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312 results for “change in distribution”
Thermal tolerance in Drosophila: repercussions for distribution, community coexistence and responses to climate change
<p>Here we combined controlled experiments and field surveys to determine if estimates of heat tolerance predict distributional ranges and phenology of different Drosophila species in southern South America. </p> <p>We contrasted thermal death time curves, which consider both magnitude and duration of the challenge to estimate heat tolerance, against the thermal range where populations are viable based on field surveys in an 8-yr longitudinal study. </p> <p>We observed a strong correspondence of the physiological limits, the thermal niche for population growth, and the geographic ranges across studied species, which suggests that the thermal biology of different species provides a common currency to understand how species will respond to warming temperatures both at a local level and throughout their distribution range. </p> <p>Our approach represents a novel analytical toolbox to anticipate how natural communities of ectothermic organisms will respond to global warming.</p>
Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles
<p>Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioral ecology is the expectation that the distribution of resources shapes patterns of social interactions.</p> <p>We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions.</p> <p>We experimentally manipulated the distribution of an essential food and reproductive resource in semi-natural populations of forked fungus beetles (Bolitotherus cornutus). We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half-way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioral differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments.</p> <p>We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length, and global clustering coefficient) did not differ between environments with dramatically different distributions of resources.</p> <p>Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggests that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.</p>
Fig. 6 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 6. Result of the analysis of Binomial tests (CliMond 2090 (2081–2100)): A — T. graeca; B — T. hermanni.
Fig. 3 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 3. Niche clustering (Geographic space, CliMond 1975 (1970–2000)) from: A — T. graeca (1. T. g. ibera, 2. T. nikolskii, 3. T. g. anamurensis, 4. T. g. floweri, 5. T. g. antakyensis, 6. T. g. pallasi, 7. T. g. armenica, 8. T. g. perses, buxtoni, 9. T. g. terrestris); B — T. hermanni (1. T. h. hermanni, 2. T. h. hervegovinensis, 3. T. h. boettgeri), red circles showing the approximate ranges of subspecies according to "Turtles…, 2017" World" (2017).
Fig. 2 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 2. The "Ecological envelope" — relationship bio01 "Annual mean temperature", °C & bio12 "Annual precipitation", mm (DivaGis): A — T. graeca; B — T. hermanni.
Fig. 5 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 5. Potential (probabilistic) model of T. hermanni world expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).
Fig. 4 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change
Fig. 4. Potential (probabilistic) model of T. graeca expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021 a). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).
Fig. 1. The potential distribution map for B in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 1. The potential distribution map for B. bombina under contemporary climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig. 4. The potential distribution map for B. bombina under projected 2050 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 4. The potential distribution map for B. bombina under projected 2050 climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig 3 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig 3. Response curve showing how the logistic prediction changes as the environmental variable Bio2 (Mean diurnal temperature range, oC, X-axis) is varied, keeping all other environmental variables at their average sample value. The curve shows the mean response of the 10 replicate Maxent runs (red) and and the mean +/– one standard deviation (blue).
Supplementary data from 'Predicting the distribution of Australian frogs and their overlap with Batrachochytrium dendrobatidis under climate change'
<p><strong><span>Aim: </span></strong><span>Amphibians, with over 40% of assessed species listed as threatened, are disproportionately at risk in the global extinction crisis. Among the many factors implicated in the current and ongoing loss of amphibian biodiversity are climate change and the disease chytridiomycosis, caused by the fungus <em>Batrachochytrium dendrobatidis </em>(<em>Bd</em>). These two threats are of particular concern in Australia, where <em>Bd </em>has been implicated in the declines of at least 43 frog species, and climate change is emerging as an additional threat. Here, we explore how climate change is likely to affect the distributions of Australian frog species and <em>Bd </em>to the year 2100, as well as how the spatial and climatic niche overlap between <a>chytridiomycosis-declined </a></span><span>frogs and <em>Bd</em> could shift.</span></p> <p><strong><span>Location: </span></strong><span>Australia</span></p> <p><strong><span>Methods: </span></strong><span>We used species distribution modelling to infer the current and future distribution of 141 Australian frog species and <em>Bd</em>, under two emissions scenarios. We used metrics of niche similarity, including Schoener's D and the Niche Margin Index, to quantify predicted alterations to spatial interactions between <em>Bd</em> and frog species.</span></p> <p><strong><span>Results: </span></strong><span>Climate change is likely to have a variable impact on frog distributions in Australia, with some 23 and 47 species, primarily in southern Australia, predicted to lose at least 30% of their current distributions under low and high emissions scenarios, respectively. In contrast, 69 and 68 species, respectively, have potential to increase their distributions, primarily in northern Australia. While the distribution of <em>Bd </em>is predicted to decrease, the proportional spatial and niche overlap between <em>Bd </em>and susceptible frog species is predicted to remain little changed, and in some cases, to increase.</span></p> <p><strong><span>Main conclusions: </span></strong><span>Although effects will be variable across the continent, climate change is likely to be a threatening factor to a number of Australian frog species. Additionally, chytridiomycosis is likely to remain a significant threat to many frog species, as any reductions to the pathogen's distribution largely coincide with geographic range contractions of chytridiomycosis-susceptible species.</span></p>
Fig. 4 in An assessment of potential distribution and climate change impacts on a critically endangered primate, the Delacour's langur
Fig. 4. Occurrence records of the Trachypithecus francoisi group based on previous research studies (Nadler et al., 2003; Workman, 2010a; Ebenau et al., 2011; Hendershott et al., 2016; Blair et al., 2021).
Fig. 3 in An assessment of potential distribution and climate change impacts on a critically endangered primate, the Delacour's langur
Fig. 3. Predicted distribution of climatically suitable habitat for the Delacour's langur under a range of different future climate change scenarios: A, MIROC6 models; B, CNRM-ESM2-1 models; C, IPSL-CM6A-LR models.
Fig. 2 in An assessment of potential distribution and climate change impacts on a critically endangered primate, the Delacour's langur
Fig. 2. Potential current distribution of the Delacour's langur generated from Maxent based on eight uncorrelated WorldClim bioclimatic variables.
Fig. 1 in An assessment of potential distribution and climate change impacts on a critically endangered primate, the Delacour's langur
Fig. 1. Occurrence records of the Delacour's langur derived from previous research studies (Nadler & Long, 2001; Nadler et al., 2003; Workman, 2010b; Ebenau et al., 2011; Wojciechowski, 2013; Nadler, 2015; Hoang & Dung, 2016; Linh et al., 2019; Nguyen et al., in press) and our field surveys.
Figure 3 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 3. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2061-2080, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Figure 2 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 2. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2041-2060, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Рис. 3. ПоΔразΔеΛение Обжоровского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги. Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны; 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 3. Obzhorovsky transect by zones and subzones of the lower reaches of the Volga delta. Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 3. ПоΔразΔеΛение Обжоровского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги. Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны; 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 3. Obzhorovsky transect by zones and subzones of the lower reaches of the Volga delta. Designations: 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
Рис. 2. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 2). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 2. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 2). 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow in The number and distribution of the raccoon dog (Nyctereutes procyonoides Gray) and the common jackal (Canis aureus L.) in 2021-2022 in Astrakhan Nature Reserve under the influence of hydrological changes
Рис. 2. ПоΔразΔеΛение Αамчикского участка на зоны и поΔзоны районирования низовьев ΔеΛьты ВоΛги (часть 2). Обозначения: 1 — верхняя часть русΛовой зоны; 2 — среΔняя часть русΛовой зоны: 3 — нижняя часть русΛовой зоны; 4 — куΛтучная зона и вытечки протоков Fig. 2. Damchiksky transect by zones and subzones of the lower reaches of the Volga delta (part 2). 1 — upper part of the streambed; 2 — middle part of the streambed; 3 — lower part of the streambed; 4 — cultuk zone and the streambed outflow
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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.