https://blue.feedland.org/?river=http://data.feedland.org/blue/feeds/CBarros.xml It's just a feed for now Mon, 22 Jul 2024 19:10:34 GMT feedland v0.6.43 https://cyber.harvard.edu/rss/rss.html Mon, 22 Jul 2024 19:10:35 GMT http://rpc.rsscloud.io:5337/pleaseNotify Mon, July 22, 2024 3:10 PM EDT <p>Our new paper on &quot;Spatially explicit predictions of food web structure from regional-level data&quot; is finally out! Congrats to @gabdans.bsky.social for his great work on leading this piece :) <a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2023.0166">https://royalsocietypublishing.org/doi/10.1098/rstb.2023.0166</a></p> <p>We show how uncertainty and spatial variability in network structure can be explicitly accounted for when infering trophic networks across large spatial scales.</p> <p>Our probabilistic framework builds potential trophic networks based on a probabilistic metaweb of interactions and the probability of species&#39; presences in each given location.</p> <p>Across Canada, we noticed a latitudinal signal on species richness and interactions across the potential networks, but this didn&#39;t necessarily align with richness/interaction hotspots, nor with variation in network motifs.</p> <p>Our framework can help inform biodiversity hotspot analyses, but also where more sampling should be targeted to deal with network composition/structure uncertainty. </p> <p>It can also be used in a forecasting context to explore potential effects of enviromental change on network structure.</p> Mon, 22 Jul 2024 19:10:34 GMT https://blue.feedland.org/?item=484201 https://blue.feedland.org/?item=484201 Our new paper on "Spatially explicit predictions of food web structure from regional-level data" is finally out! Congrats to @gabdans.bsky.social for his great work on leading this piece :) https://royalsocietypublishing.org/doi/10.1098/rstb.2023.0166 We show how uncertainty and spatial variability in network structure can be explicitly accounted for when infering trophic networks across large spatial scales. Our probabilistic framework builds potential trophic networks based on a probabilistic metaweb of interactions and the probability of species' presences in each given location. Across Canada, we noticed a latitudinal signal on species richness and interactions across the potential networks, but this didn't necessarily align with richness/interaction hotspots, nor with variation in network motifs. Our framework can help inform biodiversity hotspot analyses, but also where more sampling should be targeted to deal with network composition/structure uncertainty. It can also be used in a forecasting context to explore potential effects of enviromental change on network structure.