Trait evolution across the tree‐of‐life of freshwater macroinvertebrates Video
The rates of species and trait diversification vary across the Tree‐of‐Life and over time. Whereas species richness and clade age generally are decoupled, the correlation of accumulated trait diversity of clades (trait disparity) with clade age remains poorly explored. Total trait disparity may be coupled with clade age if the growth of disparity (disparification) within and across clades is continuous with time in an additive niche expansion process (linear‐cumulative model), or alternatively if the rate of trait disparification varies over time and decreases as ecological space becomes gradually saturated (disparity‐dependent model). Using a clock‐calibrated phylogenetic tree for 143 freshwater macroinvertebrate families and richness and trait databases covering more than 6400 species, we measured trait disparity in 18 independent clades that successively transitioned to freshwater ecosystems and analyzed its relation with clade age. We found a positive correlation between clade age and total disparity within clades, but no relationship for most individual traits. Traits unique to freshwater lifestyle were highly variable within older clades, while disparity in younger clades shifted towards partially terrestrial lifestyles and saline tolerance to occupy habitats previously inaccessible or underutilized. These results argue that constraints from incumbent lineages limit trait disparity in younger clades that evolved for filling unoccupied regions of the trait space, which suggests that trait disparification may follow a disparity‐dependent model. Overall, we provide an empirical pattern that reveals the potential of the disparity‐dependent model for understanding fundamental processes shaping trait dynamics across the Tree‐of‐Life. Full paper: https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.02886
About the Site 🌐
This site provides links to random videos hosted at YouTube, with the emphasis on random. 🎥
Origins of the Idea 🌱
The original idea for this site stemmed from the need to benchmark the popularity of a video against the general population of YouTube videos. 🧠
Challenges Faced 🤔
Obtaining a large sample of videos was crucial for accurate ranking, but YouTube lacks a direct method to gather random video IDs.
Even searching for random strings on YouTube doesn't yield truly random results, complicating the process further. 🔍
Creating Truly Random Links 🛠️
The YouTube API offers additional functions enabling the discovery of more random videos. Through inventive techniques and a touch of space-time manipulation, we've achieved a process yielding nearly 100% random links to YouTube videos.
About YouTube 📺
YouTube, an American video-sharing website based in San Bruno, California, offers a diverse range of user-generated and corporate media content. 🌟
Content and Users 🎵
Users can upload, view, rate, share, and comment on videos, with content spanning video clips, music videos, live streams, and more.
While most content is uploaded by individuals, media corporations like CBS and the BBC also contribute. Unregistered users can watch videos, while registered users enjoy additional privileges such as uploading unlimited videos and adding comments.
Monetization and Impact 🤑
YouTube and creators earn revenue through Google AdSense, with most videos free to view. Premium channels and subscription services like YouTube Music and YouTube Premium offer ad-free streaming.
As of February 2017, over 400 hours of content were uploaded to YouTube every minute, with the site ranking as the second-most popular globally. By May 2019, this figure exceeded 500 hours per minute. 📈
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