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141 results for “species density”
Data from: eDNA surveys to detect species at very low densities: a case study of European carp eradication in Tasmania, Australia
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Remotely sensed forest understory density and nest predator occurrence interact to predict suitable breeding habitat and the occurrence of a resident boreal bird species
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Data from: Contrasting effects of large density changes on relative testes size in fluctuating populations of sympatric vole species
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Densities of mollusc species at different sites along the Chinese coast
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Data from: Loss of animal seed dispersal increases extinction risk in a tropical tree species due to pervasive negative density dependence across life stages
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Seasonal seabird density by species and seasonal density anomaly for four seabird species off Southern California from from sampling aboard California Cooperative Oceanic Fisheries Investigations (CalCOFI) cruises, 1987 - 2006.
The observational data have been collected as part of long-term fisheries oceanography and seabird monitoring programs designed to inform management of California Current Ecosystem living marine resources. Information collected on seabird distribution and abundance at sea off southern California has been obtained since May 1987 as part of the California Cooperative Oceanic Fisheries Investigation (CalCOFI; http://www.calcofi.net). This program, initiated by R.R. Veit and J.A. McGowan, with funding from the National Science Foundation, has resulted in density (no. birds/km2) estimates of ~60 species of seabirds over the period 1987-present (Veit et al. 1996, 1997, Hyrenbach and Veit 2003, Yen et al. 2006).
Data from: Community-based wildlife management area supports similar mammal species richness and densities compared to a national park
<p>Community-based conservation models have been widely implemented across Africa to improve wildlife conservation and livelihoods of rural communities. In Tanzania, communities can set aside land and formally register it as Wildlife Management Area (WMA), which allows them to generate revenue via consumptive or non-consumptive utilization of wildlife. The key, yet often untested, assumption of this model is that economic benefits accrued from wildlife motivate sustainable management of wildlife. To test the ecological effectiveness (here defined as persistence of wildlife populations) of Burunge Wildlife Management Area (BWMA), we employed a participatory monitoring approach involving WMA personnel. At intermittent intervals between 2011 and 2018, we estimated mammal species richness and population densities of ten mammal species (African elephant, giraffe, buffalo, zebra, wildebeest, waterbuck, warthog, impala, Kirk's dik-dik, and vervet monkey) along line transectss . We compared mammal species accumulation curves and density estimates with those of time-matched road transect surveys conducted in adjacent Tarangire National Park (TNP). Mammal species richness estimates were similar in both areas, yet observed species richness per transect was greater in TNP compared to BWMA. Species-specific density estimates of time-matched surveys were mostly not significantly different between BWMA and TNP, but elephants occasionally reached greater densities in TNP compared to BWMA. In BWMA, elephant, wildebeest, and impala populations showed significant increases from 2011 to 2018. These results suggest that community-based conservation models can support mammal communities and densities that are similar to national park baselines. In light of the ecological success of this case study, we emphasize the need for continued efforts to ensure that the BWMA is effective. This will require adaptive management to counteract potential negative repercussions of wildlife populations on peoples' livelihoods. This study can be used as a model to evaluate the effectiveness of wildlife management areas across Tanzania. Community-based conservation models have been widely implemented across Africa to improve wildlife conservation and livelihoods of rural communities. In Tanzania, communities can set aside land and formally register it as Wildlife Management Area (WMA), which allows them to generate revenue via consumptive or non-consumptive utilization of wildlife. The key, yet often untested, assumption of this model is that economic benefits accrued from wildlife motivate sustainable management of wildlife. To test the ecological effectiveness (here defined as persistence of wildlife populations) of Burunge Wildlife Management Area (BWMA), we employed a participatory monitoring approach involving WMA personnel. At intermittent intervals between 2011 and 2018, we estimated mammal species richness and population densities of ten mammal species (African elephant, giraffe, buffalo, zebra, wildebeest, waterbuck, warthog, impala, Kirk's dik-dik, and vervet monkey) along line transectss . We compared mammal species accumulation curves and density estimates with those of time-matched road transect surveys conducted in adjacent Tarangire National Park (TNP). Mammal species richness estimates were similar in both areas, yet observed species richness per transect was greater in TNP compared to BWMA. Species-specific density estimates of time-matched surveys were mostly not significantly different between BWMA and TNP, but elephants occasionally reached greater densities in TNP compared to BWMA. In BWMA, elephant, wildebeest, and impala populations showed significant increases from 2011 to 2018. These results suggest that community-based conservation models can support mammal communities and densities that are similar to national park baselines. In light of the ecological success of this case study, we emphasize the need for continued efforts to ensure that the BWMA is effective. This will require adaptive management to counteract potential negative repercussions of wildlife populations on peoples' livelihoods. This study can be used as a model to evaluate the effectiveness of wildlife management areas across Tanzania.</p>
Data from: Sexual conflict and ecology: species composition and male density interact to reduce male mating harassment and increase female survival
Sexual conflict is a pervasive evolutionary force that can reduce female fitness. Experimental evolution studies in the laboratory might overestimate the importance of sexual conflict since the ecological conditions in such settings typically include only a single species. Here, we experimentally manipulated conspecific male density (high or low) and species composition (sympatric or allopatric) to investigate how ecological conditions affect female survival in a sexually dimorphic insect, the banded demoiselle (Calopteryx splendens). Female survival was strongly influenced by an interaction between male density and species composition. Specifically, at low conspecific male density, female survival increased in the presence of heterospecific males (C. virgo). Behavioural mating experiments showed that interspecific interference competition reduced conspecific male mating success with large females. These findings suggest that reproductive interference competition between con- and heterospecific males might indirectly facilitate female survival by reducing mating harassment from conspecific males. Hence, interspecific competitors can show contrasting effects on the two sexes thereby influencing sexual conflict dynamics. Our results call for incorporation of more ecological realism in sexual conflict research, particularly how local community context and reproductive interference competition between heterospecific males can affect female fitness.
Data from: Breakdown of phylogenetic signal: a survey of microsatellite densities in 454 shotgun sequences from 154 non model eukaryote species
Microsatellites are ubiquitous in Eukaryotic genomes. A more complete understanding of their origin and spread can be gained from a comparison of their distribution within a phylogenetic context. Although information for model species is accumulating rapidly, it is insufficient due to a lack of species depth, thus intragroup variation is necessarily ignored. As such, apparent differences between groups may be overinflated and generalizations cannot be inferred until an analysis of the variation that exists within groups has been conducted. In this study, we examined microsatellite coverage and motif patterns from 454 shotgun sequences of 154 Eukaryote species from eight distantly related phyla (Cnidaria, Arthropoda, Onychophora, Bryozoa, Mollusca, Echinodermata, Chordata and Streptophyta) to test if a consistent phylogenetic pattern emerges from the microsatellite composition of these species. It is clear from our results that data from model species provide incomplete information regarding the existing microsatellite variability within the Eukaryotes. A very strong heterogeneity of microsatellite composition was found within most phyla, classes and even orders. Autocorrelation analyses indicated that while microsatellite contents of species within clades more recent than 200 Mya tend to be similar, the autocorrelation breaks down and becomes negative or non-significant with increasing divergence time. Therefore, the age of the taxon seems to be a primary factor in degrading the phylogenetic pattern present among related groups. The most recent classes or orders of Chordates still retain the pattern of their common ancestor. However, within older groups, such as classes of Arthropods, the phylogenetic pattern has been scrambled by the long independent evolution of the lineages.
Data and climate variable selection from: Effects of density, species interactions and environmental stochasticity on the dynamics of British bird communities
<p>Our knowledge of the factors affecting species abundances is mainly based on time-series analyses of a few well-studied species at single or few localities, but we know little about whether results from such analyses can be extrapolated to the community level. We apply a Joint Species Distribution Model to long-term time-series data on British bird communities to examine the relative contribution of intra- and interspecific density dependence at different spatial scales, as well as the influence of environmental stochasticity, to spatio-temporal interspecific variation in abundance. Intraspecific density dependence has the major structuring effect on these bird communities. In addition, environmental fluctuations affect spatiotemporal differences in abundance. In contrast, species interactions had a minor impact on variation in abundance. Thus, important drivers of single-species dynamics are also strongly affecting dynamics of communities in time and space.</p>
Camera trap data: Density dependence of daily activity in three ungulate species
<p><span><span><span><span><span><span><span><span><span><span><span>Daily activity in herbivores reflects a balance between finding food and safety. The safety-in-numbers theory predicts that living in higher population densities increases safety, which should affect this balance. High-density populations are thus expected to show a more even distribution of activity – i.e. spread – and higher activity levels across the day. We tested these predictions for three ungulate species; red deer (<i>Cervus elaphus</i>), roe deer (<i>Capreolus capreolus</i>) and wild boar (<i>Sus scrofa</i>). We used camera traps to measure the level and spread of activity across ten forest sites at the Veluwe, the Netherlands, that widely range in ungulate density. Food availability and hunting levels were included as covariates. Daily activity was more evenly distributed when population density was higher for all three species. Both deer species showed relatively more feeding activity in broad daylight and wild boar during dusk. Activity level increased with population density only for wild boar. Food availability and hunting showed no correlation with activity patterns. These findings indicate that ungulate activity is to some degree density dependent. However, while these patterns might result from larger populations feeling safer as the safety-in-numbers theory states, we cannot rule out that they are the outcome of greater intraspecific competition for food, forcing animals to forage during suboptimal times of the day. Overall, this study demonstrates that wild ungulates adjust their activity spread and level based on their population size.</span></span></span></span></span></span></span></span></span></span></span></p>
The effects of host plant species and larval density on immune function in the polyphagous moth Spodoptera littoralis
<p>Immune functions are costly and immune investment is usually dependent on the individual's condition and resource availability. For phytophagous insects, host plant quality has large effects on performance, e.g. growth and survival, and may also affect their immune function. Polyphagous insects often experience a large variation in quality among different host plant species, and their immune investment may thus vary depending on which host plant species they develop on. Larvae of the polyphagous moth <i>Spodoptera littoralis</i>have previously been found to exhibit density-dependent prophylaxis as they invest more in certain immune responses in high population densities. In addition, the immune response of <i>S. littoralis </i>has been shown to depend on nutrient quality in experiments with artificial diet. Here, I studied the effects of natural host plant diet and larval density on a number of immune responses to understand if host plant species affects immune investment in generalist insects, and if the density-dependent prophylaxis could be mediated by host plant species. While host plant species in general did not mediate the density-dependent immune expression, particular host plant species was found to increase larval investment in certain functions of the immune system. Interestingly, these results indicate that different host plants may provide a polyphagous species with protection against different kinds of antagonisms. This insight may contribute to our understanding of the relationship between preference and performance in generalists, as well as having applied consequences for sustainable pest management.</p>
Fig. 1 in Comparison of carabid densities in different cover crop species in north Florida
Fig. 1 Number of Tetracha carolina and Selenophorus palliatus per day found in cover crops in Quincy, Florida in 2012 (mean ± SE; n = 8).
FIGURE 4 in Using an Asteraceae checklist to understand collection history, species density and conservation implications: a case study in the state of Alagoas, Northeastern Brazil
FIGURE 4. Distribution of Asteraceae species by vegetation type in Alagoas.
FIGURE 2 in Using an Asteraceae checklist to understand collection history, species density and conservation implications: a case study in the state of Alagoas, Northeastern Brazil
FIGURE 2. Habit proportion by vegetation type in the state of Alagoas, Brazil
FIGURE 1 in Using an Asteraceae checklist to understand collection history, species density and conservation implications: a case study in the state of Alagoas, Northeastern Brazil
FIGURE 1. Geographic location of Alagoas and vegetation types included in its limits.
Nutrients and wood density in coarse root, trunk and branches in Bornean tree species
<b>Description: </b><p>Carbon (C), nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), and magnesium (Mg) concentrations in woody tissue and bark, and wood density, were quantified for ten common Bornean tree species. The study took advantage of the ongoing logging in August 2014, sampling trees felled in the logging sites (near D Matrix, exact locations of the sampled trees not recorded). Each tree species had three replicates. The diameter of the sampled trees ranged from 12 cm to 44 cm. Sampled trees had no visible damage (e.g. no insect damage, chlorotic leaves and bent stems). The species IDs of the sampled trees were confirmed at the herbarium of the Forest Research Centre in Sepilok, Sandakan.<br><br>Several points along the length of the tree were sampled: coarse root; trunk bottom, trunk middle and trunk top (highest point of the main stem); and the lowest branch of the tree, from branch bottom, branch middle and branch tip. The sampled components were cut directly after felling using a chainsaw. The samples were then transported to the chemistry laboratory of the Forest Research Centre in Sepilok, Sandakan.<br><br>Bark (both the outer bark and phloem) was separated from the wood, except for the trunk top and branch tip samples, where the bark layer was too thin. Wood samples were divided into sapwood and heartwood if possible, based on a distinguishable colour difference after mechanical sanding. Sapwood and heartwood for most branch and coarse root samples were not separated due to the absence of colour boundary, but they probably consists mostly of sapwood.<br><br>For chemical analyses, all samples were oven dried at 50°C to constant weight for five days and ground with a Thomas Wiley Mill to pass through a 100-mush (212-μm) sieve. Each sample was digested following the sulphuric acid-hydrogen peroxide-lithium sulphate digest procedure for vegetation described in Allen (1989). Phosphorus in the digest was determined using the molybdenum-blue method described in Anderson and Ingram (1993) and read at 880 nm on a spectrophotometer (HITACHI UV-VIS, Tokyo, Japan), while K, Ca and Mg contents were measured on an atomic absorption spectrophotometer (GBC Scientific Equipment, Victoria, Australia). Total C and N contents (as total element contents) were determined by a dry combustion method at 900°C using an Elementar Vario Max CN analyzer (Elementar Analysensysteme, Hanau, Germany). Analysis protocols are described in more detail in Majalap and Chu (1992).<br><br>Wood density was quantified for trunk bottom and trunk middle samples. A strip, running through the wood disk slightly off centre, proportionally covering both sapwood and heartwood, was cut, and three (minimum of two, maximum of five, depending on the lenght of the strip) cubes (tangent ~20 mm × axial ~20 mm × radial ~20 mm length, exact dimensions recorded) were cut for wood density measurements, following the procedure ISO 3131:1975 (E) Wood -- Determination of Density for Physical and Mechanical Tests. Wood density was defined as the oven-dry mass per unit volume of fresh wood cube. Dry mass was measured after oven drying the samples at 103±2°C until the dry mass was stabilised. <br><br>References:<br>Allen, S. E. (1989). Chemical analysis of ecological materials (2nd ed.). Oxford, England; Boston; Blackwell Scientific Publications.<br>Anderson, J. M., & Ingram, J. S. I. (1993). Tropical soil biology and fertility: a handbook of methods (2nd ed.). Wallingford, UK: C.A.B. International.<br>Majalap, N., & Chu, N. H. (1992). Laboratory Manual for Chemical Analysis. Sandakan, Sabah, Malaysia: Forest Research Centre, Sabah Forestry Department.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/113"><b>Changing carbon dioxide and water budgets from deforestation and habitat modification</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Sime Darby Foundation (Grant, SAFE Core data)</li><li>Joint Japan/World Bank Graduate Scholarship Program (JJ/WBGSP) (Scholarship, JJ/WBGSP)</li><li>European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131)</li><li>NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (SABC) (Research licence JKM/MBS.1000-2/2 (187))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=8158811">here</a></p><p><b>Files: </b>This consists of 1 file: Inagawa_TreeComponentData_Nutrients_WoodDensity_SAFEdatabase_2023-07-10.xlsx</p><p><b>Inagawa_TreeComponentData_Nutrients_WoodDensity_SAFEdatabase_2023-07-10.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>TreeInfo</b> (described in worksheet TreeInfo)</p><p>Description: Local name, confirmed species id, family, and diameter of the sampled trees</p><p>Number of fields: 6</p><p>Number of data rows: 30</p><p>Fields: </p><ul><li><b>TreeNumber</b>: Running number of the sampled trees (Field type: id)</li><li><b>Species_LocalName</b>: Local name of the sampled tree (Field type: comments)</li><li><b>Family</b>: Family (Field type: comments)</li><li><b>Species</b>: Tree species (Field type: taxa)</li><li><b>DBH</b>: Diameter at the 1.3 m heigth (Field type: numeric trait)</li><li><b>SamplingLocation</b>: Location within the SAFE landscape (Field type: location)</li></ul></li><li><p><b>Nutrients</b> (described in worksheet Nutrients)</p><p>Description: Data on nutrients in bark and wood, along the length of the tree (coarse roots, along the trunk, branches) and radially (sapwood and heartwood).</p><p>Number of fields: 11</p><p>Number of data rows: 420</p><p>Fields: </p><ul><li><b>LabNumber</b>: Code assigned to each sample at the receiving chemistry lab (Field type: id)</li><li><b>TreeNumber</b>: Running number of the sampled trees (Field type: id)</li><li><b>SamplingPoint</b>: Site of the sample, from the bottom to the top of the tree: coarse root (CR), trunk bottom (TB), trunk middle (TM), brach bottom (BB), branch middle (BM), branch top (BT), trunk top (TT) (Field type: categorical)</li><li><b>TissueType</b>: Tissue type: bark, heartwood, sapwood, wood (if not possible to separate the sample into heart and sapwood), wood and bark (in case of branch tip and trunk tip, not possible to separate wood and bark). (Field type: categorical trait)</li><li><b>Species</b>: Tree species (Field type: taxa)</li><li><b>K_total</b>: Total potassium content (Field type: numeric trait)</li><li><b>Ca_total</b>: Total calsium content (Field type: numeric trait)</li><li><b>Mg_total</b>: Total magnesium content (Field type: numeric trait)</li><li><b>P_total</b>: Total phosphorus content (Field type: numeric trait)</li><li><b>N_total</b>: Total nitrogen content (Field type: numeric trait)</li><li><b>C_total</b>: Total carbon content (Field type: numeric trait)</li></ul></li><li><p><b>WoodDensity</b> (described in worksheet WoodDensity)</p><p>Description: Data on wood density at the bottom and at the middle of the trunk.</p><p>Number of fields: 13</p><p>Number of data rows: 207</p><p>Fields: </p><ul><li><b>TreeNumber</b>: Running number of the sampled trees (Field type: id)</li><li><b>Species</b>: Tree species (Field type: taxa)</li><li><b>SamplingPoint</b>: Site of the wood sample, either trunk bottom (TB) or trunk middle (TM). (Field type: categorical)</li><li><b>Replicate</b>: Replicate wood cube sampled for each wood disk (Field type: replicate)</li><li><b>Length_Tangential_Fresh</b>: Length of the wood cube edge in tangential direction, before drying (fresh). (Field type: numeric trait)</li><li><b>Length_Axial_Fresh</b>: Length of the wood cube edge in axial direction, before drying (fresh). (Field type: numeric trait)</li><li><b>Length_Radia_Fresh</b>: Length of the wood cube edge in radial direction, before drying (fresh). (Field type: numeric trait)</li><li><b>Mass_Fresh</b>: Fresh mass of wood cube, before drying. (Field type: numeric trait)</li><li><b>Length_Tangential_Dry</b>: Length of the wood cube edge in tangential direction, after oven drying. (Field type: numeric trait)</li><li><b>Length_Axial_Dry</b>: Length of the wood cube edge in axial direction, after oven drying. (Field type: numeric trait)</li><li><b>Length_Radial_Dry</b>: Length of the wood cube edge in radial direction, after oven drying. (Field type: numeric trait)</li><li><b>Mass_Dry</b>: Dry mass of wood cube, after oven drying (Field type: numeric trait)</li><li><b>WoodDensity</b>: Wood density, calculated as dry mass divided by fresh volume (Field type: numeric trait)</li></ul></li></ol><p><b>Date range: </b>2014-08-01 to 2015-11-30</p><p><b>Latitudinal extent: </b>4.1830 to 4.1830</p><p><b>Longitudinal extent: </b>114.0220 to 114.0220</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Plantae <br> -  -  Tracheophyta <br> -  -  -  Magnoliopsida <br> -  -  -  -  Malvales <br> -  -  -  -  -  Malvaceae <br> -  -  -  -  -  -  <i>Pentace</i> <br> -  -  -  -  -  -  -  <i>Pentace borneensis</i> (as synonym: <i>Pentace laxiflora</i>)<br> -  -  -  -  -  Dipterocarpaceae <br> -  -  -  -  -  -  <i>Shorea</i> <br> -  -  -  -  -  -  -  <i>Shorea johorensis</i> <br> -  -  -  -  -  -  -  <i>Shorea leprosula</i> <br> -  -  -  -  -  -  -  <i>Shorea micans</i> <br> -  -  -  -  -  -  -  <i>Shorea parvifolia</i> <br> -  -  -  -  -  -  <i>Parashorea</i> <br> -  -  -  -  -  -  -  <i>Parashorea malaanonan</i> <br> -  -  -  -  Fagales <br> -  -  -  -  -  Fagaceae <br> -  -  -  -  -  -  <i>Lithocarpus</i> <br> -  -  -  -  -  -  -  <i>Lithocarpus leptogyne</i> <br> -  -  -  -  Gentianales <br> -  -  -  -  -  Rubiaceae <br> -  -  -  -  -  -  <i>Neolamarckia</i> <br> -  -  -  -  -  -  -  <i>Neolamarckia cadamba</i> <br> -  -  -  -  Rosales <br> -  -  -  -  -  Cannabaceae <br> -  -  -  -  -  -  <i>Trema</i> <br> -  -  -  -  -  -  -  <i>Trema orientalis</i> <br> -  -  -  -  Malpighiales <br> -  -  -  -  -  Euphorbiaceae <br> -  -  -  -  -  -  <i>Macaranga</i> <br> -  -  -  -  -  -  -  <i>Macaranga pearsonii</i> <br></div><p></p>
Data from: Density-dependent and species-specific effects on self-organization modulate the resistance of mussel bed ecosystems to hydrodynamic stress
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Fine-scale root community structure in the field: species aggregations change with root density
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The effects of host plant species and larval density on immune function in the polyphagous moth Spodoptera littoralis
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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.