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201 results for “habitat modelling”
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index) in Comparative characterization of the ecology of native (Henosepilachna vigintioctomaculata) and invasive (Leptinoatrsa decemlineata) species under the conditions of the monsoon climate in the southern part of the Russian Far East
Рис. 6. МоΔеΛирование экоΛогических ниш коΛораΔского жука ΔΛя ΔаΛьневосточного, европейского и североамериканского ареаΛов метоΔом метрического Δвухмерного шкаΛирования с применением коэффициента Жаккара Fig. 6. Models of ecological niches of the Colorado potato beetle for the Far Eastern, European, and North-American habitats (metric multidimensional scaling, Jaccard index)
Fig. 3 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 3. Current (A and F) and future distribution models (B-E, G-J) for Haemadipsa rjukjuana in Korea. Dark gray represents over 0.5 MaxEnt value (suitable habitat) and light gray represents below 0.5 (unsuitable habitat). A is projected to the current climate conditions (2020), and F was built with the restricted spatial area between Heuksando Island and Gageodo Island. B-E are projections of the Maxent model to SSP585 of GISS-E2-1 climate scenarios by NASA and G-J were SSP585 of INM-CM4-8 scenarios by The Institute of Numerical Mathematics. B-E and G-J are respectively 2040, 2060, 2080, and 2100.
Fig. 2 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 2. Projection of MaxEnt Haemadipsa rjukjuana distribution model from Heuksando Island and Gageodo Island to the current climate condition of South Korea. Red color (lower value) represents less suitable habitats and blue (higher value close to 1.0) represents suitable habitats for H. rjukjuana.
Fig. 1 in New record of a blood-feeding terrestrial leech, Haemadipsa rjukjuana Oka, 1910 (Haemadipsidae, Arhynchobdellida) on Heuksando Island and possible habitat estimation in the current and future Korean Peninsula using a Maxent model
Fig. 1. The map of study sites (inset) and the Korean Peninsula. Haemadipsa rjukjuana was identified from the regions shaded in gray.
Figure 4 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 4. (A) Current distribution of the Maghreb magpie in North Africa, (B) binary map of habitat suitability with a threshold> 0.6.
Figure 6 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 6. Response curves of the explanatory variables included in the species distribution model (SDM) for Pica mauritanica. (MTWQ: mean temperature of wettest quarter).
Figure 5 in Modeling habitat suitability and current distribution of the Maghreb magpie (Pica mauritanica)
Figure 5. Two-dimensional plots of Pica mauritanica niche hypervolume with the most influential variables.
Fig. 1 in LiDAR sensors in smartphones can enrich herbarium specimens with 3D models of habitat at high precision and little cost
Fig. 1. Example of a 3D point-cloud model of specimen habitat obtained with the LiDAR scanner of an iPad Pro. A, Plan view of the model with potential use cases, including annotation and extraction of general habitat characteristics; B, Side view with measurements that can be extracted from the model at centimetre precision (DBH, diameter at breast height); C, Average times needed for physical herbarium specimen collection (orange) and LiDAR scanning (purple) in the field over 20 replicates; time for scanning depends on the area scanned and the habitat.
Airflow modelling predicts seabird breeding habitat across islands
<p>Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (<em>Uria aalge</em>) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions.</p>
Habitat Assessment and Restoration Planning (HARP) Model for the Snohomish and Stillaguamish River Basins
<p>Model code (R) to accompany the 2023 NOAA report "Habitat Assessment and Restoration Planning (HARP) Model for the Snohomish and Stillaguamish River Basins"</p>
A habitat connectivity reality check for fish physical habitat model results and decision making for river restoration
<ol> <li>Fish physical habitat models are a tool for guiding restoration efforts in lotic ecosystems but often they overestimate restoration outcomes because currently they do not incorporate habitat connectivity. This persistent issue can, in extreme cases, result in little or no improvement to fish populations after the restoration, wasting valuable conservation resources.</li> <li>We present a case study where practitioners applied a fish habitat model for multiple life history stages of gravel spawning fishes to a 52 kilometer stretch of the Iller River but did so at a microscale implementation (every 200 meters). This approach provided an opportunity to assess the connectivity of gravel spawning fishes to find suitable habitats for all life history stages and seasonal movements.</li> <li>We used the assessed habitat estimates (availability of distinct habitat types within the 200 m reaches) to calculate the minimum distance a fish would need to go as it hypothetically “grew up” from egg to full spawning adult. We call this technique a reality check as it results in a decisive understanding of which areas were ultimately necessary to fulfill the life cycle of gravel spawning fishes, which standard assessments do not show.</li> <li>Our results show that complete connectivity still require long movement distances for vulnerable life stages to find suitable habitat. This contradicts standard practice, as restoration schemes and decision making often assume that connectivity inherently leads to more fish production without added habitat restoration.</li> <li>We recommend practitioners should perform this habitat connectivity approach when assessments implement fish habitat suitability models at similar scales. As a result, decision makers can evaluate proposed restoration sites and measures more realistically.</li> </ol>
Species detection histories used in Killion et al. (2023): Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models
<p>Camera trap species detection histories used for occupancy models in "Integrating Spaceborne Estimates of Structural Diversity of Habitat into Wildlife Occupancy Models". </p>
Data and model code for: Habitat use patterns suggest that climate-driven vegetation changes will negatively impact mammal communities in the Amazon (ACV)
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Data from: Some like it cold: A general habitat association model for smallmouth bass in stratified lakes
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Habitats as predictors in species distribution models: Shall we use continuous or binary data?
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Data from: Large-scale eDNA sampling and hierarchical modeling elucidates the importance of stream habitat for eastern hellbender (<em>Cryptobranchus a. alleganiensis</em>) occupancy and eDNA detection
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Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
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Environmental DNA data of aquatic insects for habitat suitability models
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Airflow modelling predicts seabird breeding habitat across islands
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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.