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174 results for “Biodiversity assessment”
Data from: Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system
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Data from: Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA
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Data from: Biodiversity assessment among two Nebraska prairies: a comparison between traditional and phylogenetic diversity indices
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Data from: Spectral diversity area relationships for assessing biodiversity in a wildland-agriculture matrix
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Data from: Optimal taxonomic groups for biodiversity assessment: a meta-analytic approach
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Data from: Global biodiversity assessment and hyper-cryptic species complexes: more than one species of elephant in the room?
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Molecular ecological network analyses: An effective conservation tool for the assessment of biodiversity, trophic interactions, and community structure
<p>Global biodiversity is threatened by the anthropogenic restructuring of animal communities, which rewires species interaction networks in real-time as individuals are extirpated or introduced. Conservation science and adaptive ecosystem management demands more rapid, quantitative, and non-invasive technologies for robustly capturing changing biodiversity and quantifying species interactions. Here we develop molecular ecological network analyses (MENA) as an ecosystem assessment tool to address these needs. To construct the ecological network, we used environmental DNA from feces to identify the plant and mammal diet of two carnivores: puma (<i>Puma concolor</i>) and bobcat (<i>Lynx rufus</i>); two omnivores: coyote (<i>Canis latrans</i>) and gray fox (<i>Urocyon cinereoargenteus</i>); and two herbivores: black-tailed deer (<i>Odocoileus hemionus</i>) and black-tailed jackrabbit (<i>Lepus californicus)</i> in a well-studied Californian reserve<i>. </i>To evaluate MENA as a comprehensive biodiversity tool, we applied our framework to identify the structure of the network, patterns of trophic interactions, key species, and to assess its utility in capturing the biodiversity of the area. The high dietary taxonomic resolution enabled the assessment of species diversity, niche breadth and overlap. The network analysis revealed a dense ecological network with a high diversity of weakly connected species and a community that is highly modular and non-nested. The significant prevalence of tri-trophic chain and exploitative competition patterns indicates (i) the removal or reintroduction of a top predator would trigger a trophic cascade within this community, directly affecting their prey and indirectly the plant communities, and (ii) the potential impact of indirect effects between two predators that consume the same prey. These results suggest that the recent resurgence of puma in the study area may impact the herbaceous and woody vegetation and the population size of other predators. This effect of fluctuating predator populations and plant communities could be predicted through MENA's fine-scale assessment of the diet selection and the identified keystone species. Although just using a subset of species, MENA more rapidly, accurately, and effectively captured the broader biodiversity of the area in comparison to other methodologies. MENA reconstructed and unveiled the hidden complexity in trophic structure and interaction networks within the community, providing a promising toolkit for biodiversity and ecosystem management.</p>
FIGURE 2 in A benefit to biodiversity assessment: An Indian survey demonstrates that Cheiriphotis geniculata is a misidentification of the valid species Photis geniculata Barnard, 1935 (Crustacea: Amphipoda)
FIGURE 2. Sampling site in Valanthakad island, Vembanad lake, Kerala, India.
FIGURE 7. Photis geniculata a in A benefit to biodiversity assessment: An Indian survey demonstrates that Cheiriphotis geniculata is a misidentification of the valid species Photis geniculata Barnard, 1935 (Crustacea: Amphipoda)
FIGURE 7. Photis geniculata a) gnathopod 1; b) gnathopod 2 (♂); c) gnathopod 2 (♀).
Supplementary material 3 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic bacteria assessment for Zambia
Supplementary material 4 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic protist assessment for Zambia
Supplementary material 2 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Guidelines for scoring species
Supplementary material 5 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Assessment for vector species
Supplementary material 1 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
All data from horizon scanning for Zambia
Assessing spatial patterns of phylogenetic diversity of Mexican mammals for biodiversity conservation
<p>Phylogenetic diversity is a biodiversity measurement that describes the amount of evolutionary history contained by the taxonomic units in a region. It has proven to be an important metric for determining conservation priorities. Mammalian phylogenetic diversity patterns have been suggested as potential surrogates of biodiversity for establishing priority areas for conservation. This study aims to identify areas of high mammalian phylogenetic diversity in Mexico, a megadiverse country with high mammalian richness, and to assess how well protected areas encompass the phylogenetic diversity. IUCN distribution data for 479 Mexican mammals were used to estimate species richness. Data for the molecular markers cytB, 12S and COI, were gathered from GenBank and from laboratory extractions for reconstructing a maximum-likelihood phylogenetic tree. Spatial patterns in phylogenetic diversity were estimated by summing the branch lengths of the phylogenetic tree representing species presence across grid cells. The results were compared with the distribution of protected areas in Mexico in order to assess if phylogenetic diversity is effectively conserved. The southeastern part of Mexico was found to be the most diverse. The breadth of the phylogenetic tree was well represented within the protected areas. Beta-diversity analyses showed that the species composition between protected and unprotected areas is very similar. Protected areas group based on the phylogenetic composition of mammal species into three clusters corresponding to the Nearctic, Neotropical, and Mexican Transition Zone biogeographical regions, which suggests that protected areas could be managed based on these clusters.</p>
Figure 5 from: Thompson AC, Bazelet CS, Naskrecki P, Samways MJ (2017) Adapting the Dragonfly Biotic Index to a katydid (Tettigoniidae) rapid assessment technique: case study of a biodiversity hotspot, the Cape Floristic Region, South Africa. Journal of Orthoptera Research 26: 63-71. https://doi.org/10.3897/jor.26.14552
Figure 5 - Map of ecosystem threat statuses and the average KBI scores (i.e. KBI/Site) of each ecosystem.
Figure 3 from: Thompson AC, Bazelet CS, Naskrecki P, Samways MJ (2017) Adapting the Dragonfly Biotic Index to a katydid (Tettigoniidae) rapid assessment technique: case study of a biodiversity hotspot, the Cape Floristic Region, South Africa. Journal of Orthoptera Research 26: 63-71. https://doi.org/10.3897/jor.26.14552
Figure 3 - Distribution of South African and Cape Floristic Region katydid species among Tettigoniidae subfamilies.
Figure 2 from: Thompson AC, Bazelet CS, Naskrecki P, Samways MJ (2017) Adapting the Dragonfly Biotic Index to a katydid (Tettigoniidae) rapid assessment technique: case study of a biodiversity hotspot, the Cape Floristic Region, South Africa. Journal of Orthoptera Research 26: 63-71. https://doi.org/10.3897/jor.26.14552
Figure 2 - Composition of South African (A, C, E) and Cape Floristic Region (B, D, F) katydid assemblages as characterised by their distribution (A, B), mobility (C, D), and trophic level (E, F) relative to their IUCN threat status.
Figure 1 from: Thompson AC, Bazelet CS, Naskrecki P, Samways MJ (2017) Adapting the Dragonfly Biotic Index to a katydid (Tettigoniidae) rapid assessment technique: case study of a biodiversity hotspot, the Cape Floristic Region, South Africa. Journal of Orthoptera Research 26: 63-71. https://doi.org/10.3897/jor.26.14552
Figure 1 - Proportion of South African (A, C, E, G) and Cape Floristic Region (B, D, F, H) katydid assemblages as characterised by the KBI assessment criteria (Threat Status, Distribution, Trophic level and Mobility).
Figure 4 from: Thompson AC, Bazelet CS, Naskrecki P, Samways MJ (2017) Adapting the Dragonfly Biotic Index to a katydid (Tettigoniidae) rapid assessment technique: case study of a biodiversity hotspot, the Cape Floristic Region, South Africa. Journal of Orthoptera Research 26: 63-71. https://doi.org/10.3897/jor.26.14552
Figure 4 - Distribution of Katydid Biotic Index (KBI) among ecosystem threat statuses (mean ± s.e.).
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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.