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112 results for “habitat dynamics”
Data from: Analyzing movement behavior and dynamic space-use strategies among habitats using multi-event capture-recapture modeling
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Data from: Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird
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Data from: Spatial dynamics of habitat use informs reintroduction efforts in the presence of an invasive predator
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Data from: Epigenetic variation reflects dynamic habitat conditions in a rare floodplain herb
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Data from: Temporally dynamic habitat suitability predicts genetic relatedness among caribou
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Data from: Strong dispersal in a parasitoid wasp overwhelms habitat fragmentation and host population dynamics
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Data from: Ghosts of habitats past: environmental carry-over effects drive population dynamics in novel habitat
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Figure 1 in Investigating the influence of habitat type and weather conditions on the population dynamics of land snails Vertigo angustior Jeffreys, 1830 and Vertigo moulinsiana (Dupuy, 1849). A case study from western Poland
Figure 1. (A) Precipitation in the studied sites in consecutive months of 2008; dashed bars – sampling months. (B) and (C) Abundance of individuals: juveniles (white bars) and adults (black bars) of Vertigo angustior (B) and Vertigo moulinsiana (C) in each sampling event in the Ilanka and Pliszka sites in 2008.
Data from: Why we should care about movements: Using spatially explicit integrated population models to assess habitat source-sink dynamics
<p>1. Assessing the source-sink status of populations and habitats is of major importance for understanding population dynamics and for the management of natural populations. Sources produce a net surplus of individuals (per capita contribution to the metapopulation >1) and will be the main contributors for self-sustaining populations, whereas sinks produce a deficit (contribution < 1). However, making these types of assessments is generally hindered by the problem of separating mortality from permanent emigration, especially when survival probabilities as well as moved distances are habitat-specific.<br> 2. To address this long-standing issue, we propose a spatial multi-event Integrated Population Model (IPM) that incorporates habitat-specific dispersal distances of individuals. Using information about local movements, this IPM adjusts survival estimates for emigration outside the study area.<br> 3. Analyzing 24 years of data on a farmland passerine (the northern wheatear Oenanthe oenanthe) we assessed habitat-specific contributions, and hence the source-sink status and temporal variation of two key breeding habitats, while accounting for habitat- and sex-specific local dispersal distances of juveniles and adults. We then examined the sensitivity of the source-sink analysis by comparing results with and without accounting for these local movements.<br> 4. Estimates of first-year survival, and consequently habitat-specific contributions, were higher when local movement data were included. The consequences from including movement data were sex specific, with contribution shifting from sink to likely source in one habitat for males, and previously noted habitat differences for females disappearing.<br> 5. Assessing the source-sink status of habitats is extremely challenging. We show that our spatial IPM accounting for local movements can reduce biases in estimates of the contribution by different habitats, and thus reduce the overestimation of the occurrence of sink habitats. This approach allows combining all available data on demographic rates and movements, which will allow better assessment of source-sink dynamics and better informed conservation interventions.</p>
Data from: Ecological constraints coupled with deep-time habitat dynamics predict the latitudinal diversity gradient in reef fishes
We develop a spatially explicit model of diversification based on paleohabitat to explore the predictions of four major hypotheses potentially explaining the latitudinal diversity gradient (LDG), namely, the 'time-area', 'tropical niche conservatism', 'ecological limits' and 'evolutionary speed' hypotheses. We compare simulation outputs to observed diversity gradients in the global reef fish fauna. Our simulations show that these hypotheses are non-mutually exclusive and that their relative influence depends on the time scale considered. Indeed, simulations suggest that reef habitat dynamics produced the LDG during deep geological time, while ecological constraints shaped the modern LDG, with a strong influence of the reduction in the latitudinal extent of tropical reefs during the Neogene. Overall, this study illustrates how mechanistic models in ecology and evolution can provide a temporal and spatial understanding of the role of speciation, extinction and dispersal in generating contemporary biodiversity patterns.
Supplementary material 1 from: Zhao M, Tian Y, Dong N, Hu Y, Tian G, Lei Y (2022) Spatial and temporal dynamics of habitat quality in response to socioeconomic and landscape patterns in the context of urbanization: A case in Zhengzhou City, China. Nature Conservation 48: 185-212. https://doi.org/10.3897/natureconservation.48.85179
Notes on the data
Fig. 5. A in Temporal dynamics of fruit-feeding butterflies (Lepidoptera: Nymphalidae) in two habitats in a seasonal Brazilian environment
Fig. 5. A) Temporal variation in Nymphalidae species richness captured in the dry and wet seasons and in the 2 transitional periods between those seasons, wet to dry (T.wd) and dry to wet (T.dw), in savanna (Cerrado sensu stricto, ss) and gallery forest habitats in the Fazenda Água Limpa and the Reserva Ecológica do Roncador, Brasília, DF. The rarefaction curves compare the 4 climatic periods—dry (black triangles) and wet (black circles) seasons, and the transitional periods from wet to dry (T.wd, white circles) and from dry to wet (T.dw, white triangles)—in B) gallery forest and C) savanna separately.
Spatial dynamics of pathogen transmission in communally roosting species: Impacts of changing habitats on bat-virus dynamics
<p>1. The spatial organisation of populations determines their pathogen dynamics. This is particularly important for communally roosting species, whose aggregations are often driven by the spatial structure of their environment.</p> <p>2. We develop a spatially explicit model for virus transmission within roosts of Australian tree-dwelling bats (<i>Pteropus</i> spp.), parameterised to reflect Hendra virus. The spatial structure of roosts mirrors three study sites, and viral transmission between groups of bats in trees was modelled as a function of distance between roost trees. Using three levels of tree density to reflect anthropogenic changes in bats habitats, we investigate the potential effects of recent ecological shifts in Australia on the dynamics of zoonotic viruses in reservoir hosts.</p> <p>3. We show that simulated infection dynamics in spatially structured roosts differ from that of mean-field models for equivalently sized populations, highlighting the importance of spatial structure in disease models of gregarious taxa. Under contrasting scenarios of flying-fox roosting structures, sparse stand structures (with fewer trees but more bats per tree) generate higher probabilities of successful outbreaks, larger and faster epidemics, and shorter virus extinction times, compared to intermediate and dense stand structures with more trees but fewer bats per tree. These observations are consistent with the greater force of infection generated by structured populations with less numerous but larger infected groups, and may flag an increased risk of pathogen spillover from these increasingly abundant roost types.</p> <p>4. Outputs from our models contribute insights into the spread of viruses in structured animal populations, like communally roosting species, as well as specific insights into Hendra virus infection dynamics and spillover risk in a situation of changing host ecology. These insights will be relevant for modelling other zoonotic viruses in wildlife reservoir hosts in response to habitat modification and changing populations, including coronaviruses like SARS-CoV-2. </p>
Figure 4 from: Lessel T, Marx M, Eisenbeis G (2011) Effects of ecological flooding on the temporal and spatial dynamics of carabid beetles (Coleoptera, Carabidae) and springtails (Collembola) in a polder habitat. ZooKeys 100: 421-446. https://doi.org/10.3897/zookeys.100.1538
Figure 4 - Mean number of individuals of hygrophilic (A/B) and xerophilic/mesophilic (C/D) carabid beetle species at the fallow (A/C) and ruderal area (B/D) during different moisture conditions. Abbreviations: ef = ecological flooding (higher Rhine water levels); spe = flood caused by a strong precipitation event; dc = drought conditions; ° outliers. Different letters represent statistically significant differences (Mann-Whitney U-test).
Figure 3 from: Lessel T, Marx M, Eisenbeis G (2011) Effects of ecological flooding on the temporal and spatial dynamics of carabid beetles (Coleoptera, Carabidae) and springtails (Collembola) in a polder habitat. ZooKeys 100: 421-446. https://doi.org/10.3897/zookeys.100.1538
Figure 3 - Mean number of individuals per trap and day (± SE) and total carabid beetle species number at location 1 (fallow area) and location 6 (ruderal area) (n=3) during the vegetation period of 2008. Hygrophilic species (black bars) and xerophilic as well as mesophilic species (grey bars) are shown. Abbreviations: ef = ecological flooding; spe = strong precipitation event.
Figure 1 from: Lessel T, Marx M, Eisenbeis G (2011) Effects of ecological flooding on the temporal and spatial dynamics of carabid beetles (Coleoptera, Carabidae) and springtails (Collembola) in a polder habitat. ZooKeys 100: 421-446. https://doi.org/10.3897/zookeys.100.1538
Figure 1 - Location of the polder "Ingelheim" in Germany and location of the different areas and pitfall trap localities (L1–L6) within this polder (A). Abbreviations: LA 0: ruderal area; HB 0: fallow area; LA 0 + HB 0: transition area between LA 0 and HB 0; HA 0: agricultural fields; L1–6: locations of the six pitfall trap groups (three pitfall traps per locality). The pictures show the main flood gate (left) and the ecological flood gate (right), and an ecological flooding in March 2007 (B) and the fast drying event in the ruderal area after ecological flooding in April 2007 (C).
Figure 6 from: Lessel T, Marx M, Eisenbeis G (2011) Effects of ecological flooding on the temporal and spatial dynamics of carabid beetles (Coleoptera, Carabidae) and springtails (Collembola) in a polder habitat. ZooKeys 100: 421-446. https://doi.org/10.3897/zookeys.100.1538
Figure 6 - PCA of springtail communities in the fallow area (location 1) and the ruderal area (location 6) during ecological flooding, the flood caused by a strong precipitation event and drought conditions. Only species with more than 1% dominance value in at least one area are included. Abbreviations of the species: I.pal=Isotomurus palustris; I.vir=Isotoma viridis; L.cya=Lepidocyrtus cyaneus; O.vil=Orchesella villosa; P.aqu=Podura aquatica; S.aqu=Sminthurides aquaticus. Percentage variation explained by the two PCA axes are included.
Figure 2 from: Lessel T, Marx M, Eisenbeis G (2011) Effects of ecological flooding on the temporal and spatial dynamics of carabid beetles (Coleoptera, Carabidae) and springtails (Collembola) in a polder habitat. ZooKeys 100: 421-446. https://doi.org/10.3897/zookeys.100.1538
Figure 2 - PCA of carabid beetle communities in the fallow area (location 1) and the ruderal area (location 6) during ecological flooding, the flood caused by a strong precipitation event and drought conditions. Only species with more than 1% dominance value in at least one area are included. Abbreviations of the species: A.mar=Agonum marginatum; A.bif=Amara bifrons; A.sim=Amara similata; B.lam=Bembidion lampros; B.pro=Bembidion properans; B.qua=Bembidion quadrimaculatum; C.pur=Carabus purpurascens; H.aff=Harpalus affinis; H.ruf=Harpalus rufipes; H.sma=Harpalus smaragdinus; N.bre=Nebria brevicollis; O.ard=Ophonus ardosiacus; P.cup=Poecilus cupreus; P.ant=Pterostichus anthracinus; P.mel=Pterostichus melanarius; P.nig=Pterostichus nigrita. Percentage variation explained by the two PCA axes is included.
Figure 5 from: Lessel T, Marx M, Eisenbeis G (2011) Effects of ecological flooding on the temporal and spatial dynamics of carabid beetles (Coleoptera, Carabidae) and springtails (Collembola) in a polder habitat. ZooKeys 100: 421-446. https://doi.org/10.3897/zookeys.100.1538
Figure 5 - Mean individual numbers per trap and day (± SE) and total species numbers of springtails of the pitfall traps of location 1 and location 6 (n=3) over the vegetation period 2008. Hygrophilic and hygrotolerant species (black bars) and xerotolerant as well as mesophilic species (grey bars) are shown. Abbreviations: ef = ecological flooding; spe = strong precipitation event.
Figure 7 from: Lessel T, Marx M, Eisenbeis G (2011) Effects of ecological flooding on the temporal and spatial dynamics of carabid beetles (Coleoptera, Carabidae) and springtails (Collembola) in a polder habitat. ZooKeys 100: 421-446. https://doi.org/10.3897/zookeys.100.1538
Figure 7 - Mean number of individuals of hygrophilic/hygrotolerant (A/B) and xerotolerant/mesophilic (C/D) collembolan species at the fallow (A/C) and ruderal area (B/D) during different moisture conditions. Abbreviations: ef = ecological flooding (higher Rhine water levels); spe = flood caused by a strong precipitation event; dc = drought conditions; ° outliers. Different letters represent statistically significant differences (Mann-Whitney U-test).
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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.