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129 results for “Biodiversity change”
Scale dependence and mechanisms of grazing-induced biodiversity changes depend on herbivore type in semiarid grasslands
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Data from: Reviewing the Great American Biotic Interchange: Climate change as a trigger for biodiversity dispersal
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Geographical trends of soil-associated biodiversity changes due to tree plantations in South America: biome and climate constraints revealed through meta-analysis
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Data from: Multiple facets of biodiversity are threatened by mining-induced land-use change in the Brazilian Amazon
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Up in the air: threats to Afromontane biodiversity from climate change and habitat loss revealed by genetic monitoring of the Ethiopian Highlands bat
<p>Whilst climate change is recognised as a major future threat to biodiversity, most species are currently threatened by extensive human-induced habitat loss, fragmentation and degradation. Tropical high altitude alpine and montane forest ecosystems and their biodiversity are particularly sensitive to temperature increases under climate change, but they are also subject to accelerated pressures from land conversion and degradation due to a growing human population. We studied the combined effects of anthropogenic land-use change, past and future climate changes and mountain range isolation on the endemic Ethiopian Highlands long-eared bat, <i>Plecotus balensis</i>, an understudied bat that is restricted to the remnant natural high altitude Afroalpine and Afromontane habitats. We integrated ecological niche modelling, landscape genetics and model-based inference to assess the genetic, geographic and demographic impacts of past and recent environmental changes. We show that mountain range isolation and historic climates shaped population structure and patterns of genetic variation, but recent anthropogenic land-use change and habitat degradation are associated with a severe population decline and loss of genetic diversity. Models predict that the suitable niche of this bat has been progressively shrinking since the last glaciation period. This study highlights threats to Afroalpine and Afromontane biodiversity, squeezed to higher altitudes under climate change while losing genetic diversity and suffering population declines due to anthropogenic land-use change. We conclude that the conservation of tropical montane biodiversity requires a holistic approach, using genetic, ecological and geographic information to understand the effects of environmental changes across temporal scales and simultaneously addressing the impacts of multiple threats.</p>
Data from: How do habitat amount and habitat fragmentation drive time-delayed responses of biodiversity to land-use change?
<p><span>Land-use change is a root cause of the extinction crisis, but links between habitat change and biodiversity loss are not fully understood. While there is evidence that habitat loss is an important extinction driver, the relevance of habitat fragmentation remains debated. Moreover, while time-delays of biodiversity responses to habitat transformation are well-documented, time-delayed effects have been ignored in the habitat loss vs. fragmentation debate. Here, using a hierarchical Bayesian multi- species occupancy framework, we systematically tested for time-delayed responses of bird and mammal communities to habitat loss and to habitat fragmentation. We focused on the Argentine Chaco, where deforestation has been widespread recently. We used an extensive field dataset on birds and mammals, along with a time series of annual woodland maps from 1985-2016 covering recent and historical habitat transformations. Contemporary habitat amount explained bird and mammal occupancy better than past habitat amount. However, occupancy was affected more by past rather than recent fragmentation, indicating a time-delayed response to fragmentation. Considering past landscape patterns is therefore crucial for understanding current biodiversity patterns. Not accounting for land-use history ignores the possibility of extinction debt and can thus obscure impacts of fragmentation, potentially explaining contrasting findings of habitat loss vs. fragmentation studies.</span></p>
Data from: Anticipating arrival: tackling the national challenges associated with the redistribution of biodiversity driven by climate change
1. The redistribution of species in response to climate change is expected to significantly challenge environmental management and conservation efforts around the globe. To date, we have had restricted understanding of the benefits and risks that species redistribution may pose to individual countries, and a limited appreciation of the variability in current opportunities for developing effective monitoring approaches that build on existing national frameworks. 2. To assess the present level of ecological, economical and societal risks and opportunities associated with new arrivals driven by changes in climatic conditions, we conducted a review of the available information on climate driven changes in animal species (both terrestrial and marine) composition and distribution in the United Kingdom over the past ten years (2008-2018). 3. We found evidence that at least 55 species have colonised new locations in the country due to climate change in past decade, with 22 of them suspected to impact positively or negatively the receiving ecosystems and/or nearby human communities. Ten of these 55 species were identified thanks to social media. 4. Synthesis and applications. Our work identifies pressing monitoring gaps relevant to the management of species on the move and discusses the potential for social media to help address current information needs. It also calls for more theoretical work to enable the quick identification of species likely to be problematic (or beneficial) and locations likely to experience significant ecological and societal impacts from biodiversity's redistribution under a changing climate.
Data from: Interactive effects of climate change and biodiversity loss on ecosystem functioning
Climate change and biodiversity loss are expected to simultaneously affect ecosystems, however research on how each driver mediates the effect of the other has been limited in scope. The multiple stressor framework emphasizes non-additive effects, but biodiversity may also buffer the effects of climate change, and climate change may alter which mechanisms underlie biodiversity-function relationships. Here, we performed an experiment using tank bromeliad ecosystems to test the various ways that rainfall changes and litter diversity may jointly determine ecological processes. Litter diversity and rainfall changes interactively affected multiple functions, but how depended on the process measured. High litter diversity buffered the effects of altered rainfall on detritivore communities, evidence of insurance against impacts of climate change. Altered rainfall affected the mechanisms by which litter diversity influenced decomposition, reducing the importance of complementary attributes of species ("complementarity effects"), and resulting in an increasing dependence on the maintenance of specific species ("dominance effects"). Finally, altered rainfall conditions prevented litter diversity from fuelling methanogenesis, because such changes in rainfall reduced microbial activity by 58%. Together, these results demonstrate that the effects of climate change and biodiversity loss on ecosystems cannot be understood in isolation and interactions between these stressors can be multifaceted.
No state change in pelagic fish production and biodiversity during the Eocene-Oligocene Transition
The Eocene-Oligocene (E/O) boundary ~33.9 million years ago, has been described as a state change in the Earth system marked by the permanent glaciation of Antarctica and a proposed increase in oceanic productivity. Here we quantified the response of fish production and biodiversity to this event using microfossil fish teeth (ichthyoliths) in seven deep-sea sediment cores from around the world. Ichthyolith accumulation rate (a proxy for fish biomass production) shows no synchronous trends across the E/O. Ichthyolith accumulation in the Southern Ocean and Pacific Gyre sites is an order of magnitude lower than the equatorial and Atlantic sites, demonstrating that the Southern Ocean was not a highly productive ecosystem for fish before or after the E/O. Further, tooth morphotype diversity and assemblage composition remained stable across the interval, indicating little change in the biodiversity or ecological role of open ocean fish. While the E/O boundary was a major global climate change event, its impact on pelagic fish was relatively muted. Our results support recent findings of whale and krill diversification which suggest that the pelagic ecosystem restructuring commonly attributed to the E/O transition likely occurred much later, in the late Oligocene or Miocene.
Data from: The impacts of climate change and disturbance on spatio-temporal trajectories of biodiversity in a temperate forest landscape
The ongoing changes to climate challenge the conservation of forest biodiversity. Yet, in thermally limited systems, such as temperate forests, not all species groups might be affected negatively. Furthermore, simultaneous changes in the disturbance regime have the potential to mitigate climate-related impacts on forest species. Here, we (i) investigated the potential long-term effect of climate change on biodiversity in a mountain forest landscape, (ii) assessed the effects of different disturbance frequencies, severities and sizes and (iii) identified biodiversity hotspots at the landscape scale to facilitate conservation management. We employed the model iLand to dynamically simulate the tree vegetation on 13 865 ha of the Kalkalpen National Park in Austria over 1000 years, and investigated 36 unique combinations of different disturbance and climate scenarios. We used simulated changes in tree cover and composition as well as projected temperature and precipitation to predict changes in the diversity of Araneae, Carabidae, ground vegetation, Hemiptera, Hymenoptera, Mollusca, saproxylic beetles, Symphyta and Syrphidae, using empirical response functions. Our findings revealed widely varying responses of biodiversity indicators to climate change. Five indicators showed overall negative effects, with Carabidae, saproxylic beetles and tree species diversity projected to decrease by more than 33%. Six indicators responded positively to climate change, with Hymenoptera, Mollusca and Syrphidae diversity projected to increase more than twofold. Disturbances were generally beneficial for the studied indicators of biodiversity. Our results indicated that increasing disturbance frequency and severity have a positive effect on biodiversity, while increasing disturbance size has a moderately negative effect. Spatial hotspots of biodiversity were currently found in low- to mid-elevation areas of the mountainous study landscape, but shifted to higher-elevation zones under changing climate conditions. Synthesis and applications. Our results highlight that intensifying disturbance regimes may alleviate some of the impacts of climate change on forest biodiversity. However, the projected shift in biodiversity hotspots is a challenge for static conservation areas. In this regard, overlapping hotspots under current and expected future conditions highlight priority areas for robust conservation management.
Data from: Space can substitute for time in predicting climate-change effects on biodiversity
"Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.
Data from: Biodiversity ensures plant-pollinator phenological synchrony against climate change
Climate change has the potential to alter the phenological synchrony between interacting mutualists, such as plants and their pollinators. However, high levels of biodiversity might buffer the negative effects of species-specific phenological shifts and maintain synchrony at the community level, as predicted by the biodiversity insurance hypothesis. Here, we explore how biodiversity might enhance and stabilise phenological synchrony between a valuable crop, apple and its native pollinators. We combine 46 years of data on apple flowering phenology with historical records of bee pollinators over the same period. When the key apple pollinators are considered altogether, we found extensive synchrony between bee activity and apple peak bloom due to complementarity among bee species' activity periods, and also a stable trend over time due to differential responses to warming climate among bee species. A simulation model confirms that high biodiversity levels can ensure plant–pollinator phenological synchrony and thus pollination function.
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>
R workspace: A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances
<p>R workspace and associated code used in the the manuscript '<span>A scalable and transferable approach to combining emerging conservation technologies to identify biodiversity change after large disturbances'</span></p>
Supplementary material 3 from: Schmidt K, Walz A (2021) Ecosystem-based adaptation to climate change through residential urban green structures: co-benefits to thermal comfort, biodiversity, carbon storage and social interaction. One Ecosystem 6: e65706. https://doi.org/10.3897/oneeco.6.e65706
Mean values of microclimatic parameters between 9am and 9pm, based on measurements in the four courtyards (CY): CY 1: light green, CY 2: dark green, CY 3: orange, CY 4: red
Supplementary material 6 from: Schmidt K, Walz A (2021) Ecosystem-based adaptation to climate change through residential urban green structures: co-benefits to thermal comfort, biodiversity, carbon storage and social interaction. One Ecosystem 6: e65706. https://doi.org/10.3897/oneeco.6.e65706
Results from tree mapping and allometric equations, indicating above-ground biomass and carbon stocks
Supplementary material 5 from: Schmidt K, Walz A (2021) Ecosystem-based adaptation to climate change through residential urban green structures: co-benefits to thermal comfort, biodiversity, carbon storage and social interaction. One Ecosystem 6: e65706. https://doi.org/10.3897/oneeco.6.e65706
Results from habitat mapping and biodiversity scores. Domin values = 1: < 4% cover with few individuals; 2: < 4% with several individuals; 3: < 4% with many individuals; 4: 4–10%; 5: 11–25%; 6: 26–33%; 7: 34–50%; 8: 51–75%; 9: 76–90%; 10: 91–100% cover
Extended data for "Potential for positive biodiversity outcomes under diet-driven land use change in Great Britain"
<p>Extended data tables for Ferguson-Gow et al 2022 "Potential for positive biodiversity outcomes under diet-driven land use change in Great Britain".</p> <p>Extended data table 1. The 814 species that comprised the final dataset.</p> <p>Extended data table 2. The 24 land cover classes in the land cover dataset.</p>
Replication data for Chen and Khanna. (Global Environmental Change Advances, 2024), "Heterogeneous and Long-Term Effects of a Changing Climate on Bird Biodiversity"
<p>This dataset contains code and data to replicate the results for "Heterogeneous and Long-Term Effects of a Changing Climate on Bird Biodiversity" by Luoye Chen and Madhu Khanna.</p>
Fig. 6 in How might sea level change affect arthropod biodiversity in anchialine caves: a comparison of Remipedia and Atyidae taxa (Arthropoda: Altocrustacea)
Fig. 6 Profile of cave networks in a karst platform with a steep shelf at left and shallow shelf at right. (a) Past glacial maximum (18 kya), (b) current sea level. Double arrows represent vertical connections between horizontal cave passages. Dashed lines represent upper layers of meteoric groundwater and marine layers, which change as sea level rises from (a) -120 m to (b) 0 m, creating variation in connectivity of cave passage. On the edges of continental plates (left side of diagram), passages remain vertically connected throughout the rock profile as sea level falls. On more gradual slopes (right side of diagram), vertical connections are less common, causing breaks in anchialine cave networks as sea level falls
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.