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45 results for “extinction rate”
Data for article: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils
<p>Supplementary information for:</p> <p><strong>A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils</strong></p> <p>Torsten Hauffe, Mathias M. Pires, Tiago B. Quental, Thomas Wilke, and Daniele Silvestro</p> <p> </p><ul> <li> Simulations <ul> <li>Scripts <ul> <li>Scenario1_SamplingHeterogeneity.R: Script to simulate biogeographic histories with sampling heterogeneity</li> <li>Scenario3_SealevelInvasion.R: Script to simulate biogeographic histories where sea level facilitates dispersal and invasion induces extinction</li> <li>Scenario3_DiversityDependence.R: Script to simulate diversity-dependent biogeographic histories</li> <li>Scenario4_TraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> <li>Scenario5_CategoricalTraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> </ul> </li> <li>Results <ul> <li>Scenario1_SamplingHeterogenetiy_alpha05.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 0.5</li> <li>Scenario1_SamplingHeterogenetiy_alpha1.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 1</li> <li>Scenario1_SamplingHeterogenetiy_alpha2.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 2</li> <li>Scenario1_SamplingHeterogenetiy_alpha10.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 10</li> <li>Scenario2_Independent_dispersal_and_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_independent_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_independent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and invasion induced extinction</li> <li>Scenario3_Independent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-independent dispersal and extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_independent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Independent_dispersal_and_Diversity_dependent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and extinction</li> <li>Scenario4_Independent_dispersal_and_extinction.txt: Results of scenario 4 with trait-independent dispersal and extinction</li> <li>Scenario4_Trait_dependent_dispersal_and_independent_extinction.txt: Results of scenario 4 with trait-dependent dispersal and independent extinction</li> <li>Scenario4_Independent_dispersal_and_trait_dependent_extinction.txt: Results of scenario 4 with independent dispersal and trait-dependent extinction</li> <li>Scenario4_trait_dependent_dispersal_and_extinction.txt: Results of scenario 4 with trait-dependent dispersal and extinction</li> <li>Scenario5_CatTrait_dependent_dispersal_and_independent_extinction.txt: Results of model 2 with categorical traits (e.g family) influence dispersal but no influence of a category-specific continuous traits</li> </ul> </li> </ul> </li> <li>Carnivora <ul> <li>BinnedOccurrence: Folder with 100 replicates of binned occurrences of max. 330 carnivoran genera throughout the Neogene</li> <li>BodyMass: Folder with 100 replicates of body mass for 330 carnivoran genera</li> <li>Sealevel: Folder with sea level through the Neogene</li> <li>Temperature: Folder with the temperature record of the Neogene</li> <li>Families: Folder with families as taxonomic proxy for phylogeny. FamilyGeneraNumeric.txt is the numeric coding used for the Bayesian analyses of carnivoran biogeography</li> </ul> </li> </ul> <p></p>
Alya Eulerian LES results fire extinction nozzle low mass flow rate (paraview files)
<p>The present data complements the Deliverable 5.2. Toolchain V2 to couple CFD and multiple droplet and continuous droplet models.It includes a paraview file (*pvd) for the continuous droplet model (Eulerian-Eulerian) with an inlet mass flow rate of 0.05 kg/s. </p> <p>The unstructured mesh employed to simulate the injector system is composed by 81.6 million tetrahedrons that include the internal ow in the nozzle and the discharged atmosphere. Three levels of refinement are considered to characterize the internal ow and the near eld after the expansion, the jet penetration up to 15D (being D=2mm, the diameter of the nozzle) and the surrounding air with mesh resolution of 0.1, 0.3 and 1.5 mm respectively. A conservative level set is used in combination a a second order low dissipation finite element scheme and the LES model Vreman. </p> <p>Considered variables are: </p> <p> <PDataArray type="Float64" Name="AVD32_average"/> : average particle size distribution</p> <p> <PDataArray type="Float64" Name="AVDEN_average"/> : average density</p> <p> <PDataArray type="Float64" Name="AVL _average"/> : average liquid fraction</p> <p> <PDataArray type="Float64" Name="AVL2 _average"/>: average square liquid fraction </p> <p> <PDataArray type="Float64" Name="AVS _average"/>: average surface density</p> <p> <PDataArray type="Float64" Name="AVS0 _average"/>: average surface density 0</p> <p> <PDataArray type="Float64" Name="CON01_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="CON02_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="CON03_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="CON04_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="D32 _average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="DENSI_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="PRESS_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="SIGM0_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="SIGMA_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="TURBU_average"/>: not relevant (instantaneous values)</p> <p> <PDataArray type="Float64" Name="VELOC_average" NumberOfComponents="3"/>: average velocity</p> <p> <PDataArray type="Float64" Name="VISCO_average"/>: not relevant (instantaneous values)</p>
Fig. 6 in The phanerozoic diversity of agglutinated foraminifera: Origination and extinction rates
Fig. 6. Ranked per−capita extinction (A) and origination (B) rates, showing events of highest amplitude.
Fig. 1 in The phanerozoic diversity of agglutinated foraminifera: Origination and extinction rates
Fig. 1. Number of agglutinated foraminiferal genera in each geological stage based on updated ranges of 764 genera (including genera reported only from a single stage).
Fig. 2 in The phanerozoic diversity of agglutinated foraminifera: Origination and extinction rates
Fig. 2. Phanerozoic mean standing diversity of agglutinated foraminifera and mean sea level. The mean sea level curve was generated using Time Scale Creator, version 4.0.2 [http://www.tscreator.com]
Fig. 5 in The phanerozoic diversity of agglutinated foraminifera: Origination and extinction rates
Fig. 5. Per−capita (A) and percent (B) origination rates of agglutinated foraminiferal genera over the Phanerozoic.
Fig. 3 in The phanerozoic diversity of agglutinated foraminifera: Origination and extinction rates
Fig. 3. Stratigraphic ranges of agglutinated foraminiferal suborders (Kaminski 2004) over the Phanerozoic.
Bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting as predicted by the Madingley General Ecosystem Model
<p>The datasets contain data generated using the Madingley General Ecosystem Model for experiments decribed in the paper: "T. Barychka, G.M.Mace and D.W.Purves (2021) The Madingley General Ecosystem Model predicts bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting. Oikos."</p> <p>The Madingley General Ecosystem Model was used to generate predictions of bushmeat yields, extinction rates and broader ecosystem impacts for a range of harvesting intensities of duiker-sized endothermic herbivores. Duiker antelope (such as <i>Cephalophus callipygus</i> and <i>Cephalophus dorsalis</i>) are the most heavily hunted species in sub-Saharan Africa, contributing 34%-95% of all bushmeat in the Congo Basin. In the Madingley, the harvested group was described as "Heterotroph – Herbivore – Terrestrial – Mobile – Iteroparous– Endotherm", with adult bodymasses of 13-21 kg and juvenile bodymasses of >100 g. Harvesting period was set at 30 years (<em>n </em>=30).</p> <p>In the first experiment, we used the Madingley model to predict bushmeat yields ("Harvested Biomasses") and extinction rates ("Density") from harvesting duiker-sized herbivores using proportional harvesting strategy, with harvest rates ranging from 0 to 0.90. These were compared to the estimates of bushmeat yields and survival probabilities for two duiker antelope species (<i>Cephalophus callipygus</i> and <i>Cephalophus dorsalis) </i>from conventional single-species Beverton-Holt model. </p> <p>In the second experiment, we used the Madingley model to generate data on the state ("State") of the harvested ecosystem. The datasets contain estimates of biomasses, abundances, adult and juvenile bodymasses, etc. of the harvested duiker-sized herbivores as well as unharvested herbivores, omnivores and carnivores present in the simulated ecosystem. In the paper we focused on abundances; however, other estimates e.g., adult bodymasses can be used to conduct further studies on the effects of harvesting in tropical ecosystems.</p> <p>Main results of the experiments are that: 1) the Madingley model gave estimates for optimal harvesting rate, and extinction rate, that were qualitatively and quantitatively similar to the estimates from conventional single-species Beverton-Holt model; 2) the Madingley model predicted a background local extinction probability for the target species of at least 10%; 3) at medium and high levels of harvesting of duiker-sized herbivores, the Madingley model predicted statistically significant, but moderate, reductions in the densities of the targeted functional group; increases in small-bodied herbivores; decreases in large-bodied carnivores; and minimal ecosystem-level impacts overall.</p>
Codes for simulation and data for: The relationship between local and regional extinction rates depends on species distribution patterns
<p>The rapid loss of biodiversity poses a great threat to ecosystem functions and services. Credible estimation of species extinction rates is essential for understanding the magnitude of biodiversity loss and for informing conservation, but this has been a challenge because estimated extinctions are unverifiable due to the lack of data. In this study, we investigated the relationship between local and regional extinctions and assessed the effects of range size, spatial segregation, and patchiness of species distribution on this local-regional extinction relationship. We found that regional extinction rates had a convex relationship with local extinction rates, that is, the regional extinction rate was most likely to be lower than the average local rate. The regional rates deviated from local rates as the sampling area decreased. The difference between local and regional extinction rates (local-regional extinction difference) became larger if a higher number of species had larger range sizes and patchiness. We also detected that there were interactive effects among these factors. Species segregation had a weak positive relationship with the local-regional extinction difference if more species had relatively large range sizes. As the sampling areas increased, the range size showed smaller positive effects on local-regional differences, but patchiness showed larger positive effects. The local-regional extinction relationship of this study provides insights into the spatial scaling of biodiversity loss and offers some important cues for estimating regional extinctions from local data in future studies.</p>
The effects of sex on extinction dynamics of Chlamydomonas reinhardtii depend on the rate of environmental change
<p>The continued existence of sex, despite many the costs it entails, still lacks an adequate explanation, as previous studies demonstrated that the effects of sex are environment-dependent: sex enhances the rate of adaptation in changing environments, but the benefits level off in benign conditions. To the best of our knowledge, the potential impact of different patterns of environmental change on the magnitude of these benefits received less attention in theoretical studies. In this paper, we begin to explore this issue by examining the effect of the rate of environmental deterioration (negatively correlated with population survival rate), on the benefits of sex.</p> <p>To investigate the interplay of sex and the rate of environmental deterioration, we carried out a long-term selection experiment with a unicellular alga (<em>Chlamydomonas</em> <em>reinhardtii</em>), by manipulating mode of reproduction (asexual, facultative or obligate sexual) and the rate of environmental deterioration (an increase of salt concentration). We monitored both the population size and extinction dynamics.</p> <p>The results revealed that the relative advantage of sex increased at the intermediate rate and plateaued at the highest rate of environmental deterioration. Obligate sexual populations had the slowest extinction rate under the intermediate rate of environmental deterioration, while facultative sexuality was favoured under the high rate-treatment.</p> <p>To the best of our knowledge, our study is the first to demonstrate that the interplay of sex and the rate of environmental deterioration affects the probability of survival, which indicates that mode of reproduction may be an important determinant of survival of the anthropogenic-induced environmental change.</p>
Bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting as predicted by the Madingley General Ecosystem Model
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Extinction and ecosystem function debt across dispersal rate and behavior in a heterogeneous metacommunity model
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Codes for simulation and data for: The relationship between local and regional extinction rates depends on species distribution patterns
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The effects of sex on extinction dynamics of Chlamydomonas reinhardtii depend on the rate of environmental change
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Empirical and methodological challenges to the model-based inference of diversification rates in extinct clades
<p>Changes in speciation and extinction rates are key to the dynamics of clade diversification, but attempts to infer them from phylogenies of extant species face challenges. Methods capable of synthesizing information from extant and fossil species have yielded novel insights into diversification rate variation through time, but little is known about their behavior when analyzing entirely extinct clades. Here, we use empirical and simulated data to assess how two popular methods, PyRate and Fossil BAMM, perform in this setting. We inferred the first tip-dated trees for ornithischian dinosaurs, and combined them with fossil occurrence data to test whether the clade underwent an end-Cretaceous decline. We then simulated phylogenies and fossil records under empirical constraints to determine whether macroevolutionary and preservation rates can be teased apart under paleobiologically realistic conditions. We obtained discordant inferences about ornithischian macroevolution including a long-term speciation rate decline (BAMM), mostly flat rates with (PyRate) or without (BAMM) a steep diversification drop, and episodes of implausibly accelerated speciation and extinction (PyRate). Simulations revealed little to no conflation between speciation and preservation, but yielded spuriously correlated speciation and extinction estimates while time-smearing tree-wide shifts (BAMM) or overestimating their number (PyRate). Our results indicate that the small phylogenetic datasets available to vertebrate paleontologists and the assumptions made by current model-based methods combine to hinder inferences about the diversification of extinct clades. We provide guidelines for interpreting the results of the existing approaches in light of their limitations, and suggest how the latter may be mitigated.</p>
Data from: Evolution of a hotspot genus: geographic variation in speciation and extinction rates in Banksia (Proteaceae)
Background: Hotspots of angiosperm species richness and endemism in Mediterranean-climate regions are among the most striking, but least well-understood, geographic patterns of biodiversity. Recent studies have emphasized the importance of rapid diversification within hotspots, compared to non-hotspot regions, as a major contributor to these patterns. We constructed the first near-complete phylogeny of Banksia (Proteaceae) to test whether diversification rates have differed between lineages confined to the southwest Australian hotspot and those found throughout southern, eastern and northern Australia. We then tested for variation in diversification rates among the bioclimatic zones within the southwest hotspot itself. Results: Although Banksia species richness in the southwest is ten times that of the rest of the continent, we find little evidence for more rapid diversification in the southwest, although this result is inconclusive. However, we find firmer support for substantial rate variation within the southwest hotspot, with more rapid diversification in the semi-arid heaths and shrublands, compared to the high-rainfall forests. Most of the Banksia diversity of the southwest appears to be generated in the heaths and shrublands, with a high migration rate out of this zone boosting diversity of the adjacent forest zone. Conclusions: The geographic pattern of diversification in Banksia appears more complex than can be characterized by a simple hotspot vs. non-hotspot comparison, but in general, these findings contrast with the view that the high diversity of Mediterranean hotspots is underpinned by rapid radiations. Steady accumulation of species at unexceptional rates, but over long periods of time, may also have contributed substantially to the great botanical richness of these regions.
A flexible method for estimating tip diversification rates across a range of speciation and extinction scenarios
<p>Estimates of diversification rates at the tips of a phylogeny provide a flexible approach for correlation analyses with multiple traits and to map diversification rates in space, while also avoiding the uncertainty of deep time rate reconstructions. Available methods for tip rate estimation make different assumptions, and thus their accuracy usually depends on characteristics of the underlying model generating the tree. Here we introduce MiSSE, a trait-free, state-dependent speciation and extinction approach that can be used to estimate varying speciation, extinction, net-diversification, turnover rates, and extinction fraction at the tips of the tree. We compare the accuracy of tip rates inferred by MiSSE against similar methods and demonstrate that, due to certain characteristics of the model, the error is generally low across a broad range of speciation and extinction scenarios. MiSSE can be used alongside regular phylogenetic comparative methods in trait related diversification hypotheses, and we also describe a simple correction to avoid pseudoreplication from sister tips in analyses of independent contrasts. Finally, we demonstrate the capabilities of MiSSE, with a renewed focus on classic comparative methods, to examine correlation between plant height and turnover rates in eucalypts, a species-rich lineage of flowering plants.</p>
Figure 1 in Do extraordinarily high growth rates in Permo-Triassic dicynodonts (Therapsida, Anomodontia) explain their success before and after the end-Permian extinction?
Figure 1. Stratigraphical ranges of the anomodonts used in this study. Modified from Angielczyk & Kurkin (2003) and Angielczyk & Walsh (2008). Oudenodon range updated from Botha & Angielczyk (2007). Kingoria is now referred to as Dicynodontoides following Angielczyk et al. (2009). Vertical solid bars and open bars indicate ranges and ghost lineages, respectively. Abbreviations: Chsn, Changhsingian; Ciste., Cistecephalus Assemblage Zone; Eodicyn., Eodicynodon Assemblage Zone; Ind., Induan; Lystro., Lystrosaurus; Olen., Olenekian; Prist., Pristerognathus Assemblage Zone; PTB, Permo-Triassic boundary; Tap., Tapinocephalus Assemblage Zone; Tropid. Tropidostoma Assemblage Zone; Wn, Wordian. Numbers indicate million years ago. Stratigraphical chart follows Catuneanu et al. (2005).
Figure 4. Permo-Triassic dicynodont bone histology. A in Do extraordinarily high growth rates in Permo-Triassic dicynodonts (Therapsida, Anomodontia) explain their success before and after the end-Permian extinction?
Figure 4. Permo-Triassic dicynodont bone histology. A, juvenile Dicynodon humerus SAM-PK-K5576d; B, subadult Dicynodon humerus NMQR3633a; C, subadult Lystrosaurus maccaigi ulna NMQR3663b; D, juvenile Lystrosaurus declivis tibia NMQR735b; D, E, adult Lystrosaurus humerus (possibly Lystrosaurus declivis) NMQR3678; F, late subadult Kannemeyeria femur NMQR2674b. Arrows indicate enlarged channels in all genera. Scale bars: D, E = 500 mm; A, B, C, F = 413 mm.
Figure 6. Permo-Triassic eutherapsid bone histology. A in Do extraordinarily high growth rates in Permo-Triassic dicynodonts (Therapsida, Anomodontia) explain their success before and after the end-Permian extinction?
Figure 6. Permo-Triassic eutherapsid bone histology. A, subadult gorgonopsian Scylacops femur SAM-PK-10188; B, adult therocephalian Pristerognathus femur SAM-PK-11557; C, late subadult nonmammalian cynodont Cynognathus femur SAM-PK-K6235a; D, early subadult nonmammalian cynodont Diademodon ulna SAM-PK-K8971c. Enlarged channels are absent from these taxa. Arrows indicate growth rings. Scale bars: B, D = 500 mm; A, C = 413 mm.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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