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385 results for “Environmental factors”
Data from: Environmental factors influence both abundance and genetic diversity in a widespread bird species
Genetic diversity is one of the key evolutionary variables that correlate with population size, being of critical importance for population viability and the persistence of species. Genetic diversity can also have important ecological consequences within populations, and in turn, ecological factors may drive patterns of genetic diversity. However, the relationship between the genetic diversity of a population and how this interacts with ecological processes has so far only been investigated in a few studies. Here, we investigate the link between ecological factors, local population size, and allelic diversity, using a field study of a common bird species, the house sparrow (Passer domesticus). We studied sparrows outside the breeding season in a confined small valley dominated by dispersed farms and small-scale agriculture in southern France. Population surveys at 36 locations revealed that sparrows were more abundant in locations with high food availability. We then captured and genotyped 891 house sparrows at 10 microsatellite loci from a subset of these locations (N = 12). Population genetic analyses revealed weak genetic structure, where each locality represented a distinct substructure within the study area. We found that food availability was the main factor among others tested to influence the genetic structure between locations. These results suggest that ecological factors can have strong impacts on both population size per se and intrapopulation genetic variation even at a small scale. On a more general level, our data indicate that a patchy environment and low dispersal rate can result in fine-scale patterns of genetic diversity. Given the importance of genetic diversity for population viability, combining ecological and genetic data can help to identify factors limiting population size and determine the conservation potential of populations.
Invasive tree cover covaries with environmental factors to explain the functional composition of riparian plant communities
<p>Invasive species are a major cause of biodiversity loss worldwide, but their impact on communities and the mechanisms driving those impacts are varied and not well understood. This study employs functional diversity metrics and guilds - suites of species with similar traits - to assess the influence of an invasive tree (<em>Tamarix</em> spp.) on riparian plant communities in the southwestern United States. We asked: 1) What traits define riparian plant guilds in this system? 2) How do the abundances of guilds vary along gradients of <em>Tamarix </em>cover and abiotic conditions? 3) How does the functional diversity of the plant community respond to the gradients of <em>Tamarix </em>cover and abiotic conditions? We found nine distinct guilds primarily defined by reproductive strategy, as well as height, seed weight, specific leaf area, drought and anaerobic tolerance. Guild abundance varied along a covarying gradient of local and regional environmental factors and <em>Tamarix </em>cover. Guilds relying on sexual reproduction, in particular those producing many light seeds over a long period of time were more strongly associated with drier sites and higher <em>Tamarix </em>cover. <em>Tamarix </em>itself appeared to facilitate more shade tolerant species with higher specific leaf areas than would be expected in resource poor environments. Additionally, we found a high degree of specialization (low functional diversity) in the wettest, most flood-prone, lowest <em>Tamarix </em>cover sites as well as in the driest, most stable, highest <em>Tamarix </em>cover sites. These guilds can be used to anticipate plant community response to restoration efforts and in selecting appropriate species for revegetation.</p>
Figure 2 in The behaviour of orientation of openings of burrows by Liolaemus lutzae (Squamata: Liolaemidae): is it influenced by environmental factors?
Figure 2. (Above) Direction of the openings of retreats (n = 59, in degrees) dug by Liolaemus lutzae, and (below) terrain slope direction (n = 45, in degrees) in which the retreats were dug at the restinga of the Parque Natural Municipal de Grumari, municipality of Rio de Janeiro, Brazil. The arrows represent the mean vector (µ) and the mean vector length (r) (see Table 2).
Figure 1 in The behaviour of orientation of openings of burrows by Liolaemus lutzae (Squamata: Liolaemidae): is it influenced by environmental factors?
Figure 1. (Above) Direction of the openings of retreats (n = 132, in degrees) dug by Liolaemus lutzae, and (below) terrain slope direction (n = 115, in degrees) in which the retreats were constructed at the restinga of the Reserva Ecológica Estadual de Jacarepiá, municipality of Saquarema, Brazil. The arrows represent the mean vector (µ) and the mean vector length (r) (see Table 1).
FIG. 3 in Influence of Environmental Factors on Short-Term Movements of Butter Frogs (Leptodactŋlus latrans)
FIG. 3.—Particularly long-distance movements of individuals of Leptodactŋlus latrans at Agronomic Experimental Station of the Federal University of Rio Grande do Sul, Brazil. Arrows indicate the direction of the movements. When their temporary pond dried up, Frogs 3 and 5 moved to different marshy areas, and Frog 4 moved to a permanent pond. Frogs 10 and 19 moved from the same temporary pond to the same small stream. Frog 10 subsequently returned to the original temporary pond, whereas Frog 19 moved to a new permanent pond. Frogs 12 and 15 moved relatively long distances (37.6 and 31.3 m, respectively) from the same shallow area (<0.5 m) to deeper areas (1.0–1.5 m in depth) of the pond on the same night. Both frogs remained in the deeper areas for 15 d, showing little or no movement (<2 m). On another night, Frogs 12 and 15 moved 35.1 and 43.5 m, respectively, from the deeper areas to shallow areas. A color version of this figure is available online.
FIG. 2 in Influence of Environmental Factors on Short-Term Movements of Butter Frogs (Leptodactŋlus latrans)
FIG. 2.—The eight lunar phases considered in this study and the assignment of days since new moon and days from the nearest new moon. Adapted from Grant et al. (2009).
FIG. 1 in Influence of Environmental Factors on Short-Term Movements of Butter Frogs (Leptodactŋlus latrans)
FIG. 1.—Example of Leptodactŋlus latrans habitat at the Agronomic Experimental Station of the Federal University of Rio Grande do Sul, Brazil. The surveyed wetlands were each surrounded by pasture and underbrush matrices and all sited in a former Atlantic Forest area. A color version of this figure is available online.
FIG. 4 in Influence of Environmental Factors on Short-Term Movements of Butter Frogs (Leptodactŋlus latrans)
FIG. 4.—Mean displacement by individuals of Leptodactŋlus latrans during each lunar phase at the Agronomic Experimental Station of the Federal University of Rio Grande do Sul, Brazil. Each bar corresponds to a lunar phase, and bar length indicates the mean displacement of frogs (m). The new moon is positioned on the top of the figure, and the full moon is positioned at the bottom (additional details in Fig. 2).
Fig. 4.—Climatic niche overlaps A and B in Identifying regional environmental factors driving differences in climatic niche overlap in Peromyscus mice
Fig. 4.—Climatic niche overlaps A and B differed among allopatric, parapatric, and sympatric species pairs of Peromyscus mice throughout North America. Bayesian 95% highest posterior density intervals estimates showed that sympatric species pairs had higher average overlap than parapatric or allopatric pairs and that parapatric pairs had higher average overlap than allopatric pairs.
Fig. 2 in Identifying regional environmental factors driving differences in climatic niche overlap in Peromyscus mice
Fig. 2.—Species richness map derived from geographic ranges of 43 species of Peromyscus mice available in the IUCN database (NatureServe and IUCN 2018). The remaining species mostly comprise island forms with ranges too small to be visualized in this map.
Fig. 3 in Identifying regional environmental factors driving differences in climatic niche overlap in Peromyscus mice
Fig. 3.—Illustration of the relative climatic niche overlap between species pairs of North American Peromyscus mice.
Fig. 1.—A in Identifying regional environmental factors driving differences in climatic niche overlap in Peromyscus mice
Fig. 1.—A visual summary of the three distribution modes and associated scenarios of range and climatic niche overlaps between species. The blue and green colors represent two different species within a pair. In this illustration, different parts of the triangle (a mountain) will exhibit different climatic conditions. When the two species (blue and green mouse) are aligned horizontally (either on the same mountain or on separate mountains), they will experience the same climatic conditions. When one species is above the other (either on the same mountain or on separate mountains), they experience different climatic conditions.
Three datasets of global monthly gross primary productivity (GPP) during 2003-2018 derived from SIF, NIRv and LAI and their best-matching environmental factors
<p>As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous remote sensing vegetation indices (VIs) have participated in the estimation of global GPP. However, the relative performance of various VIs in estimating GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood.</p> <p>We used the Random Forest (RF) algorithm to identify the factors with the most powerful explanation of GPP and to explore the importance of these predictors. We trained six RF models to select features, i.e., two types of models (Plant Functional Type [PFT]-specific and universal) for each vegetation index (SIF, NIRv, and LAI). Each model comprised 100 decision trees, was sampled without replacement, and was trained using 70% of the data. Model performance was evaluated using out-of-bag (OOB) R-squared (R<sup>2</sup>) and root mean square error (RMSE) values. The predictor with the lowest importance score in the iteration was removed and the whole procedure was then repeated until only the vegetation index, CO<sub>2</sub>, and PFTs were left. The predictors used to estimate GPP were identified based on the performance curve of OOB R<sup>2</sup> and RMSE. The determination of the model is based on the principle that further reductions in the number of predictors would considerably reduce model performance, while increasing the number of predictors would not significantly improve model performance.</p> <p>Here we provide a set of high-spatial resolution (1/12°) global gridded products of monthly GPP for 2003-2018 generated for each vegetation index based on a generic model with an optimal configuration, i.e., an optimal combination of VI and other relevant variables using the RF algorithm. R<sup>2</sup> of three optimal VI-based GPP estimation models ranges from 0.84 to 0.85, and RMSE ranges from 1.51g C·m<sup>−2</sup>·d<sup>−1</sup> to 1.54g C·m<sup>−2</sup>·d<sup>−1</sup>. More information about the datasets can be found in Zhao and Zhu (2022) <strong><em>Remote Sensing</em></strong>.</p> <p><em>Zhao W, Zhu Z. Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity[J]. Remote Sensing, 2022, 14(24): 6316.</em></p>
Fig. 1 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 1. Relationship between usnic acid content in Cladonia mitis and the latitude of the collection sites (R = 0.547, p =0.019). The circles denote samples from open area, while squares denote samples from forest area.
Fig. 4 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 4. The projection of samples on the plane defined by the first two latent components of the PLS model. The circles denote samples from open area, while squares denote samples from forest area.
Fig. 2 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 2. Relationship between usnic acid content in Cladonia mitis and the altitude (in the range of 50 and 500 m above sea level) of the collection sites (n = 13). The circles denote samples from open area, while squares denote samples from forest area.
Fig. 3 in Quantitative variations of usnic acid and selected elements in terricolous lichen Cladonia mitis Sandst., with respect to different environmental factors - A chemometric approach
Fig. 3. The weights of the first two latent components of the partial least square model. Usnic acid and Pb concentrations are response parameters, all other parameters are predictors.
Genetic and Environmental Risk Factors of Type 1 Autoimmune Diabetes and Its Early Complications
ClinicalTrials.gov study NCT02212522. IPD Sharing: NO. Countries: 1. Publications: 1.
Investigation of Environmental Factors Associated With Transmission of T. Solium in Endemic Villages of Zambia
ClinicalTrials.gov study NCT03874689. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Environmental Factors Associated With Peripheral Neuropathies in French Guiana
ClinicalTrials.gov study NCT07341997. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
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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.