysiognomies freshwater swamp forests (MDB), cerradão (CER) and semideciduous seasonal forest (FES). (a) Cumulative richness of MDB; (b) cumulative richness of CER; (c) cumulative richness of FES; (d) total cumulative richness.
Figure 3 in Faunistic analysis of longhorn beetles (Cerambycidae: Coleoptera) in Cerrado and Atlantic Forest areas: biodiversity hotspots of Brazil
Figure 3. Correlation between abundance of Cerambycidae and climatic variables in the Municipal Botanical Garden of Bauru. (a) Correlation between abundance and average temperature; (b) correlation between abundance and accumulated precipitation. Source: UNESP Bauru Meteorological Institute (2021).
Figure 2 in Faunistic analysis of longhorn beetles (Cerambycidae: Coleoptera) in Cerrado and Atlantic Forest areas: biodiversity hotspots of Brazil
Figure 2. Seasonality for Cerambycidae species in the phytophysiognomies freshwater swamp forests (MDB), cerradão (CER) and semidecidual seasonal forest (FES), present in the Municipal Botanical Garden of Bauru, from October 2019 to September 2020.
Figure 1 in Faunistic analysis of longhorn beetles (Cerambycidae: Coleoptera) in Cerrado and Atlantic Forest areas: biodiversity hotspots of Brazil
Figure 1. New records of Cerambycidae species for São Paulo state, Brazil. (a) Eburodacrys elegantula Gounelle, 1909; (b) Compsibidion maronicum (Thomson, 1867); (c) Macroeme sobrina (Gounelle, 1909); (d) Acorethra aureofasciata Gounelle, 1911; (e) Eclipta nigriventris (Melzer, 1934); (f) Eclipta seminigra (Gounelle, 1911); (g) Odontocera albicans (Klug, 1825); (h) Ceralocyna militaris (Gounelle, 1911); (i) Aegoschema migueli Monné and Mermudes, 2007. Scale bar, 5mm.
Figure 5 in Faunistic analysis of longhorn beetles (Cerambycidae: Coleoptera) in Cerrado and Atlantic Forest areas: biodiversity hotspots of Brazil
Figure 5. Cluster similarity analysis for the phytophysiognomies freshwater swamp forests (MDB), cerradão (CER) and semideciduous seasonal forest (FES), present in the Municipal Botanical Garden of Bauru, based on species composition.
Supplementary material 1 from: Jacobs JM, Bergeron JAC (2017) Addition of a new Quedius Steph. (Coleoptera, Staphylinidae) species to the biodiversity of Albertan mixedwood forest, Canada. ZooKeys 668: 61-68. https://doi.org/10.3897/zookeys.668.12320
Locality table for the holotype and paratype specimens of Quedius spencei Jacobs & Bergeron, sp. n. :
Biodiversity Habitat Index forest indicator
<p>This collection contains 30-arcsecond grid-resolution results for the Biodiversity Habitat Index associated with the publication 'Ecosystem extent is a necessary but not sufficient indicator of the state of global forest biodiversity.' </p>
Data from: Impact of habitat fragmentation on the spatial structure of the Eastern Arc Forests in East Africa: implications for biodiversity conservation
The Eastern Arc Mountains in Tanzania and Kenya are one of 35 global biodiversity hotspots. The Eastern Arc forests are, as are many other tropical biodiversity hotspots, highly fragmented. Understanding the impact of habitat fragmentation (i.e., habitat loss and subdivision) on the spatial structure of the Eastern Arc forests is important because forest spatial structure highly influences species richness, persistence, and extinction debt. Here we examine the impact of habitat fragmentation on the spatial structure of the Eastern Arc forests at a patch scale using very high resolution aerial imagery having a spatial resolution of 0.5–1.5 m. Forest area across the 13 Eastern Arc Mountains is 405,852 ha and is distributed into 311 fragments ≥ 10 ha in size with a median fragment size of 84 ha. The 18 largest forest fragments in the Eastern Arc Mountains contain greater than three-quarters of total forest area. Average fragment isolation, as assessed by median distance to nearest fragment and median distance to the nearest larger fragment, is 867 and 1533 m, respectively. Of total forest area, 14% is < 100 m from the forest edge and 33% is < 300 m from the forest edge. Establishing forested linkages among the largest and closest forest fragments through forest regeneration and protection of secondary regenerating forest as well as providing protected area status to the remaining non-protected forest including unprotected smaller forest fragments are important to enhancing the long-term persistence of many plant and animal species here.
Data from: Forest plant community as a driver of soil biodiversity: experimental evidence from collembolan assemblages through large-scale and long-term removal of oak canopy trees Quercus petraea
Plant–soil interactions are increasingly recognized to play a major role in terrestrial ecosystems functioning. However, few studies to date have focused on slow dynamic ecosystems such as forests. As they are vertically stratified by multiple vegetation strata, canopy tree removal by thinning operations could alter forest plant community through tree canopy opening. Very little is known about cascading effects on soil biodiversity. We conducted a large-scale, multi-site assessment of collembolan assemblage response to long-term canopy tree removal in sessile oak Quercus petraea temperate forests. A total of 33 experimental plots were studied covering a large gradient of canopy tree basal area, stand age and local abiotic contexts. Collembolan abundance strongly declined with canopy tree removal in early forest successional stage and this was mediated by negative effect of understory plant community composition changes, i.e. shift from moss and forb to tree seedling, fern, shrub and grass species. Negative effect of this composition shift on collembolan species richness was largely offset by positive effect of the increase in understory plant species richness. This gives support to both the plant mass-ratio and functional diversity hypotheses. Collembolan functional groups had contrasting response patterns, which were mediated by different ecological factors. Epedaphic (r-strategist) abundance and species richness increased with canopy tree removal in relation with the increase in understory plant species richness. In contrast, euedaphic (K-strategist) abundance and species richness declined with canopy tree removal in early forest successional stage in relation with changes in understory plant community composition and species richness, as well as microclimatic conditions. Overall, our study provides experimental evidence that forest plant community can be a strong driver of collembolan assemblages. It also emphasizes the role of trees as foundation species of forest ecosystems that can shape soil biodiversity through their regulation of understory plant community and ecosystem abiotic conditions.
Data and R code from: Fire-induced loss of the world's most biodiverse forests in Latin America
<p>Fire plays a dominant role in deforestation, particularly in the tropics, but the relative extent of transformations and influence of fire frequency on eventual forest loss remain unclear. Here we analyze the frequency of fire and its influence on post-fire forest trajectories between 2001-2018. We account for ~1.1% of Latin American forests burnt in 2002-2003 (8,465,850 ha). Although 40.1% of forests (3,393,250 ha) burned only once, by 2018~48% of the evergreen forests converted to other, primarily grass-dominated uses. While greater fire frequency yielded more transformation, our results reveal the staggering impact of even a single fire. Increasing fire frequency imposes greater risks of irreversible forest loss, transforming forests into ecosystems increasingly vulnerable to disturbance and degradation. Reversing this trend is indispensable to both mitigate and adapt to climate change globally. As climate change transforms fire regimes across the region, key actions are needed to conserve Latin American forests.</p>
Carbon-biodiversity relationships in a highly diverse subtropical forest
<p><span>Carbon-focused climate mitigation strategies are becoming increasingly important in forests. However, with ongoing biodiversity declines, we require better knowledge of how much such strategies account for biodiversity. We particularly lack information across multiple trophic levels and on established forests, where the interplay between carbon stocks, stand age and tree diversity might influence carbon–biodiversity relationships. Using a large dataset (>4,000 heterotrophic species of 23 taxonomic groups) from secondary, subtropical forests, we tested how multitrophic diversity and diversity within trophic groups relate to aboveground, belowground, and total carbon stocks at different levels of tree species richness and stand age. Our study revealed that aboveground carbon, the key component of climate-based management, was largely unrelated to multitrophic diversity. By contrast, total carbon stocks—i.e. including belowground carbon—emerged as a significant predictor of multitrophic diversity. Relationships were non-linear and strongest for lower trophic levels, but non-significant for higher trophic level diversity. Tree species richness and stand age moderated these relationships, suggesting long-term regeneration of forests may be particularly effective in reconciling carbon and biodiversity targets. Our findings highlight that the biodiversity benefits of climate-oriented management need to be evaluated carefully, and only maximizing aboveground carbon may fail to account for biodiversity conservation requirements.</span></p>
ScienceDex guides
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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.